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Recently, with the rapid developments of economy and industry, how to reduce heavy air pollution has been a challenging task for Chinese people and the Chinese government. In order to study air pollutants and their chemical transformation in North China, variations of the concentrations of atmospheric constituents were analyzed for four representative sites in North China during 2005-2015. Satellite-derived vertical column densities (VCDs) of SO₂, NO₂, O₃, HCHO, and aerosol optical depth (AOD) over these four sites were used together with ground-based radiation and meteorological measurements at each site. On the base of the analysis, a photochemical mechanism relating the formation of PM_2.5 and O₃ in North China was given. In particular, the key role of volatile organic compounds (VOCs) in chemical and photochemical reactions is found to be prominent in the summer season. We make some suggestions for air pollution control in North China, especially to reduce anthropogenic VOC emissions and artificial biogenic VOC emissions.

Key words: Trace gases, particulate matter (PM), emission, solar radiation, air pollution control.

We study air quality over China using satellite observations, especially their spatial and temporal variability. For the period 2007-2017 we derived monthly SO₂ and NO_x emissions for China on a provincial level. To derive NO_x emission we applied the inversion algorithm DECSO v5.1 to OMI NO₂ retrievals of the newly developed QA4ECValgorithm. In DECSO modelled NO₂ concentrations are constraint by the NO₂ satellite observations using a Kalman filter technique. SO₂ is derived for each year by applying a correction factor to the MEIC SO₂ emissions based on the derived provincial trend in SO₂.
Both the SO₂ and NO_x emissions are used in the regional chemical-transport model CHIMERE. This model participates in an ensemble forecast of 9 operational models for East China. The ensemble forecast is compared to in-situ observations and performs better than each individual model.
The results will be validated using ground observations of the network of in-situ instruments in the cities and also using measurements at specific ground stations in different types of forests.

Using the inversion algorithm DECSO we derived monthly NO_x emissions on a 0.25 x 0.25 degree resolution over East Asia for an 11-year period (2007 to 2017) based on OMI observations. We used these emissions to analyse trends and seasonal cycle of maritime emissions over Chinese seas. No effective regulations on NO_x emissions have been implemented for ships in China, which is reflected in the trend analysis of maritime emissions. The effect of maritime emissions on the air quality over land will be discussed. Simulations by an atmospheric chemistry transport model show a notable influence of maritime emissions on air pollution over coastal areas, especially in summer. The satellite-derived spatial distribution and the magnitude of maritime emissions over Chinese seas are in good agreement with bottom-up studies based on the Automatic Identification System of ships.

Dose-Response Functions (DRFs) are a very important tool for estimating the deterioration – degradation of structural materials, used in both modern constructions and cultural heritage monuments, due to atmospheric pollution and climatological parameters. To date, the available in literature DRFs make use of ground based air pollution and climatological data in order to model materials’ deterioration. This limits the possibility of using DRFs only in areas where the necessary ground based data, are available. In this study are presented the first results of the attempt to develop new kind of DRFs that will model the deterioration – degradation of materials, in particular carbon steel and limestone, using only satellite data. This new kind of DRFs is expected to help in monitoring materials deterioration – degradation to areas where there are no available ground based data as well as expanding the usage of satellite data by introducing a totally new field of implementation. The term “Satellite Sensed Data-Dose Response Functions (SSD-DRFs)” is proposed for this new kind of DRFs.

Atmospheric particulate matter (PM) from both natural and anthropogenic emission sources can bring adverse effects on public health. Long-term exposure to particular matter with aerodynamic diameters less than 2.5 μm (PM_2.5) can cause lung and respiratory diseases and even premature death. PM_2.5 not only threatens people's health, but also causes the decrease of atmospheric visibility and the degradation of the city scenery. In recent years, with the rapid development of industrialization and urbanization, PM_2.5 has become the primary air pollutant in China, especially in most major cities, such as Beijing, Shanghai and Guangzhou, where the fastest economic growth has occurred. To understand the effects of PM_2.5 on the Earth’s environmental system and human health, it is necessary to routinely monitor PM_2.5. Given the considerable advantages of satellite remote sensing, especially the large coverage provided at the spatial scale and stable continuity at the time scale, aerosol optical depth (AOD) retrieved from satellite sensors has been widely considered to be a good method for atmospheric PM monitoring. There was a distinct spatial pattern of correlation between AOD retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based in situ PM concentrations. In this study we established three types of AOD-PM_2.5 retrieving model based on Physical and Artificial Neural Network technology. Firstly, we using satellite retrieved AOD and other meteorological parameters such as the planetary boundary layer height (PBLH), temperature (TEMP), relative humidity (RH), U wind component (U), V wind component (V), surface pressure (SP), and large-scale precipitation (LSP), to establish GA-BP ANN AOD-PM_2.5 retrieve model. The test correlation coefficient R reached 0.83. This model is seasonally and regionally stable. The satellite AOD and ANN retrieved PM_2.5 has the similar trend and distribution, and the trained model have practical as well as theoretical value. Then, a physically-based model is developed to estimate the concentration of PM_2.5, in which, fine mode aerosol optical depth (AOD) at 440, 550 and 675nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM_2.5) data, relative humidity (RH) and boundary layer height (BLH) data are used. We proposed a new way to derive the integrated extinction efficiency <Qext> by using aerosol parameters in AERONET together with PM_2.5observations. The results show that R²can reach to 0.70 and Root Mean Square Error (RMSE) is 33.67 μg/m³ at Beijing site at 440 nm. The results are comparable with other findings based on physically-based methods. Finally, we also tested the performance of Specific Particle Swarm Extinction Mass Conversion Algorithm using remotely sensed data. Ground-level observed PM_2.5, Planetary Boundary Layer Height (PBLH) and relative humidity (RH) reanalyzed by European Centre for Medium-Range Weather Forecasts (ECMWF), aerosol optical depth (AOD), Fine Mode Fraction (FMF), particle size distribution, refractive indices from AERONET of Beijing area are used. The validation of PM_2.5 from SPSEMCA algorithm to AERONET observation data and MODIS monitoring data achieve acceptable results, R = 0.70, RMSE = 58.75μg/m³ for AERONET data, R = 0.6, RMSE = 48.36 μg/m³ for MODIS data, respectively. Then the trend of temporal and spatial distribution of Beijing and surrounding areas has been revealed. On the whole, this study will provide practical method for PM_2.5 estimation based on satellite data.

The present study aims to investigate spatio-temporal evolution and trend in the aerosol optical properties (aerosol optical depth, AOD; Ångström exponent, AE), qualitatively identify different aerosol types and sources over an urban city, Nanjing in the Yangtze River Delta, East China. For this purpose, the Collection 5.1 Level-2 data obtained from the MODIS sensor onboard Terra and Aqua satellites, the MISR, and the OMI for the period between 2002 and 2015 have been analyzed. A notable spatiotemporal heterogeneity was observed in the optical properties of aerosols on the seasonal scale over East China. The seasonal mean AOD₅₅₀ (AE_470-660) was found to be maximum with 0.97 ± 0.48 during summer (summer) (1.16 ± 0.33) and a minimum of 0.61 ± 0.28 during the winter (spring) season (0.80 ± 0.28). AE_470-660 found higher in summer indicate relative abundance of fine mode aerosols over the coarse mode. Annual mean Terra AOD₅₅₀ showed a strong decreasing trend (–0.70% year^-1), while the Aqua exhibited a slight increasing trend (+0.01 year^-1) during the study period. We also used the HYSPLIT model for presenting cluster trajectory analysis which revealed that the airmasses from different source regions contributed greatly to aerosol loading. Using the AOD-AE method (hereafter called as Technique-I), five major aerosol types were identified. In all the seasons, the mixed (MX) type of aerosol is dominant followed by the biomass burning/urban-industrial (BU) and desert dust (DD) aerosol types during summer and spring seasons, respectively. Further, the sub-classification of aerosol types was carried out considering into account of the characteristics of absorbing aerosol index (AAI) (hereafter called as Technique-II). The two clustering techniques showed reasonable consistency in the obtained results. The various aerosol types (absorbing and non-absorbing) and their change over a region are highly helpful in fine tuning the models to decrease the uncertainty in the radiative and climatic effects of aerosols.

With the rapid development of China's economy and high rate of industrialization, environmental pollution has become a major challenge for the country. The present study is aimed at analyzing spatiotemporal heterogeneities and changes in trends of different aerosol optical properties observed over China. To achieve this, Collection 6 Level 3 data retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS; 2002-2016) and Ozone Monitoring Instrument (OMI; 2005-2016) sensors were used to investigate aerosol optical depth (AOD₅₅₀), Ångstrӧm exponent (AE_470-660) and absorption aerosol Index (AAI). The spatial distribution of annual mean AOD₅₅₀ was noticed to be high over economically and industrialized regions of east, south and northeast regions of China; while low aerosol loadings were located over rural and less developed areas of west and northeast of China. High AE_470-660 (>1.0) values were characterized by the abundance of fine-mode particles and vice-versa, likely attributed to large anthropogenic activities. Similarly, high AOD with corresponding high AE and low AAI were characterized over the urban-industrialized regions of central, east and south of China during most of the months; being more pronounced in June and July. On seasonal scale, AOD values were found to be high during spring followed by the summer and autumn, and low during the winter season. It is also evident that all aerosol parameters showed a single peak frequency distribution in all seasons over entire China. Further, the annual, monthly and seasonal spatial trends revealed a decreasing trend in AOD over most regions of China, except in the southwest of China which showed a positive increasing trend. Significant increasing trends were noted in AAI for all the seasons, particularly during autumn and winter, resulted in a large amount of absorbing type of aerosols produced from biomass burning and desert dust.

Cloud and aerosol are the important part of the gas system and plays an important role in the radiation budget. The Changes in aerosols and associated radiative forcing, and their impact on the climate system have been highly valued by the scientific community in the recent years due their large uncertainty factor. In recent decades, the rapid development of social economy in East China, aerosol particles emissions and production of secondary aerosol pollutants by photochemical reactions are also increasing, caused serious environmental and climate problems. Hence, it is important to study the temporal and spatial distribution of aerosols in this region, and understand the aerosol-cloud interactions.

In the present study, we examined the spatial and temporal variations in aerosol optical depth (AOD) at 550 nm and its relationship with various cloud parameters derived from the Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra satellite during 2000-2016. High mean AOD values were observed in almost all regions during the summer season, with low values in autumn/winter seasons. The Angstrom exponent that increases with AOD is opposite; to what would be the case if swelling of particles due to hygroscopic growth near cloudy areas played a major role in the MODIS data. We then analyzed the relationships between AOD and four other cloud parameters, namely water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP). The correlation between AOD and CF was greater than 0.5 in Shandong province, and in the northern part of the study area, it is lower than 0.2. The analyses showed strong positive correlations between AOD and WV over Fujian and Zhejiang Provinces. The correlation between AOD and CF was positive for urban and desert regions; and negative over coastal locations of East China. AOD showed a similar correlation with CTP and CTT in all regions with negative correlation coefficient which also follows the second indirect effect of aerosols; but positive correlations were found in some parts of the southern region.

Thirteen years of continuous tropospheric NO₂ observations from OMI provide insight into the air quality levels in China. The use of these daily NO₂ concentrations for emission estimates of NO_x attempts to fill in for inaccuracies and temporal discontinuities of the bottom-up emission inventories, keeping the NO_x emission estimates up-to-date. Furthermore, this top-down approach inherently takes into account rapid changes in emissions caused by economic activity, political regulations or even events such as fires.

Within the framework of the newly developed EU QA4ECV project and the GlobEmission project, algorithms have been developed to derive NO₂ concentrations and NO_x emissions, respectively, from space with more accuracy. We demonstrate the detection of spatially and temporally heterogeneous changes in NO_x emissions derived from the DECSO v5.1 algorithm after it is applied to OMI NO₂ observations derived with the DOMINO v2 and QA4ECV algorithms over China from 2004 up to 2017. QA4ECV provides improved OMI NO₂ retrieval products with detailed uncertainty estimates and quality indicators. We investigate to what extent it is now possible to also improve trend detection limit as compared to the previous DOMINO v2 datasets. We observe a distinct increasing trend until about 2012 and a distinct decreasing trend from about 2012 onwards.

To put these trends into perspective we compare them with public data on energy consumption, traffic records, shipping and economic activity, and the environmental policies of China and we study their impact on the emission trends. The emissions are also compared to bottom-up emissions and their inconsistencies are discussed.

Tropospheric vertical column densities (VCDs) of NO2, HCHO, O3 and SO2 retrieved from MAX-DOAS network can be used to study temporal and spatial evolution of atmospheric pollution, and its transport and regional characteristics. Besides, MAX-DOAS network measurements can also be used to validate results from WRF-Chem model and products derived from spaced-based instrument, such as OMI and OMPS. We apply the MAX-DOAS profiles to the satellite retrievals and find that the accuracy of these modified satellite products has been improved. These modified satellite products can be performed every day with high spatial resolution and can be used to monitor air pollutants in real time and analysis chemical processes. The combination of results from ground-based and space-based measurements assists government in establishing appropriate emission control strategies.

The Environmental trace gas Monitoring Instrument (EMI) onboard Chinese high-resolution remote sensing satellite GaoFen-5 is an UV-VIS imaging spectrometer. The primary objective of EMI is to quantitatively measure global distribution of tropospheric and stratospheric trace gas. EMI is a nadir-viewing push broom spectrometer with a moderate resolution of 0.3 to 0.5 nm in the range 240 to 710 nm. The oxygen A-band is the best suited to retrieve cloud information. But the oxygen A-band lies outside the spectral range of EMI. So we retrieve the effective cloud fraction and effective cloud altitude using the O₂-O₂ absorption band at 477nm and 360nm. Similar to OMI cloud algorithm, from the measured radiance and irradiance spectra a reflectance spectrum is made, and a DOAS fit is applied to this reflectance spectrum, yielding the continuum reflectance and O₂-O₂ slant column density. Then these parameters are converted into cloud fraction and cloud height by interpolating in the look-up table. The look-up tables are generated by DOAS fit on spectra simulated using the VLIDORT, in the forward and inverse model, the cloud is replaced by a Lambertian surface with the albedo A_L=0.8. For fixed values of geometry, ground surface albedo, and ground surface altitude, which is obtained from the DOAS fit continuum reflectance as of function of cloud fraction and cloud height, the O₂-O₂ SCD is also a function with these parameters. So look-up tables are created for all relevant geometries, ground surface albedo and altitudes. Simultaneously, we use the O₂-O₂absorption bandaround 360nm to retrieve the cloud fraction and cloud height. The results of the two methods are integrated as the final cloud parameters. The effective cloud fraction and effective cloud height are used for cloud correction in the retrieval of trace gas like NO₂ and O₃.

The stationary satellite of Himawari-8 has high spatial and temporal resolution, so it can be monitored in real time. In the visible(VIS) band ,assuming different surface cover type, solar and the satellite angle and the aerosol type ,using discrete ordinate radiative transfer model (vlidort), combined with the historical data of aerosol profile and singe scattering albedo of ground-based instruments and calipso lidar ,to establish a simulation of the albedo of the top of the atmosphere .In the inversion process, it can assimilate modis high-precision surface reflectivity products, and adopt different inversion method and cloud removal algorithm according to different surface characteristics.Through the spring test in Beijing, the absorption of aerosol has a major influence on the inversion of aerosol optical thickness, and the wind and sand weather in Beijing spring has high scattering characteristics.

China Ozone pollution caused by photochemical reaction becomes a serious problem in China in the recent years. In this study, ozone profiles retrieved from the Ozone Monitoring Instrument (OMI) and the Ozone Mapper and Profiler Suite (OMPS) using the Optimal Estimation method (OEM) over China from 2013 to 2017. We apply soft calibration to OMI and OMPS radiance to eliminate the systematic component of fitting residuals. Tropospheric Ozone Columns (TOCs) is directly derived from the total column using the known tropopause. Hyper-spectral resolution Fourier transform infrared spectrometry (FTS) data at Hefei (31.86˚N, 117.27˚E), ozonesonde data at Hong Kong (22.20˚N, 114.10˚E) and Beijing (39.92˚N, 116.46˚E) are used to validate the tropospheric ozone column (TOC). The monthly variation of tropospheric ozone column (TOC) from 2013 to 2017 are also evaluated. In addition, we characterize the ozone and aerosol concentrations in the troposphere and surface UV irradiance to quantify the effects of aerosol particles and surface UV irradiance on the variability of tropospheric ozone.

We retrieve sulfur dioxide (SO2) vertical columns from the Ozone Monitoring instrument (OMI) and Ozone Mapping and Profiler Suomi National Polar-orbiting Partnership spacecraft (OMPS) using optimal estimation method (OEM) over China from 2013 to 2017. Comparison between OEM retrievals and the principal component analysis (PCA) product shows that a general good agreement between using the different algorithms is obtained with a correlation coefficient of 0.7249 and a slope of 0.8789 over eastern China. Validations with ground-based Multi Axis Differential Optical Absorption Spectroscope (MAX-DOAS) measurements show that a monthly averaged ground-based SO2 results and coincident OMI SO2 results using OEM agree very well. The seasonal cycle of SO2 is consistent in both data sets with a maximum in winter, on average, 6×10e16 molecules*cm−2 in Xianghe (a key pollution area), and a minimum in summer, which has a mean value of 2×10e16 molecules*cm−2 there. Winter is the domestic heating season. Both show that SO2 originates mainly form human sources rather than natural ones. The spatio-temporal distribution over China shows that the pollution is mainly concentrated in Beijing-Tianjin-Hebei area and Sichuan Basin. And the yearly averaged SO2 results show that the SO2 vertical columns are decreasing from 2013, 4.57×10e16 molecules*cm−2, to 2017, 0.89×10e16 molecules*cm−2 in those key pollution areas. SO2 and NO2 are major aerosol precursors, and SO2 and NO2 respectively are sources of pollution mainly from coal- fired power plants and motor vehicle emissions. We also investigate the relationship between SO2 emission and aerosol production in Beijing. A stronger correlation between the SO2 concentrations and aerosol optical depths (AODs) measured by the MODIS satellite instrument than NO2 concentrations with AODs obtained in winter suggests that anthropogenic SO2 is the major contributor to the aerosol content during the period of the year.

The TROPOspheric Monitoring Instrument (TROPOMI) is a passive nadir-viewing satellite borne imaging spectrometer on board the Sentinel-5 Precursor (S5P) satellite which was launched on 13^th October 2017. Compared to previous satellite instruments such as SCIAMACHY, GOME-2 and OMI, TROPOMI provides much higher spatial resolution with a ground pixel size of ~25km² (3.5km × 7km) at nadir. TROPOMI provides global observation of cloud, aerosol and multiple atmospheric trace gases and greenhouse gases. The operational NO₂, HCHO and O₃ products of TROPOMI are compared to ground based MAX-DOAS and FTS observations in China. In addition, the influence of aerosol and trace gases vertical distribution profiles on TROPOMI retrieval are estimated by using MAX-DOAS derived aerosol and trace gases profiles in the satellite retrieval. The Environmental Monitor Instrument (EMI) is one of the hyper-spectral payloads on board on the Chinese Gao Fen 5 (GF-5) satellite. The satellite is expected to be launch in May 2018. EMI provides global observations of atmospheric trace gases with a moderate resolution of ~624km² (48km × 13km). Combing with the higher spatial resolution TROPOMI data, we could estimate the spatial averaging effect over pollution hotspot of the EMI observations.

This paper presents formaldehyde (HCHO) column observed by Ozone Mapping and Profiler Suite (OMPS) and the TROPOspheric Monitoring (TROPOMI) instrument. OMPS-NM on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite was launched successfully on 28 October 2011, and TROPOMI on board the Copernicus Sentinel-5 Precursor satellite was launched successfully on 13 October 2017. Both of them cross the equator each afternoon at about 13:30 local time (LT), thus provides a great opportunity to compare HCHO column observed by OMPS and TROPOMI instruments. Besides, tropospheric HCHO columns observed by OMPS was compared with that measured by the high resolution Fourier transform infrared spectrometry (FTS) which is located in the western suburbs of Hefei (117.17E, 31.9N) in this paper, and they show the similar trend with the correlation coefficient (R) of 0.78. Tropospheric HCHO column observed by OMPS and TROPOMI both keeps good agreement with that measured by MAX-DOAS with the correlation coefficient (R) of 0.76.

It is presented in this paper the recent progresses of ESA-MOST China Dragon Cooperation Program (ID. 32249) in the field of microwave marine remote sensing including (1) GF-3 SAR ocean wind retrieval: the first view and preliminary assessment; (2) Preliminary analysis of Chinese GF-3 SAR quad-polarization measurements to extract winds in each polarization; (3) Assessments of ocean wind retrieval schemes and geophysical model functions used for Chinese GF-3 SAR data at each polarization; (4) Combined co- and cross-polarized SAR measurements under extreme wind conditions; (5) Joint retrieval of directional ocean wave spectra from SAR and RAR; (6) The first quantitative ocean remote sensing by using Chinese interferometric imaging radar altimeter onboard TG-2.

Sentinel-1, Gaofeng-3 and Radarsat-2 offer the unique possibility to observe the ocean surface at high resolution in both co- and cross- polarizations. This work shows how this new capabilities allow a new vision of the ocean surface over extreme events such as Hurricanes or Typhoon.

A database has been completed to gather all Sentinel-1 acquisitions over hurricane eyes. A collection of about 50 images now exist and a strategy to optimize the acquistitions over hurricane has been developed, proposed and tested with ESA Sentinel-1 Mission planning team.

Based on this data, an algorithm for cean surface wind speed measurements has been developped. Its performances are compared to analysis performed by hurricane experts in the hurricane centres, airborne measurements and parametric models.

SAR Radar-cross section over extreme are also directly compared to brigthness resolution from SMOS and SMAP L-band radiometer. In situation of rain rate less than 20 mm/hr, a striking linear relationship is found between both active and passive sensors. As interpreted, this can correspond to a regime change of the air-sea interactions during extreme events.

A new geophysical model function (GMF), called C_SARMOD2, has been developed to relate high resolution C-band Normalized Radar Cross Section (NRCS), acquired in VV polarization over the ocean, to the 10 m height wind speed. A total of 3078 RADARSAT-2 and Sentinel-1A VV-polarized SAR images acquired under different wind speed conditions were collocated with in situ buoy measurements. The paired dataset was used to derive transfer functions and coefficients of C_SARMOD2, and then to validate the wind speed retrievals. With almost no bias and a root mean square error of 1.84 m/s. Two representative quad- and dual-polarization SAR images acquired from coastal regions are used as case studies to examine C_SARMOD2 performances. The case study and statistical validation results suggest that the proposed C_SARMOD2 has the potential to measure coastal wind speeds at sub-kilometer resolutions. Although derived from low resolution NRCS measurements, this study also confirms the great robustness of CMOD5.N and recent CMOD7 when applied to SAR data. In addition, it shows that with the new generation of SAR satellite-borne sensors, it is no longer mandatory to rely on scatterometers in order to build a GMF that will be used for SAR applications. Such an approach is particularly important in view of the upcoming RADARSAT Constellation Mission (RCM) with new polarization configurations. Moreover, it also opens new perspectives on the derivation of GMFs in HH-polarization. However, these results also suggest that for coastal areas, the increase of the resolution to define the GMF is less important than adding other geophysical parameters to improve wind retrieval performance. This advocates for the necessity of revisiting the methodologies for ocean surface wind speed measurements in coastal areas.

Abstract:

Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. GF-3 provides many SAR images for oceanography with its high resolution and large coverage. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean.

The paper proposed an empirical algorithm for significant wave height from the GF-3 wave mode data, called QPCWAVE_GF3, which contains six image and spectra parameters of radar incidence angle, normalized radar cross section, imaging normalized variance, azimuth Cut-Off, peak wavelength and direction.

The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys. The assessment shows a good agreement with root mean square error from 0.5m to 0.6m, and scatter index around 20%.

Keywords: Gaofen-3; significant wave height; empirical algorithm

This study proposed a joint method to retrieve directional ocean wave spectra from synthetic aperture radar (SAR) and real aperture radar (RAR). The method broke through the limitations existed in the single-sensor wave retrieval, by combining two sensors’ characteristics. First, the H_s was estimated from the SAR cutoff using an empirical model. On the other hand, the relative wave spectra at large scale were derived from RAR modulation spectra. After that, the first guess spectra were estimated by relative wave spectra and SAR-derived H_s. Finally, the full wave spectra at small scale were retrieved from the SAR image cross spectra with the help of first guess spectra using the Max-Planck-Institute scheme. The 180° ambiguity of retrieved wave spectra was removed using the imaginary part of SAR cross spectra. Both simulation and collocated data were used to validate the joint method. This method helps to complement traditional wave retrieval methods.

Vegetation and soil moisture are key parameters in the studies of global water and carbon cycles. In this study, based on the commonly used zero-order radiative transfer model ( model), a two-step approach for retrieving vegetation optical depth (VOD) and soil moisture using only SMOS H-polarized multi-angular measurements was presented. At a first step, VOD is estimated by minimizing the soil signal and separating the vegetation signal from the multi-angular brightness temperature. In the retrieval, the angular feature of soil emission is used and the VOD is retrieved directly from the refined H-polarized multi-angular brightness temperature without any field correction or auxiliary soil or vegetation data. This retrieved algorithm is first validated by theoretical modeling and experimental data. The results demonstrate that VOD can be reliably estimated using this algorithm. The retrieved VOD is then compared with aboveground biomass, which shows strong correlation. Global mean VOD for the years of 2010 to 2011 generally shows a clear global pattern and corresponds well to the land cover types. The VOD of nine representative regions that are homogeneously covered with different vegetation types is compared with Normalized Difference Vegetation Index (NDVI). The results indicate that the VOD can generally reveal vegetation seasonal changes and can provide unique information for vegetation monitoring. At a second step, after estimating VOD, soil moisture can be retrieved based on model using H-polarized multiangular brightness temperature. By analyzing a simulated database using the advanced integral equation model (AIEM), an effective surface roughness parameter that considers the influence of rms height, correlation length and correlation function shape on surface reflectivity was presented. Using this effective surface roughness parameter, a new parameterized surface reflectivity model is based on a simple-empirical model, the Hp model, is developed. Comparison with AIEM simulations over a wide range of soil conditions indecates a good performance of this model. This approach is then applied on SMOS data, retrieved soil moisture in Africa exhibitus reasonable patterns and temporal changes. Validation using in situ soil moisture from two soil moisture monitoring networks of Yanco Region and Little Washita watershed over 2010-2011 indicates that this approach performs well in surface soil moisture retrieval. Retrieved soil moisture agrees well with the in situ measurements with the RMSE of about 0.04 m³/m³.

Soil Moisture Content (SMC) is a key parameter in the study of agriculture and the global water cycle. In recent years, with the development of Global Navigation Satellite System, a new SMC remote sensing technique called GNSS-Interferometry and Reflectometry (GNSS-IR) was proposed by K. M. Larson et al. Compared with traditional remote sensing technique, it can provide retrievals at intermediate spatial scales with high time resolution, and is easier in its operation and management.

The GNSS-IR technique utilizes the composite signals formed by the interference effect occurred between the direct and the ground reflected navigation signals. These signals, which contain the physical information of the soil, are routinely recorded in a normal geodetic receiver in the form of Signal-to-Noise Ratio (SNR) data. Part of the efforts made in this field are to model SNR more accurately and extract SMC-related metrics from the SNR data. Recent contributions made by our group were to propose a semi-empirical SNR model which aimed at reconstructing the direct and reflected signal from SNR data and at the same time extracting frequency and phase information that is affected by soil moisture as proposed by K. M. Larson et al. This model worked as a curve-fitting model, and it was built through approximating the direct and reflected signal by a second-order and fourth-order polynomial, respectively, based on the well-established SNR model. Compared with other models (K. M. Larson et al. 2008, T. Yang et al. 2017), this model can improve the Quality of Fit (QoF) with little prior knowledge needed and can allow soil permittivity to be extracted from the reconstructed signals.

In this oral presentation, we will showed how this model was validated through simulation and experimental data processing. The data we used were collected by previous researchers at Lamasquère, France. Main results and finding are as follows:

Firstly, the QoF obtained using this model was improved by around 40%. It could ensure good fitting quality even in the case of irregular SNR variation. This advantage also results in better estimation of the frequency and phase information. However, we found that the improvement on phase estimation could be neglected.

Secondly, SMC could be retrieved from reconstructed signals. The results were satisfactory when the satellite elevation angle is between 5 degrees and 15 degrees. Additionally, the soil moisture calculated from the reconstructed signals was about 15% closer in relation to the ground truth measurements.

Finally, some phenomena were discovered regarding retrieval ambiguity and error sensitivity. These will also be stated and discussed in this oral presentation.

Microwave (active and passive) and optical sensors are and will be deployed at an alpine meadow test site near Maqu city (China) on the Tibetan Plateau to study soil-vegetation-atmosphere processes and to validate physically based earth observation models used for retrievals from satellite data. Presented will be details and first results of a broad band full polarimetric scatterometer. The purpose of the scatterometer is to retrieve soil moisture content (SMC) and basic vegetation properties (vegetation water content, biomass, leaf area index) of the alpine meadow. This retrieval is done through inversion of backscattering models that link the measured backscattering coefficient σ⁰ to the SMC and aforementioned vegetation properties.

The scatterometer consists of a vector network analyser (VNA) connected to two dual polarization broadband antennas elevated 5 m above the surface. The radar return for co- and cross- polarization is measured over a 1 – 10 GHz frequency range (3 MHz resolution). The scatterometer calibration and validation was performed by means of a rectangular metal plate and metal dihedral reflector. For the co-polarization channels the measured radar cross section of the dihedral reflector matched a theoretical model within ±1 dB for 3 – 10 GHz. The calculation of σ⁰ from the measured radar return for the given site geometry and the frequency dependent radiation patterns will be explained as well.

Two experiments were performed. With the first experiment the antenna azimuth- and zenith angles were varied so that σ⁰ was measured over different parts of the surface under various angles of incidence θ_i. The azimuth range was -20° to 20° with 5° increments and the antenna zenith angles were varied such that θ_i varied from 35° to 70° with 5° increments. For the second experiment the orientation of the antennas were fixed and σ⁰ was recorded every hour during a long period (August 2017 – February 2018).

Analysis of the results from the first experiment will be presented to demonstrate the electromagnetic homogeneity of the ground surface. Since for the second experiment the antenna orientations were fixed it was not possible to measure multiple non-overlapping spatial samples of the surface. Therefore, to decrease the inherent uncertainty of the measured σ⁰ due to fading, frequency averaging will be applied to the data of the second experiment. A first glance at the results of the second experiment shows that during a 12 day period in August 2017 σ⁰ changed in parallel to in-situ measured SMC at 5 cm depth. We observe a decay of the σ⁰ during dry days and a sudden increase of σ⁰ after rainfall. The temporal behaviour of σ⁰ holds for most of the frequencies with all polarization channels.

Topographic mapping and surface motion estimation with spaceborne SAR sensors are the main topics of the Dragon-4 project "Multi-baseline SAR processing for 3D/4D reconstruction (id 32278-2)” under the framework of THREE- AND FOUR- DIMENSIONAL TOPOGRAPHIC MEASUREMENT AND VALIDATION (id 32278). In Dragon-4, we work on different test sites investigating the following topics:

1. Topographic mapping with SAR. Interferometric SAR (InSAR) is the main method for the generation of digital elevation models (DEM) with SAR observations. The StereoSAR-assisted InSAR topographic mapping strategy is presented in the following three steps. Firstly, the StereoSAR DEM can be employed as a reference to remove the main topographic phase from the InSAR interferogram, which can reduce the fringe frequency and facilitate the phase unwrapping of the InSAR interferogram. Then, the StereoSAR DEM can be used to calibrate the unwrapped phase of InSAR to determine the absolute phase deviation. Finally, the StereoSAR DEM and InSAR DEM are fused by weighted averaging, in which the determination of reasonable weights is the key issue to be solved. Thus the random height error can be effectively reduced and the StereoSAR DEM can also fill in the void in the original InSAR DEM. The effectiveness of the proposed methods was demonstrated by experimental results with high-resolution TerraSAR-X data pairs for the test site of Mount Song, one of the five sacred mountains in China.

2. Urban subsidence analysis.SAR systems can measure distances and movements with high precision. Using for example PS-InSAR, deformations can be estimated with a very high precision. The long-term surveillance of urban subsidence and the infrastructure stability in Shanghai is our major research goal since Dragon-1. With data starting from ERS-1, over ENVISAT ASAR, ALOS PALSAR, up to modern systems like TerraSAR-X, COSMO-SkyMed, PALSAR-2, and Sentinel-1, we continuously monitor the subsidence over Shanghai for far over a decade now. Furthermore, the subsidence distribution of Wuhan is derived from the long-term Sentinel-1 data stack. The InSAR-derived results also highlight active motions of built-up areas and infrastructures, such as some communities and railways segments. It can benefit safety screening and risk assessment. Furthermore, the Sentinel-1 data stacks are also applied in monitoring the infrastructures such as bridges.

Abstract - 3D SAR imaging by tomographic processing of multibaseline interferometric data has emerged for operational spaceborne monitoring of forest biomass in incoming or next ESA missions. However, a few open issues, or improvement needs, still stand, in particular of radiometric accuracy of the most diffused superresolution processing, the adaptive Capon method. After its introduction in SAR Tomography by University of Pisa a decade and a half ago, despite the widespread experimental use its basic structure, and issues, remained unchanged. In this work, an alternative improved (double) adaptive algorithm for spaceborne forest SAR Tomography is tested, characterized, and tuned by simulations, showing that it can furnish a better tomographic performance trade-off than Fourier and classic Capon Tomography. First corresponding low frequency real SAR data tests are also performed.

3D SAR Tomography (TomoSAR) [1-2] is a well established technique, for which operational interests related to spaceborne SAR missions have emerged, in particular for urban and forest applications. TomoSAR stems from advanced multibaseline (MB) Interferometry, exploiting the MB cross-track array typically constructed by multiple SAR passes for beamforming and steering along the vertical axis, estimating the 3D distribution of the backscattered power in volumetric scenarios. This is typically accomplished by spatial (baseline) spectral analysis [1,2], each scattering component at a given height originating a corresponding spatial frequency component in the MB data vector.

In particular, concerning the incoming ESA mission BIOMASS for forest monitoring, the well know, tested and widespread superresolution Capon method [2,3] is foreseen to be exploited, which in a simple adaptive and light burden manner is able to get a height resolution beyond the overall baseline-related Rayleigh limit, and reduce layer cross-talk i.e. height sidelobes especially for typically non perfectly uniform baselines. This, in parallel to Fourier TomoSAR, that offers limited layer resolution capability and sensible cross-talks. Unfortunately, beyond the attention given to long-term temporal decorrelation [4] affecting all the TomoSAR methods for forest applications that are not based on companion satellite concepts, it is well known that Capon TomoSAR is affected by radiometric issues, presenting in the practical applications, with limited number of looks and residual data miscalibration, power losses in the height-resolved backscatterers, resulting in a non-linear behaviour.

It is thus the goal of this work to tune and test an alternative adaptive method [5] for TomoSAR imaging, offering height superresolution and sidelobe cleaning with improved radiometric capabilities. Both simulated analyses will be developed of the 3D imaging quality and radiometric fidelity, and first low frequency real forest data tests carried out.

Insights in the Capon radiometric issues are first given. In particular, a source of the power losses resulting in the Capon non-linearities is the self-cancellation phenomenon intrinsic in the Capon concept. To get the height superresolution and cross-talk reduction, the Capon algorithm relies on the knowledge of the MB array response (steering vector) and of the spatial (baseline) correlation matrix, to adaptively reject the interfering scattering coming from height directions different from that currently targeted during the height scan [2,3].

In this process, deviations of the actual steering vector from the nominal one, related to residual miscalibrations typically after atmospheric compensation, and imperfect correlation estimates, lead Capon to misinterpret the data component from the targeted height as an unwanted interference to be reduced, so tending to cancel also the signal of interest, resulting in non-linear radiometric sensitivity. This can be only partially controlled by the well-known diagonal loading method, that tends to brake the critical adaptive interference rejection.

The method presented here to cure these issues of Capon TomoSAR is based on a specific preconditioning of the MB data before adaptive spectral estimation [5]. In particular, a pre-estimate of the current targeted component is (partially) compensated in the data to bypass the misinterpretation in the adaptive processing that triggers the self-cancellation. Two different partial compensation strategies are experimented in this work. The new TomoSAR method can be considered to be double adaptive, and advantageously trade-off superresolution, in particular the sub-Rayleigh resolution level, with the reduced radiometric losses i.e. improved linearity.

First simulated analyses are reported for a controlled characterization of the radiometric behaviour of the proposed method, with the Fourier beamforming and (loaded) Capon as comparison methods.

Typical realizations are shown of Tomo profiles for the new method and the reference algorithms. The MB array is composed by 6 almost uniformly spaced passes, which is a typical BIOMASS mission scenario, different residual phase miscalibration levels are applied, and the processed looks follows typical figures for the forest application. The backscattering scenario consists of two both equi and different power speckled sources, height-compact for an easier investigation, with typical forest SNR, and height separation slightly sub-Rayleigh. It is shown how the new method can offer a very good recovery of the expected peaks level, with amplitudes very close to the Fourier ones, overcoming the sensible Capon radiometric loss, still producing a well satisfactory superresolution and low sidelobes.

In the attempt to optimize the global tomographic performance trade-off, height accuracy is also analyzed and the partialization factor of the compensation step in the method tuned, finding an advantageous knee point. A more extended characterization of the Capon radiometric issues and of the new tuned method is also performed, producing sensitivity plots.

First real data trials are also performed of the new double adaptive method, for a line of low frequency airborne MB SAR data, taken over a forest. The proposed advance can be useful in the context of both the BIOMASS and the SAOCOM-CS programs.

[1] A. Reigber, A. Moreira, “First demonstration of airborne SAR tomography using multibaseline L-band data,” IEEE TGRS, 38(5), pp.2142–2152, 2000.

[2] F. Gini, F. Lombardini, and M. Montanari, “Layover solution in multibaseline SAR interferometry,” IEEE TAES, 38(4), pp.1344–1356, 2002.

[3] F. Lombardini, J. Ender, L. Rößing, et al., “Experiments of interferometric layover solution with the three-antenna airborne AER-II SAR system,” Proc. IGARSS 2004.

[4] F. Lombardini, F. Cai, “Temporal decorrelation-robust SAR tomography,” IEEE TGRS, 52(9), pp.5412-5421, 2014.

[5] F. Lombardini, F. Viviani, “Radiometrically robust superresolution tomography: first analyses,” Proc. IGARSS 2016.

Synthetic Aperture Radar (SAR) provides precise range and range-difference measurements. These measurements suffer from speckle noise, when there are more than one dominant scatterer in a resolution cell. However, when focusing on dominant and stable point-like scatterers, often called permanent scatterers (PS), the measurement of the backscattered signal is not affected by speckling and allows for precise measurement of distance differences using the interferometric phase differences. This is the reason for the importance of stable point-scatterers in SAR remote sensing, which are the base for techniques like PS-InSAR, but also for absolute position measurements with SAR geodesy.
Such point-scatterers can be found in rather large numbers in urban areas. The spatial density of these PS points depends on the structure of the area of interest, but also on the used wavelength, with shorter wavelengths providing a denser PS network.
In many areas outside of cities, there are only few stable point-scatterers to be found. Artificial targets, like corner-reflectors, can be an alternative solution for areas without ‘natural’ available point-scatterers. However, as corner-reflectors are large and expensive, they cannot be widely used outside of secured test areas, because they are prone to misuse and theft.
We propose the use of small, inexpensive artificial targets that can be used in large numbers under such circumstances. We demonstrate the use of such targets with TSX. For C-band in Sentinel, larger targets are necessary. We will demonstrate the possibility to also use cheap targets in C-band for co-pol and cross-pol cases.

Mapping forest height makes a great contribution to quantitative estimation of forest above ground biomass, leading to a better knowledge of carbon stocks stored in forests. In recent years, polarimetric SAR interferometry (Pol-InSAR) and SAR tomography (TomoSAR) techniques have become major tools for forest height retrieval based on SAR measurements. In polarimetric SAR interferometry, forest height is retrieved from single baseline polarimetric data, under the assumption of the random volume over ground (RVoG) model. For SAR tomography, instead, fully 3-D back-scattering profiles are reconstructed by jointly focusing data from multiple flights and forest height is then obtained by analyzing the shape of the vertical profiles. In this work, we aim at comparing these two techniques in the context of P-Band SAR retrieval of forest parameters in tropical areas. To accomplish this goal, both techniques are applied to the same SAR dataset at P-band, which is the one acquired by ONERA in French Guiana during the TropiSAR campaign. PolInSAR and TomoSAR forest height maps are then analyzed using Lidar measurements.

Forest above ground biomass (AGB) retrieval by P-band SAR Tomography has largely been studied in recent years, mostly in the frame of studies related to the forthcoming spaceborne Mission BIOMASS. Using SAR Tomography, it has been demonstrated that the backscattered power at the canopy layer is strongly correlated to the forest AGB. In the context of spaceborne missions, however, it is difficult to achieve enough passes for SAR tomography. Interferometric ground notching has recently been proposed as a new method to single out volume scattering contributions. The method takes as input a single pair of SAR images to obtain a ground-notched image by canceling out the backscattered power coming from the ground level. Most interestingly, the correlation between ground-notched intensity and forest AGB has been demonstrated to be very significantly improved w.r.t. the case of single images. In this paper, we evaluate the impact of temporal decorrelation on interferometric ground notching. A model is presented to show the impact of temporal decorrelation, and an experimental assessment is provided by analyzing data from the P-Band campaign BIOSAR-1, where multiple baselines where acquired both on the same day and with a time span of 23, 30, and 53 days. The experimental results show that ground-notched intensity is more stable for tall-forested areas, whereas the low vegetation is more affected by temporal decorrelation. Current work is ongoing to extend the analysis to tropical sites.

The monitoring of line-infrastructure, such as railways and dams, benefits from the synergy of SAR interferometry (InSAR) using multiple satellite missions. Different orbital and instrument viewing geometries, as well as spatial and temporal coverage and resolution, optimize the amount of information that can be extracted from the data. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. Therefore, advanced integrated products and generic performance assessment metrics are necessary. Here, we propose several new monitoring products and quality metrics for a-priori and a-posteriori performance assessment using multisensor InSAR, based on the assumption that:

1) coherent scatterers can be found representing the same physical phenomenon,

2) alignment of the datasets in space and time is possible, and

3) the influence of (nonperiodic) longitudinal movements compared to transversal and normal motion is limited.

The methods and metrics address two main operational questions.

1) Can we measure a particular deformation in a specific direction, at a specific location, and how well can we measure that?

Sensitivity values and Sensitivity circles are introduced, leading to a deformation variance as a function of the infrastructure orientation and the orthogonal elevation angle. The particular observability yields Minimal Detectable Deformations (MDDs) that can be observed with a given confidence level.

2) What can a particular combination of sensors produce as deformation products, and how does this compare with another combination of sensors?

We state the method for Line-of-Sight decomposition specifically to an asset-based coordinate system, and provide the variance covariance matrix in this coordinate system. The Dilution of Precision (DoP) is introduced as a scalar-valued quality metric, which is convenient to compare different sensor combinations.

Once InSAR data have been processed into deformation estimates, we introduce two operationally relevant end-products. First, the Significance Deformation Map (SDM) shows all locations on the selected asset where deformation is significant, given a confidence level as agreed with the asset-manager. Second, the Longitudinal Anomaly Profiles (LAPs) are a convenient way for instant information on the occurrence, location, and significance of anomalies along the track.

The proposed methods and metrics are demonstrated on a 125 km railway line-infrastructure asset in the Netherlands. All work contributes to a more structured, repeatable, and generic approach in the operational monitoring of line infrastructure.

This work has been recently published by the IEEE Journal of Selected Topics for Applied Remote Sensing, entitled ‘Monitoring line-infrastructure with multi-sensor SAR interferometry: products and performance assessment metrics’, doi: 10.1109/JSTARS.2018.2803074

Elevated highways, as one of the most important infrastructures, make contributions to a convenient and efficient public traffic, whose operational safety is the foundation of city development. Thus, deformation monitoring is the necessary prerequisite to normal operation of elevated roads. Persistent Scatterer SAR Interferometry (PSI) is a mature tool for land subsidence monitoring in an urban area, and its reliability has been verified by many studies.

In this research, we processed a long time-series of high-resolution TerraSAR-X satellite dataset in Shanghai from 2013 to 2017 to explore the spatio-temporal patterns along the elevated highways. Then with ground leveling data for InSAR accuracy verification, we compared and analyzed results between InSAR and leveling. The spatial distribution and temporal evolution of deformation characteristics of elevated highways were explored with joint analysis of PSI results, regional land subsidence, dynamic loads and the historical construction activities.

According to our results, regional land subsidence is a major factor for the deformation of Elevated highways because foundation of elevated highway is made up of end bearing pile foundation, which can generate frictional resistance between pile body and soil layer. The second factor we may consider is vehicle dynamic loads during the operational stage. We try to build a model between deformation and dynamic loads based on big data. And another one is the time when the elevated roads built. As is known, the land subsidence may undergo similar evolution after the engineering completion. So there are some relations between the completion time and deformation. Moreover, other factors such as groundwater, surrounding projects, etc. may play some but very small roles and we ignore them here.

Synthetic Aperture Radar (SAR) image co-registration is a fundamental but crucial procedure for interferometric SAR applications. Mainstream SAR coregistration algorithms are based on either cross-correlation or geometrical mapping. Both algorithms suffer from high computational expenses. The cross-correlation based coregistration, which is widely applied to conventional stripmap SAR data, requires many sub-image patches and an oversampling operation to derive the robust offsets with one tenth pixel accuracy. While the geometrical co-registration, which is widely applied in S-1 Interferometric Wide Swath (IW) images, is quite time consuming due to wide imaging coverage of TOPS mode and the iterative range-Doppler method. The massive parallelism of Graphic Processing Units (GPUs) can be used to improve the calculation efficiency of the two algorithms. The two new parallel algorithms are developed in NVIDIA’s Compute Unified Device Architecture (CUDA). The parallel cross-correlation based algorithm is implemented on batched processing for small matrices operation. The parallel geometrical processing is optimized by parallel pipelines. The efficiency improvement of the two parallel algorithms can be observed via the contrast experiments on Envisat stripmap and Sentinel-1 IW data.

Sentinel-1 Capability of Surface Deformation Estimation over a Wide Area in North-Eastern Algeria

Abstract

Monitoring ground deformation over a wide area with classical geodetic techniques, such as Geodetic Levelling and Vertical inclinometer, is very time consuming and expensive. On the other hand, interferometric synthetic aperture radar (InSAR) has been successfully used over the last two decades to produce high spatial density displacement maps in centimeter/millimeter accuracy with relatively low cost. InSAR gives the opportunity to study various phenomena, like fault creep, landslides, and subsidence induced by groundwater extraction.
This work is focusing on detecting land deformation and study geohazards such as landslide and subsidence over large areas in northeast Algeria by using Sentinel-1 data. Sentinel-1 data is provided by the Copernicus Program satellites constellation conducted by the ESA. Sentinel-1data is acquired in TOPS (Terrain Observation by Progressive Scans) mode, which is mainly designed for the purpose of wide coverage capability. We used all available data acquired over the study area between 2015 and 2018.
Stacks containing hundreds of SLC images covering the study area in ascending and descending orbits were analysed for land surface deformation using SarProZ and applying Persistent Scatterer Interferometry (PSI) technique.
This study main objectives are: first, increasing the detection capability of active landslides and ground subsidence; second, monitoring and analyzing the temporal evolution of the detected deformations by providing a time series deformation maps and velocities maps over a period of time. In the end, to understand deformations distribution and investigate geomorphological and geological causes.
Results revealed the characteristics of the study area where large areas of sparse vegetation and rocky surfaces, especially at high altitude and around the main mountainous structures, have a low density of measured PS points. On the other hand, areas where the vegetation cover decreases, especially in the urban areas, the density of Persistent Scatterers increases. Obtained deformation maps in these areas are composed of dense PS points. Where, the biggest amount of PSs is found in the urban area, the rest in the monotonous rocky areas.
A deformation time series was calculated for each Persistent Scatterer. Each point associated with the value of the annual linear velocity (mm/yr), estimated over the analyzed period and the displacement accumulated at each sensor acquisition date (mm). The measures are referred to the movement of the ground point in the satellite Line of Sight (LOS) direction.

Recently, the Sentinel-1 data has received extensive attention due to its large coverage and free availability. It is often used for monitoring of large-scale volcanoes and earthquakes, but it also offers an opportunity for subsidence monitoring in built-up areas although it is conventionally not included as high-resolution imagery.

The study area in Wuhan is along the Yangtze River, and has developed rapidly in recent years. Rapid urbanization and extensive carbonate rock strips as well as soft soil layers underground in Wuhan have contributed to land subsidence in most parts of Wuhan. The risks to the safety of buildings and public infrastructures are concerned by municipal departments and citizens. It is crucial to detect land subsidence to facilitate understanding of the evolutionary processes so that proper measures can be taken to carry out effective planning and construction and to mitigate further loss.

The time-series analysis method is adapted to derive the deformation from Sentinel-1 images. In this case, StaMPS is introduced for data processing, which does not need to assume a special deformation model, directly through the three-dimensional phase unwrapping algorithm to obtain surface deformation information ^[1]. It is used to extract deformation on constructions combining with high-resolution imagery ^[2]. Whereas application of Sentinel-1 data in monitoring deformation in built-up areas is relatively few.

In this experiment, totally 44 scenes of the IW mode data of the Sentinel-1A are collected. Using the free Sentinel-1 data, time-series PS-InSAR technology ^[3] is applied to obtain the deformation rate of Wuhan City and there are several areas with severe subsidence, with a maximum subsidence rate of -27mm/y. Furthermore, the InSAR-derived displacement map also highlights active motions of built-up areas and infrastructures, such as some communities and railways segments. It helps with safety screening and risks assessment.

An overall subsidence of -15mm/y to -27mm/y occurs in Anjuyuan Community, with a cumulative deformation of 30mm. From the obtained time-series curve of the PS points, the subsidence rate of the community accelerated from August 2016 to November 2016, during which the subsidence exceeded 10mm, which is equivalent to the subsidence in the previous 16 months. It should be paid more attention to.

Remarkable subsidence occurs in the Fazhanercun and Jingnan Community in Hankou district. The maximum subsidence rate is -27mm/y, and the maximum deformation is even -38mm. According to the survey, since the start of reconstruction project of the Village in the south of the community, the walls of some households in this community were cracked, and the front steps were severely separated from the ground, which is consistent with the experimental results, indicating that the processing results of Sentinel-1A dataset is reliable.

An analysis of a section of a railway passing through Hankou Railway Station shows that the majority railway of this section suffers from different degrees of subsidence, and the deformation rate in the vertical direction is between -11.64mm/y and 6.18mm/y. Besides, the differential subsidence in the railway curve is relatively large, and it is worthy of attention. The deformation rate in the vertical direction of a section of Wuhan-Guangzhou Railway passing through Wuhan Railway Station is approximately -5mm/y to 5mm/y. Except for a slight uplift near the Wuhan Railway station, this section is overall sinking slightly and there is no uneven subsidence.

REFERENCES

[1]. Hooper, A.J., Persistent scatter radar interferometry for crustal deformation studies and modeling of volcanic deformation [D]. Stanford University, 2006.

[2]. Qin. X, Liao. M, Yang. M, and Zhang. L. Monitoring structure health of urban bridges with advanced multi-temporal InSAR analysis [J]. Annals of GIS. 2017, 23(6):1-10.

[3]. FERRETTI A, PRATI C, ROCCA F. Permanent scatterers in SAR interferometry [J]. IEEE Transactions on Geoscience & Remote Sensing, 2001, 39: 8-20

Land reclamation is a well-known solution for land augmentation on coastal areas to serve the population increase, rapid urbanization, and economic development. Many countries have expanded its coastal land by using this method including England, Korea, Germany(Flemming and Nyandwi 1994), Ireland, Netherlands, Spain, Bangladesh, Nigeria, and China. Remote sensing has represented a high capability in detecting environmental changes caused by land reclamation in coastal areas due to its wide coverage, periodical revisit, and different data types and techniques. In this study, we have utilized microwave remote sensing data of Sentinel-1 to estimate the stability of reclaimed areas’ surface in Shenzhen City, Guangdong Province and Hong Kong, Southern China.

This study area located in the southeastern part of the Pearl River Estuary (PRE). The Pearl River is the third largest river in China and the second biggest river measured by mean annual runoff. The river’s water flows through eight different outlets into the South China Sea. Four of which are located in the north and west of the PRE. The coastal area of this area is under an intense pressure of land reclamation and almost can be considered as an artificial environment due to the recent economic development. This area has witnessed an enormous urban encroachment on agricultural land and coastal swamps during the last four decades due to the open-door policies in China since 1978. This urban expansion was a result of high population density and rapid industrialization in the Pearl River Delta since then.

Reclaimed areas in the study area were defined based on the land expansion on the estuary’s water in Shenzhen and Hong Kong coastal areas since 1973. The first available Landsat Multispectral Scanner System MMS images for the PRE which had been acquired in December 1973, were used to map the PRE water surface body in 1973 as the study’s area of interest (AOI). Then, the most recent images of Sentinel-2 were used to define the recent coastal line in our study area. Normalized Difference Water Index (NDWI) was applied to all images to extract the water body of our study area. This method based on the high reflectance of water in the green light wavelength and its low reflectance in the near-infrared wavelength.

After extracting reclaimed areas in the period between 1973 and 2018; Sentinel-1 data was used to detect surface deformation on these areas by applying PS-InSAR technique using SarProZ software. 65 Ascending images between June 2018 and January 2018 were utilized for this purpose.

Results revealed more PS points on the older impervious surfaces land cover of reclaimed areas compared to recent built-up and cultivated areas. The overall results showed stable surface in most reclaimed areas. However, some reclaimed areas represented surface deformation reached in some places to -30 mm/year such as areas southern Bao’an airport in Shenzhen City and some wave breakers in Hong Kong.

InSAR has proved a powerful technique for mapping surface deformation, developing rapidly in recent twenty years. But one-dimensional InSAR LOS measurement has limited its application to retrieve 3-D surface displacements, as it is only sensitive to surface movements towards or away from the satellite. The most straightforward approach is to integrate InSAR LOS measurements with homogenous data (Offset tracking, MAI) or heterogeneous data (GPS data, leveling). In this paper, we reconstruct the three-dimensional deformation field with surface-parallel flow assumption based on the knowledge of DEM information on ground deformation. In addition to, due to the different influence of the errors in different observation data, the iteration method by correcting characteristic value with maximum likelihood estimation is used to literately process the function model to get the accurate random model through prior information, and also the exact weight function. We apply this method on the Jiaju landslide and the results shows, horizontal displacement of Jiaju landslide appears to move along the landslide direction in the east-west direction, vertical deformation rate of the north part is large which exceeding -2cm/y, while the south part is -0.5cm/y.

Forest dominates the terrestrial carbon cycle and forest above-ground biomass (AGB) has been the critical index for carbon sequestration capacity. However, any individual method, such as ground-measurement-based method, remote-sensing-based method, and ecological model-based model, cannot efficiently describe the changing processes and driven mechanisms of forest AGB dynamics. Based on multi-mode remote sensing, time-space dynamic knowledge of forest ecological process, and continuous multi-disciplinary ground observation data, this project is planning to model spatial-temporal continuous, physical quantity-synergy forest AGB dynamics.
Firstly, a highly accurate regional forest AGB product obtained by applying multi-mode remote sensing and scaling connection, is used as the AGB basis. Then, the uncertainties of simulation of forest growth processes are alleviated by use of model-model and model-data fusion strategies. Finally, modeling of forest AGB dynamics is accomplished by combining forest AGB basis with succeeding dynamic forest growth processes, which taking the effects of tree mortality, forest disturbance into account. The methodology of spatio-temporal synergetic modeling of Forest AGB dynamic information proposed by this project, can explore the eco-physiological mechanisms of spatio-temporal pattern of forest AGB dynamics and the driven forces of natural and anthropogenic disturbances. Moreover, this methodology can extend the spatial and temporal dimensions of forest AGB dynamics and in order to precisely improve forest quality and promote the national ecological environment construction.

This paper presents different processing techniques for the polarimetric 3-D imaging of forested areas using multi-baseline interferometric SAR data, and processed through tomographic imaging techniques.

The case of spaceborne SAR acquisitions is analyzed and specific techniques and concepts are proposed to cope with the important limitations of this kind of acquisitons, compared to airborne dat sets, in terms of resolution, decorrelation and number of images.

in particular, tandem-like acquisition modes, based on the simultaneous measurement of interferometric pairs, represent a high-potential alternative for the tomographic imaging of scenes with rapidly decorrelating scattering features using a spaceborne SAR. The counterpart related to this independent interferometric sampling lies in the restricted amount of available information, whose processing requires specific techniques. These methods as well as their potential for boreal forest characterization are evaluated using data sampled over various campaigns.

In this report, we will introduce the main research progress of land use/cover classification and forest quantitative information extraction based on Chinese and European Spaceborne SAR Data. First, the study of land use/cover classification based on China's first C-band SAR satellite is introduced. It mainly contains two aspects of research: (1) Deep learning for large-scale land cover type classification with GF-3 Dual-Pol SAR Data; (2) Study on full polarimetric SAR image classification method based on stokes vector features and GA-SVM. Secondly, the research progress of forest canopy height estimation in complex terrain regions based on European TanDEM-X satellite interferometric SAR data is introduced. In addition, the research progress on the analysis of spatial baseline configuration of SAR tomography is briefly introduced.

Synthetic aperture radar (SAR) can penetrate through rain and clouds and can be used for earth observation in all weather conditions. SAR tomography (TomSAR) is a new type of SAR technique that has emerged in recent years and has a great advantage in three-dimensional detection of the forest. A fundamental requirement for forest height estimation using TomoSAR technique is to have precise knowledge of spatial baseline parameters, mainly including baseline spatial location and SAR imaging geometry. The parameters such as the quantity, length, angle, height of the space baseline, and the local incidence angle of the ground were studied to analyze the influence of the interferometric coherence, the location of the scattering center, and the estimation of accuracy in the forest height estimation. Based on that, a space baseline configuration strategy was derived. The strategy was demonstrated through numerical simulations of SAR, in order to validate the effectiveness of baseline configuration strategy, and using real data from the ESA campaigns TropiSAR. The results of space baseline configuration analysis can also prepare for the upcoming aviation remote sensing campaigns.

Methods of plantation types classification by remote sensing mostly use remote sensing image with medium spatial resolution (above 10m and more than 16-50m). Due to the probable problem of mixed pixels, these images are more suitable for macroscopic monitoring tasks of regional forest resources, such as national forest resources continuous inventory operations. However, this study is aimed at the task of forest resource planning and design investigation. The purpose is to achieve fine classification of small class plantations. So, the most suitable remote sensing images are very high panchromatic and multispectral remote-sensing images,which are similar to GF-1( 1m / 4m ) or GF-2 ( 2m / 8m ) satellites.

Although there are many literatures on general land cover/utilization type classification methods based on very high panchromatic and multispectral remote sensing images, there are few studies on the detailed classification methods applied to plantation forest types.Moreover, most of the methods used are still traditional linear-nonlinear classification methods, and the image features used need manual extraction.

At present, big data-based intelligent methods such as computer vision technology have achieved great success. Deep learning such as deep convolutional neural network has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. Although some scholars have applied the convolutional neural network model to remote sensing image classification, most of them are aimed at hyperspectral remote sensing data, especially for hyperspectral data with high spatial resolution (mainly using airborne hyperspectral data for experiments). The improvement of convolutional neural network model structure mainly lies in the simultaneous capture of spatial and spectral features of hyperspectral remote sensing images.

In this study, very high panchromatic and multispectral remote-sensing images are the main data sources, and the classification method of plantation types based on deep convolutional neural network is developed. The spatial-spectral characteristic information of panchromatic and multispectral data was fully utilized to achieve the classification of plantation forests.This is of important implication to the forest resource planning and design survey of the small-class plantation type renewal business.

The research contents of this paper include the following two aspects:

1. Network structure improvement method based on deep convolutional neural network for plantation forest type remote sensing classification.

In order to make comprehensive use of the high-spatial feature information of the panchromatic band and the spectral information of the multi-spectral band, taking into account the differences in spatial geometrical characteristics between the plantation and the non-plantation forest, the existing structure of the deep convolutional neural network is improved.Take GF-2 images as experimental data and Wangyedian Forest Farm as research area to achieve forest farm classification.

The main ideas for improvement are as follows:

(1) Oversampling a multispectral image yields the same resolution as a full-color image. After overlaying the panchromatic and multispectral images, a deep convolutional neural network was used for classification.

(2) Resample the panchromatic image to the same resolution as the multispectral image. After superimposing the two, they are input into the deep convolutional neural network for classification.

(3) Perform convolution, pooling, and other operations on panchromatic and multispectral images, respectively. The resulting categorical features are superimposed and input to the classifier. Finally, output the category label.

(4) Compare the classification results obtained by adopting the above improvement ideas with the existing classification methods.

2. Sample acquisition method for training and precision test in deep convolutional neural network model

Obtaining objective and true training and precision test sample data requires a lot of labor and material resources. Therefore, the number of training samples is always limited. However, a classification method based on a convolutional neural network requires a large number of high-confidence samples. It is important to explore the model to establish a practical method for collecting the required samples in the field. This study will explore a method for collecting ground truth data (model training and accuracy test samples) based on drone aerial photography through experiments.

Map aerial photography images of drone onto GF1/2 images. The maps of the types of ground features (including planted forest types) in each aerial photographed area were obtained by visual interpretation. This map is used for training and accuracy testing of deep convolutional neural network models.

The 500m*500m ground truth data obtained by aerial photography and visual interpretation will be cropped/resampled.
Ground truth data of different sizes such as 250m*250m, 100m*100m, and 50m*50m are obtained. Experiments on the effects of data blocks of various sizes on the classification accuracy of deep convolutional neural networks. Explore a more effective aerial drone-based training and precision inspection sample acquisition method for forestry-type deep convolutional neural network classification.

GF-3 satellite is the first China C-band SAR satellites, with a variety of polarizations, 12 different working modes and a quick site access time.In this paper, the large-scale land cover type mapping of Hulunbeier is completed by using GF-3 dual-pol SAR data.Benefited from the acquisition of massive data and the popularization of high performance computing resources such as graphics Processing Unit (GPU), deep learning has been pleasantly surprised in the field of classification. Based on the theory of deep learning, this paper uses the deep convolutional Highway Unit neural network to give full play to the ability of deep learning to effectively deal with massive data.Types of ground objects classified include forests, grasslands, waters, artificial ground, arable land and other.Calculation of confusion matrices based on ground truth measurement map collected in September 2017.The deep convolutional Highway Unit neural network by the dual-pol SAR images, the proposed approach in the paper can reduce speckle, fully excavate the regularity of SAR images in time and space and effectively improve the accuracy of classification.

Measuring forest height on a large scale is of importance to forest resource management and biomass estimation. This study demonstrates the use of TanDEM-X interferometric coherence for retrieving forest height over mountainous terrain. First, non-volumetric decorrelation was corrected from the observed coherence in order to obtain the volumetric coherence, and then based on the SINC model, the amplitude of volumetric coherence was used to estimate forest height. Then inversion results were compared against light detection and ranging (LiDAR) and field measurement data. The study showed that the inversion accuracy of SINC model is influenced by severe topography, and the large-slope induced errors are mitigated to some extent by combining ascending and descending passes.

Abstract: A new classification method of Polarimetric SAR image based on polarization scattering feature is developed, and the effectiveness of Stokes vector feature as a classification feature is explored, and the method of feature selection (GA-SVM) with genetic algorithm coupled with SVM effectively solves the problem of insufficient generalization ability of classifier. It provides a new idea for the classification of polarimetric SAR images based on polarization scattering characteristics. Taking Yigen farm of Hulunbeier city as the experimental area, the Full Polarization SAR image of GF-3 was used as test data, and the effectiveness of the method was validated by the ground data obtained by the field survey. Firstly, the polarization target decomposition component and image texture parameters are extracted based on the polarimetric SAR data as classify feature sets. Then, Stokes vectors of 3 kinds of typical polarization incidence modes are simulated and their decomposition components of each mode are calculated, and both Stokes vector elements and Stokes decomposition components are added as the Stokes vector features of the data to the classification feature set. Finally, the test image is classified by SVM classifier using the optimal features combination selected by GA-SVM. Based on the classification feature set and feature selection method in this paper, a good classification effect is achieved, the overall accuracy reaches the 90.00% and the kappa coefficient is 0.87. Compare to the classification result based on the original features set, the total accuracy is increased by 8.56%. For the same feature set and classifier, the accuracy of the Rlieff algorithm compared with the method without feature selection improves by 1.76%, and SVM-RFE algorithm improves the accuracy by 6.72%. Based on the GA-SVM feature selection method developed in this paper, and the classification set including the Stokes features, the overall accuracy of the classification is increased from 83.02% to 90%, and the misclassification phenomenon of certain types is reduced. The main conclusions of this study as follows: (1) The feature selection method of GA-SVM can improve the classification accuracy of the target SVM classifier while effectively reducing the classification feature dimension, (2) the Stokes vector element and its decomposition feature can be used as the classification feature to effectively enhance the accuracy of nonparametric model classifier.

Chinese carbon dioxide observation satellite (TanSat) launched in 22 Dec 2016, after on-broad test and calibration, the TanSat starts to record the back-scattered sunlight spectrum from scientific earth observation and produced first XCO₂ data from February to July in 2017.

The optimal estimation theory was involved in TanSat XCO₂ retrieval algorithm in a full physics way with simulation of the radiance transfer in atmosphere. Gas absorption, aerosol and cirrus scattering and surface reflectance associate with wavelength dispersion have been considered in inversion for better correction the interference errors to XCO₂. In this presentation, I will show the preliminary results of XCO₂ retrieved from TanSat measurements, and inter-compared with OCO-2 results in an overlap footprint measurement over north Australia. Validation study with TCCON measurements indicate a better than 4 ppm with 8 stations. The first global XCO₂ map has represented a milestone in Chinese GHGs satellite. TanSat will release level 2 and higher level products to support carbon emission estimations and climate change studies.

Satellites have the power to provide new insights into the regional sources and sinks of the major greenhouse gases (GHG) CO₂ and CH₄ thanks to their high data density and coverage. Satellite observations play an important role in diagnosing regions where carbon is taken up and released and how these fluxes respond to climate extremes such as droughts. Recently, the focus has further expanded with the aim of separating natural from anthropogenic emission sources.

Since the launch of the first dedicated GHG sensor GOSAT almost a decade ago, a series of new sensors have been launched (NASA OCO-2, TanSat, S5P…) with further sensors expected to be launched in the coming years (GOSAT-2, MicroCarb, OCO-3…). As a consequence, we are now presented with the opportunity of an emerging ad-hoc constellation of GHG satellites providing us with an opportunity to intercompare and eventually combine observations from multiple satellites to increase data coverage and density. However, one concern is potential biases between the datasets and careful validation against ground-based datasets is essential.

China is a key region for the global carbon and methane cycle with significant emission sources. However, the frequent occurrence of high aerosol loads over China combined with insufficient ground-based validation opportunities leaves satellite observations uncertain. In this presentation, we will focus on an evaluation of satellite observations of CO2 and CH4 over China. We will intercompare GOSAT, OCO2 and TanSat datasets and assess them against model calculations that are constrained with surface in-situ data. Finally, we will discuss the use of space-based data to identify and highlight main emission areas of GHGs.

We discuss the recent recearch activities that have taken place at the Finnish Meteorological Institute related to satellite observations of greenhouse gases with links to the DRAGON project “Monitoring greenhouse gases from space: Cal/Val and applications with focus in China and high latitudes“.

The FTIR instrument in Finnish Meteorological Institute’s premise in Sodankylä is one of the sites participating in the Total Carbon Column Observing Network (TCCON). The retrievals include column-averaged, dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4). Since 2013, series of AirCore balloon launches have been performed to obtain accurate in-situ profiles of methane, carbon dioxide and carbon monoxide from troposphere to lower stratosphere. As a high latitude site, Sodankylä contributes to the validation of satellite observations of greenhouse gases in boreal and arctic regions. The recent and on-going validation activities and campaigns that have taken place in Sodankylä are also discussed.

In addition, we have studied the spatiotemporal distribution of greenhouse gases by analysing spatially and temporally OCO-2 and GOSAT data. The developed methods and results are applicable also to TanSat data.

Anthropogenic CO₂ emissions from fossil fuel combustion have large impacts on climate. In order to monitor the increasing CO₂ concentrations in the atmosphere, accurate spaceborne observations—as available from the Orbiting Carbon Observatory-2 (OCO-2)—are needed. In our recent work [Hakkarainen et al., 2016] we provided a new approach to study anthropogenic CO₂ emission areas by deseasonalizing and detrending OCO-2 XCO₂ observations for deriving XCO₂ anomalies. The spatial distribution of the XCO₂ anomaly matches the features observed in the maps of the Ozone Monitoring Instrument NO₂ tropospheric columns, used as an indicator of atmospheric pollution, as well as the features observed in the ODIAC emission dataset. In addition, the results of a cluster analysis confirmed the correlation between CO₂ and NO₂ spatial patterns.

In this work, we study this idea further and provide the global XCO₂ anomaly maps for three full years 2015, 2016 and 2017. The patterns observed in these maps are compared with inventory-based estimates given by the Lagrangian particle dispersion model FLEXPART driven by the high-resolution ODIAC emission dataset. We also analyze the changes observed in XCO₂ anomaly maps and compare these changes to the inventory-based estimates, as well as to the changes observed in other trace gases (NO₂ and SO₂).

References

Hakkarainen, J., I. Ialongo, and J. Tamminen (2016), Direct space-based observations of anthropogenic CO2 emission areas from OCO-2, Geophys. Res. Lett., 43, 11,400–11,406, doi:10.1002/2016GL070885.

CO₂ anthropogenic emissions contribute 37 Gt CO₂ to the global carbon budget. The emissions of carbon dioxide arise from different anthropogenic activities with the dominant contribution from burning of fossil fuel especially for power generation and the industry and transport sectors. The quickly growing economy of China has, in recent years, become the largest emitter of CO₂ generating about 30% of all emissions. Cities and urban areas, where the human activities are very intense, are responsible for over 70% of all CO₂ emissions highlighted the need for a better understanding of emissions form these urban areas.

New satellite observations of CO₂, in particular from NASA’s OCO-2 launched in 2014 and the Chinese TanSat launched in 2016 provide the opportunity to monitor CO₂ concentration over polluted areas in the scale of big cities and individual point sources due to the small size of its footprint of ~ 4 km².

The goal of this study is to investigate if and how well we can observe anthropogenic enhancement of total column dry air CO₂ mole fraction (XCO₂) from space over densely populated areas as well as for individual large cities. We will focus on Eastern China as well as selected megacities globally.

We will show an evaluation of current model calculations such as Copernicus Atmosphere Monitoring Service (CAMS) against space-based data and present first result based on a high-resolution modelling approach that allows interpretation of individual soundings/orbits.

In recent years, the continuous rise in the concentrations of greenhouse gases has drawn a great attention of the international community, especially for carbon dioxide (CO₂) and methane (CH₄). The sources and sinks of CO₂ and CH₄ in the atmosphere are distributed unevenly and subjected to a variety of transport. The high-precision observation of CO₂ and CH₄ provides a foundation for the assessment of carbon emissions and a basis for the study of carbon cycle Much effort has been done by the government and research groups to monitor and quantify the magnitude and distribution of greenhouse gases, including both domestic and international.

High-accuracy continuous measurements of CO₂ and CH₄ at Xinglong (40°24'N, 117°30'E, 940 m a.s.l.) located at ~150km northeast of Beijing from May 2016 to December 2017 were made based on cavity ring down spectroscopy (CRDS) analyzer. The calibration data, seasonal and diurnal variations, influence of local wind and regional transport have been discussed. To understand different regional background in China, the seasonal cycles of CO₂ and CH₄ at Xinglong have been compared the results from Mt.Waliguan and Shangdianzi.

The calibration result shows that the accuracies of measurements of the instrument meet the compatibility goals of WMO/GAW. CO₂ concentration is high in winter and low in summer while CH₄ concentration is high in summer as well as in middle autumn and low in spring. The seasonal variation for CO₂ is mainly due to the combination influence of photosynthesis and respiration of plants. The regional transportation is another important factor. The air mass from southwest-south-southeast of the station such as Beijing, Hebei and Jinan may contribute to the relative high concentrations, especially in summer and autumn. The higher CH₄ concentration occurs in summer and middle autumn due to the gradual degradation of anaerobic microorganisms emitting large quantities of CH₄ in local area and fossil combustion emissions from Beijing-Tianjin-Hebei in summer and autumn. What’s more, Mongolia is another potential source area for both CO₂ and CH₄ in autumn and winter.

The diurnal patterns of CO₂ and CH₄ both show relatively low value in the noon. For CO₂ it is because the strong sunshine in the noon which is conductive to the stronger mixing of near-surface air with lower CO₂ concentrations aloft. While for CH₄ it is the increasing OH radicals in the atmosphere after sunrise that leads to the decrease of CH₄.The stable boundary in the afternoon and at night helps the accumulations of CO₂ and CH₄ in the afternoon.

The seasonal variations of CO₂ comparisons between Xinglong, Shangdianzi and Mt.Waliguan show that the patterns are the similar in the 3 sites for CO₂,high in summer and low in other seasons. The pattern for CH₄ at Xinglong is high in summer, middle autumn and winter while low in spring. However, the variation of atmospheric CH₄ in Mt.Waliguan and Shangdianzi is only high in summer and smaller than other 2 stations.

The GaoFen-3 (GF-3) is the first Chinese spaceborne SAR in C-band for civil applications. In the paper, we provide an overview on demonstrating capabilities of the GF-3 SAR on ocean, coast and polar observations, by presenting some representative cases, such as polarimetric characteristics of typical intertidal flat in the Subei shoal, the German Bight, observation of offshore wind turbine wakes in the North Sea and East China Sea, observation of internal waves generating in the Luzon Strait and their propagation to DongSha Atoll, as well as discrimination of sea ice and open water in the Arctic. Derivation of marine-meteo parameters in high spatial resolution is one of the most attractive applications of spaceborne SAR for ocean observation. This is also a primary goal of launching the GF-3 SAR, we also presented the current problem of using GF-3 for retrieval of oceanic dynamic parameters and some possible solutions.

Radar altimeter is an important part of ocean phenomena monitoring. Profile radar altimeters (such as Topex/Poseidon, Jason-2 and Sentinel-3) have provided abundant data for the ocean phenomena research in the past decades. But they only measure one-dimensional profile height along the satellite track, a 200~300 km gap usually exists between two successive tracks and the spatial resolution of their data products is sparse. Wide-swath altimeter (such as SWOT) calculate the target point height using the interferometric phase measured by two antennas of the altimeter. It can greatly improve the spatial resolution of the data product and makes up for the lack of profile altimeter. However, in the process of generating a digital elevation model with a wide-swath altimeter, the baseline roll error will cause height error which increases in the cross-track direction. Taking the design parameters of SWOT as an example, a baseline error of only 0.36arcsec (1/10000°) would result in an average height error of roughly 6 cm at the swath of 10km to 60km. In the present case, it’s difficult to control the measurement accuracy of baseline roll within 0.1arcsec by measuring instruments such as a gyroscope, and it is impossible to meet the high-accuracy requirement of 1-2cm. Therefore, it is necessary to adopt other methods to calibrate the measurement value of the baseline roll.

Due to the special geometrical relationship between the nadir point and two antennas, using the nadir interference phase can obtain a more accurate baseline roll angle value, estimate the flight attitude more effectively and improve the height measurement accuracy. The nadir interference phase is related to baseline length and roll angle. The contribution of measurement error of baseline length to the measurement error of roll angle is much smaller than that contributed by the measurement error of interferometric phase. The accuracy of interferometric phase is decided by both the hardware guaranteed accuracy and the echo guaranteed accuracy. An accuracy of 0.05° for open ocean surface is not difficult to achieve. Under this assumption, the measurement accuracy of roll angle can be as high as 0.0246arcsec.

A two-dimensional ocean surface which is simulated by the Pierson-Moscowitz spectrum. The slant range and interferometric phase of the resolution unit are obtained based on geometrical relationship. It is assumed that the interferometer can measure the nadir interferometric phase with an accuracy of 0.05°, while a gyroscope can measure the baseline roll angle with an accuracy of 0.36arcsec simultaneously. Reconstruct the sea surface using parameters of the SWOT mission and combining with the baseline roll that calculated by the nadir interference phase or measured by the simulated gyroscope, and then calculate the elevation error within the swath. The numerical simulation results show that the accuracy of the baseline roll can reach 0.03arcsec with a nadir interferometric phase accuracy of 0.05°, and the average height error within the swath of 10km-60km is 0.48cm.

Using the relationship between the nadir interference phase and the baseline roll angle to measure the roll angle indirectly, can achieve a more accurate baseline roll angle measurement than the general hardware. The height error caused by the baseline roll error could be reduced and the accuracy of data products of wide-swath altimeter could be improved which make it an effective way to calibration height error of wide-swath altimeter.

Tracing the evolution of subaqueous topography in coastal water enables us to understand the effects of intensive coastal development on bays and estuaries. Analysis of a series of historical bathymetric acoustic surveys has revealed large changes in morphology from 1960s to 2010s in Longkou Bay, China. Water depths were extracted from digitized admiralty charts to explore the accretion-erosion characteristics in a Geographical Information System (GIS) environment, providing quantitative estimates of morphological changes. Multibeam echosounders (MBES) were used to map and analyze the geomorphologic features caused by the construction of artificial islands. Results illustrated that the shoreline and bathymetry of Longkou Bay changed dramatically in recent decades. The subaqueous area decreased by about 15%, while land area increased by more than 13 km2 in the study area during the last 50 years. From 1960s to 1990s, the evolution of Longkou Bay was mainly governed by natural processes with a patchy distribution of deposition and erosion, and there were few signs of being related to large-scale human activities. During the period of 1990s to 2010s, intensive coastal developments including large port engineering projects, channel dredging and artificial islands construction became the main processes affecting morphological changes in the Longkou Bay. The high-resolution bathymetric results near the artificial island showed that the seafloor was dredged at many sites, leaving large areas of borrow pits. The sudden change of the underwater topography will lead to the destruction of local benthic habitat and effective measures need to be taken to protect and remediate heavily disturbed subaqueous environment.

The refraction and reconnection of internal solitary waves (ISWs) around the Dongsha Atoll (DSA) in the northern South China Sea (SCS) are investigated based on spaceborne synthetic aperture radar (SAR) observations and numerical simulations. In general, a long ISW front propagating from the deep basin of the northern SCS splits into northern and southern branches when it passes the DSA. In this study, the statistics of Envisat Advanced SAR (ASAR) images show that the northern and southern wave branches can reconnect behind the DSA, but the reconnection location varies. A previously developed nonlinear refraction model (NRM) is set up to simulate the refraction and reconnection of the ISWs behind the DSA, and the model is used to evaluate the effects of ocean stratification, background currents, and incoming ISW characteristics at the DSA on the variation in reconnection locations. The results of the first realistic simulation agree with consecutive TerraSAR-X (TSX) images captured within 12 h of each other,which proves the validity of the NRM model around the DSA. Further sensitivity simulations show that ocean stratification, background currents, and initial wave amplitudes all affect the phase speeds of wave branches and therefore shift their reconnection locations while shapes and locations of incoming wave branches upstream of the DSA profoundly influence the subsequent propagation paths. This study clarifies the variation in reconnection locations of ISWs downstream of the DSA and reveals the important mechanisms governing the reconnection process, which can improve our understanding of the propagation of ISWs near the DSA.

Most researchers pay more their attentions on the characteristics of oil spills when they construct the feature set for establishing an automatic sea surface oil spill monitoring system by spaceborne SAR. In this paper, taking oceanic internal waves as an example of oil look-likes, a Fourier spectrum feature subset is proposed based on the analysis of the characteristics of look-alikes. The aim is to reduce the false alarms resulting from oceanic internal waves and then to decrease the false discovery rate (FDR) of the automatic monitoring system. The proposed feature subset consists of 10 Fourier spectrum features. 53 SAR images rich in internal waves acquired over South China Sea are collected for this experimental study. An adaptive threshold image segmentation algorithm and its post-processing are applied on the SAR images to automatically generate dark target set with reasonable proportion between oil spills and look-alikes. Training and testing of classifier are conducted with 77 original features and 87 features including the additional 10 Fourier spectrum features, respectively. The results show that the system's FDR decreases from 19.6% to 14% due to the introduction of the Fourier spectrum feature subset.

TheOcean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400–1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (K_d(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available K_d(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R²) = 0.81, root-mean-square error (RMSE) = 0.99 m⁻¹ and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R² = 0.83, RMSE = 1.06 m⁻¹ and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI K_d(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of K_d(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The K_d(490) values for Lake Taihu exhibit notable spatial and temporal variations. K_d(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties.

The identification of spatial and temporal dynamics of phytoplankton and macrophytes is crucial for deepening the knowledge of lake primary productivity and shifts in trophic status of inland water bodies. Earth Observation (EO) can provide sensitive information on both groups of primary producers, but their possible coexistence within the same site is often not taken into account by satellite-based analyses. Indeed, macrophyte and phytoplankton coexistence is not rare event, especially in shallow eutrophic lakes subject to cyanobacteria blooms, and common methods based on optical VNIR spectral response features for estimating water constituents often fail in distinguishing dense surface accumulation of cyanobacteria forming at peak of bloom events with floating and emergent macrophyte cover. Few authors have tackled this issue in scientific literature, the most effective approach to our knowledge being the one proposed by Oyama et al. (2015; Remote Sensing of Environment, 157: 35-47), based on the exploitation of SWIR range reflectance. On this topic, Bresciani et al. (2014; Remote Sensing of Environment, 146: 124-135) have demonstrated the potential of combined optical and SAR data in delivering accurate information on algae blooms and scum events in Curonian lagoon (Lithuania).

In this work, we take advantage of new generation EO sensors (i.e. ESA-Copernicus Sentinel-1 and -2) for investigating the capabilities of optical (broadband multi-spectral) and SAR (C-band) data integration in providing an effective method for distinguishing cyanobacteria scum and floating macrophytes in Lake Taihu (Jiangsu, China). Matchup pairs of Sentinel-2 and Sentinel-1 data acquired with less than 5 day difference have been pre-processed to derive surface reflectance and backscattering coefficient (sigma0), respectively. Statistics of spectral reflectance and Water Adjusted Vegetation Index (WAVI; Villa et al., 2014; Int. J. Appl. Earth. Obs. Geoinf., 30: 113-127) derived from Sentinel-2 data, as well as sigma0 in VV and VH polarization combinations derived from Sentinel-1 data, have been calculated and used to assess the separability of cyanobacteria scum and floating macrophyte pixels response. Finally, a rule-based framework has been designed, parametrized and applied to Sentinel-1 and -2 data to produce maps of algae scum and macrophytes on Lake Taihu in different times of the primary producers cycle, spanning from April to October.

In China, freshwater is an increasingly scarce resource. Study location Dongting Lake is China’s second largest freshwater lake in the middle reaches of Yangtze River catchment. Its wetlands deliver important ecosystem functions such as freshwater supply, water purification, flood and climate regulation to name only a few. The development of comprehensive water resource management and nature conservation strategies requires detailed mapping and monitoring of inland waters. The generation of such information requires either large human resources for conventional ground surveying or expensive data. In addition to costly methods, the monitoring of large wetlands such as the Dongting Lake study site with a water surface of up to 3.200 km² is difficult due to its inaccessibility during annual flood period.

Remote sensing offers a mature and comprehensive tool to solve this task with large area coverage at very low costs. Until today, in addition to satellite data with lower temporal resolution such as Envisat ASAR or Landsat, daily MODIS satellite data are a frequently selected source for capturing lake dynamics with medium spatial resolution of 250 m up to 1 km. Satellite data from the new European Sentinel fleet has been available since 2014 and provides high-resolution information free of costs. In this study we present the application of Sentinel-1 time series data for spatio-temporal high-resolution wetland mapping. New is the level of detail that can be achieved with Sentinel-1 data. Potential and limitations are analyzed and mid-term results presented.

In recent years, the climate change and human activities have a great influence on lake environments and ecosystem^[1]. As the second largest freshwater lake of Yunnan Province, Erhai Lake, is the indispensable drinking water source in Dali. However, due to the increasing human activities, Erhai lake have suffered a great deal of environmental stressors, such as eutrophication, heavy metal pollution, etc^[2]. Water quality deterioration and eutrophication led to the occurrence of algal blooms and affected the normal ecological function of lakes^[3]. Thus, the primary task for protecting Erhai Lake is monitoring and risk pre-warning of blooms. When algal bloom occurs, the content of chlorophyll a in the water increases, resulting in significant differences in the spectral characteristics between the bloom and non-bloom waters, so that cyanobacterial bloom can be detected by remote sensing.

The launch of MultiSpectral Instrument (MSI) orbited on Sentinel-2A (S2) and Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) on board ESA Sentinel-3A (S3) opened a new world in water environmental remote sensing^[4]. Lacking of in situ data and complexity of air and water conditions are the challenges of validating and evaluating S2 and S3 products. It provides multispectral data at high, medium and low resolution to meet different purposes by combinating S2 and S3 missions.

The aims of the study are: 1) to find out how Senitnel 2 MSI contributes to the long-term monitoring of algal blooms in inland lakes, 2) to get distribution of algal bloom and water surface temperature (WST) in spatial and temporal scales, 3) to figure out how Sentinel 3 plays a role in inland water monitoring and the driving factors of algal bloom, with the combination of high resolution MSI images.

In this study, a total of 22 Sentinel-2A MSI images with 50% cloud cover combining with Landsat OLI images (a total of 13 images) were used to monitoring algal blooms in Erhai Lake during November 2016 and December 2017. The VB-FAH (Virtual-Baseline Floating microalgae Height) index was used to identify and extract water bloom by using Sentinel-2A MSI and Landsat OLI sensors. Besides, 84 images of Sentinel-3 SLSTR level-1 products (from October 2016 to December 2017) and MODIS Terra Global 1Km Grid (short as MOD11A1, from January 2003 to December 2016) were used to analyze the relationship between WST and algal bloom. The SLSTR WST Products were processed by using split-window algorithm^[5].

Accompany with Landsat OLI and Sentinel-2A algal bloom maps, the processes of algal bloom development during October 2016 to December 2017 was presented. It’s apparently indicated that algal bloom was first observed in October 2016, located in the central of lake and then move to north Erhai Lake from October 2016 to the late spring of 2017. In order to describe the continuous process of algal bloom, the occurrence and duration of the algal bloom are set as follows: if the algal bloom is observed in two scenes within 3-5 days, and during this time the meteorological conditions are stable, then these two observation can be treat as one observation of bloom, and the time of first scene is recorded as the start of this algal bloom. Following the rules that mentioned above, with the aid of Sentinel 2 MSI monitoring on 22 and 24 November ,the three observations of algal bloom in November 2016 (11-20, 11-23, 11-27, detected by Landsat ) should be synthesized as one large scale bloom, which means Sentinel 2 MSI has a greater contribution to the long-term monitoring of algal blooms in Erhai lake^[6,7].

To demonstrate the spatial variability of the SLSTR maps, WST in Erhai Lake present a typical seasonal changes with the lowest temperature apparent in December 2016 and the highest temperature apparent in April 2017. A south to north difference was observed in every climatological monthly mean WST maps. Water temperature in south lake is 0.03K lower than what in north lake for the total of 70 images. The year 2016 had a relatively cool October according to SLSTR L1B result, which is consistent with the metrological air temperature record by Chinese metrological centre.

The monthly average WST in 2016 and 2017 were calculated from the MOD11A and Sentinel 3. Comparing with the 14years average monthly WST from MOD11A during 2003-2017. The monthly average WST in Erhai in 2016 is 0.9-2.5°C higher than 14 years average value. In autumn and early winter of 2016, there was a wide range of algal bloom, indicating that the higher WST was a favorable factor for the propagation and eruption of algal bloom. Otherwise, in 2017, the monthly mean WST of SLSTR is 0.5-3.5°C higher than 14 years average WST of MOD11A (2003-2017), higher WST was found in January and February when algal bloom occurred.

SLSTR can help finding relationship between algal bloom and water surface temperature. Higher water surface temperature is incentive to the eruption of algal bloom.

Wetlands are an important natural resource that requires monitoring. Water area is an important factor to the wetland monitoring. Methods of water extraction from optical images were very mature, such as NDWI, MNDWI and etc. But in rainy and cloudy area, there are always no enough optical images could be used while the underlying surface keeps in a highly dynamic changing speed. Synthetic Aperture Radar(SAR) data are helpful in these conditions and they could be used to map and monitor changes in surface water extent and flooded vegetation areas. These two factors are very helpful for the wetland management to understand the wetland vegetation distributions and changes. We reviewed a few techniques to extract water from optical and SAR images, including NDWI for the optical images and grey-level thresholding for SAR images and compared their differences in different seasons. Since the penetration character in SAR images, We compared the difference water extraction results from optical images and SAR images. And then, We used the polarimetric decompositions to map flooded vegetation to distinguish it from the surface water area. We used H/alpha composition method to detect the flooded vegetation area and found that it is useful to improve the accuracy of water area extraction from SAR images. We recommend that SAR data are very important to acquire the water area in the delta of wetland and differences from Optical and SAR images are very helpful to the wetland management to obtain the accurate water area data.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science.

Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover.

We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

Biodiversity stakes within Yangtze watershed are very important at national level by also international ones. These very rich ecosystems, being key wintering areas for many waterfowl of SE Asia, are suffering from rapidly changing environments due to human activities. It’s crucial to understand what the key factors and their effects are. As data within large spatial and temporal scales are difficult to get, remote sensing and spatial analysis technology turns out to be a useful tool to access information. Works are on progress over Poyang Lake, in regards to vegetation recognition and dynamic particularly within the core of the Poyang Lake natural Reserve, but also over smaller and less known sensitive areas such as Wuchang and Shengjin lakes (Anhui Province).

Since its launch in 2015, the Sentinel-2 mission is capturing the world with an unprecedented combination of spatial and temporal resolutions, going from 10 to 60 m of spatial resolution and 5 days of revisit time at the equator, all in free access. The interval of spectral bands monitored goes from coastal blue at 443 nm to Short-Wave InfraRed (SWIR) at 2190 nm, divided in 13 bands. The mission is composed of 2 identical satellites, Sentinel-2A sent on June 2015, and Sentinel-2B sent on March 2017. Sentinel-2 allows a precise and systematic follow-up of hydrological systems regarding water surface area or vegetation coverage.

The water extents of Wuchang and Shengjin lakes have been extracted from Sentinel-2 time series, using all exploitable images since the beginning of the acquisitions in 2015. Being an optical sensor not all images are usable due to potential high cloud coverage. A total of 32 dates have been used and 10 Landsat 8 (Libra) have been added to try to reduce the temporal gaps in the Sentinel-2 acquisitions caused by cloudy conditions. The final time series have an average of 1 image every 22 days, going from the 20^th October of 2015 to the 7^th of April 2018. The number of available images is higher since March 2017, thanks to the launch of the Sentinel-2B satellite. Extractions were done using a SERTIT-ICube automatized routine based on a supervised Maximum Likelihood Classification, trained with Pekel water occurrence product. These extractions allow to recreate the dynamic of the two lakes and show the drought and wet periods. During the 3 years interval, the surface peaks on July 2016 for both lakes. The lowest level appears at two different dates for each lake; on January 2018 for Wuchang, on February 2017 for Shengjin. Wuchang Lake surface area appears to be more variable than Shengjin Lake, with many local maximum and minimum between the end of 2017 and April 2018.

In addition to Sentinel-2 and Landsat 8, SPOT images have been downloaded from the Theia-world website through the SPOT World Heritage program. The latter gives access to archive data from satellites SPOT 1-2-3-4-5 and extents the study duration span. A total of 19 images were either completely or partially available over Shengjin and Wuchang lakes; 2 SPOT-1, 2 SPOT-3, 13 SPOT-4 and 2 SPOT-5, from December 1987 to April 2009. The time between two images during this period is too large to capture the lakes dynamic but can be used to calculate a total water occurrence product.

In the case of Wuchang Lake, floating vegetation is a problem for automatic water surface extraction. The lake is covered by vegetation during long periods of time and the water below can’t be detected by automatic radiometric means. Nevertheless, Sentinel-2 stays a pertinent and powerful tool for hydrological monitoring of lakes confirming the expectation from the remote sensing wetland community before launch. The presence of IR and SWIR bands induces a strong discrimination between water and other classes, and the systematic acquisitions create dense time series, making analysis more consistent. It makes possible to sensor events occurring over short periods of time. These midterm results illustrate the pertinence and power of multi-source optical satellite data for environmental analysis and confirm the expectations in the Sentinel- 2 constellation.

Colored dissolved organic matter (CDOM), the key component in aquatic environment, plays an important role in biogeochemical processing. The optical characteristics of CDOM potentially permit remote sensing of CDOM. However, retrieval of CDOM for inland turbid water is challenging because of CDOM absorption at blue spectral range overlapped by the absorption caused by chlorophyll a and amounts of total suspended matter contained in turbid water. CDOM inversion algorithms developed and applied to specific regional locations may not be directly applicable for other water environment. Moreover, various CDOM sources present distinct CDOM absorption characteristics spatially and spectrally. In this study, in situ reflectance and water samples were used to develop models for estimating CDOM absorption in a complex freshwater environment in Poyang Lake, China. Poyang Lake is the largest fresh water lake in China. It is a complex flood-path lake with significant annual water level variations caused by hydrological conditions and monsoon climate. The lake also exerts an irreplaceable role for drinking water supply, flood control, waterway shipping and conservation of biological diversity. However, in recent years, the water environment of Poyang Lake has been affected by anthropogenic impacts such as sand mining and major hydrologic engineering. The in situ water reflectance spectra, CDOM absorption spectra and other water-color parameters from 92 samples collected in four representative study areas between 2015 and 2016 were analyzed. Band ratio models were established to estimate CDOM absorption coefficient at 355 nm [ag(355)] based on the correlation analysis between reflectance and ag(355). The results indicated that the band ratio models performed well for estimating ag(355) when the 92 samples were divided into two datasets with the threshold of concentration of total suspended matter (TSM) as 10 mg/L. The band ratios of R(689) / R(497) and R(767) / R(826) were selected to establish model for retrieval of ag(355) in clean water (TSM < 10 mg/L) and turbid water (TSM ≥ 10 mg/L), respectively. The determination coefficients (r2) of calibration models for clean and turbid water were 0.70 and 0.73, respectively. The percentage root-mean-square errors (%RSME) of validation models for clean and turbid water were 13.2% and 11.6%, respectively. The simulated Sentinel-2 and Landsat-8 bands based on reflectance spectra were used to examine potential capability for retrieving CDOM using these sensors. The results indicated that Sentinel-2 would perform better than Landsat-8 for estimating ag(355). The Sentinel-2 band ratio of B4 / B2 or B5 / B2 and B7 / B8 or B7 / B8A would be useful for retrieval CDOM in clean and in turbid water of Poyang Lake, respectively. The performance of the models for Sentinel-2 image acquired on July 26, 2016 was stable both in clean and turbid water. Further evaluate performance for extended temporal application is required in Poyang Lake. The findings from this study provide algorithms foundation for monitoring spatial and temporal dynamic of CDOM in Poyang Lake using remote sensing.

Coastal environments and, in particular, ground motions in coastal areas are in practice often poorly known, and in many cases, only little information is available about the relevant patterns and time evolution. For this reason, it is strategic the continuous monitor of coastal delta regions by the use of advanced Earth Observation (EO) systems that are capable to detect and monitor the evolution of surface deformation phenomena, recovering their spatial extent on the ground and following their temporal variability. This is beneficial for the subsequent interpretation of natural/anthropogenic processes causing surface motion.

The activities performed within this present Dragon IV project have mostly been focused on the analysis of modification processes that characterize two important Delta river areas in China: the Yangtze and the Pearl River Deltas. Principally, we focused on Shanghai area but also a few experiments have been carried out on PRD. Both delta regions are significantly affected by sea-level rise and natural/anthropogenic deformation phenomena, making it clear the need of extended analyses for a better understanding of the mechanisms responsible for the observed surface modifications, and for the planning of actions devoted to risk prevention for populations living in coastal areas.

More specifically, the main aim is to retrieve long-term displacement time-series from EO data, specifically satellite Synthetic Aperture Radar (SAR), of the investigated areas through advanced differential interferometric synthetic aperture (DInSAR) techniques [1]-[2].

To evaluate the combined risk of sea level rise, storm surges, and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas has also resulted mandatory. Added-value EO data-products, such as the updated DEMs of coastal areas subject to sea-level rise, the time-series of terrain displacement, mean displacement velocity maps, and time-series of SAR backscattering maps, have been obtained by exploiting archives of SAR data at different spatial resolution spanning more than 10 years, from 2007 to 2018.

A few experiments have been conducted. In particular, the combined use of ENVISAT, Cosmo-SkyMed, and Sentinel-1 data have permitted to recover long-term displacement time-series of the ocean-reclaimed lands. New combination methods for the retrieval of the components of surface deformations from InSAR-driven LOS-projected measurements have been applied, and the most relevant results have been published on peer-reviewed journals [3]-[5].

Further investigations are currently being in progress to assess the risk of flooding of the coastal region of Shanghai, by benefiting from the retrieved InSAR deformation maps and a digital elevation model (DEM) of the area. The latter has been generated by using 2012 TanDEM-X bistatic SAR data [6]. The results of all these investigations will also be presented in separate communications at the mid-term D4 meeting.

We would like to remark that these studies are the result of a strict cooperation between the European and Chinese research institutions involved in the project.

Finally, some results evidencing the current ground deformation of the Pearl River Delta (PRD) region, obtained using Sentinel-1 acquisitions acquired over the last two years will be presented. In particular, we have selected this test-site area as a laboratory to evaluate the performance of a new multi-grid phase unwrapping approach. We moved from the observation that new-generation satellite data are characterized by larger spatial coverage and/or improved spatial resolutions, thus leading to augmented computational problems. In particular, the number of observation points in each SAR scene tends to considerably increase, thus posing new challenges. To overcome this problem, some multi-grid phase unwrapping methods, based on partitioning a scene in several overlapped, multi-resolution grids of pixels, and on their proper recombination [7]-[8], can be profitably adopted. In our experiments we focused on the PRD region and we provided InSAR-based analyses over multi-grids of pixels characterized by different spatial pixel spacing (i.e., from 500m x 500 m to 25m x 25m). Further developments consist in adaptively identifying the correct (most adequate) spatial spacing grid in each area, separately, depending on the observed spatial rate of deformation, so as to use finer grids in areas with significantly large rates of deformation and worse pixel spacing where deformation has a low spatial rate. Noteworthy, the efficient use of multiple grids of resolution can permit both to unwrap/process large interferograms (even on a continental basis) and, then, progressively “zoom in” given regions in conformity with the Nyquist sampling condition. Very preliminary results will be presented at the D4-meeting. A hybrid multi-scale experiment has also been performed on the Shanghai coastal area.

[1] Berardino, P., G. Fornaro, R. Lanari, E. Sansosti (2002), A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE Trans. Geosci. Remote Sens., 40(11), 2375-2383.

[2] A. Ferretti, C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE Trans. Geosci. Remote Sens., 39(1), 8-20.

[3] Zhao Q., Pepe A., Gao W., Lu Z., Bonano M., He M.L., Wang J., Tang X. (2015) A DInSAR investigation of the ground settlement time evolution of ocean-reclaimed lands in Shanghai, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1763-1781.

[4] A. Pepe, M. Bonano, Q. Zhao, T. Yang, H. Wang, “The Use of C-/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai’s Ocean-Reclaimed Lands through the SBAS-DInSAR Technique, “ Remote Sensing 2016, 8, 911; doi:10.3390/rs8110911.

[5] Lei Yu, Tianliang Yang, Qing Zhao, Min Liu and Antonio Pepe, “The 2015-2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis,” Remote Sens. 2017, 9, 1194.

[6] Krieger G., Moreira A., Fiedler H., Hajnsek I., Werner M., Younis M., Zink M. (2007) TanDEM-X: A satellite formation for high resolution SAR interferometry. IEEE Tans. Geosci. Remote Sens., 45, 3317-3340.

[7] M. D. Pritt, “Multigrid phase unwrapping for interferometric SAR,” in IGARSS 95, Florence, Italy

[8] Antonio Pepe, L. D. Euillades, M. Manunta, R. Lanari: "New Advances of the Extended Minimum Cost Flow Phase Unwrapping Algorithm for SBAS-DInSAR Analysis at Full Spatial Resolution," IEEE Transaction on Geoscience and Remote Sensing, vol. 49, n° 10, October 2011, pp. 4062-4079.

Global mean sea-levels have risen during the 20^th century, and they will accelerate rising by up to ~60 cm by 2100 (Nicholls and Cazenave, 2010). However, the projections remain uncertain in estimating the rate of increase in melting of glaciers, Greenland and Antarctic ice sheets. The fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) suggests the higher values for sea level rise base on newer ice-sheet observations (IPCC, 2014). This will assuredly increasingly submerge risk in the low-lying areas of coastal zones throughout this century. Assessing and mapping the coastal inundation risk under sea level rise has been conducted in Charlestown, RI, USA (Grilli et al., 2017), Italian coastal plains (Antonioli et al., 2017), coastal zones of Poland (Paprotny et al., 2017), the Southeast Queensland (Mills et al., 2016), the Venice city of Italy (Sperotto et al., 2015), and Shanghai (Wang et al., 2012).

Moreover, non-climate-related anthropogenic processes, such as ground subsidence due to groundwater extraction, ground settlements due to large scale land-reclamation, and fast and non-linear subsidence phenomena of artificial sea wall, will exacerbate the risk to coastal zones and megacities and amplify local vulnerability. Making the situation worse is the combination of sea-level rise resulting from climate change, local sinking of land resulting from anthropogenic and natural hazards. Previous study has already stressed the significance of relative sea level rise in increasing coastal flood frequency (Karegar et al., 2017; Little et al., 2015; Cayan et al., 2008; Carminati et al., 2002; Shi et al., 2000).

The coastal vulnerability of mega-city, Shanghai, which is located at the Yangtze River Delta, is currently being amplified by the compounding effects of the time-dependent ground subsidence and the accelerated rate of sea level rise (Yin et al., 2013). The provided examples of delta regions affected by the combination of sea-level rise and significant modifications over time make clear the need of extended analyses for the understanding of the mechanismsat the base of the surface modifications of coastal areas, estimating of future regional relative sea level change, and evaluating the potential submerged land area. The main goals of this study are to provide a full characterization of the scene modifications over time and causes of the coastal region environments, to provide estimates of future regional sea level change, and to project coastal submerged area.

In this study, the use of well-established remote sensing technologies, based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques, and the hydrodynamic model-FloodMap projections will be employed for these purposes. The results obtained in this study represents an asset for the planning of present and future scientific activities devoted to the monitoring of such fragile environments. These analyses are essential to assess the factors that will continue to amplify the vulnerability of the low-elevation coastal zones.

In order to evaluate the combined risk of sea level rise and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas is generated with InSAR. The time-series of terrain deformation and mean deformation velocity maps, will be obtained by exploiting archives of Synthetic Aperture Radar (SAR) data with different levels of spatial resolution spanning a long time interval of about 10 years since the beginning of 2007 to 2017. SAR data will be collected by the former (i.e., the ESA ENVISAT-ASAR) and the new generation of radar sensors (the Cosmo-SkyMed constellations and Sentinel-1A). Large scale DInSAR analysis over wide areas will be performed by exploiting the Small BAseline Subset (SBAS) algorithm. The InSAR derived DEM with different spatial resolution and a 2D hydrodynamic model-FloodMap will be employed to investigate the evolving flood risk in the eastern coastal area of Shanghai and to derive coastal submerged area.

As global warming problem is becoming serious in recent decades, the global sea level is rising continuously. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. Previous studies have shown that there is severe land subsidence in the reclamation area in the east of Shanghai [1-3]. Land subsidence greatly exacerbates the risk of sea level rise. How to obtain land subsidence data efficiently is crucial.

DInSAR technology can quickly obtain a large range of land subsidence information, and the accuracy can reach the millimeter level. Today, land subsidence monitoring using DInSAR technology has been widely used in coastal cities such as Shanghai, Guangzhou and Hong Kong [1-5]. However, limited by the satellite launch time and life cycle, it is difficult to obtain a long time series of land subsidence. Antonio et al. [2,3]. used the subsidence model derived from laboratory centrifuge tests and the Singular Value Decomposition (SVD) to joint the land subsidence time series of the three satellites of ENVISAT/ASAR, COSMO-SkyMed, and Sentinel-1A. In this way, a subsidence time series of up to ten years is obtained.

Based on this, we have found that the deviation of land subsidence time series obtained by using different joint strategies (Using a different order to ioint three satellite platform subsidence time series) at some high coherence points is larger. In this paper, By exploiting a set of 35 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 , a set of 61 SAR images acquires by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, and a set of 33 SAR images acquires by the Sentinel-1A (S1A) sensors from December 2013 to March 2016, coherent point targets identified by using the Small Baseline Subset (SBAS) algorithm. Then, the subsidence time series of high coherence points was obtained.

We use the algorithm proposed in [1,2] to joint the subsidence time series of the three satellite platforms. We adopt different joint strategies: Strategy 1 is to first combine the subsidence time series of CSK and S1A, and then combine the CSK_S1A subsidence time series with the ENV subsidence time series; Strategy 2 is to first combine the subsidence time series of ENV and CSK, and then combine the ENV_CSK subsidence time series with the S1A subsidence time series. We set a threshold for the Euclidean distance of the subsidence time series obtained by the two joint strategies, and call the high-coherence point with a Euclidean distance greater than the threshold as "bad pixel" and the high-coherence point with a Euclidean distance less than the threshold as " Good pixel". Then, through the consistency matching algorithm of the un-joint subsidence time series of three satellite platforms between the bad point and the good point, the joint subsidence time series of bad pixels is corrected.

Meanwhile we use the monthly mean tide level series from Lvsi Station (1959 ~ 2011), and combine the Ensemble Empirical Mode Decomposition(EEMD), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Back Propagation (BP) Neural Network to propose two improved EEMD-GA-BP and EEMD-PSO-BP method for regional sea level change prediction. The use of GA and PSO optimize BP Neural Network can improve the accuracy, and PSO is superior to GA. Multi-platform long-term land subsidence time series and precise sea level predict time series provides a realistic meaning for the impact of relative sea level change on the coastal areas.

[1]Zhao Q, Pepe A, Gao W, et al. A DInSAR Investigation of the Ground Settlement Time Evolution of Ocean-Reclaimed Lands in Shanghai[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(4):1763-1781.

[2]Pepe A, Bonano M, Zhao Q, et al. The use of C-/X-band time-gapped SAR data and geotechnical models for the study of Shanghai's ocean-reclaimed lands through the SBAS-DInSAR technique[J]. Remote Sensing, 2016, 8(11):911.

[3]Yu L, Yang T, Zhao Q, et al. The 2015–2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis[J]. Remote Sensing, 2017, 9(12):1194.

[4]Zhao Q, Lin H, Jiang L, et al. A Study of Ground Deformation in the Guangzhou Urban Area with Persistent Scatterer Interferometry[J]. Sensors, 2009, 9(1):503-18.

[5]Zhao Q, Lin H, Gao W, et al. InSAR detection of residual settlement of an ocean reclamation engineering project: a case study of Hong Kong International Airport[J]. Journal of Oceanography, 2011, 67(4):415-426.

Continuing investigations of tidal variability at multiple tide gauges in Hong Kong and the South China Sea (SCS) have identified correlations with the potential to amplify extreme water levels and nuisance flooding at certain locations. Observed changes are hypothesized to be due to mechanisms active on multiple spatial scales. The regional behaviour of the SCS may have changed tidal evolution via MSL rise, upper-ocean warming (and hence, stratification), or modulations in the baroclinic conversion at the entrance of the SCS (the Luzon Strait). The baroclinic tidal signal can be enhanced at the northern shelf of the SCS and can generate multiple PSI-type interactions that yield amplifications in minor tides such as M₃ that can be observed in Hong Kong. Additionally, the enclosed regions of Hong Kong have undergone massive land reclamation projects that may have changed the resonant and/or frictional response of the harbors to the regional dynamics. Previous works reported on the tidal anomaly correlations (TACs) to detrended MSL fluctuations, shown to be most important in harbour regions such as Victoria Harbor in Hong Kong. In this work, we highlight the intertidal correlations of diurnal (D₁) tides to semidiurnal (D₂) tides, which are positively reinforced through the northern SCS, and the correlations of overtide (OT) fluctuations to D₁ and D₂, shown to be negatively reinforced (i.e., anti-correlated) across the same region. The consideration of all water level variabilities may help explain the large TACs previously reported and may have serious implications for future water levels in Hong Kong.

We present an updated method for semi-empirical determination of significant wave height (H_s) estimation applied to Sentinel-1 SAR data. The method is based on existing semi-empirical algorithms, intending to identify a narrow-band swell-wave spectrum on the open-ocean waters and focused on linear imaging mechanism of tilt modulation on radar-looking directional surface wave. Utilizing the radar backscatter cross-section, we develop and evaluate a linear method to estimate H_s in various environment conditions without any prior knowledge from external input variables. Our method is divided into two components; first, the estimation of dominant wavelength using a 2-Dimensional Fast Fourier Transform (2D-FFT) and dimensionless coefficient, second, an estimation of ocean surface roughness and slope variation. The routines are aided by: adaptive filtering of the radar cross-section using median filters and Gaussian filters in different domains, linear fitting, and a detailed dependency analysis based on wave type and varying wind speed. Standard meteorological buoy data from the National Buoy Data Center (NDBC) is used for validation of the estimated Hs and input for the dependency analysis. These stations are selected to test differing water depths, wave properties, and wind conditions. This study employs Level-1 GRD Sentinel-1A and 1B SAR images from 2016 to 2017 covering locations of in-situ NDBC stations located near Hawaii, used for validation. Results show that the method performs well in estimating H_s under low to moderate wind forcing conditions (4 – 10 ms^-1) for any wave type in open-water areas. Lower performances are found under very low and strong wind conditions, and in wind-wave dominant environments.

This study focuses on the C-band radar backscatter features of the shallow water topography of Subei Bank in the Southern Yellow Sea using 25 ENVISAT (Environmental Satellite) ASAR (advanced synthetic aperture radar) and ERS-2 (European Remote-Sensing Satellite-2) SAR images acquired between 2006 and 2010. Under different sea states, SAR imagery shows different bathymetric features: the wide bright patterns with an average width of 6 km are shown under low to moderate wind speeds and correspond to sea surface imprints of tidal channels formed by two adjacent sand ridges, while the sand ridges appear as narrower (only 1 km wide), fingerlike, quasi-linear features on SAR imagery in high winds. Two possible SAR imaging mechanisms of coastal bathymetry are proposed in the case where the flow is parallel to the major axes of tidal channels or sand ridges. Two vortexes will converge at the central line of the tidal channel in the upper layer and form a convergent zone over the sea surface when the surface Ekman current is opposite to the mean tidal flow, therefore the tidal channels are shown as wide and bright stripes on SAR imagery. For the SAR imaging of sand ridges, all the SAR images were acquired at low tidal levels. In this case, the ocean surface waves are possibly broken up under strong winds when propagating from deep water to the shallower water, which leads to an increase of surface roughness over the sand ridges.

To satisfy the growing land demand for industrial and urban development, man-made lands, reclaimed from the sea, are used to build airports, harbors, and industrial areas. However, in such reclaimed areas, foundation settlements caused by unconsolidated soils are of public concern, and may induce severe damage to buildings and infrastructures. In such a context, Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) technique [1] is able to retrieve ground displacements, with centimeter to millimeter accuracy, by exploiting the phase difference between two SAR images acquired over the investigated area at different times and from different orbital positions.

Advanced DInSAR approaches, such as the Persistent Scatterer Interferometry (PSI) [2] and the Small BAseline Subset (SBAS) technique [3], nowadays represent effective tools for remotely detecting, mapping and monitoring surface deformation phenomena, thanks to their capability to produce spatially dense velocity maps as well as long-term displacement time-series corresponding to coherent targets location.

This study is focused on the retrieval of deformation signals over the ocean-reclaimed lands of Shanghai, China, and it is mostly devoted to the development of an ad-hoc procedure based on the combination of multiple-scale of resolution information. Over the last recent years, several investigations [4]-[5] have been carried out to study the deformation of the coastal area of Shanghai. In particular, the time evolution of ground deformation occurring over the coastal zone was derived from 2007 to 2017 [5] by jointly analyzing sequences of X-band (COSMO-SkyMed) and C-band (Sentinel-1A and ENVISAT/ASAR) SAR images. To achieve this task, a novel approach to link the time-gapped COSMO-SkyMed and ENVISAT/ASAR data was applied and an SVD-based combination approach to link time-overlapped COSMO-SkyMed and Sentinel-1A SAR data was developed. More precisely, the temporal evolution of the sensor-line-of-sight terrain deformation occurring over the coastal area of Shanghai was retrieved by independently processing the available sets of SAR images. This was done by analyzing sequences of multilooked differential SAR interferograms generated from the stacks of SAR images collected from 2007 to 2010 by the ENVISAT sensor, from 2013 to 2016 by the CSK sensors’ constellation, and from 2015 to 2016 by the Copernicus S1A radar instrument. The well-established Small BAseline Subset (SBAS) differential interferometry technique [3] was used for retrieving the LOS deformation time-series for each SAR sensor. Subsequently, for each coherent pixel found both in the ENVISAT and CSK datasets, the time-gapped ENVISAT and CSK LOS deformation time series were preliminarily converted in vertical (subsidence) deformation and, then, linked by using a time-dependent geotechnical centrifuge model.

Starting from December 2017 a new set of CSK data is available relevant to the same track used in the previous investigations; however (due to the lack of a regular planning of SAR acquisitions over the investigated area) there is a big lapse of more than one year between the old (from February 2014 to March 2016) and the new CSK SAR dataset. This leads us to the impractability of obtaining long-term SBAS deformation time-series by exclusively using CSK data. This drawback, at the same time, lead us the possibility to conduct an experiment based on the application of a hybrid multi-scale SBAS strategy. The idea is to focus on a group of highly coherent point-wise targets that preserve their coherence, also after one year, and to generate the CSK 2014-2018 surface displacement time-series related to those pixels. The applied advanced approach, originally presented in [6], for the identification of the highly-coherent point-wise scatterers and for the subsequent generation of the displacement time-series will be adopted. Subsequently, the achieved time-series will be compared with those obtained by linking the two independent (time-gapped) sequences of CSK data (similarly to what done in [5]) by the model adopted in [4]-[5] through the solution of a non-linear optimization problem based on the use of the Levenberg-Marquadt technique. The goal of this present investigation is to prove that, at least in correspondence to the highly coherent targets on the ground, the expected deformations behavior dictated by the used model is in general agreement with the achieved results. This finding is interesting to check the validity of the model used in our previous investigations [4]-[5]. The preliminarily results will be presented and discussed at the next Dragon-IV mid-term meeting.

[1] R. Bürgmann, P. A. Rosen, and E. J. Fielding, “Synthetic aperture radar interferometry to measure Earth’s surface topography and its deformation,” Annu. Rev. Earth Planet. Sci., vol. 28, no. 1, pp. 169–209, May 2000.

[2] A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 1, pp. 8–20, Jan. 2001.

[3] P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2375–2383, Nov. 2002

[4] Q. Zhao, A. Pepe, W. Gao, Z. Lu, M. Bonano, M. He, X. Tang, “A DInSAR Investigation of the Ground Settlement Time Evolution of Ocean-Reclaimed Lands in Shanghai,” IEEE Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1763-1781, April 2015.

[5] Y. Lei, Y. Tianliang, Qing Zhao, Min Liu and Antonio Pepe, “The 2015-2016 Ground Displacements of the Shanghai Coastal Area Inferred from a Combined COSMO-SkyMed/Sentinel-1 DInSAR Analysis,” Remote Sens. 2017, 9, 1194.

[6] Antonio Pepe, L. D. Euillades, M. Manunta, R. Lanari: "New Advances of the Extended Minimum Cost Flow Phase Unwrapping Algorithm for SBAS-DInSAR Analysis at Full Spatial Resolution," IEEE Transaction on Geoscience and Remote Sensing, vol. 49, n° 10, October 2011, pp. 4062-4079.

Abstract

Due to large-scale infrastructure construction and land reclamation, the problem of land subsidence in Shanghai is becoming more and more serious, which will have a major impact on urban public safety. Shanghai has established leveling networks and GPS networks to detect land subsidence, but due to cost constraints, the resolution is relatively insufficient (Amighpey et al. 2016). In recent years, InSAR technology has been widely used to monitor urban land subsidence due to its low cost and high precision (Sansosti et al. 2010; Hooper et al. 2012). Sentinel-1A data were available in the single-look-complex (SLC) format and acquired through the interferometric wide swath (IW) mode by employing the terrain observation by progressive scans (TOPS) acquisition mode, which provides large swath widths of 250 km at ground resolutions of 5 m x 20 m. In order to study the distribution and spatial pattern of land subsidence in Shanghai, a set of 33 SAR images acquired by the Sentinel-1A from July 2015 to August 2017 (ascending passes, VV polarization, with a side-looking angle of about 39˚and a satellite heading angle of about 348˚) were exploited to get coherent point targets as long as land subsidence velocity maps and time series which were identified by using the Small Baseline Subset (SBAS) algorithm (Berardino et al. 2002; Lanari et al. 2007). SBAS is based on the use of multiple-master multilook interferograms generated after a proper selection of Small Baseline (SB) SAR data pairs. LOS displacement time-series are computed by solving a least-squares (LS) minimization problem, based on the application of the singular value decomposition (SVD) method, to the sequence of unwrapped multilook interferograms. In this paper Sentinel-1A data were processed by the SBAS toolbox implemented within the commercial ENVI’s SARScape modules from EXELIS VIS Information Solutions with the coherence threshold of 0.35 and the maximum temporal baseline set to 180 days.

Urban land subsidence is mainly caused by infrastructure construction and groundwater extraction (Galloway et al. 2011; Wang et al. 2017). We use Landsat optical satellite data to analyze the changing trend of coastline in Shanghai since the 1990s (Landsat5、7、8, resolutions of 30m x 30m, every five years collected in winter). It is found that the coastline is expanding dramatically and most of the coastal areas with obvious settlement are the regions which has been reclaimed in the recent decade. In the central city, subsidence areas are densely located along subway lines. Since Shanghai began to reduce the exploitation of groundwater from the 1970s, the current settlement in Shanghai is mainly due to the unstable geological structure of the reclamation area and the construction of a large number of above ground and underground infrastructure in the city. By focusing on the land subsidence trend in high-rise buildings in urban areas and coastal areas, we found that the settlement in the coastal area is still significant, and the high-rise buildings in the area along the Huangpu River also have a subsidence of up to 2 cm/y.

Reference

Amighpey, M., & Arabi, S. (2016). Studying land subsidence in Yazd province, Iran, by integration of InSAR and levelling measurements. Remote Sensing Applications: Society and Environment, 4, 1-8. doi: 10.1016/j.rsase.2016.04.001

Sansosti, E., Casu, F., Manzo, M. & R, L., 2010. Space-borne radar interferometry techniques for the generation of deformation time series: an advanced tool for earth’s surface displacement analysis, Geophys. Res.Lett., 37, L20305, doi:10.1029/2010GL044379.

Hooper, A., Bekaert, D., Spaans, K. & Arikan, M., 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation, Tectonophysics, 514-517, 1–13.

Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383.

Lanari, R.; Casu, F.; Manzo, M.; Zeni, G.; Berardino, P.; Manunta, M.; Pepe, A. An overview of the small baseline subset algorithm: A DInSAR technique for surface deformation analysis. Pure Appl. Geophys. 2007,164, 637–661.

Galloway, D. L., & Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8), 1459-1486. doi: 10.1007/ s10040-011-0775-5

Wang, H., Feng, G., Xu, B., Yu, Y., Li, Z., Du, Y., & Zhu, J. (2017). Deriving Spatio-Temporal Development of Ground Subsidence Due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China. Remote Sensing, 9(10). doi: 10.3390/rs9101004

In this study, the possible submergence area of the Yangtze River Delta (YRD) under the background of sea level rise is investigated combining both satellite data and numerical models. The sea level rises (SLRs) in the East China sea in the middle and end of 21^st century are first predicted based on the statistical analysis of historical satellite altimeter data. The mean SLR values in 2050 and 2100 are 20 cm and 35cm, respectively. Then a regional tidal wave model of the East China sea is constructed using the Finite-Volume, primitive equation Community Ocean Model (FVCOM), and a new storm surge inundation model of the YRD is developed (here we take typhoons Fung-wong and Wipha as examples) to analyze the possible submergence area. The results show that if there is no coastal protection, the maximum possible inundation caused by SLR through tidal wave propagation in 2100 is 2.5×10³ km², 87.2% larger than that at the current sea level, and the maximum submergence area during the two storm surges is 8.3×10²~2.7×10³ km². Considering a 4 m-high breakwater along the coastlines, there is no submergence in the above cases under the SLR in 2100, while the inundation is about 15.4 km² during typhoon Wipha when the breakwater is 2 meter high. The submergence mainly occurs in Jiangsu Province, especially Yan Cheng and Lian Yungang cities. It is suggested that the height of the breakwater should be not less than 2 m considering the impact of sea level rise and storm surges.

The assessment of land use sustainability requires a precise and objective accounting of land degradation and regeneration rates. This is a main basis of the international initiative on achieving Land Degradation Neutrality (LDN), defined by the United Nations Convention to Combat Desertification as ‘a state whereby the amount and quality of land resources, necessary to support ecosystem functions and services and enhance food security, remains stable or increases within specified temporal and spatial scales and ecosystems’.

We present here a pilot study developed in the drylands of China and based on geospatial data archives of vegetation and climate for the hydrological years 2002 through 2012. Net Primary Productivity (NPP) yearly summaries, derived from MERIS satellite data by the CASA algorithm, were regressed in a stepwise form against matching aridity index data and year number. Such regressions were made pixel by pixel using the data transformed to standardized residuals, to enable comparisons between the effects of both predictors. Effects of time, after discarding aridity, were assumed as land degradation or regeneration depending on the sign of the standard partial regression coefficient (SPRC), negative and positive respectively. Significance was set at 90%. Then, the Mann-Whintney U test was used to compare the relative magnitudes of negative and positive SRPC, both by aridity zones and land uses. Spatial resolution was of 4 km. The drylands domain was taken from a published study on the determination of the Potential Extent of Desertification in China.

Overall, degrading trends prevail over regeneration ones, which is particularly noticeable in grasslands, deserts and croplands, and in all the aridity zones. Further to that, land degradation rates were found significantly faster than regeneration rates in grasslands and deserts, and in the semi-arid and dry sub-humid zones. Croplands, on the contrary, did result in faster regeneration than degradation, albeit the latter prevails in extent as mentioned above.

These results are still being interpreted. In general, they must be seen in the context of a high variability mosaic, where strong intensification of productive land uses (e.g. croplands) coexists with strict environmental conservation policies applied to large areas of inherited desertification that still may have not had time to show a trend change (e.g. grasslands), and with natural or seminatural areas with no detectable trend.

In parallel with the interpretation, the next step will be an essay of accounting LDN using a methods endorsed by UNCCD.

As a special vegetation types, Ulmus pumila L. sparse forest were widely distributed in the Otindag Sandy Land. It is an important component of the Otindag Sandy Land ecosystem, and also plays an important role in windbreak and sand fixation, climate regulation and grassland ecosystem maintenance. Most of the researches of Elm sparse forest information extraction are mostly based on traditional methods such as survey, field visits and historical documents. Thus, they are laborious and have great financial resources, and the investigation period is long and difficult to update and difficult to meet the demand of obtaining a wide range of Elm spatial distribution. Therefore, techniques for automatically identification the spatial distribution of Elm trees is necessary. With the development of remote sensing techniques, the spatial resolution of remote sensing images is getting higher and higher. The crown of each tree can be clearly seen in the high resolution remote sensing image. According to the characteristics of the geometric shape, size and spatial pattern displayed on the image, tree crown information can be estimated accurately. The accurate information of Elm sparse forest canopy is a prerequisite for other scientific and rational research, and it is also an important reference for decision makers.

Based on the homemade GF-2 data, the hinterland of Otindag Sandy Land where the Elm widely distributed was selected as the study area, and a technique for Elm Sparse Forest information extraction was promoted. First of all , by analyzing the performance of different objects of remote sensing images in the Otindag Sandy Land, the extracted NDVI was nonlinearly stretched to construct a feature image for detecting Elm spots; Secondly, by combining different sizes of filtering kernels and standard deviation, Gaussian filtering is adopted to generate multi-scale feature space to meet the needs for different scales of Elm distribution extraction; Next, the Laplacian operator is applied to the multi-scale feature space, and then detect the bright spot center on different scales, ie The center of the Elm target; Finally, based on field survey results, the accuracy of Elm detection results was evaluated.

Desertification is one of the most important environmental problems in drylands of China, and the damage is very serious. It is of great significance to carry out monitoring of desertification in large areas to grasp the status and dynamics of desertification and formulate scientific and effective prevention and control strategies. Soil organic matter is one of the important indicators of desertification conditions. However, due to data lack and disturbances of vegetation signals, etc., large areas of soil organic matter acquisition have always faced greater difficulties. Compared with traditional ground-based observations, remote sensing technology has the potential to provide more reliable, time- and labor-saving estimates of soil organic matter content in large areas, which in turn provides data support for desertification monitoring and assessment.

This study, uses Google Earth Engine (GEE) with mass remote sensing data provision and cloud computing capabilities, exploring different machine learning methods such as CART, Random Forest (RF), and Support Vector Machine (SVM) to estimate the soil organic matter content of desertified land in China drylands, based on Sentinel-2 high-resolution image reflectance (non-growth season), topographic data, climate data, characteristic spectral index data, and ground measured soil organic matter content data (0-20cm). Overall, CART showed better accuracy than RF and SVM. The CART model obtained moderate results with R² of 0.48 and RMSE 0.35 without considering ancillary factors. By including the terrain, climate and characteristic spectral factors, the model accuracy improved greatly (R² can reach 0.86, the RMSE to 0.16, and the precision increased by 53%), which fully highlighting the importance of including the characteristic index and climate and topography factor when estimating the soil organic matter content. In particular, compared with other existing soil products in the region, this study obtained a full-coverage, higher-resolution and more reliable spatial distribution map of soil organic matter content, which could provide better support for desertification monitoring in China drylands in the future. .

The Third Pole Environment centered on the Tibetan plateau and the Himalayas feeds Asia’s largest rivers which provide water to 1.5 billion people across ten countries. Due to its high elevation, TPE plays a significant role in global atmospheric circulation and is highly sensitive to climate change. Intensive exchanges of water and energy fluxes take place between the Asian monsoon, the plateau land surface (lakes, glaciers, snow and permafrost) and the plateau atmosphere at various temporal and spatial scales, but a fundamental understanding of the details of the coupling is lacking especially at the climate scale.

Based on in-situ measurements, satellite remote sensing and numerical modeling, several main achievements have been acquired to promote the understanding of water and energy cycles over the Tibetan Plateau. (1) In-situ measurements: The warm season characteristics of turbulence structure and transfer of turbulent kinetic energy over alpine wetlands and alpine meadow are analyzed. We found that the turbulence intensities decrease rapidly with increasing wind velocity under conditions of wind velocity smaller than 2 m s^-1 and the pulse of CO₂ flux is very small at noon time because of the high temperatures. We also identified that variations in soil moisture had important effects on carbon exchange in the alpine steppe ecosystem. Both the photosynthesis and respiration were active under high soil moisture content, and suppressed during periods of water shortage. Further, precise measurements of evaporation and understanding of the physical controls on turbulent heat flux at different time scales over a high-elevation small lake are also performed. A total evaporation value of 812 mm is reported for the small lake and the energy budget is generally closed during the open water period. Also we analyzed the variability and trends of daily precipitation extremes over the northern and southern side of central Himalaya. The results suggest that increases in precipitation have been accompanied by an increasing frequency of extremes over the southern central Himalaya while no relation could be established between the precipitation extreme indices and circulation indices for higher altitudes. (2) Remote sensing: an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODIS sensors was established, with RMSE value of 2.65℃ and mean bias of smaller than 1 ℃. Combining satellite remote sensing data and surface meteorological forcing data, land surface heat fluxes at multi-spatiotemporal scales over the Tibetan Plateau have been achieved. The parameterization schemes for diffused and reflected downward shortwave radiation flux of the TESEBS model were improved by introducing the parameters sky-view factor (SVF) and terrain configuration factor (C_t). In addition, a parameterization approach of effective roughness length was introduced into the SEBS model to account for subgrid-scale topographical influences. The results show that sensible heat flux decreased overall while latent heat flux increased over the majority TP over 2001 to 2012. (3) Model simulation: Lake-air interactions at Nam Co lake were analyzed through evaluating two popular lake-air exchange models: a bulk aerodynamic transfer model (B Model) and a multi-layer model (M Model). It was found that both models underestimated turbulent fluxes. This was due to inaccurate values of the Charnock coefficient and the roughness Reynolds number which are both important parameters for calculating the roughness length for momentum over water. A new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2) and the results show significant improvements in snow internal process and soil water phase changes. Also Regional Atmospheric Modeling System (RAMS) was applied to the study of the effect of the topographical altitude of the Tibetan Plateau (TP) on a severe drought event which took place in eastern China from November 2008 to January 2009. (4) Hydrological model: Flow generated from Upper Indus Basin (UIB) originates in Hindukush-Karakoram-Himalaya region, Pakistan. The initial water supply reinstates after winter, depending upon the accumulated snow aggregate and subsequent temperature. Seasonal temperature dictates the state and fate of snow and glacier melt during summer. Recently, developing evidence of warming at high-mountains is accelerated regarded as Elevation dependent warming (EDW). We have identified trends, analyzed variability, and assessed changes in annual and seasonal maximum, minimum, mean and diurnal temperature range. (5) Training of young scientists in the area of climate and environment. Four PhD students have been sent to European partner for joint training. Two of them have got PhD degree in University of Twente under the supervision of European PI (Prof. Z.(Bob) Su) and Chinese PI (Prof. Yaoming Ma). Several European students from our partner also come to China regularly for joint field visiting and academic exchange.

The Third Pole Environment plays a significant role in global atmospheric circulation and is highly sensitive to climate change and its impact on Asia’s largest rivers which provide water to 1.5 billion people across ten countries. A fundamental understanding of intensive exchanges of water and energy fluxes between the Asian monsoon, the plateau land surface and the plateau atmosphere at various temporal and spatial scales especially at the climate scale is crucial to understand the role of TPE on global climate and the impact of climate change on TPE.

The CLIMATE-TPE project aims to improve understanding the interactions between the Asian monsoon, the plateau surface (including its permafrost and lakes) and the Tibetan plateau atmosphere in terms of water and energy budgets in order to assess and understand the causes of changes in cryosphere and hydrosphere, in relation to changes of plateau atmosphere in the Asian monsoon system, and to predict the possible changes in water resources in the Third Pole Environment. A core innovation of the project is to verify or falsify recent hypotheses (e.g. links between plateau heating and monsoon circulation, snow cover and monsoon strength, soil moisture and timing of monsoon) and projections of the changes of glaciers and permafrost in relation to surface and tropospheric heatings on the Tibetan plateau as precursors of monsoon pattern changes and glaciers retreat, and their impacts on water resources in South East Asia.

We use earth observation, in-situ measurements and modelling to advance process understanding relevant to monsoon scale predictions, and improve and develop coupled regional scale hydroclimatic models to explain different physical links and scenarios that cannot be observed directly. Three work-packages (WP) are defined to address three specific objectives. 1) advancement of the understanding of microwave scattering and emission under complex terrains with permafrost and freeze – thawing conditions. The focus is to reduce current uncertainties in microwave satellite observations over complex terrain and improve retrieval accuracies of soil moisture and freeze-thaw states by deploying in-situ observations, laboratory experiment and numerical modelling. 2) Advancement of physical understanding and quantification of changes of water and energy budgets in the TPE. The focus here is to integrate current understandings in the mechanism of changes in water and energy budget in TPE using satellite data products and numerical modelling. Objective 3) Advancement of quantifying changes in surface characteristics and monsoon interactions. All variables related to water and energy budgets in TPE will be subject to systematic analysis to ensure their consistence in terms of climate data records. The variables will include albedo, vegetation coverage, soil thermal and hydraulic properties, LST, soil moisture, lake levels and land use changes among others.

In this contribution we focus on WP1: Observation and modelling of microwave scattering and emission under complex terrains and including permafrost and freeze and thawing.

Since 2006 the Tibetan plateau observatory for soil moisture and soil temperature (Tibet-Obs, Su et al., 2011, HESS) has been in operation and has provided valuable dataset for land-atmosphere process studies. The networks and collected data have been used for calibration and validation of satellite soil moisture retrieval algorithms and data products as well as for improving numerical model parameterizations (Su et al., 2013, JGR; Zheng et al., 2015a, b, JHM; 2017a, JHM, b, JGR) and for understanding passive and active microwave signals (Dente et al., 2015, RSE; Wang et al., 2016, JAG; Lv et al., 2014, RSE). Most recently an in-situ microwave radiometer (ELBARA III from ESA) has been operating at the Maqu site of the Tibet-Obs, as such coherent process observation, process modeling and radiative transfer modeling can be conducted to examine land-atmosphere interactions. We report here recent results of these experiments in combined radiative transfer and heat-water transfer processes (Zheng et al., 2017, TGRS) and in understanding SMOS/SMAP observation signals and data products (Lv et al., 2018, RS).

Young scientists engaged in this project:

1. Dr. Rogier van der Velde, University of Twente, email: r.vandervelde@utwente.nl, remote sensing of soil moisture
2. Dr. Yijian Zeng, University of Twente, email: y.zeng@utwente.nl, data assimilation
3. Dr. Xin Wang, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China, remote sensing of soil moisture
4. Dr. Xuelong Chen (ITP) land-atmosphere interactions, boundary layer processes
5. Dr. Donghai Zheng (ITP) water cycle in the upper Yellow River basin, hydrological modelling
6. Shaoning Lv (PhD student, UT-NIEER) Microwave emission and soil moisture, SMOS/SMAP signals
7. Qiang Wang (PhD student, UT) Soil moisture monitoring using Aquarius data
8. Binbin Wang (PhD student, UT-ITP) Energy balance of high plateau lakes
9. Xu Yuan (PhD student, UT) The role of sea surface salinity in the Indian Ocean and its effect on the South Asian monsoon
10. Hong Zhao (PhD student, UT) Retrieval of soil thermal and hydraulic parameters from satellite observations
11. Dongyu Jia (PhD student, NIEER) land-atmosphere interaction study in cold region
12. Prof. Weiqiang Ma, Institute of Tibetan Plateau, CAS, email: wqma@itpcas.ac.cn, remote sensing and atmospheric modelling of land-atmosphere processes
13. Lian Liu (PhD student, ITP) Retrieval of thermal and hydraulic parameters from satellite observations on the glaciers
14. Dr. Cristina Aguilar, University of Cordoba, e-mail: caguilar@uco.es, ecohydrological modelling
15. Dr. Rafael Pimentel(postdoc, UC)remotely sensed data fusion and assimilation for snow and hydrological modelling
16. Gabriel Delgado Leal(PhD student, UC) Water and energy fluxes regime in snow mountain regions
17. Marta Sáenz de Rodrigáñez (PhD student, UC) Remote sensing data fusion to generate long and high resolution time series of snow and hydrological variables
18. Dr. Jian Peng (LMU/OU) Thermal and microwave remote sensing of surface energy and water fluxes
19. Dr. Christiaan van der Tol, University of Twente, email: c.vandertol@utwente.nl, remote sensing of fluorescence
20. Jan Hofste (PhD student, UT),University of Twente, email: j.g.hofste@utwente.nl, remote sensing of land surface by scatterometry and spectroscopy

A global daily evapotranspiration product without spatial-temporal gaps for 2000-2017 is delivered by using an energy balance (EB) algorithms and MODIS satellite data. A global turbulent exchange parameterization scheme was developed and used in an energy balance model, which uses land-air temperature gradient to estimate the turbulent sensible heat (H), and take the latent heat flux as a residual of the available energy (net radiation minus ground heat flux) and sensible heat. It provides us with the first ever moderate resolution estimates of ET without spatial-temporal gaps on a global scale. The performance of evapotranspiration (ET) data has been evaluated in comparison to 230 flux sites measurements representative of a broad range of biomes and climates at the global scale. The gap-filling algorithm reproduces observed ET with reasonable accuracy. The daily ET product has a mean bias of 0.04 mm/day, with the RMSE value of 1.56 (±0.25) mm/day.

Surface soil moisture (SSM) plays a significant role in various domains of science, including agriculture, hydrology, meteorology and ecology. However, the spatial resolution of microwave SSM products is too coarse for regional applications. This study estimates SSM directly from data of the Chinese geostationary meteorological satellite FY-2E, without establishing empirical relationships between SSM measurements and satellite derived proxies of SSM. The derived SSM has a spatial resolution of 5 km and is based on an elliptical-new SSM retrieval model developed from the synergistic use of the diurnal cycles of Land Surface Temperature (LST) and Net Surface Shortwave Radiation (NSSR). Validation of the model is conducted based on ground measurements over the source area of the Yellow River (SAYR) on the northeastern Tibet Plateau. The FY-2E-derived SSM using the elliptical-new model exhibited good consistency with the ground measurements, with R of 0.845, RMSE of 0.064 m³/m³ and bias of 0.017 m³/m³. In addition, since the spatial resolution of microwave SSM products is too coarse, various downscaling methods are proposed to improve the spatial information. In this study, the CCI SSM product is downscaled to 5 km through a simple vegetation-temperature-condition-index (VTCI) method, which is simpler in terms of input requirements and implementation and has similar accuracy compared to other downscaling methods. The comparison of the VTCI downscaling model against in-situ measurement in Maqu, Luqu and Ruoergai also shows good agreement with R of 0.73, RMSE of 0.08 m³/m³ and bias of 0.04 m³/m³. Furthermore, the spatial patterns of elliptical-new SSM retrieval and VTCI downscaled SSM are also compared. The results show that FY-2E-derived SSM is similar to the downscaled SSM, with SSM over the Eastern region higher where has lakes than SSMs in the west. Lowest SSMs from both methods appear in the northernmost region. In order to provide more accurate SSM for these two methods, high accuracy vegetation indexes, LST and NSSR are still needed.

There are tens of thousands of lakes on the Tibetan Plateau and lakes show significant influences on catchment scale water and heat budget and influence local climate modeling. However, the observation and simulation of the high-elevation lakes are still quite limited. Thus, based on eddy covariance observations and meteorological data over open water periods from a small lake in 2012-2013 on the Tibetan Plateau, we achieved the goals of understanding the characteristics and driving forces of lake-air interaction process, obtaining the evaporation and energy budget, and testing evaporation estimation methods at temporal scale of 10 days over high altitude small lakes. The optimized parameters of roughness length for momentum are suitable for lake-atmosphere heat flux simulation by bulk aerodynamic transfer method. Wind speed shows high correlations at temporal scale of 30 minutes while temperature gradient and water vapor gradient has much higher correlations at temporal scales of daily and monthly. Under neutral conditions, the water vapor gradients have no influence on latent heat flux. The accumulated heat during April to August is fast released during September to November. The average evaporation over the entire open water period is 812 mm and the energy budget is generally closed with a closure ratio of 0.97. The constructed data series provide a good data set for evaporation methods evaluation. The energy budget based method show much better performances than methods of radiation based and Dalton type.

Sentinel-2 MSI offers improved spatial resolution over sensors dedicated to water observations (e.g. OLCI). This makes it an attractive sensor to attempt water quality observations in coastal regions and inland water bodies. However, the band configuration of Sentinel-2 MSI is not optimized to resolve the optical features that are diagnostic of waterbody health. It is therefore worth investigating whether the SNR statistics of the MSI are good enough for water quality retrieval.

The predominant source of error in remote sensing of water quality is atmospheric correction, followed by selection of suitable water constituent retrieval algorithms. Here we present a comparative analysis of atmospheric correction solutions for MSI including Polymer, C2RCC, Acolite, iCOR, l2gen and Sen2Cor. In situ data from optically diverse sources (Baltic Sea, Western English Channel, and lakes in Estonia, Italy and the Netherlands) are used to generate > 1000 match-ups to compare the suitability and limitations of the different tools for different water environments. We further show how the dynamic algorithm selection of the Calimnos processing chain is implemented for Sentinel-2, leading to retrieval of a set of distinct Optical Water Types which describes how MSI 'sees' waterbodies.

The GaoFen-4 (GF-4) remote sensing satellite is China's first civilian high-resolution geostationary optical satellite, which has been launched at the end of December 2015. The GF-4 has the unique advantages of high temporal resolution (20s) and high spatial resolution (50m). In order to explore GF-4’s potential in ocean bloom monitoring, the GF-4 images were used in red tide detection in the Bohai Sea and drifting velocity of the green tide in the Yellow Sea. Results showed that the GF-4 images had great potential in small patches of red tide detection and could provide data support for accurate monitoring of green tide short-term movement.

Daily coverage of single ocean color sensor can only reach 10%~15% because of cloudy and rainy weather, solar flare, track gap, etc. Merging datasets from different missions into unified data products is a valid way to increase the spatial and temporal coverage of ocean color satellites.

ESA started the GlobColour project in 2005 with the aim of providing a continuous dataset of merged Level 3 ocean color products (1997–present). Based on long time series of merged ocean color products (2003–2016) from the ESA GlobColour dataset, spatial and temporal distributions on effective observation days of ocean color satellites of the East Yellow and East China Seas are analyzed. The results show that the average number of effective observation days for the East China Seasregion is 51±6.8 days per year. Large numbers of such days appeared for the West Korea Bay and western Liaodong Bay, at 100±8.3 days per year. Small numbers of days appeared for the southwestern southern Yellow Sea, northwestern East China Seas, and the branch of Kuroshio Current region, at 40±10.1 days per year. Effective observation days of the Yellow and East China Seas had strong seasonal characteristics. The Bohai Sea, northern Yellow Sea, and southern Yellow Sea had two peaks in March and October. The East China Seas had a single peak in July. A lack of effective observation reduces the quality of monthly ocean color products, with greater than 15% bias if the monthly data are averaged from 3 days and 30% bias if the monthly data are from 1 day only.

SeaWiFS and MERIS stopped successively in 2010~2012 and the number of ocean color sensors decreased. We tried to add Medium Resolution Spectral Imager (MERSI)/FY-3 of China in merged Chl-a products. The results show that the average daily coverage of merged products increases by ~9% when MERSI data are added in the merging process. Sampling frequency (temporal coverage) is greatly improved by combining MERSI data, with the median sampling frequency increasing from 15.6% (~57 days/year) to 29.9% (~109 days/year). The new merged products agree within ~10% of the merged Chl-a product from GlobColour. Time series of the Chl anomalies are similar to GlobColour products.

Environmental monitoring and early warning of water quality from space is now feasible at unprecedented spatial and temporal resolution following the latest generation of satellite sensors. The transformation of this data through classification into labelled, tracked event information is of critical importance to offer a searchable dataset.

Advances in image recognition techniques through Deep Learning research have been successfully applied to satellite remote sensing data. Deep Learning approaches that leverage optical satellite data are now being developed for remotely sensed multi- and hyperspectral reflectance. The combination of spectral with spatial feature extracting Deep Learning networks promises a significant improvement in the accuracy of classifiers using remotely sensed data.

This project aims to re-tool and optimise spectral-spatial Convolutional Neural Networks originally developed for land classification as a novel approach to identifying and labelling dynamic features in waterbodies, such as algal blooms and sediment plumes in high-resolution satellite sensors.

With accelerating climate change and receding summer ice cover, the problems of marine environments in the Arctic Ocean appear to be increasing. Ocean color remote sensing is one of the most effective methods with relatively low cost to monitor marine ecosystems. The quality assurance (QA) system of remote sensing reflectance (Rrs) developed by Wei et al. (2016) is used to assess the MERIS radiometric products in the Arctic Ocean over the 2002 ~2012 time period, which is scored according to the spectral shapes and amplitudes of Rrs spectra. Results show that monthly QA average scores are about 0.75 with little fluctuation (<0.1) and the life-cycle quality of the MERIS radiometric products keeps relatively steady. The majority of the Arctic Ocean has a QA score close to 0.7 while relatively lower QA scores (<0.4) are mainly found in the Kara Sea, Laptev Sea and Hudson Bay. There is less valid MERIS data in the spring and winter, which mainly distributes in the Norwegian Sea and the Denmark Straits with QA scores of about 0.7. More valid MERIS radiometric data are obtained in the summer and autumn, and the radiometric products in the Norwegian and Bering Sea are observed with relatively higher QA scores (>0.8).

Optical satellites of HY-1C and HY-1D will be launched by China in 2018 and 2019, onboard which the Coastal Zone Imager (CZI) is one of the shared payloads. CZI is designed to monitor the coastal zone by providing the optical images in the four bands (blue, green, red and near infrared) with wide swath (950km) and moderate spatial resolution (50m). Quantitative retrieval of the environmental parameters including water quality will be achieved for the coastal zone management.

In this paper, the atmospheric correction algorithm for the operational CZI data processing is proposed. Based on the atmosphere radiation transfer (6S) model and CZI band response functions, lookup tables have been established for the calculation of Rayleigh scattering, aerosol scattering and scattering transmittance for different aerosol models. With these lookup tables, two schemes of atmospheric correction algorithm have been developed. For the clear water with nearly null water-leaving radiance in the CZI red and near infrared bands, the aerosol scattering contributions are firstly estimated at these bands to determine the aerosol models close to actual situation. Then, the aerosol scattering at the blue and green bands is estimated with the lookup tables for the selected aerosol models. For the turbid coastal waters with significant water-leaving contributions in the red and near infrared bands, the aerosol optical depth observed by the Chinese Ocean Color and Temperature Scanner (COCTS), which is concurrent with CZI, is used as auxiliary data to determine aerosol models.

China is rich in neritic and tideland resources. Floating raft aquaculture is an important part of the coastal marine environment monitoring. With rapid development of the aquaculture industry and driven by interests, high-density culturing and occupation of key ecological function areas including core and buffer zones of natural reserves, and illegal use of public facilities protective zones for culturing including ports and waterways have been exacerbated year by year. Therefore, systematic and deep studies on distribution and area of Floating raft aquaculture can provide additional decision-making information for fisheries authorities to rationally plan the Floating raft aquaculture, and offer a reliable scientific basis for controlling culturing density, curbing the deterioration of culturing environment, and preventing and controlling mariculture diseases.

Taking the seawater and the floating raft aquaculture in the Jiangsu Lianyungang offshore area as the classified objects, and the domestic GF1 high-resolution remote sensing data as the data source, this paper explores the method of extracting the raft cultivation information with the domestic high- resolution satellite imagery. The GF1 satellite data are preprocessed, and spectral and texture features are combined and applied to the support vector machine(SVM) algorithm. Then, the classification results of extracted seawater and floating raft aquaculture are compared and analyzed. In this paper, blue and green bands sensitive to culturing information are firstly screened for calculation of texture features. Then, 8 characteristic variables of gray level co-occurrence matrix, namely mean value, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment and correlation, are adopted. Texture feature variables are combined with the red, green, and blue spectral data to form 19-layer feature variables. In order to capture key samples and removes redundancy, those feature variables are processed by principal component conversion. The components of the first 8 principal components with large quantities of information and well-retained culturing information are screened and used for classification experiment of support vector machine. To verify the classification accuracy, this method is compared with the classic maximum likelihood estimation and minimum distance methods. The experimental results show that the method of applying texture features in extraction of culturing information method can improve the classification accuracy of floating raft aquaculture. Compared with the maximum likelihood estimation and minimum distance methods, this method has its classification accuracy improved by 3%-5% as a whole.

A watershed, regarded as the best unit for practicing hydrological cycle and water resource research, possesses all of the complexities of the land surface system. Thus, integrating multi-source observations, hydrological modeling into a model-data assimilation framework at watershed scale is the most comprehensive way to understand the complexities of process of hydrological cycle, and is of utmost significance to provide insight into water resource management. This study presents a comprehensive overview on the progress of observation, modeling and data assimilation of watershed scale hydrological cycle. Specifically, several key progresses have been observed at: 1) development of an integrated watershed system model and closure of hydrological cycle at watershed scale, 2) improvement data assimilation algorithm and development data assimilation system, and 3) development of key water cycle elements estimating algorithms and products.

To understand complex watershed systems and to support integrated river basin management, we proposed a new modeling framework to incorporate emerging knowledge into integrated models through data exchange interfaces [1]. The model is expected to represent the coevolution of the water-land-air-plant-human nexus in a watershed and provide capability of decision-making support and to provide an overarching framework for linking natural and social sciences. Based on the framework of the watershed system model, we analyzed the hydrological cycle in the Heihe River Basin [2]. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012.

We proposed soil moisture assimilation scheme that jointly assimilated the brightness temperature of Advanced Microwave Scanning Radiometer-Earth Observing System and Land Surface Temperature products of Moderate Resolution Imaging Spectroradiometer [3]. The data assimilation scheme could correct model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter. In addition, we developed a physically based hydrological data assimilation system using the gridded and parallelized Soil and Water Assessment Tool distributed hydrological model [4]. The system integrated remotely sensed and ground-based observational data with the Parallel Data Assimilation Framework. The system could accurately characterize watershed hydrological states and fluxes. As to the application of data assimilation to hydrological flux, significant progress has been obtained as well. For instance. Pan et al. [5] assimilated the two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM and Fengyun-2D: FY-2D) into the weather research and forecasting model under framework of the 4D-Var data assimilation method in Heihe River Basin. The improved precipitation forecasting has been observed.

Remote sensing retrieval algorithms for key hydrological elements, such as soil moisture, evapotranspiration, have been witnessed progress. For instance, Li et al. [6] estimated continuous daily evapotranspiration at a 90-m spatial resolution using the Surface Energy Balance System (SEBS) by fusing high-temporal resolution Moderate Resolution Imaging Spectroradiometer and high spatial-resolution Advanced Space-borne Thermal Emission Reflectance Radiometer images. Ma et al. [7] proposed a probabilistic inversion algorithm for soil moisture estimation based on Bayes’ theorem and the Markov Chain Monte Carlo technique. They not only obtained highly accurate soil moisture estimation, but also quantified the uncertainties in the inversion algorithm.

Overall, significant progress has been made in the hydrological observation, modeling and data assimilation at watershed scale in recent year. We believe that more fruitful results would be expected in near future under these bases.

References

[1] X. Li, G. Cheng, H. Lin et al., “Watershed system model: the essentials to model complex human‐nature system at the river basin scale,” Journal of Geophysical Research Atmospheres, vol. 123, no. 6, pp. 3019-3034, 2018.

[2] X. Li, G. Cheng, Y. Ge et al., “Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins,” Journal of Geophysical Research Atmospheres, vol. 123, no. 2, pp. 890-914, 2018.

[3] W. Chen, H. Shen, C. Huang et al., “Improving soil moisture estimation with a dual ensemble Kalman smoother by jointly assimilating AMSR-E brightness temperature and MODIS LST,” Remote Sensing, vol. 9, no. 3, pp. 273, 2017.

[4] Y. Zhang, J. Hou, J. Gu et al., “SWAT‐Based Hydrological Data Assimilation System (SWAT‐HDAS): Description and Case Application to River Basin‐Scale Hydrological Predictions,” Journal of Advances in Modeling Earth Systems, vol. 9, no. 8, 2017.

[5] X. D. Pan, X. Li, G. D. Cheng et al., “Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin,” Remote Sensing, vol. 9, no. 9, pp. 963, 2017.

[6] Y. Li, C. Huang, J. Hou et al., “Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China,” Agricultural & Forest Meteorology, vol. 244, pp. 82-97, 2017.

[7] C. Ma, X. Li, C. Notarnicola et al., “Uncertainty Quantification of Soil Moisture Estimations Based on a Bayesian Probabilistic Inversion,” IEEE Transactions on Geoscience & Remote Sensing, vol. 55, no. 6, pp. 3194-3207, 2017.

The surface energy balance of glaciers and snowpack is the main driver of the mass balance. A detailed analysis of Landsat images of the entire Qinghai – Tibet Plateau over the period 1995 – 2015 has documented a large variability of glacier spectral reflectance and albedo in relation with surface materials. The area of debris-covered glaciers accounted for approximately 20% of the total glacial area, and slightly decreased between 1995 and 2015. The area of glaciers at elevation under 5800 m decreased significantly over 20 years. The number of small glaciers, i.e. < 1 km², decreased most, while the largest contribution to the reduction in glacial area was due to the larger glaciers, i.e. > 10 km². To understand how closely glacier melting is related to surface properties, we need to map changes in glacier volume at high spatial resolution. A few satellites acquire stereo – images at high spatial resolution, i.e. ALOS / PRISM and Zi Yuan-3/ TLC, but spatial and temporal coverage is far from satisfactory. We focused on two case – studies on the Zhadang and Parlong nr.4 glaciers, where concurrent ground measurements are available. Preliminary results show that melting rate of glaciers correlates with the albedo and surface temperature. Decrease in glacier thickness over multiple years was clearly related with the mean surface temperature over the same period of time. The quality and spatial resolution of ground measurements of mass balance in the Parlong glaciers gave the opportunity to evaluate the relationship between the mean surface temperature and changes in glacier thickness over the entire glacier. This analysis gave an estimate of the mean surface temperature at which glacier melting starts.

At the same time, glacier surface velocity has been estimated and mapped using high resolution images, i.e. Gao Fen – 1. High spatial resolution maps of surface velocity can be related to maps of albedo and surface temperature and of glacier melt (change in thickness) at comparable spatial resolution.

Atmospheric forcing of the surface energy balance in glacial and snow – covered areas is being characterized using WRF fields generated at multiple spatial resolutions by applying a nested implementation with the inner domain having a 500 m x 500 m grid size. The accuracy of air temperature and wind speed is being evaluated with ground measurements at the Parlong 4 glacier. Radiative interactions of the land surface, particularly the glacial and snow – covered areas, with clouds and aerosols are being evaluated under a new project.

Quantitative information on water losses is important to understanding the global terrestrial water cycle and land – atmosphere interactions. Following the former study of last year, the ETMonitor estimated global evapotrasnspiration in 2008-2013 with a spatial resolution of 1 km was carefully validated mainly based on ground observation from FLUXNET, spatial variaiton was also cross-compared with other available global evapotranspiration products. The estimated ET agreed well with the in situ observations at site scale, with overall high correlation, low bias, and low root mean square error. Meanwhile, the estimated ET variation could capture the expected overall global ET patterns, and its spatial and temporal patterns were consistent with the current available global ET products such as the upscaled ET dataset from FLUXNET observations and GLEAM ET product, but is superior by high spatial and temporal resolutions. The separation between plant transpiraiton and soil evaporation made by the ETMonitor was also validated based on ground observation based on isotope technique and showed overall good agreement between ETMonitor estimation and isotope observation in partitioning transpiration and evaporation. In details, the ratio of transpiration and evaporation to the total ET generally agreed well with the isotope observation in the growing season in 2012 in one of the ground site of HiWATE experiment, while relative large bias was found in the beginning of the growing season when the soild surface was covered by mulching film.

Precipitation was the major regional water source and precipitaion based indocators were widely adopted in drought monitoring. However, large differene could be found among different earth observation based precipiation products. We evaluated the accuracy of multiple satellite-based precipitation products including the tropical rainfall measuring mission multisatellite precipitation analysis (TMPA) (TMPA 3B42RT and TMPA 3B42 version 7) and the Climate Prediction Center MORPHing technique (CMORPH) (CMORPH RAW and CMORPH BLD version 1.0) datasets and evaluated the impact of the accuracy and temporal coverage of these data products on the reliability of the standardized precipitation index (SPI) estimates for drought monitoring. The satellite-based SPI was compared with the SPI estimate using in situ precipitation observations from 2221 meteorological observation sites across China from 1998 to 2014. The SPI values calculated from the products calibrated with rain gauge measurementswere generally more consistent with the SPI obtained with in situ measurements than those obtained using noncalibrated products. The short data record of satellite precipitation data products was not the primary source of large errors in the SPI estimates, suggesting that the SPI estimate using satellite precipitation data products can be applied to drought assessment and monitoring. The satellite-based SPI can capture typical drought events throughout China, with the limitation that it is based on precipitation only and that different durations of antecedent precipitation are only suitable for specific drought conditions.

The water reource of different basins in China was also obstaind based on ETMonitor estimated ET combing with CMORPH precipitation data. ETMonitor was applied to obtaind regional ET dataset in China and southeast Asia in 2001 -2015 focusing on the key study region, the estimation adopted the ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product as one of the inputs. The estimated ET agreed well with flux tower based observations from AsiaFLUX and the estimated ET from water balance method in basin scale. The obtained precipitaiton - ET showed strong correlation with the statistical data of differnet basins from Chinese Water Resource Department, while relative large disagreement mainly occurred at the region with large ground water consumption. Both precipitation and ET presented increasing trends in China, and it generally resulted in a non-significant increasing of available water resource in China. It may benefit the overall water resource supply nowadays, however increasing wate shortage were aslo found in the major grain producing regions and dense population regions. The results were partly contributed to the National Remote Sensing Monitoring for Sustainable Devepoment Report in China (2017).

Evapotranspiration (ET) is a key terrestrial water cycle at the land-atmosphere interface, and the earth observation are expected to provide spatially and temporally continuous information on large scale ET variation. In currenty study, ETMonitor was applied to obtaind regional daily ET dataset in China and southeast Asia in 2001 -2015, and the estimation adopted the ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product as one of the key inputs. To validate the estimated ET, in situ flux tower oberservations from AsiaFLUX datasets were first colleted. The quility of colleted latent heat flux at 30min inteval was carefully checked to obtain observed daily ET. Generally, high correlation coefficient was found escept in tropical humidity forest, where relative low correlation coefficient could be found mostly due to the unclear seasonal ET variation, while the low root mean squre error suggest the good agreement between our estimation and the flux tower observation. Meanwhile, the estimated ET was also compared with the water balance method estimated annual ET (ET_wb) at basin scale. ET_wb of the major basins in mainland of China was estimated as the the precipitation minus the sum of observed runoff and total water storage by GRACE. Their good agreement hilight the good potential of earth observation in basin water source evaluation. Furthermore, the spatial pattern of estimated ET was compared with other ET products, e.g. the MODIS official ET product, Global Land Evaporation Amsterdam Model (GLEAM v3a) ET products, the FLUXNET observations upscaled ET products, Surface Energy Balance System (SEBS) ET products. The estimated ET dataset can represent overall reasonable geographical patterns and seasonality, and it agrees well with other ET products in terms of spatial variation, however with the advantage of either high spatial-temporal resolution or high accuracy.

The main objective of this project is to improve the estimate of water balance under natural and human pressure on the Heihe basin in China by using MOST, ESA and NASA satellite data coupled with three distributed hydrological models (FEST-EWB & SHAW-DBHM, HeiFLOW). This will be achieved simulating evapotranspiration, soil moisture, discharge, SWE and groundwater dynamic at different spatial and temporal scales.

Multi-source remote sensing data, from visible to thermal infrared and microwave, will be used for forcing, calibration, validation and data assimilation of/into basin scale hydrological models. Vegetation parameters, snow coverage, LST and soil water content, lakes extent and water level height, and meteorological forcings will be retrieved.

In this year presentation, FEST-EWB model is run for the whole Heihe River basin at spatial resolution of 0.05° and temporal resolution of 1 hour. Results are provided in terms of hourly evapotranspiration, soil moisture and land surface temperature maps for the 2012.

FEST-EWB model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET) that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to LST as retrieved from operational remote sensing data (MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale.

Evapotranspiration estimates are then compared at local scale with two eddy covariance data and at basin scale with the estimates from the ETMonitor model.

In the framework of the DRAGON4 Project, the National Institute of Geophysics and Volcanology of Rome (INGV, Italy) and the Northeastern University of Shenyang (China) collaborate to study the surface movement over several industrial regions in Northeast China. The traditional heavy industrial base of Northeast China, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, has played an important role in the economic development of the region, although severe consequences on the local environment are taking place due to the continuous mining activities. Various geohazards, such as subsidence, landslides, ground breakage and building inclinations, have been threatening the safety of local people and the environment for decades. The continuous monitoring of the effects of the mentioned geohazards is thus of great importance for the local population well-being. The main objectives of the study are: to take advantage of the availability of dense remote sensing data sets in order to analyze the geohazards and their environmental impacts in the region; and then forecast when and how these geohazards might occur in the future and provide technical support for disaster prevention and damage reduction.

Time series InSAR, as a general term of a variety of algorithms, is able to analyze the spatial and temporal deformation over large areas. With a single SAR image data stack only deformation along the line-of-sight direction could be analyzed. In this analysis we use time-series InSAR results from multiple stacks (from ascending and descending orbits) to monitor slow motions gravitational deformations.

As an important type of glacier, mountain glaciers are often regarded as sensitive recorders and indicators of global climate change. Additionally, glacier movement, one of the most important features of glaciers, can cause serious natural disasters, such as debris flow and glacial lake outburst floods, that threaten human production and life. Thus, monitoring glacier movement has great significance for predicting glacial flow and hazards. COSMO-SkyMed is a constellation of four X-band high-resolution radar satellites with a minimum revisit period of 12 hours. Based on ascending and descending COSMO-SkyMed data acquired at nearly the same time, the surface velocity of the Yiga Glacier, located in the Jiali County, Tibet, China, is estimated in four directions using an offset tracking technique during the periods of 16 January to 3 February 2017 and 1 February to 19 February 2017. Through the geometrical relationships between the measurements and the SAR images, the least square method is used to retrieve the 3D components of the glacier surface velocity in the eastward, northward and upward directions. Four conclusions can be drawn. First, by applying the offset tracking technique to the intensity information of ascending and descending passes of SAR images and combining the four measurements with different directions, the 3D velocity field of glaciers can be estimated. Second, as a constellation with four radar satellites, COSMO-SkyMed has a short revisit time that can acquire ascending and descending images with very similar time periods. This technique has great potential to validate the true 3D velocity of glaciers using different image pairs mapping the same deformation field. Third, the Yiga Glacier had a stable velocity during the observation period from 16 January to 19 February 2017. The distribution of the glacier surface velocity is related to the elevation change. A maximum velocity of approximately 2.4 m/d is observed in the middle part of the glacier because the steepest slope is located there. With steadily decreasing elevation, the velocity in the upper middle and the lower middle portions of the Yiga Glacier stabilizes at approximately 40 cm/d. Finally, the low RMSE in the non-ice region indicates that the results are reliable.

As the third largest country with coal reserves, but China is the largest coal product and consume country in the word. The goafs formed by underground coal extraction often bring severe damages and geohazards to coal mining areas, characterized by uncertainty, slowly and unpredictable over a relatively long time period after post-mining. Generally speaking, detecting the spatial distribution of surface deformation caused by underground goafs for effectively is the basic work for response the subsidence control and geohazards assessment. Compared to traditional geophysical techniques, the satellite-based imagery geodetic observations, such as Differential SAR Interferometry (InSAR) technique, has been considerated as a powerful tool for potentially large-spatial coverage deformation monitoring of the earth’s surface with an accuracy within centimeters to millimeters. As a typical city which abundant in coal resource and thus developed, Xuzhou city, has been experiencing a large-scale and high-intensity coal mining activities over past more than a century, causing large-area land subsidence even collapse phenomenon.In this study, the Multi-Temporal InSAR analysis techniques that both Persistent Scatterers Interferometry (PSI) and Small BAseline Subset (SBAS) methods is implemented, to investigate and analyze the land subsidence over the Xuzhou region, and to conduct an in-depth assessment about the stability of several interested underground goafs, using 62 SAR imagery acquired by Copernicus’ Sentinel-1A satellite spanning July 2015 until Apir 2018. The maps of annal-average subsidence velocity and displacement time-series were generated. And, the reliability of the monitoring results was cross-verified by comparing the PSI results to the SBAS method and highlight the differences. The MT-InSAR results reveal that there have four significant subsidence areas in the Xuzhou region, mainly locating in Tongshan-Quanshan District, Jiawang District, Fengxian and Peixian Country, the main driving factor of land subsidence is underground coal-mining except Fengxian Country, and the most server subsidence take place in Tongshan-Quanshan District where the maximum subsidence rate about 48.6mm/a, which keep better consistency with coal-mining borde in the spatial pattern. Moreover, aiming to the several typical underground goafs, the subsidence characteristics of their were analyzed, and we found that the trend of subsidence over underground goafs present a remarkable behavior that the stabilize continuously in temporal and the subsidece area is shrinking in spatial.It is self-evident thatdetected displacement time series over underground goafs provide valuable insight into the spatial and temporal evolution of corresponding deformation phenomena in recent years, thus it contribute to offer essential insight to the long-term stability assessment of the subsidence coal-mining induced of the Xuzhou region.

Abstract:For a long time, monitoring of mining subsidence requires a lot of money and time, besides monitoring and prediction can not be effectively integrated.So in this paper, the mining area is monitored by using time series InSar, then the data of experimental results and support vector regression (SVR) are combined to predict the dynamic change of mining subsidence.Finally, the rapid monitoring of mine deformation,integration of monitoring and prediction are realized.Firstly,we use PS-InSar and SBAS-InSar technology to get the subsidence scope and development trend of mining area,then the monitoring results after weighted assessment are used as training ang learning samples of SVR algorithm to establish prediction function;Finally, by using the established prediction function, the rolling prediction is carried out based on the results of regression analysis.To test the proposed method,We taking Xinjiang sulphur gully coal mine as an example,and use 36 Scenes of sentinel-1 imagery from 2015 to 2017 to carry out experimental research and analysis.The result shows that：Time series Insar can well monitor the subsidence scope and development trend of mining area,Further more the result of the prediction error of mining subsidence is also better.The experimental results show the feasibility of the method.

Key words: Time series Insar.;Subsidence prediction; mining subsidence; SVR

The Zhouqu–Wudu segment of the Bailongjiang Basin in Northwest of China with a total area of 8917 km² lies in the middle south of the west wing of Qinling orogen. It is controlled by Qinghai–Tibet tectonic belt and Wudu arc structure, and affected by unlift of the Qinghai–Tibet plateau. This segment is located in the Qinling Mountains, and is surrounded by the Qinghai–Tibet Plateau, the Loess Plateau and the Sichuan Basin as the three major geomorphic units. Because of its geophysical conditions, the Bailongjiang Basin is one of the most severely landslide affected regions in China. More than 2000 medium and large landslides have been reported within the Wudu and Zhouqu segment. In this paper, 50 newly launched Sentinel-1 scenes from November 2014 to September 2016 are gathered, and a preprocessing chain of TOPS with SBAS-InSAR are generated to obtain the time series deformation, the active area within the five typical giant fossil landslides in the study area were detected, the maximum deformation and the average deformation were verified by field investigation and the displacement monitor measurements in the local landslide early warning system.

Approximately half of the world’s population is at the risk of at least one vector-borne parasitic disease. The survival of intermediate hosts of vector-borne parasitic diseases is governed by various environmental factors, and remote sensing can be used to characterize and monitor environmental factors related to intermediate host breeding and reproduction, and become a powerful means to monitor the vector-borne parasitic diseases. In this research, satellite remotely sensed data has been used to obtain the environmental factors (vegetation, soil, temperature, terrain et al.), which are related to the living, multiplying and transmission of intermediate host. Then based on ground truth data, the remote sensing monitoring model of the intermediate host has been developed, which can enhance the remote sensing monitoring capabilities of the vector-borne parasitic disease and provide the theoretical foundation and technical support for diseases prevention and control.

To promote the radiometric quality of satellite remote sensing products and assure the comparability of product quality amongst multi-series satellites, we should build a continuous transfer chain from remote sensor to the radiometric reference standard, estimate those parameters characterizing sensor’s performance, and conduct quality controls on remote sensing data and products during sensor lifetime. However, when the sensor is on-orbit, its radiometric quality is usually hard to be traced to SI because of breakage of the reference transfer chain. The traditional field vicarious radiometric calibration method which uses ground target measurement value as radiometric reference, can be influenced by various uncertainties due to scaling effect, atmospheric condition, environment change, etc., so it is hard to reach high calibration accuracy. In pursuit of on-orbit optical sensor high-accuracy calibration and product quality consistence, we carried out the following exploratory work:
1) Research on the ground-based radiometric reference transfer chain in sensor calibration under the framework of RadCalNet (the Radiometric Calibration Network, initiated by CEOS/WGCV). The RadCalNet initiative was sponsored cooperatively by NASA, ESA, CNES and AOE, with object to verify EO sensor’s radiometric calibration accuracy and ensure radiometric measurement consistency from different EO sensors. Through the activities of establishment of calibration sites, increasing observation frequencies, decreasing of total calibration errors, RadCalNet ensures the traceability of radiometric measurement benchmark for space remotely sensors. And moreover, it can provide the opportunities to carry out on-obit calibration for those space agencies who do not have the conditions for organizing the in-situ calibration activities. Under the framework of RadCalNet, AOE designed and manufactured automated surface spectrum measurement system. Now, this system has been deployed on different permanent targets in Baotou site. It can automatically measure the surface reflected radiance and atmospheric parameters continuously. Then, following the requirement of data products defined by RadCalNet data center, Level 1 BOA (bottom of atmosphere) reflectance standard products and level 2 TOA (top of atmosphere) sensor-independent reflectance products are generated automatically. Besides, AOE and NPL carried out cooperation research on uncertainty analysis of the whole chain of radiometric calibration benchmark product generation for Baotou site. In this study, the contributions to the total uncertainty for different factors, including obtaining of surface reflectance, homogeneity and BRDF of surface target, measurement of atmospheric parameters, atmospheric radiative transfer calculation, solar irradiance spectra model, and etc., have been elaborately analyzed to calculate the uncertainty of radiometric calibration in Baotou site.
2) Research on space radiometric benchmark transfer calibration. Introducing radiometric benchmark sources, which can be traced to SI, in some specific benchmark satellite, via synchronously observation of surface scenes between benchmark satellite and other satellite, is a feasible way to transfer the space radiometric measurement benchmark from spaceborne standard radiometric calibration system to operational optical remote sensing satellite with high accuracy. Under this framework, we can provide high accurate and consistent space radiometric measurement benchmark for those optical satellite, to significantly promote calibration accuracy and ensure the consistency and stability of data quality amongst multi-series satellites. Based on this idea, we design a space radiometric benchmark transfer calibration plan with accurate spatial-temporal-spectral-angular matching and coupling transform as core techniques. In this plan, we comprehensively use the ground surface scene model, atmospheric radiative transfer model and optical sensor model, build the satellite-satellite radiometric measurement mapping relationship, carry out the spatial-temporal variety and anisotropy characteristic analysis for surface stable targets, establish the constraint mechanism in spatial-temporal-spectral-angular matching, construct the satellite-satellite radiometric measurement transform model though simulation and observation data, to finally realize space radiometric benchmark transfer calibration.
The above two researches have been validated and the superiority of each method have been analyzed with Chinese and European satellite, such as Sentinel-2a/2b、GF-1/2、ZY301/02, with ground-based and aerial-based campaign. This work points out the direction of the next step.

Long-term aerosol data records provide information on changes in the aerosol properties due to both natural (meteorological, climatological, dust storm, fires) and anthropogenic effects (such as industrialization, urbanization and policy measures aimed at improvement of air quality). An often-used indicator for the aerosol burden is the aerosol optical depth (AOD), i.e. the column-integrated extinction coefficient. AOD can be monitored from satellites using radiometers, but instruments used for this purpose have a limited lifetime and often are not designed for aerosol monitoring. As part of the Aerosol-cci project, the Along Track Scanning Radiometer (ATSR-2) flying on the European Space Agency (ESA) ERS-2 satellite from 1995 to 2003 and the advanced ATSR (AATSR) flying on ESA’s ENVISAT (2002-2012) were used together to create a 17-years (1995-2012) global AOD data record over land and ocean (Popp et al., 2016). This data set is planned to be extended with AOD retrieved from the Sea and Land Surface Temperature Radiometer (SLSTR), an instrument similar to ATSR but with a backward instead of forward view, the first of which flies on Sentinel-3A launched in early 2016. However, this leaves a gap of about 4 years between the end of the AATSR and the start of the SLSTR data records. To fill this gap, we investigated the use of AOD data available from the application of the ALAD algorithm developed by RADI for AOD retrieval over land using data from the Advanced Very High Resolution Radiometer (AVHRR) (Xue et al., 2017). ALAD combines eight different AVHRR instruments to produce an AOD time series starting in 1983 (Xue et al., 2017). Hence, the ALAD AOD data set could also be used to extent the information from the ATSR data record backward from 1995 to 1983, provided that a satisfactory match can be obtained between the overlapping ATRS and AVHRR data sets.

In this study we used ATSR AOD data produced with FMI’s Aerosol Dual View (ADV) algorithm (Kolmonen et al., 2016; Sogacheva et al., 2017) and ALAD data sets retrieved for the whole period from 1983 to 2014 over the North China Plain (NCP). In addition, MODIS-Terra AOD C6.1 data are used for comparison and ground-based sun photometer AOD data from AERONET (Holben et al., 1998) are used as reference. The validation versus AERONET shows the good performance of the AVHRR AOD up to about 0.5 and for ATSR up to about 1.3, which leads to large differences during high AOD episodes such as often observed over the NCP in the summer. However, during the winter, when AOD is often moderate, AVHRR provides better coverage. Part of the difference between AVHRR and ATSR AOD may be explained by the difference in wavelength between the ATSR- and AVHRR-retrieved AOD (550 nm and 630 nm, respectively).

References

Kolmonen, P., Sogacheva, L., Virtanen, T.H., de Leeuw, G. , and Kulmala, M.: The ADV/ASV AATSR aerosol retrieval algorithm: current status and presentation of a full-mission AOD data set, International Journal of Digital Earth, 9:6, 545-561, doi: 10.1080/17538947.2015.1111450, 2016.

Popp, T., de Leeuw, G., Bingen, C., Brühl, C., Capelle, V., Chedin, A., Clarisse, L., Dubovik, O., Grainger, R., Griesfeller, J., Heckel, A., Kinne, S., Klüser, L., Kosmale, M., Kolmonen, P., Lelli, L., Litvinov, P., Mei, L., North, P., Pinnock, S., Povey, A., Robert, C., Schulz, M., Sogacheva, L., Stebel, K., Stein Zweers, D., Thomas, G., Tilstra, L.G., Vandenbussche, S., Veefkind, P., Vountas, M., and Xue, Y.: Development, production and evaluation of aerosol Climate Data Records from European satellite observations (Aerosol_cci), Remote Sens. 2016, 8, 421; doi:10.3390/rs8050421, 2016.

Sogacheva, L., Kolmonen, P., Virtanen, T. H., Rodriguez, E., Saponaro, G., and de Leeuw, G.: Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer, Atmos. Meas. Tech., 10, 491-505, doi:10.5194/amt-10-491-2017, 2017.

Xue, Y., He, X., de Leeuw, G., Mei, L., Che, Y., Rippin, W., Guang, J., Hu, Y. : Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe. Remote Sensing of Environment, 198: 471-489, 2017.

Different from the work last year, the paper develops a all-weather and all-day passive sub-millimeter precipitation retrievals algorithm for Microwave Humidity and Temperature Sounder (MWHTS) onboard the Chinese Feng Yun 3C (FY-3C) satellite. The retrieval algorithm employs a number of neural network estimators trained and evaluated using the validated global reference physical model NCEP/WRF/ARTS, and works for seawater. NCEP data per 6 hours are downloaded to run the Weather Research and Forecast model WRF, and derive the typical precipitation data from the whole world. The Atmospheric Radiative Transfer Simulator ARTS is feasible for performing simulations of atmospheric radiative transfer. Rain detection algorithm has been used to generate level 2 products. Retrievals are reliable for surface precipitation rate higher than 0.1 mm/h at 15km resolution, which is in good agreement with those retrieved using the Precipitation retrieval algorithm version 1(ATMP-1) for Advanced Technology Microwave Sounder (ATMS) aboard Suomi NPP satellite.

Meanwhile, calibration and validation between similar instruments onboard different satellites are also important to ensure the validity of observations and accuracy of precipitation retrievals. In the ongoing work, we are going to carry out the calibration and validation among FY-3C MWHTS, FY-3B MWHS and ATMS，and some preliminary results can be shown in the conference materials.

A ground-based MAX-DOAS and a Bruker IFS 125HR have been deployed in Xianghe Station, Northern China, of the Institute of Atmospheric Physics, Chinese Academy of Sciences, and another Bruker IFS 125M has been installed in Xinglong Station. The MAX-DOAS has been running for more than ten years, providing a large number of high quality data of NO₂, SO₂, etc., for deriving their trends, and for validating the satellite products of OMI, GOME-2, and SCIMACHY. In Xianghe station, CIMEL sunphotometer, gas analyzers, automatic meteorological station, and a 100-meter tower can provide aerosol optical properties, air quality status, and meteorological conditions in the planet boundary layers. The two Bruker FTIR instruments in Xianghe and Xinglong stations aim at providing the greenhouse gas such as CO₂, CH₄, N₂O, and for validating GOSAT, OCO-2, and TanSat products in future. The FTIR in Xinglong station has been operating for more than one year, and some data has been used for validation of GOSAT products. Some new progresses in validating satellite products over northern China using ground-based FTIR and MAX-DOAS instruments will be reported here.

Atmospheric aerosol optical remote sensing makes use of ultraviolet, visible and infrared sensors to collect information of the particles in atmosphere by detecting radiation scattered from targets. Black carbon (BC) is the most important light-absorbing aerosol in the current atmosphere because of its strong positive climate forcing from direct radiative and snow albedo effects. Both effects are significantly affected by BC optical properties. Observations have shown that BC particles have complex structures due to stochastic aggregating. Thus, a reliable remote sensing of BC aerosols and estimate of BC climatic effects requires accurate computations of optical properties for BC particles with complex structures. Currently most simulation methods employ single-sized spherical particle as primary spherule to construct the whole aggregates, this may introduce some extra uncertainty. In this study we use DDA method to compute optical properties of aggregates constructed by nonspherical and spherical particle with same structural parameters. Preliminary results are presented and show great differences exist between them, the results could be used for evaluating uncertainty introduced by particle modelling and calibrating results of remote sensing.

The Baotou site is one of four instrumented sites being established as part of the Radiometric Calibration Network (RadCalNet). RadCalNet is an initiative of the CEOS WGCV. It has been designed to provide satellite operators with SI (Système International d'Unités)-traceable top-of-atmosphere (TOA) spectrally-resolved reflectances from a coordinated network of instrumented land-based test sites. An automated radiometric calibration system was established on the Baotou Cal&Val test site in China to provide an operational high-accuracy and high-stability vicarious calibration and validation site for high resolution remote sensing instruments.
The composition of the system is first described in this paper, which provides bottom-of-atmosphere (BOA) reflectance in 30 minutes intervals in 10 nm steps over the spectral range from 400 nm to 1000 nm. The sources of uncertainty from the laboratory calibration of the spectrometer, through to field observations of radiance and the calculation of ground reflectance, through to the propagation to TOA reflectance were analyzed in this paper. An initial uncertainty analysis of the automated radiometric calibration was made by considering three aspects: uncertainty associated with the spectrometer (ground viewing instrument) measurements; uncertainty associated with the BOA reflectance calculation; and the uncertainty associated with averaging this to the requirements of the Radiometric Calibration Network, RadCalNet. Preliminary analysis shows that the combined uncertainty associated with RadCalNet BOA reflectance would be approximatively 3.0 % within the spectral range 500 nm ~ 900 nm. The uncertainty associated with the TOA reflectance propagated from BOA reflectance measured by single observation is approximatively estimated as 3.5% ~ 5.0%, ignoring the influence of the surrounding pixels.

Here we applied our tropospheric nitrogen dioxide (NO2) retrieval algorithm, which was implemented for the Chinese Environmental trace gases Monitoring Instrument (EMI), to the TROPOspheric Monitoring Instrument (TROPOMI). Generally, the Differential Optical Absorption Spectroscopy (DOAS) technique was used to retrieve slant column densities (SCDs) of NO2, and air mass factor (AMF) are calculated for light path correction. The solar and viewing geometries, surface albedo and pressure, cloud properties, and modelled gas profile was used as input parameters for online AMF calculations. The tropospheric NO2 was estimated from the total column by using the modified reference sector method. Results show good agreements with independent ground-based MAX-DOAS measurements.

The project aims at exploiting microwave satellite measurements to generate innovative added-value products to observe coastal areas also under extreme weather conditions. The following added-values products are addressed: coastal water pollution, coast erosion, ship and metallic target detection, typhoon monitoring.

To better state the goals, the project is framed into three subtopics: 1) SARCO - SAR-based Coast Observation; 2) Ship and Coastal Water Pollution Observation with Polarimetric SAR Architectures (SCoPeSAR); 3) SHENLONG: Sea-surface High-wind ExperimeNts with Long-range (satellite) Observations using Numerical Geophysical methods.

To reach the above-mentioned goals, single-polarization and polarimetric models will be analyzed and/or developed to generate added-value products that consist of: a) time-evolution of oil seeps in Gulf of Mexico; b) ship detection methods using European and Chinese SAR data; c) data assimilation scheme to assimilate Sentinel-1 SAR winds in the Weather Research and Forecasting (WRF) model for typhoon observation purposes; d) a new SAR azimuth cut-off scheme to estimate wind from SAR imagery.

Typhoons are among the most powerful and destructive natural disasters. Accurate forecasting of Typhoon track and intensity is very important to disaster prevention and reduction. Satellite observations can effectively compensate for the shortcomings of traditional methods of sea surface measurement and provide all-weather observation over the sea surface, which is of great significance to improve the numerical prediction of strong convective weather over ocean. The spaceborne radar observes the backscattering caused by the sea surface roughness, and then, the sea surface wind can be retrieved. Within this context, the Synthetic Aperture Radar (SAR) is an important data source for sea surface monitoring, since a variety of meteorological hydrological elements can be retrieved by SAR observation, and it has been used in data assimilation in recent years. SAR imagery is also used to monitor strength and structure of typhoons. The accuracy of sea surface winds retrieved from SAR has been found to be comparable to that of scatterometer data, and these wind fields can be used with a data assimilation system to provide the initial conditions for the numerical weather prediction (NWP) model.

In this study, a data assimilation scheme is proposed to assimilate the Sentinel-1 SAR retrieved winds in the Weather Research and Forecasting (WRF) model. Numerical simulation experiments of the typhoon Lionrock (2016) are carried out to test and compare different data assimilation methods. A series of Sentinel-1A EW swath mode dual-polarization (VV/VH) images are used to retrieve sea surface wind speed. Their overpass time were around 20:35 UTC on 29 August 2016. We use two different methods to derive the sea surface wind maps. The first is based on the use of the VV+VH dual pol geophysical model functions, while the second is based on the azimuth cut-off method. The Weather Research and Forecasting model data assimilation system (WRFDA) developed by the National Center for Atmospheric Research (NCAR) is adopted in this study. The grid size of the assimilation region is 260×250; the horizontal resolution is 15 km; and the vertical discretization is 30 layers. The time of assimilation is 0900 UTC 29 August 2016. The NCEP FNL Operational Global Analysis data are used as the initial field and boundary conditions. We take the 21-h forecast adjustment from 1200 UTC 28 August 2016 to 0900 UTC 29 August 2016 as the background field of the assimilation system. After the assimilation, a 30-h forecast is made, which is a forecast to 1500 UTC 30 August 2016. In this study, a set of assimilation and comparison experiments is carried out. Preliminary results show that the forecast track from SAR observations agree better with the best track than the control experiment.

Satellite Synthetic Aperture Radar (SAR) has been proved to be a key tool for a broad range of environmental applications in the context of oceans and coastal areas monitoring, including ship detection, coastline extraction, land use/cover classification, oil spill observation and sea surface parameters retrieval. In particular, the capability of satellite SAR measurements to support operational activities in case of natural disasters and environmental hazards as the Deepwater Horizon oil spill accident occurred in the Gulf of Mexico in 2010 or the most recent Sanchi accidental oil spill occurred off the eastern coast of China.

In this study, we focused on one of the richest areas in offshore oil seepages, i. e, the northern part of the Gulf of Mexico near the Mississippi river delta, where the Taylor Energy oil drilling platform is located (28°56’17’’N,88°58’16’’W). The platform was destroyed by the Hurricane Ivan in 2004 and, since then, the underwater wells were continuously leaking oil. It was estimated that more than 100 oil gallons enters into the marine environment from the Taylor Energy platform site. This results in surface oil slicks whose average thickness and life–time are about 1 μm and 4 days, respectively.

The area was continuously observed from satellite SAR platforms since the accidental oil spill occurred. Space-borne SAR imagery witness that this coastal area was almost persistently affected by this anthropogenic oil seep as the slicks were detected in about 80% of the data collected over the site. Even if strictly speaking this leakage cannot be considered as a natural oil seep, the underwater origin of the oil seep together with the involved weathering and aging processes are fairly the same.

Hence, it represents a good opportunity to have a large and consistent time series of SAR imagery that covers a well-known oil seepage. A large time series of dual-polarimetric co-polarized TerraSAR-X high-resolution (1.2 x 6.6 slant range x azimuth nominal spatial resolution) SAR imagery, collected in StripMap mode between July 2011 and April 2016 in a wide range of incidence angles (25° - 45°) and sea state conditions (low-to-moderate wind conditions applied, i. e., 1.5 m/s – 8.5 m/s), is exploited.

In this study, despite of the rather high noise floor that characterizes TerraSAR-X StripMap SAR imagery (an estimated noise equivalent sigma zero, NESZ, in the range -20 dB – -23 dB), the time series is effectively exploited to monitor the Taylor Energy oil spill. A multi-polarization analysis, that includes co-polarized intensity and phase difference information, is undertaken on which the oil spill detection and characterization is grounded.

Wind speed retrieval is a topic of great interest since wind estimation is very useful for a number of meteorological and oceanographic applications: wind is the major responsible of problematic like coastal erosion, climate change, marine life and so on. Most of the remote-sensing satellite radar systems are able to provide sea-surface wind field information, and they can be considered the main sea-surface wind information source. Within this context, active microwave remote sensing, in particular scatterometer and Synthetic Aperture Radar (SAR), is worldwide recognized as one of the best suitable tools to perform a reliable sea-surface wind speed retrieval. Radar backscatter intensities and their statistical properties contain quantitative information about the state of the sea surface roughness and, therefore, can be used to derive sea-surface wind information. When using radar systems such as the scatterometer and the SAR, the backscatter signal from the sea surface is dominated by the so-called Bragg resonant mechanism (mainly for wind speeds lower than 15m/s). In this case, there is a strong relationship between Normalized Radar Cross Section (NRCS) and wind speed linked by a Geophysical Model Function (GMF), while an alternative spectral based approach to retrieve wind speed is represented by the azimuth cut-off procedure.

When dealing with SAR microwave sensors, Doppler misregistration in azimuth are induced by gravity wave orbital motion. This issue is the major responsible of a distortion of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. In literature, the azimuth cut-off method is used to retrieve wind speed and several studies have been carried out to analyze the dependence of λc on sea surface parameters. In particular, there is a linear relationship between λc values and geophysical parameters, like wind speed and significant wave height. Recently, in [1] the ACF-based λc approach has been improved to deal with high wind speed regimes, e.g.; extreme weather conditions. The key issues that allow to extend the method to high wind regimes concern the tuning of the method with respect to pixel spacing, box size and the homogeneity of the SAR imagery. In particular, the box size is set at 1 km × 1 km and the median filter window is set at 90-120 m.

In this study, this novel SAR azimuth cut-off implementation is applied to an actual SAR dataset collected under high wind regimes, like tropical cyclones. Finally, the soundness of this improved azimuth-cut-off method under extreme weather conditions is discussed.

[1] M. Portabella, V. Corcione, X. Yang, Z. Jelenak, P. Chang, G. Grieco, A. Mouche, F. Nunziata, W. Li, “Analysis of the SAR-derived wind signatures over extra-tropical storm conditions”, Dragon 4 Symposium, Copenhagen, Denmark, 26-30 June

Tropical cyclone is a generic term that designs a rapidly rotating storm system characterized by a
low pressure center that produces strong winds and heavy rainfall. Cyclones are differently named according to the ocean basin where they develop: “Hurricanes” in the Northern Atlantic and Northeast Pacific Ocean, “Typhoons” in the Northwest Pacific Ocean, and “Cyclones” in the Indian Ocean and Southwest Pacific Ocean. Although tropical cyclones are among the most dangerous and destructive natural disasters, current models are still not able to give an accurate forecast of their intensity and track. Hence, within this context, spaceborne active microwave sensors, like the Synthetic Aperture Radar (SAR), are of paramount importance because of their all-weather and all-day capabilities together with a fine spatial resolution. In particular, SAR data can be used to directly monitor position and intensity of the storms, to analyze their structure or the rainfall-rate but also to estimate wind speed and direction. The purpose of this study is to apply the azimuth cut-off approach under extreme weather conditions.
In literature, the azimuth cut-off method is used to retrieve wind speed and several studies have
been carried out to analyze the dependence of λc on sea surface parameters. In particular, there is a linear relationship between λc values and geophysical parameters, like wind speed and significant wave height Recently, in [1] the ACF-based λc approach has been improved to deal with high wind speed regimes, e.g.; extreme weather conditions. The key issues that allow to extend the method to high wind regimes concern the tuning of the method with respect to some parameters: the pixel spacing, the box size and the homogeneity of the SAR imagery.
In this study, an actual SAR dataset collected in occasion of tropical cyclones is used to discuss
the validity of this improved azimuth-cut- off method under extreme weather conditions.

[1] M. Portabella, V. Corcione, X. Yang, Z. Jelenak, P. Chang, G. Grieco, A. Mouche, F. Nunziata, W. Li, “Analysis of the SAR-derived wind signatures over extra-tropical storm conditions”, Dragon 4 Symposium, Copenhagen, Denmark, 26-30 June.

Polarimetric Synthetic Aperture Radar (PolSAR) is a microwave imaging system with the capabilities to image day-and-night and penetrate cloud cover. It is becoming an effective means of monitoring the Earth’s surface. Examples of applications are disaster damage estimation, urban classification, and
object detection. In particular, ship detection with PolSAR data has attracted a great deal of attention in the last several decades. Ship location can help us deal with many troublesome issues, e.g., marine traffic, illegal fishing, illegal immigration. However, ship detection is a complex problem, which is not
only depended on the nature of ship but also depended on the sea state.

Compared with sea surface, a different backscattering behavior can be expected in ships. Due to the complicated structures, ship backscatter is often various, including single-bounce returns, double-bounce returns, multiple-bounce returns and so on [1]. By analyzing the different scattering mechanisms between sea surface and ships, many excellent works have been done on ship detection. The most straightforward approaches, such as the polarimetric whitening filter (PWF) and SPAN detectors [2], directly used the three channels of PolSAR data for ship detection. In [3], Nunziata et al. effectively utilized the reflection symmetry (RS) properties of the sea and man-made targets to detect ships. Marino et al. [4] further constructed a new scheme, called the geometrical perturbation-polarimetric notch filter method (GP-PNF), from the polarimetric target complex space to detect ships at sea.

In essence, the above ship detectors only exploit one single pixel information to extract the polarimetric features, which hardly consider the spatial information and still belong to ’pixel level’ category [5]. As a fact, the background pixels surrounding ship pixels can also provide rich information for ship detection. In this paper, we proposed a new strategy to add the phase information when computing the polarimetric covariance difference matrix (PCDM) [6]. Then a complete polarimetric covariance difference matrix (CPCDM) is developed, and a CPCDM-based algorithm is also proposed to detect ships. Experimental results demonstrate the effectiveness of the proposed algorithm.

Surface radiation balance is a very important energy source in study of the third pole region ’s evapotranspiration, snow and glacier melting. It is a controlling factor in characterizing the regional energy and water cycle’s system and it’s change. However, all currently available radiation products in this area are not suitable for regional scale study of water and energy exchange and snow/glacier melting due to their coarse resolution and low accuracies, such as the re-analyses data. The study summarizes our recent progress on the all-sky surface radiation estimation with high spatial-temporal resolution remote sensing techniques. The significant improvement of these products is the full consideration of the effect of clouds and topography on derived radiation. Our goal is to produce high-resolution (< 2km, half-hour) short- and long-wave radiation (downward and net components) to drive high-resolution hydrological model’s application and to improve our understanding the third pole region’s energy and water cycle’s system.

Glacier-climate interaction and its spatial variability over the Tibetan Plateau is still poorly understood. We present a new distributed glacier mass balance model applied on two glaciers of the Tibetan Plateau: Parlung No. 4 Glacier, 11.7 km², a temperate-maritime glacier, and Zhadang Glacier, 2.0 km², a sub-continental glacier. Enthalpy, rather than temperature, is used in the energy budget equations to simplify the computation of latent heat fluxes from water phase changes and the movement of liquid water in the snow. Two novel methods are used to distribute near-surface air temperature and wind speed from a set of Automatic Weather Stations (AWS). Further, we apply a new method to discriminate between solid and liquid precipitation based on daily mean air temperature, relative humidity, and elevation. Model results are evaluated by in-situ mass balance observations of the Parlung No. 4 Glacier and remote sensing products.

Our aims are to: i) develop a novel enthalpy-based model and test its performance on the distributed simulations of glacier mass balance and energy budget; ii) compare the physical processes typical of the summer season on two different types of glaciers on the Tibetan Plateau; iii) identify the key model sensitivities at both study sites. We present the interplay of precipitation thresholds, albedo and net radiation at these different glaciers and discuss their implications for future mass balance modelling on the Tibetan Plateau.

The response of glaciers to climate in the high-elevation Tibetan Plateau (TP) is generally poorly understood and is highly variable in space and time. A key influence on glaciers of the TP and surrounding mountain ranges is the monsoon, which for a large majority of TP glaciers overlaps with the main melting season and determines a very specific regime of mixed accumulation and ablation in summer. Monsoon effects on glacier mass balance however are still little understood. We use a distributed energy balance model, combined with high-resolution meteorological observations and new schemes for precipitation discrimination and albedo evolution to understand the effect of rain events and monsoon precipitation on the summer mass balance of a monsoon-dominated glacier of the TP. The main effect of precipitation events is to considerably alter surface conditions, maintaining higher reflectivity surface for most of the season. We show that it is challenging to reproduce this effect with traditional approaches based on simple discrimination of solid/liquid precipitation. The glacier summer mass balance is highly sensitive to precipitation thresholds discriminating between rain, sleet or snow. Precipitation acts both on the actual mass balance as well as the surface albedo. Adjustment of albedo during sleet events is crucial to correctly reproduce the glacier mass balance, and neglecting it leads to much higher mass losses and more negative mass balance over the entire glacier but especially at higher elevations, with a similar negative impact on summer mass balance than prescribing ~69% less snow accumulation for the upper-glacier. Based on static air temperature shifts of +1.5°C, it is found that the dynamic precipitation discrimination approach based on wet bulb temperatures results in a monsoon period mass balance up to 36% more sensitive than if assuming a single value threshold for solid and liquid precipitation. Our work identifies a key and complex role of precipitation events on the glacier mass balance, and a strong need for improving the modelling of local precipitation gradients and thresholds based on observations of a high spatio-temporal resolution.

Hydrologic model is a simplification of a real-world system that aids in understanding, predicting, and managing regional water resources. The quality of driving data greatly influences the accuracy of model simulation. Red River a China-Laos-Vietnam transboundary river. The upstream and middle stream of which are dry-hot valley regions with large altitude difference(1893-1916). The water resources simulation and management are difficult and complex. The new version of Chinese Land Data Assimilation Systems(CLDAS-V2) integrated advantages of point-based ground meteorological observations and remote sensing products. The products have higher 6km spatial resolution and higher quality within the China boundary. In this study, we simulated the soil moisture and runoff in Red River Basin(RRB) in 2017-2018 by using The Variable Infiltration Capacity (VIC) model based on CLDAS-V2.0 products, as well as state data (e.g. 250m DEM, MODIS 500m LAI products). The prelimary result show that the daily runoff simulation fits well with actual runoff observation in Yuanjiang station and Tukahe station in early 2018. There are several big rivers derived from Asia high plain. This study reveals the usefulness of CLDAS-V2 product in similar transboundary river basin for flood and drought management. There are good water level-runoff regression relation in RRB. Our results will be validated with water level of small water body by SAR altimetry and 1km spatial resolution downscaled SMOS Soil Moisture products[Gao et al. 2018].

Water balance in red river basin is very complex. Due to complex topography, total drop of red river is high (2574m). One of the greatest challenges for flood prediction and integrated water management in the Red River basin is a lack of information on reservoir management, as a consequence, it is not easy to estimate the water resources. Since it is a transboundary river, there are difficulties to manage the area as a whole, and the information might not be in time for flood and drought early warning.
Thanks to Microwave Remote Sensing, water resources can be monitored within any weather conditions remotely. The main objective of this project is to develop the algorithms and synergies between different Microwave Remote Sensing sensors to be able to monitor water resources in the Red River Basin. Accounting with water level products over Redriver basin, soil moisture products and runoff, can help improving water resource management in this area.
SAR altimetry with the slicing of the altimeter footprint that the SAR mode performs, allows the separate computation of all the elementary reflectors, in particular the wet ones. In this project, we took advantage of SAR altimetry to monitor small water bodies in Red River basin.
However, satellite altimeters are initially designed to monitor homogeneous surfaces such as oceans or ice sheets, which results in poor performance over small inland water bodies because of the land contamination contribution in the returned waveforms. To improve altimeter range accuracy which relates to water level measurement accuracy, the waveform needs to be retracked precisely to find out the accurate tracking point which locates on the leading edge [Deng and Featherstone, 2006]. In our study, we use the Sentinel-3 satellite with data available from June 2016 to retrieve the water level in Red river basin. The new retrackers [Makhoul e al., 2018] including the threshold, OCOG, and 2-step analytical retracker are introduced to find the tracking points precisely from the land contaminated waveforms. The waveform portion selection is applied for the water level retrieval using DEM information to locate the waveform portion which comes from nadir.
The aim of the study is to retrack water level more precisely by comparing the threshold, OCOG and 2-step analytical retracker performance along with the waveform portion selection approach performance over Red river. It is a challenging task since the water bodies are very small, the widths of which we studied are mostly around 200m to 500m, which means there are only one or two signals in the water for Sentinel-3 whose spatial resolution is 300m along track. For Redriver basin, Sentinel-3 works in closed loop tracking, which means the altimeter range window is autonomously positioned based on on-board NRT analysis of previous SRAL waveform. With the mountainous topography, it is more challenging to locate the leading-edge position of the waveform.
The water level results of the three retrackers with waveform portion selection are then compared with the onboard ocean retracker. Over places with land contamination, the analytical 2-step retracker with waveform portion selection has shown better performances than ESA L2.
The preliminary results show a smoothly time series over several water bodies in Red river basin, and waveform portion selection works much better than using the whole waveform since the DEM information is introduced to eliminate land contamination. Retracking water level from Level-1 waveforms using the combination of the retrackers and waveform portion selection is more robust to land contamination than using Level-2 data directly.
We will be producing Soil Moisture products at 1km spatial resolution for the entire Red River basin for the 2010-2017 period. The Soil Moisture products will be SMOS based and we will use the DisPATCh disaggregation scheme to increase SMOS coarse resolution. DisPATCh provides 1 km resolution SM data from coarse-scale microwave derived SM. In DisPATCh, the soil evaporation from the 0-5 cm soil layer and the vegetation transpiration from the root zone soil layer are partitioned by separating MODIS LST (Land Surface Temperature) into its soil and vegetation components. The partitioning method relies on a contextual interpretation of MODIS LST and MODIS NDVI [Moran et al. 1994]. MODIS-derived soil temperature is first used to estimate Soil Evaporative Efficiency (SEE defined as a ratio of actual to potential soil evaporation), which is known to be relatively constant during the day on clear sky conditions. DisPATCh then distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived SEE and near-surface soil moisture [Merlin et al. 2013].
These soil moisture estimates will be validated against output from modeling [Rui et al. 2018].
With water level products and soil moisture products over Redriver basin, the hydrological model for water resource estimation can be improved in future steps.

Abstract:

Soil moisture (SM) is a significant determinant of crop growth and a useful indicator of drought. It is important to evaluate and analyze existing soil moisture products for environmental monitoring and protection of the Belt and Road region. At present, there are many global soil moisture products, such as the ones retrieved from the data collected by AMSR-E (the Advanced Microwave Scanning Radiometer-Earth Observing System), AMSR-2(the Advanced Microwave Scanning Radiometer 2), FY3B/C (the Feng Yun 3rd Satellite), LPDR (the Daily Global Land Parameters Derived from AMSR-E andAMSR-2) and ESA CCI (the ESA Climate Change Initiative) at a coarse resolution of ~0.25◦.

In this study, the 8 soil moisture products were selected (AMSR-E/JAXA, AMSR-E/NASA, AMSR-2/JAXA, AMSR-2/NASA, FY3B/C, LPDR and ESA CCI). The approximate ascending and descending equator crossing time, channel and incident angle, except LPDR and ESA CCI are indicated. Among them, the LPDR product is derived from other three soil moisture products (AMSR-E, AMSR-2 and FY3B). LPDR soil moisture product was developed by using the double difference and inter-calibration methods from AMSR-2, AMSR-E and FY3B. The ESA CCI product was developed by merging many passive and active soil moisture products, such as AMSR-2, SMOS, MetOp-A and so on. In this study, the JAXA and NASA soil moisture products AMSR-E and AMSR-2 were selected. The overlapping time of AMSR-E, FY3B, LPDR and EAS CCI is 2011. The overlapping time of AMSR-2, FY3B/C, LPDR and EAS CCI is from 2014 to 2016. According to the overlapping time of soil moisture products, the comparison and validation of different soil moisture products was supported with in-situ data from ISMN (the International Soil Moisture Network) and ERA Interim/Land (0-7cm soil depth). Secondly, soil moisture content is influenced by various factors, such as soil type, land-use type, climate type and so on. The climate type implies patterns in rainfall and temperature that affect the retrievals, but also closely related to surface types. These effect factors also influence the soil moisture content. Therefore, in this study, the climate type is introduced in soil moisture product analysis at the Belt and Road region.

Keywords— soil moisture product, Belt and Road, comparison, validation

The Earth Observation (EO) mission for mapping global surface soil moisture and generating related satellite products have been witnessed a great progress in the last several decades. Among several global soil moisture products, the soil moisture products developed based on the European Space Agency Climate Change Initiative (ESA CCI) are the most complete and longest temporal serial soil moisture data records.

The latest versions (v04.2 v03.3) of CCI soil moisture products were released on Jan. 17, 2018 and Nov. 27, 2017, respectively. These two versions of the products cover the temporal range from October of 1978 to the end pf 2016. The previous versions of the products have been intensively validated. However, the evaluation of the latest version has not been reported yet. The main aim of this study is to provide an in-deep evaluation of the latest CCI soil moisture products using ground observations. To this end, ground observation from three soil moisture observation networks distributed in Tibetan Plateau, namely BBHNet, MAQU and CTP-SMTMN, are used as the reference data. The results show that the products present a little underestimation of the soil moisture over the three regions. But both versions of the products show good agreement with the temporal variation of the ground observations. Relatively, the v03.3 product is a little better than the v04.2 product.

Glaciers in the Tibetan Plateau are important climate indicators due to their rapid response to climate variability. Therefore, it is crucial to understand glacier changes and their response to climate change. Long-term series of satellite data can provide such information. The complexity of observing and understanding changes in glacier conditions is augmented by the spatial heterogeneity of the glacier surface. Automatic glacier mapping utilizing remote sensing data is even more challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock, orographic clouds and highly variable snow conditions. The vast majority of the available glacier datasets only provide the total glacier area, which means that the boundary between clean ice and debris-covered glacier is not clear. Different glacier elements have different melt rates and densities. This discrimination plays a key role in mass balance research and improved hydrological modeling.

The aim of this study was to distinguish ice cover types on a given date in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. Multitemporal analyses will be dealt with in a later study. The classification was carried out by employing an automated machine learning approach – Random Forests in combination with the analysis of topographic and textural features based on Landsat-8 image and ASTER GDEM data. The Gao Fen-1 (GF-1) PMS image was used to validate classification results. In this study, all the glacierized terrain types were classified with very high overall accuracy (>98%). The results indicated that debris-covered glaciers accounted for approximately 15.86% of the total glacier area in this region and debris covered glaciers were mainly distributed between 4600 m and 4800 m a.s.l. Additionally, analysis of the results clearly revealed that the number proportion of small glaciers (<1 km²) was 92.18%, which were distributed at lower elevation than large glaciers. In future work, the recognition of debris-free and debris-covered glaciers require further studies with more field observations and higher resolution DEM dataset.

Keywords: Automatic glacier mapping; Random Forests; Landsat; Parlung Zangbo basin

Multitemporal InSAR observations have proved to be key observations to characterize spatial and temporal variations of interseismic strain along major faults, allowing not only to retreive average interseismic velocity maps but also transient aseismic slip events, giving new lights on seismic hazard assessment. With their high temporal resolution and wide spatial coverage, Sentinel-1 (S1) InSAR data can be analyzed in time series to tackle the multi-scale issues of seismic hazard, as well as the off-fault deformation and non tectonic signals (such as seasonal hydrological loads) quantification.

We focus here primarily on the eastern border of the Tibetan plateau, from the Himalayan syntax in the south to the Ordos in the north, marked by major faults, recently broken (Longmen Shan thrusts at the origin of the Mw 7.9, Wenchuan earthquake in 2008) or known as seismic gaps unbroken for several hundred years. Geodetic data available to date (GPS, InSAR ERS / Envisat time series) show that some of these gaps are the site of aseismic slow slips (such as some segments along the Haiyuan and Xian Shui He faults, and possibly along the Himalayan front in Bhutan), which, depending on their spatio-temporal characteristics, can help to reduce the seismic hazard on these faults or, conversely, facilitate the initiation of future major ruptures. In addition, the eastern and southern borders of the tibetan plateau are marked by high mountain ranges (Longmen Shan in the east and Himalayas in the south, with elevations variations of several kilometers), subject to erosion and contrasting with basins affected by hydrological loads varying seasonally. We review here our most recent studies over this eastern border of the tibetan plateau, analyzing large scale velocity fields obtained from S1 data time series analysis over descending and ascending orbits, emphasing improvments in InSAR processing specific to S1 data.

The ~1000-km-long Chaman fault system consists of a series of subparallel left-lateral strike-slip faults and thrusts that form the transform to transpressive plate boundary between the Indian Plate and the Eurasian Plate. Studies based on geological and plate closure estimates show that the northward plate motion of India with respect to Eurasia is on the order of 35 mm/yr. Previous InSAR studies (Barnhart, 2016; Fattahi & Amelung, 2016) along the Chaman fault system based on Envisat and ALOS data have shown that the northeast-southwest trending Chaman fault itself only accounts for ~30% of this relative motion. How the remaining 70% is distributed or localized on adjacent structures remain to be determined. Such studies also revealed the existence of shallow creep along more than 300 km of the Chaman fault. The large spatial coverage of the recent Sentinel-1 data allows to tackle both the large-scale strain partitioning issue and the fault-scale creep behavior characterization.

In order to estimate strain accumulation rates along and across the Chaman fault system, we map the present-day interseismic velocity fields using long-swath (> 1250 km) Sentinel-1 (S1) TOPS radar images acquired on both ascending (T42, T71, T144) and descending (T151, T78) orbits, along the western boundary of the Indian subcontinent. Using an automatized processing workflow, we have processed time series of ~150 S1 images acquired between 2014 and 2018. Preliminary results show left-lateral shear velocities of ~20 mm/yr across the distributed plate boundary, with a complex partitioning between the main Chaman left-lateral fault, other adjacent left-lateral faults or secondary structures within the thrust belt. While ascending data are mostly sensitive to the left-lateral component of slip and vertical motion along the Chaman fault, descending data highlight horizontal and vertical motion across secondary structures branching on the main Chaman fault. Surface aseismic creep rate along the Chaman fault seems to reach up to ~10 mm/year and may extend along a ~600 km-long segment, between 28.5 ^oN and 32.5^oN, which appears significantly (50%) longer than that reported in previous studies. Surface creep thus accommodates ~30% of the tectonic loading along a significant portion of this plate boundary. Further data analysis and modelling will provide a better quantification of the creep rate amplitude and depth along fault strike, deep tectonic loading, and strain partitioning on secondary structures.

In the past 10 years after the Wenchuan earthquake, important information was obtained from analysis of InSAR data of the event, including fault geometry, slip-distribution, rupture propagation and dynamics etc. Though some GPS data collected over both sides of the earthquake fault, InSAR data with full coverage of the Sichuan basin and Longmenshan provides crucial information about the kinematic and dynamic processes of the earthquake.

For this particular region with drastic elevation changes, two end-member models were proposed to interpret the deformation mechanism of the Tibetan Plateau and generation of the Wenchuan earthquake, namely the thrust-fold belt or the viscous lower crustal flow models. Here we re-analyze the PALSAR InSAR data acquired ~10 years ago when we published the first results in both Sun et al. (2008) and Shen et al. (2009). A number of correction techniques we developed in these years are applied to the data, particularly for the ionospheric noise in meter scale. Hence the coseismic deformation is greatly improved in this analysis. Then we use a nonlinear-linear-mixed technique to invert the data for detailed rupture features of the Wenchuan earthquake. Our inversion indicates that a shallowly west-dipping segment to the south and a near-vertical segment to the northeast are good enough for fitting the InSAR data, and the displacements of a horizontal detachment extending to the west are not needed.

Thanks to ESA’s Sentinel-1 A/B satellites, a high-temporal resolution dataset over the Longmenshan region is now available, with the earliest acquisition in the middle of 2014. By using two advanced time-series analysis techniques on the TOPS mode data in both ascending and descending pass, we analyzed the deformation process on subswath-by-subwath basis covering the Longmenshan and Minshan regions, where the Wenchuan earthquake occurred in 2008 and the Jiuzhaigou earthquake happened last year respectively. Our first result indicates that the Sentinel-1 SAR dataset is promising for resolving the subtle tectonic deformation process of this region, though other sensors, such as ERS/Envisat, may suffer from heavy decorrelation in the same region.

In the rapid urbanization process of China, most rural peoples have moved to and lived in cities, making the rapid growing cities occupy lots of farmlands around these cities. On the other hand, the rural houses are abandoned and left in empty instead of transforming as corresponding farmlands. The unequal relationship between the people living and the land use has already seriously hampered the sustainable development of both cities and rural settlements, and affected China’s ideal of building well-off society in all-round way. Rapidly acquiring the information of these abandoned/empty houses in a large scale with low cost is critical for the country to make corresponding policies. And the high spatial resolution remote sensing (HSRI) techniques have a great potential in recognizing these houses because of its ability of recognizing tiny objects at a large scale region.

But currently, the HSRI of this application is limited in a small region because the recognition is still conducted by human interpretation and ground investigation. For applications of large scale region, automatic recognizing algorithm is a critical technique. However, the HSRI presents rare spectral information and plenty spatial information, which makes the well-developed pixel-based automatic classification workflow difficult in acquiring the high level information that whether a house is abandoned or empty. So we have developed an automatic recognizing solution so that the government or other developing countries could share the benefits of remote sensing techniques when making polices to deal the problem of unequal relationship between the people living and the land use in the process of urbanization.

It is hard to directly recognize whether a house is abandoned or empty in the image. However, in the process of human interpretation and ground investigation, we found that the courtyards of empty houses are often full of garbage or grass, thus distinctively different from the houses lived by people. This inspired us that we can recognize the empty house by the ratio between unclean area (garbage and grass) and the courtyard area. Guiding by this fact and the multiscale segmentation of recently popular paradigm Geographic Object-based Image Analysis (GEOBIA), we construct a primary solution and the main procedures are that: (a) acquiring the courtyards vector polygons from the cadastral data; (b) segmenting the image under the constraint of these polygons; (c) classifying the segmented image objects into Clean, Grass, Garbage, etc.; (d) computing the ratio of each courtyard; (e) recognizing the abandoned or empty houses by the ratio of each courtyard. We chose two rural settlements located at the north of Yucheng City, Shandong Province, China, for validating experiments, and acquired the images by Unmanned Aerial Vehicle remote sensing system and got the cadastral data from local government. By comparing the results of our solution and the results interpreted by human and ground investigation, it can be concluded that our solution is promising in dealing this problem.

In the future, we will consider more types of empty houses and experiment them on the high spatial resolution satellite remote sensing images so that it could be applied for large scale region investigation, and thus enable the government could share the techniques benefits when dealing the problems caused by urbanization.

Since 2008, more than half of the world population live in cities, and by 2015, nearly 4 billion people -54 per cent of the world’s population - lived in cities. That number is projected to reach 5 billion by 2030 (UN, 2018). Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services. Therefore, accurate, timely and consistent information on urban growth patterns is of critical importance to support sustainable development. The objective of this research is to develop novel methodologies to exploit Sentinel-1 SAR and Sentinel-2 MSI time series for monitoring urban changes aiming at globally applicable methods. First, model-based urban change detection method is being developed using multitemporal Sentinel-1 SAR data. Then an integrated approach between Sentinel-1 SAR and Sentinel-2 MSI data will be developed in order not only to detect changes but also to be able to label the different types of changes (e.g., agriculture or forest to urban, old low-rise urban to new high-rise urban, etc.) using a near real-time processing of the Sentinel big data. It is anticipated that urban changes in general, new builtup areas in particular, will be detected in a timely and accurate manner. The urban change information has the potential not only to support sustainable planning at municipal and regional levels, but also contribute to the monitoring objectives of UN Sustainable Developments Goal (SDG) 11: Making cities and human settlements inclusive, safe, resilient and sustainable.

Lakes constitute essential components of global water cycles and serve as important sentinel of environmental changes. During the past few decades, lakes in China have experienced dramatic changes under the influence of both climate change and human activity. Lakes in populated regions are particularly vulnerable to intensive land use/land cover (LULC) changes due to various human activities, e.g., agricultural irrigation, water diversion projects, and urban expansion. In this study, the impacts of LULC changes on the distribution and abundance of lakes in China were investigated by exploiting China Land Use/Cover Dataset (CLUD), remote sensing images, and socio-economic data. We first explored the spatiotemporal change patterns of urban lakes in China’s major cities over the period 1990–2015. The results showed the urban lakes experienced a large reduction in surface area (decreased by 24.22%), which are mainly distributed in the Yangtze Basin, accompanied by a rapid expansion of urban areas. The excessive encroachment in the urban lakes also resulted in increasing landscape fragmentation and decreasing shape complexity. Furthermore, we also investigated the effects of LULC changes on the lakes in the Yangtze Basin, a densely populated region with abundant rainfall, intensive cultivation, and rapid urbanization. Our results revealed the Yangtze Basin experienced rapid lake shrinkage, which was mainly attributed to human-induced alterations from lakes to cropland, fish ponds, and built-up areas, accounting for 34.6%, 24.2%, and 2.5% of the lake area reduction, respectively. Given the increasing vulnerability of these lake resources to anthropogenic activities, understanding the spatiotemporal changes of the lakes and the associated driving factors are issues of increasing concern.

Efficient Urban Change Detection Using Multi-Resolution Remote Sensing Data

for Large Area

Abstract:

Fast change detection and global monitoring are very important to understand large-scale activities in urban areas ate the global level, even though urban area occupies a little part of the surface of the Earth. Human activities have caused global-scale change problems, like global climate change, forest reduction and land deterioration. Urbanization is the most important form of human activities and recent years multi-resolution optical datasets has applied in urban environmental remote sensing. Unlike optical remote sensing, meter-wavelength active echoes can return the structure information of urban areas, like building height, direction and density. Hence, SAR is suitable to monitoring the geometrical and physical changes in the process of urbanization.

Since it is very important to quickly monitor and update any change, here we propose a multi-scale/resolution mapping strategy to explore the characteristics of urbanization, such as the urban structure in the horizontal and vertical directions, and urban extent. Specifically, ASCAT and Nighttime light data with very coarse resolution are used to map large-scale changes. Then, 10-meters resolution SAR images are implemented to focus on more detailed building blocks.

Although scatterometers are designed to actively measure wind speed and direction over the oceans, it also has been used to monitor urban environments [1]. The resolution is approximately 10 km (in the along beam direction) x 25 km (across the beam). This data set (ASCAT Level 1B Full resolution product) is used here to explore urban structural changes, considering the energy of backscattering signal is mainly formed by the dihedral-plane structure of buildings. Also, the Nighttime light data source is also introduced to explore dynamic changes different from those coming from the scattering characteristic of urban areas.

The large-scale change detection approach is quick and efficient but the results are rather coarse. Accordingly, a more detailed survey is needed to focus on the detected changes (i.e. fastening the research by excluding unchanged portions of the urban areas under investigation). Middle-resolution and high-resolution SAR data can be used to detect these detailed changes. In this paper, Sentinel-1A SAR, RADARSAT-2 and ALOS-PolSAR data are considered in selected regions where more detailed information of the change are meant to be detected, exploiting the method recently proposed in [2].

Preliminary results show the effectiveness and feasibility of change extraction at the multiple spatial resolution that have been considered, proving by comparison the expected consistency of changes detected at macro-scale level, and investigated at the micro-scale level. Most changes from macro-scale scatterometer data are distributed around the fringe of cities or urbanized areas. Instead, the more detailed urban structures change reflect the horizontal and vertical urbanization phenomena, including areas with construction, demolition and reconstruction activities. These are recognized as “positive” or “negative” changes and extracted from multi-temporal SAR images.

[1] S. Frolking et al. "A global fingerprint of macro-scale changes in urban structure from 1999 to 2009," Environmental Research Letters, 8.2 (2013): 024004.

[2] M. Che, P. Gamba, “2- and 3-Dimensional Urban Change Detection with Quad-PolSAR data”, IEEE Geoscience and Remote Sens. Lett., vol. 15, no. 1, pp. 68-72, Jan. 2018.

Abstract：Land subsidence is a slow geological disaster threatening the safety of the public and urban infrastructures. By 2009, more than 50 cities in China have been facing land subsidence problems, among which Beijing is one of the most severely affected. During the last decades, over-exploitation of groundwater has been the main factor for land subsidence in Beijing. Since the South-to-North Water Diversion Project was officially completed on December 24, 2014, the water supplied to Beijing has reached 840 million cubic meters by 2016. After the South Water delivered to Beijing, water shortage was greatly alleviated in Beijing. This study aims to investigate the spatio-temporal dynamics of land subsidence in Beijing before and after the South-to-North Water Diversion Project. Long-time series land subsidence during 2004-2017 were retrieved based on 39 Envisat ASAR images (2004-2010), 27 Radarsat-2 images (2011-2014) and 21 Sentinel-1 images (2015-2017) using PS-InSAR techniques. The paper analyzed the influence of South Water into Beijing on land subsidence in Beijing area, combining with the changes of the groundwater level after south water entering Beijing. The results showed that the maximum annual deformation velocity during the period of 2004-2010, 2011-2014 and 2015-2017 was -126.84 mm/year, -147.57 mm/year and -159.7mm/year, respectively. The leveling measurements are utilized to verify the InSAR results，which demonstrated that the absolute errors of the deformation velocity in the three periods are 0.9-9.8mm/year, 0.6-8.4mm/year and 0.89-5.51mm/year, respectively. The uneven settlement of the regional scale is obvious. By 2017, five subsidence funnel areas, namely Chaoyang-Tongzhou area, Chaoyang Caofang area, Chaoyang Jinzhan area, Changping Beiqijia area, and Haidian Xixiaoying area have been developed. The settlement funnel area extended to northward, east and southeast, and the area of funnels was expanding continuously. Time-series analysis was performed on all PS points with deformation rate faster than -25mm/year. It was found that most (89%) PS points showed increasing surface deformation rate from 2004-2010 to 2011-2014; 62% PS points showed decreasing deformation rate after South-to-North Water Diversion Project (from 2011-2014 to 2015-2017), which were mainly located in the Chaoyang-Tongzhou funnel area and the Changping Beiqijia funnel area. 29% PS points still showed an accelerating settlement during 2015-2017, mainly in the west of the Haidian Xixiaoying and the west of the Changping Shahe, the southwest edge of the Shunyi, the northeast of the Chaoyang and the southeast border of the Tongzhou funnel areas. Regression analysis between the time-series cumulative displacement and groundwater level at the second confined aquifer at four observation wells showed that land subsidence agreed well with groundwater level depth (R²>0.67), indicating the major control of groundwater on subsidence processes. According to the groundwater level map of 2012, 2014 and 2016, it was found that the groundwater level decreased obviously in 2014 compared with 2012, and recovered in 2016, , especially in the eastern and northern part of Chaoyang District,and the location of the subsidence funnel area was basically coincided with the groundwater funnel area. In summary, South-to-North Water Diversion Project has contributed to the mitigation of the ground subsidence in Beijing.
Keywords：land subsidence; South-to-North Water Diversion Project; PS-InSAR; Beijing

Urbanization affects considerably the thermal environment of cities and influence the spatial and temporal consumption of energy for heating and cooling. The increase of impervious surfaces alongside with the reduction of vegetated areas lead to increased air and surface temperatures. Remote sensing data is suitable for up-to-date urban land use mapping and for the assessment of the thermal environment of urban areas.

In this study a statistical approach is developed on the basis of satellite data in the visible and thermal infrared parts of the spectrum (for land use/land cover and land surface temperature respectively) in order to identify the areas where maximum and minimum temperature values are observed. The approach is tested to compact urban agglomerations to assess its validity.

The recognition of such areas is important as they reflect areas where immediate interventions are necessary to ameliorate the thermal environment (for instance by introducing nature based solutions), whereas the knowledge of their spatial distribution and temporal variations is needed for smart urbanization and smart governance practices.

Spatial information on the extent and expansion of built-up land cover is a valuable indicator for global and regional ecosystems and can inform effective policy alternatives for sustainable development. Optical remote sensing has proven to be a powerful tool to capture such information, although it suffers from limitations, especially where there is frequent cloud cover. The increased availability of synthetic aperture radar imagery (SAR) offers an additional means for assessing the surface features and monitoring land cover dynamics. However, its application in the built environment is not yet fully exploited due to the speckle nature and limited radiometric resolution. In this paper, we demonstrate a methodology for monitoring built-up land and revealing its expansion at a regional scale by taking advantage of the individual strengths of both radar and optical remote sensing data. The new method takes radiometric, interferometric, spectral, temporal and spatial-contextual signatures into account to resolve the ambiguities between natural/built-up lands and stable/changed areas by: (1) constraining the study extent to built-up areas using spectral information of optical datasets based on the Bayes theory; (2) integrating radiometric and interferometric information in a SAR stack with the spatial-contextual information in ancillary optical data, to detect accumulative change under a Markov random field. We test the method in two rapidly expanding regions in Nanjing city in China. Based on validation data from independent optical data and in-situ campaigns, the overall accuracy of change detection is high in both test sites, up to 82.9% and 85.5%, respectively. The small commission error (around 10%) for the changed class shows the potential of this method to pinpoint regional expansion without knowledge of any events on the ground, even in richly textural scenes. The results also prove the suitability of the approach for detecting the gradual changes in the built environment that cannot be captured from bi-temporal SAR data in previous studies.

The discomfort index (DI) is an important indicator that measures human heat sensation for different climatic conditions. Currently, the DI of a city is usually calculated using a few meteorological stations and hence does not accurately represent various thermal discomfort states of the city as a whole, especially in the event that the discomfort states vary depending such urban characteristics as urban density, % of greenery, aspect ratio, etc. This is a considerable drawback taken the importance of the index for assessing the quality of life within urban agglomerations and thus facilitating measures for smart urbanization.

In this study a technique to produce fine-scale DI maps is proposed and applied accordingly. The technique is based on the combination of Sentinel-2 and Landsat 8 images with in-situ measured meteorological data. The DI map clearly reveals the spatial details of the DI in different locations of the city and thus supports focused interventions with the potential to support the smart operation of a city.

In recent decades, the great development of radar remote sensing provides the opportunities for land cover mapping at larger extent. However, in the region with rich textures heterogeneous land covers exist and intermingle over short distances, relatively few studies have analyzed the potential of SAR datasets. Current studies focus more on the improvement of classifiers or multi-source data fusion. Radar image resolution and parameter estimation accuracy are not considered, thus structural features in a SAR image cannot be accurately described in details.

Covariance matrix is fundamental for the full exploitation of InSAR capabilities and widely applied in data processing. Present researches are mainly based on parameter estimation of the single element in covariance matrix without consideration of complex statistical inference. Conversely, these methods try to mitigate the source of errors at the cost of the increase of constraints. As a result, it is usually difficult to achieve the satisfied results in the real world when the assumptions are broken.

To solve this problem and further extend previous research into remote monitoring of urban environments, this study highlights the impact of the InSAR parameters on land cover mapping under the framework of InSAR covariance matrix estimation. More concretely, we will quantitatively evaluate the influences of the quality of the input variables, the classifiers and the information fusion on classification accuracy respectively, and show that the overall accuracy depends strongly on the error mitigation of input variables. On this basis, a methodology that fuses multitemporal SAR dataset for land cover mapping over scenes with rich textures will be proposed. The objective of the study is that we can obtain a full resolution land cover map with higher accuracy and simultaneously evaluate changed areas caused by urban extension. This research will be very useful for many populated cities especially the fast growing cities in Mainland China.

The Hengqin Island of Zhuhai City, Guangdong Province in Pearl River Delta is chosen as a typical experimental area. InSAR classification results with the traditional method and the exact InSAR parameter estimation method are compared. It is shown that the more accurate parameter estimation is as high as 10% and 9% for the overall classification accuracy and Kappa coefficient compared with the measured data.

This method will improve the accuracy of InSAR parameter estimation and simultaneously preserving the resolution of the image particularly over rich texture areas. It will also be very useful to monitor natural distribution over complicated scenes with larger extent where the region of interest is intricate. Therefore, the research proposed has both scientific and practical values.

Atmospheric particles have a remarkable impact on the global environment and climate change. The mineral dust, marine, polluted marine, absorbing, and other types of aerosols are important parts of the global biogeochemical cycles. The land-sea-wind circulation, different heights of boundary layers over sea and continents, the thermal and mechanical turbulence and the pollution emissions in the coastal zones have pronounced impact on the optical properties of the aerosols. In view of these, vertical resolved measurements of optical aerosol properties with calibrated and QA/QC checked lidar systems are necessary. Hence, evaluation and calibration of the data quality of observation equipment are needed urgently. The proposed project tasks are to intercalibrate the lidars from both partners by using EARLINET QA/QC procedures side by side. For this, the lidars from China are scheduled to be transported to Europe. The intercalibration and intercomparison will be conducted at TROPOS in Leipzig/Germany (http://www.tropos.de/) since often particle layers of dust, polluted marine aerosol and other types of aerosol had been observed at TROPOS, the technical infrastructure at TROPOS together with the running systems there is well established. Afterwards, the lidars will be theoretically and experimentally analyzed (including the determination of Müller Matrixes) to determine the contributions of the optical parts to the total system parameters and their uncertainties. With this system calibration and validation results, the optical particle parameters like the extinction coefficient, the backscatter coefficient, the lidar ratio, the aerosol optical thickness, the depolarization ratio, and the Ångström exponent will be measured at TROPOS during a following intensive measurement campaign of about 3 months. The mentioned intensive particle parameters will be used for aerosol type characterization from the observed data.

After this crosscheck, also the intercomparison of the measurement results from the ground-based lidars and from spaceborne lidars (carried by EARTHCARE, ADM-Aeolus, CALIPSO) will be conducted. Furthermore, the wind profiles, the turbulence, and the dynamic structure inside the atmospheric boundary layer will also be observed, which will support the research on the vertical mixing and lateral transport (including sea-land-wind) of aerosols. Through vertical wind speed detection, aerosol flux will be calculated, and thus the strength and deposition of aerosols can be estimated. After the transportation of the lidar systems to Changdao Island / China, a second joint intensive joint measurement campaign will be carried out in this project. This task will enhance the cognition of aerosols like polluted marine, polluted dust, dust, and other aerosol types. It is expected that the aerosols consist mainly of mixtures of mineral dust, pollution, and marine within the planetary boundary layer and in the lofted layers (above) at Changdao Island.

After its launch in autumn 2018, the spaceborne wind lidar ALADIN (Atmospheric LAser Doppler INstrument) on-board ESA’s Earth Explorer satellite Aeolus will allow for global observation of atmospheric wind profiles. Being the first ever satellite-borne Doppler wind lidar instrument, ALADIN will significantly contribute to the improvement in numerical weather prediction by providing one component of the wind vector along the instrument’s line-of-sight (LOS) from ground throughout the troposphere up to the lower stratosphere. The vertical resolution is 0.25 km to 2 km depending on altitude, while the precision in wind speed is envisaged to be between 1 m·s^-1 to 3 m·s^-1.

Over the past years, an airborne prototype of the Aeolus payload, the ALADIN Airborne Demonstrator (A2D), has been developed at DLR (German Aerospace Center) and deployed in several field experiments, aiming at pre-launch validation of the satellite instrument and at performing wind lidar observations under various atmospheric conditions. The A2D features a high degree of commonality with ALADIN in terms of laser source and Doppler lidar receiver design. Thus, it represents the key instrument for the planned calibration and validation activities during the Aeolus mission, as it allows validating the instrument concept, operating procedures as well as wind retrieval algorithms.

In autumn 2016, the A2D was engaged in the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX). Based in Keflavík, Iceland, this international field campaign had the overarching goal to investigate the influence of diabatic processes, related to clouds and radiation, on the evolution of the North Atlantic jet stream. Apart from providing accurate wind observations for quantifying effects of disturbances on the downstream propagation of the jet, the research flights performed during NAWDEX considerably extended the wind dataset obtained with the A2D as well as with the 2-µm coherent wind lidar on-board the same aircraft – the DLR-Falcon F20. Hence, NAWDEX was an ideal platform for assessing the performance of the two wind lidar systems in heterogeneous atmospheric scenes including strong wind shear and varying cloud conditions.

Besides the DLR-Falcon, three additional aircraft were involved in the campaign being equipped with diverse state-of-the art remote sensing instruments which enabled the observation of a large set of atmospheric parameters, while ground stations delivered a comprehensive suite of further measurements to complement the meteorological analysis. For the first time, coordinated flights were conducted involving the DLR-Falcon, the German HALO deploying an aerosol lidar, a cloud radar and dropsondes as well as the French Falcon SAFIRE with an on-board cloud radar and a UV Doppler lidar instrument. Comparative analysis of the wind data obtained during the collocated flight legs allowed quantifying the accuracy and the precision of the various instruments and demonstrated the complementarity of the different technologies for measuring wind speeds. This work will provide an overview of the NAWDEX campaign and present the results from the wind data analysis both from a meteorological and an instrument point-of-view.

The spaceborne Aerosol and Carbon dioxide Detection Lidar (ACDL) will measure the global column concentrations of carbon dioxide (CO₂) and aerosols profiles simultaneously . The column concentrations of carbon dioxide are measured by 1572 nm double-pulsed integrated path differential absorption (IPDA) lidar technique. The aerosols and clouds profiles are obtained by 532 nm high resolution spectrum lidar (HRSL) technique. Both techniques are combined in the ACDL lidar payload. The dedicated atmosphere and environment monitoring satellite will carry the ACDL lidar and is scheduled to launch in 2020. The spaceborne lidar prototype is being developed. An airborne Aerosol and Carbon dioxide Detection Lidar (AACDL) is developed and high altitude flight validation experiments are scheduled to implement in 2018.

For a double-pulsed 1.57-μm integrated path differential absorption (IPDA) lidar, the transmitted laser pulse energy is an important factor which can influence the uncertainty of the CO₂ Column concentrations measurement. Designing an 1.57μm double-pulsed laser energy monitor and to improve the accuracy of the normalized energy ratio of the transmitter pulse energies to returned echo pulse energies are presented. In the experiments, each pulse is divided into two parts .One is received by the detector directly and the other is delayed by the 200 m multimode fiber. Ground glass diffusers in front of the integrating sphere are used to reduce speckles generated by integrating sphere. Ground glass diffusers with different grits and the rotational speeds are compared. The results show that the rotated ground glass diffuser with 120 grits has the minimum standard deviation of the normalized energy ratio after a moving average. Compared to the situations without the ground glass diffuser or with static ground glass diffuser, the slopes of the Allan deviations of normalized energy ratio with rotated ground glass diffusers are more close to -0.5 in logarithmic coordinates.

In preparation of ESA’s upcoming Earth Explorer mission Aeolus which strives for the global observation of wind profiles from the ground to the lower stratosphere deploying the first-ever satellite-borne wind lidar system ALADIN, the ALADIN airborne demonstrator (A2D) has been developed at DLR (German Aerospace Center). Due to its representative design and operating principle, the A2D provides valuable information on the wind measurement strategies of the satellite instrument as well as on the optimization of the wind retrieval and related quality-control algorithms. Hence, it represents an essential testbed for the planned calibration and validation activities after the launch of Aeolus which is scheduled for end of August 2018.

The A2D was successfully employed for wind observations in the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) conducted in Iceland in autumn 2016. Within the scope of the campaign, which aimed to study the influence of diabatic processes on the evolution of the North Atlantic jet stream, 14 research flights were performed extending the wind and calibration dataset of the A2D. In particular, the recording of very high wind speeds above 80 m·s^-1 and strong wind shear of 10 m·s^-1·km^-1 was obtained by sampling an intensified jet stream close to Scotland on 27 September 2016. Broad vertical and horizontal coverage across the troposphere was achieved thanks to the complementary design of the A2D receiver comprising a Rayleigh and Mie channel for analysing both molecular and particulate backscatter signals. Validation of the instrument performance and retrieval algorithms was conducted by comparison with DLR’s coherent wind lidar which was operated in parallel on-board the same aircraft. The systematic error of the A2D line-of-sight (LOS) wind speeds was determined to be less than 0.5 m·s^-1 for both receiver channels, while the random errors range from 1.5 m·s^-1 (Mie) to 2.7 m·s^-1 (Rayleigh). This work will present the operation principle of the A2D and demonstrate selected wind results obtained during NAWDEX.

Ground-based measurements were carried out during field campaigns in April–June of 2010, 2011 and 2012 over northwestern China at Minqin, the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and Dunhuang, respectively. In this study, three dust cases were examined, and the statistical results of dust occurrence, along with physical and optical properties were analyzed. The results show that both lofted dust layers and near-surface dust layers were characterized by extinction coefficients of 0.25–1.05 km⁻¹ and high particle depolarization ratios (PDRs) of 0.25–0.40 at 527 nm wavelength. During the three campaigns, the frequencies of dust occurrence retrieved from the lidar observations were all higher than 88%, and the highest frequency was in April. The vertical distributions revealed that the maximum height of dust layers typically reached 7.8–9 km or higher. The high intensity of dust layers mostly occurred within the planetary boundary layer (PBL). The monthly averaged PDRs decreased from April to June, which implies a dust load reduction. Comparing the relationship between the aerosol optical depth at 500 nm (AOD₅₀₀) and the Angstrom exponent at 440–870 nm (AE_440–870) confirms that there is a more complex mixture of dust aerosols with other types of aerosols when the effects of human activities become significant.

“Wind Pump” is an important concept that has drawn significant attention in the recent years. Wind Pump is defined as a series of wind-driven processes that influence ocean currents and water movement, which subsequently affect marine ecological conditions. Wind Pump can change the transport of nutrients and promote the cycling of major elements in the ocean. It thus drives primary production and marine ecosystem and affects carbon fixation and global fishery resources (Tang, 2004). This presentation will introduce “Wind Pump” effects on marine systems and take some examples in the South China Sea.

Algal bloom is defined as a rapid increase or accumulation in biomass in an aquatic system. It not only can increase the primary production but also could result in negative ecological consequence, e.g., Harmful Algal Blooms (HABs). According to the two classical theories of algal blooms “critical depth” and “eutrophication”, oligotrophic waters are difficult to form a large area of algal blooms. Cruise observations were only able to capture sporadically the existence of algal blooms. Due to limitations of in-situ observational methods, most of previous studies investigated occasional or regional blooms along coastal eutrophic waters, without much success of understanding of main processes responsible in the offshore deep-ocean oligotrophic waters. Based on previous studies by taking a full advantage of remote sensing technology and multiple satellite data, we proposed the mechanism model of “Wind Pump effects”, which represent the oceanic dynamic mechanism of the bloom growth. Except for the classical coastal Ekman transport, the Wind Pumping effects explain that wind forcing affects the formation of algal bloom through a variety of mechanisms, including Ekman pumping, clip volume, stirring and mixing, and transport by wind and wind-induced surface currents.

The conventional way to study upwelling regions by remote sensing is to use infrared and optical sensors by which the sea surface temperature (SST) and the chlorophyll-a (Chl-a) concentration is measured. However, also synthetic aperture radars (SARs) are useful instruments to study upwelling regions. Upwelling regions are areas of high biological activity, where the marine beings (plankton and fish) secrete surface active substances which rise to the sea surface and damp there the short surface waves, which are responsible for the radar backscattering. Thus upwelling areas manifest themselves on SAR images often as areas of reduced normalized radar cross section (NRCS). However, not only biogenic slicks associated with upwelling regions cause a reduction of the NRCS, but also the change the stability of the air-sea interface (from neutrally-stable to stable) because in upwelling regions the SST is usually lower than over the adjacent areas. Biogenic slicks visible on SAR images as areas of reduced NRCS are often confounded with mineral oil films. Criteria for discriminating between both types of surface films are presented. Furthermore, the correlation between Chl-a distribution and biogenic slick coverage in upwelling areas, like in the South China Sea east of Hainan, the East China Sea north of Taiwan, the Atlantic Ocean west of South Africa, and the Agulhas Return Current in the Indian Ocean, is investigated. These upwelling events are studied by using Sentinel-1 SAR images, Modis SST and Chl-a maps and model data of geostrophic surface currents. It is shown that this synergism yields new insights into upwelling mechanisms.

Sea Surface Temperature (SST) is the most important parameter, which is widely applied for studying water masses, air-sea interaction, marine ecosystem and environment, and other subjects. With the development of half century, satellite remote sensing has become the dominant technique to detect the global SST. However, the satellite measured SST is more closely related to the skin temperature than the subsurface bulk temperature. It is not convictive to validate the satellite measured SST with the subsurface bulk temperature, which is generally measured at a depth of one meter or even deeper. In order to validate the satellite retrieved SST, it is necessary to measure skin temperature.

A new version of the Buoyant Equipment for Skin Temperature (BEST), has been recently manufactured. The new instrument consists of 1050 thermistors, which are integrated in one pole, and 840 thermistors are on the top part (505mm in length) of the pole at 0.6mm distance each and 210 thermistors are on the other part (1015mm in length) of the pole at distance about 5mm. The pole works with a liquid level meter, the liquid level meter uses the electrical capacitance sensors which were also arrayed at 0.6mm distance corresponding to the thermistors. The new instrument BEST was then calibrated in a thermal isolation calibration system, and totally 21 temperature points from temperature -4℃～45℃ were measured for the calibration. The calibration results show the accuracy of the BEST is 0.01K.

The new instrument was vertically floated in Haizhu lake, Guangzhou from January 30 to 31, 2018, continuously for 2 days when the weather is quite cold. It synchronically measures the temperatures of the bottom layer of the air, the skin layer and the subsurface layer of the water at every second and more than hundred thousand temperature profiles were measured. All the temperature profiles have similar distribution pattern. In the bottom of air, the closer to the water surface, the higher temperature. and under the water surface, there is a thin thermocline (or metalimnion) which is just several centimeters thick. In the thermocline the temperature increases with water depth quickly. The water generally increases in temperature by 0.65 degrees Celsius every centimeter. The thermocline has very strong intensity, which is thousand times stronger than normal thermocline occurs in the ocean columns.

Mapping the Yangtze River discharge and freshwater plume spreading is highly important for in the understanding of phytoplankton blooming and nutrient distribution and transportation from the estuary to the East China Sea. Satellite sensor synergy building on passive microwaves, imaging spectrometer and radars are explored together with in-situ observations and dynamic modeling. With new EO satellite data available, such as Chinese Gaofen-4 and the ESA Sentinel-1,2 and 3 there exist possibilities that the freshwater plume mix and transportation process on weekly to seasonal basis can be observed and modelled. Moreover, in this study the Yangtze River Plume transportation dynamics may also be studied by mapping the plumes over the past decades, which may link the variations with large damming in the catchment. We adapt some of the classical methods for retrieval of sea surface salinity distribution with optical remote sensing data by establishing relationships between colored dissolved organic matters (CDOM) and salinity. We will also opt for sea surface brightness temperature methods with which sea surface salinity is obtained by using K-S model, where the brightness temperature is derived from scattering coefficient of SAR data. A Debye Equation based synergic method for sea surface salinity inversion will be thoroughly explored, in which sea surface temperature is synergically derived from brightness temperature through high resolution optical images and sea surface emittance calculated from SAR data.

Large scale green tide (macroalgae blooms of Ulva prolifera) have ocurred in every summer in the Yellow Sea since 2007, causing serious damages on coastal ecological environment, aquaculture, tourism, transportation and so on. The green macroalgae of Ulva prolifera originate from the seaweed aquaculture zone in the Jiangsu shoal, and the blooms are mainly caused by the activity of recycling the seaweed aquaculture facilities. In this work, Gaofen (GF) optical images with high spatial resolution (16m) and high revisit frequency (4 days) and Sentinel-1 IW-GRD microwave data are used to monitor the seasonal changes in the seaweed aquaculture in Jiangsu Shoal (120.8–122°E, 31.9–33.5°N) in 2016 and 2017 with the aim of exploring the reasons on the changes in the magnitude of green tide in the Yellow Sea in the summer of 2016 and 2017.

Macroalgae have the similar spectral signature as that of green vegetation. The normalized differential vegetation index (NDVI) derived from the GF-1 reflectance spectra is used to extract the seaweed aquaculture zone. Considering the difficulty of detecting seaweed aquaculture zone under the ebb tide and bad weather conditions, Sentinel-1 IW-GRD images are used to determine whether it is seaweed aquaculture zone or not.

The result shows that the seaweed aquaculture facilities was recycled mainly in April and May. However, the area of the aquaculture zone was only 1.3 km2 on May 3rd, 2016 while it remained 137.4 km2 on May 7th, 2017. In 2017, the area of the aquaculture zone reduced to 0.7 km2 till June 9th, which shows that the completion time of recycling the seaweed aquaculture facilities in 2017 was about one month later than in 2016. We deduced that the lower magnitude of green tides in 2017 in the Yellow Sea than 2016 may be due to the delay of recycling the seaweed aquaculture facilities. In 2017, the late time of recycling the seaweed aquaculture facilities slowed down the speed of the green macroalgae into the sea, therefore, the scale of the Yellow Sea green tide decreased significantly due to the reduced release of green tide species.

It is well known that rain events leave fingerprints on synthetic aperture radar (SAR) images acquired over the ocean, but it is not always easy to identify them unambiguously, especially not on C-band SAR images. Rain becomes visible on SAR images acquired over the ocean via several mechanisms: 1) by variations of the sea surface roughness caused by downdraft winds associated with rain cells and by rain drops impinging onto the sea surface (surface scattering) generating ring waves, splash products (including stalks), and turbulence, and 2) by scattering and attenuation of the radar beam by rain drops in the atmosphere (volume scattering). Surface scattering is particularly intricate at C-band because the Bragg waves responsible for the radar backscattering at this radar frequency lie in the transition region, where the impinging raindrops can increase (usually) or decrease the backscattered radar power, and also because scattering at stalks generated by impinging rain drops can significantly enhance the radar backscattering. In addition, at very high rain rates, volume scattering and attenuation can also contribute.

In this paper we report about progress that has been made in our study of C-band radar signatures of rain over the ocean. Such studies are relevant also for retrieving sea surface wind fields from C-band scatterometer data. Rain is a main source of error in wind retrieval algorithms, especially when co- and cross-polarized scatterometer data are used, which will be the case in the future. In this study we have analyzed mainly Sentinel-1 SAR images acquired over the South China Sea and have compared them with rain data from the weather radar of the Hong Kong Observatory and from the Global Precipitation Measurement (GPM) mission. In contrast to previously analyzed ERS and Envisat SAR data, the Sentinel-1 SAR data are acquired at VV and VH polarization simultaneously, which allows investigating the role of scattering at stalks, consisting of small cylinders of water emanating from the sea surface, in more detail. Theoretical investigations show that coherent scattering at stalks is responsible for the large values of the normalized radar cross section (NRNCS) at VV and VH polarizations often observed in radar signatures of strong rain cells. This interpretation is supported also by data acquired by the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) of NASA/JPL over the Gulf of Mexico.

Yangtze Estuary is located in the margin of land, facing East China Sea. It is influenced by the interaction of land and ocean, developed special environmental characteristics. Riverine freshwater plumes appear in the estuarine area specially, which play an important role in the study of material transport and Yangtze River runoff. Salinity can directly reflect the distribution of freshwater plumes. Therefore, research on the spatial and time distribution and variation of Yangtze River salinity is significant to understanding the importance of freshwater plum and estuarine environment. Compared to the significance of salinity, the measurement of salinity cannot provide sufficient and timely dataset. Remote sensing as a new monitoring technique, is able to provide the real-time synchronous monitoring of large area fast and timely. Existing salinity satellite SOMS and Aquarius cannot apply to the estuarine area because of their low spatial and time resolution. Optical satellite like MODIS, has high spectral resolution, proved suitable to retrieve salinity in estuarine area. This study uses MODIS Terra/Aqua L1b data and field data from voyage and hydrometric station of year 2013 to 2017 to establish a half-experienced retrieval model of Yangtze Estuary. This study divides the study area into inside and outside the Yangtze river estuary. Statistical models are used to the salinity retrieval outside the estuary. A dynamic model is established to t retrieve the salinity inside the estuary, taking runoff volume and tide into consideration, because of the complex hydrological and dynamic environments. The salinity retrieval model is used to reconstruct the salinity distribution of Yangtze Estuary during recent 30 years and analyze the seasonal and spatial salinity variations.

Retrieval of ocean color information is one of the most important missiona of Sentinel-3 Ocean and Land Color Instrument (OLCI). As the successor to Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT, OLCI shows significant superiority compared with MERIS as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The superiority shows in such aspects: the sensor has 21 bands, compared to 15 bands on MERIS, a design optimized to minimize sun-glint and a resolution of 300 m over all surfaces. In this study, we estimated the water quality in the Pearl River using Sentinel-3 OLCI. We appraise the precision of the water quality, including suspended sediment, Chlorophyll-a concentration, CDOM retrieved from OLCI, MERIS, MODIS and SeaWiFS. The results shows that the OLCI shows a good improvement in water quality detection in Pearl River Estuary. The additional bands enhance the ability to extract the information of coastal water quality.

Both the green tide caused by the outbreaks of Ulva prolifera and the golden tide caused by the outbreaks of Sargassum have appeared in the Yellow Sea and the East China Sea in recent years (Xing et al, 2017). The spectral characteristics of floating macroalgae are the basis for the remote detection by optical satellite remote sensing. A total of 10 samples of Ulva prolifera and Sargassum were collected from June 9, 2017 to June 19, 2017 in the Yellow Sea (33º37´～36º30´N, 120º00´～123º30´E). The spectral reflectance of them were measured by a hyperspectral spetroradiometer and a multi-spectral imager, respectively. The hyperspectral data was used to analyze spectral characteristics. The threshold method and neural network method based on the multi-spectral image were tested for the classifying of Ulva prolifera and Sargassum.
In this work, the Virtual-Baseline Floating macroAlage Height (VB-FAH) was calculated for extracting the floating macroalgae (Xing et al, 2016). The reasonable threshold was chosen for the classifying of the two macroalgae based on the trough depth (T-depth) and the Virtual-Baseline Floating macroAlage Height (VB-FAH) (Xing et al, 2013). And the pixel is regard as Ulva prolifera if the value of T-depth is larger than 0.30 or the value of VB-FAH is larger than 0.44. For the neural network method, we did 3 tests with different inputs: the 3-band reflectance image (Image_{_R}), the 3-band reflectance and the T-depth (Image_{_R+T-depth}), the 3-band reflectance and the VB-FAH (Image_{_R+VB-FAH}). The classification results of the above two methods were compared.

Xing Q G, Yu D F, Lou M J, et al, 2013. Using in-situ reflectance to monitor the Chlorophyll concentration in the surface layer of Tidal Flat. Spectroscopy and Spectral Analysis, 33(8): 2188—2191.

References

Xing Q G, Hu C, 2016. Mapping macrolagal blooms in the Yellow Sea and East China Sea using HJ – 1 and Landsat data: Application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113—126.

Xing Q G, Guo R H, Wu L L, et al, 2017 . High-Resolution satellite observations of a new hazard of "Golden Tides" caused by floating Sargassum in Winter in the Yellow Sea. IEEE Geoscience and Remote Sensing Letters.

Locates at central Himalaya, Mt. Everest (Qomolangma) is the highest peak in the world. Famous glaciers such as Rongbuk glacier and Khumbu glacier were studied by for several long decades. Satellite geodetic observation provides important observation on glacier mass balance in the high-mountain area and plays an essential alternative to in-situ observations given the cold and harsh environment. In this research, we collected SRTM DEM observed in 2000, and bistatic TerraSAR-X/TanDEM-X SAR images observed in around 2013 and 2017. By referring SRTM as reference DEM, we obtained topographic changes between 2000 and 2013, also 2000 and 2017 by using an iterative D-InSAR method. Penetration depth differences between C- and X-band microwave on snow and ice were evaluated and corrected by comparing C- and X-band SRTM DEMs. Glacier mass balance between 2000 and 2013 was -0.38 ± 0.04 m w.e. (water equivalent) a-1, and was -0.75 ± 0.08 m w.e. a-1 between 2013 and 2017. The spatial pattern of the glacier mass loss was heterogeneous. The regional heterogeneity may possibly reflect debris-covering rates, terminating type, temperature rising rates and glacier flow rates. However, the spatial pattern in two periods kept constant. Glaciers without debris-cover at Chinese side present the slowest losing rate while lacustrine-terminating glaciers with heavy debris-covers show quickest lost rates.

Field observations and geodetic measurements suggest that glaciers in the Karakoram Range are either stable or have been expanding since 1990 and present positive or less negative mass changes. This situation is called the “Karakoram anomaly”. Previous studies found that the Central Karakoram has experienced a slight gain in glacier mass at the beginning of the 21st century. Glacier surface velocity is one of the key parameters of glacier dynamics and mass balance. The spatial-temporal characteristics of the glacier velocity in the Central Karakoram are essential to improve our understanding of glacier dynamics and the glacier responses to climate change and influences on regional water sources.

The inter-annual glacier velocity results during 1999-2003 are achieved by using a cross-correlation algorithm in the frequency domain with four pairs of Landsat-7 Enhanced Thematic Mapper Plus panchromatic images. The images were co-registered first, and the horizontal displacements were calculated with the COSI-Corr software package. Due to a lack of in situ measurements of the glacier velocity in the Central Karakoram, it was difficult to directly assess the results of the cross-correlation algorithm. Considering the stable properties of off-glacier areas that should not be displaced, the displacements of the off-glacier area have been widely used to evaluate the cross-correlation performance.

The results show that the variations in ice velocities during 1999–2003 are not obvious for most of the studied glaciers in the Central Karakoram. This indicates that the glacier velocities were quasi-stable during the study period. The uncertainty of the velocity results based on the off-glacier statistics was approximately 7 m/year in the four epochs of observation, which is less than one-half a pixel. We find that most of the glaciers on the southern slope flowed faster than those on the northern slope, which might be attributed to the differences in glacier sizes. From the transverse velocity profiles of seven typical glaciers, we infer that basal sliding is the predominant motion mechanism of the middle and upper glaciers, whereas internal deformation dominates closest to the glacier terminus.

By combusting surface vegetation and soil organic matter, wildfires can have a strong impact on tundra environment. Disturbed vegetation may need many years to recover to pre-fire phase or a mature stage. In this study, we quantified changes of C- and L-band SAR backscatter over 15 years (2002–2016) and used them to investigate vegetation regrowth affected by the Anaktuvuk River Fire in Arctic tundra environment. After the fire, C- and L-band backscatter coefficients increased by up to 5.5 and 4.4 dB in the severely burned areas compared to the unburned areas, respectively. Beyond 5 years after the fire, the C-band backscatter differences diminished between the burned and unburned areas, indicating that vegetation level in burned sites had recovered to the unburned level. This duration is longer than the 3-year recovery suggested by optical-based NDVI observations. Moreover, the L-band backscatter remained about 2 dB higher in the severely burned area than the unburned area after 10-year recovery. Such sustained differences are probably contributed by increased roughness of the surface. Our analysis indicates that long records of space-borne SAR backscatter can quantify post-fire vegetation recovery in arctic tundra environment and complement optical observations.

A single Interferometric Synthetic Aperture Radar (InSAR) interferogram provides a measurement of ground movement with centimetric accuracy, and therefore can only detect large ground motions such as those caused by co-seismic slip or volcano eruption. For detecting small amplitude and long term displacement such as post/inter seismic motion or ground subsidence, a time series of interferograms is needed to overcome the errors resulting from the atmosphere, DEM and orbit. In most of the currently available InSAR time series analysis packages, two fundamental assumptions are made, namely that (i) deformation signals are correlated in time, and (ii) atmospheric effects are correlated in space but not in time. Unfortunately, since atmospheric effects can be highly correlated with topography, the second assumption does not hold in most cases. The temporal correlation of atmospheric delays may completely mask or bias the geophysical signals and introduce unpredictable uncertainties on the velocity estimates.

To overcome this, we propose a strategy which (i) employs a generic InSAR atmospheric correction model for each interferogram by using tightly integrated HRES-ECMWF grid model output and GPS ZTD pointwise observations (global and all-time useable in near real-time); (ii) utilizes a series of model performance indicators to identify the date(s) with poor correction performance, including cross validation of ECMWF and GPS ZTD values, observed phase and modelled atmospheric delay correlations and phase standard deviations; (iii) uses an atmospheric phase screening (APS) model using partially corrected interferograms from step (i) to estimate atmospheric delays for each interferogram: higher performance of the correction model and reliable performance indicators will improve the estimation of APS; and (iv) applies the conventional time series analysis approach to extract the mean deformation rate as well as displacement time series. Our experiments with the proposed method suggest it is particularly beneficial for InSAR time series over mountain areas, as the residual atmospheric errors after correction are more likely to be randomly temporally distributed, which allows an easier minimization through time series analysis.

Landslide is one of the major and most frequently occurring geo-hazards around the world. After the 2008 Wenchuan Earthquake in China, a series of large-scale landslides were triggered. Unexpectedness and concealed nature of the landslides significantly increase the destruction degree and difficulty to prevent, exposing people’s livelihoods and infrastructure at risk.

Space borne radar remote sensing could realize macro dynamic monitoring of large-scale landslide hazards and provide an efficient way to obtain landslide surface deformation and spatio-temporal characteristics, hence contribute to early detection and early warning for local disaster risk management. This work shares several radar remote sensing applications in multiple landslide monitoring case studies in Sichuan since 2014 to till date. Long deformation evolutions of these landslides could be retrieved from time series InSAR processing with joint use of multi-platform InSAR observations. To fully investigate and validate the great potential of Sentinel-1 on landslide monitoring in complex terrain mountainous areas, and integrate the radar datasets from Sentinel-1 and TerraSAR-X, this work realized the landslide surface deformation acquisition with multi scales, short time intervals, and long time series, which also verify the great advantage of multi-platform spaceborne radar remote sensing on landslide monitoring. What’s more, combined with in situ measurements and other remote sensing observations for subsequent analysis and validation, space borne radar remote sensing applications could demonstrate great potentials to identify the spatio-temporal characteristics and investigate the failure mechanism for hazardous landslides.

This paper concludes that a comprehensive and effective Earth Observation (EO) based local landslides monitoring could avoid future human and infrastructure loses in the hill and High Mountain regions around the world.

Previous work with TerraSAR-X [1,2] indicated that landslides can be monitored on steep densely vegetated slopes in hilly terrain using sub-pixel offset tracking, sPOT over the Shuping area, Hubei, PR China. In this work, Cosmo-Skymed Spotlight data is employed at a later time period (27 June 2016 to 30 August 2016) to assess whether the mitigation measures employed to prevent further landslip have been effective using both dInSAR and sPOT processing. The results show good agreement between both methods over this 3 month time period with a small progressive motion towards the NNW of magnitude 10cm in azimuth and 5cm in slant-range. This is much smaller than the previous (accumulated) motion of up to 1m/year from February 2015-2016 using SBAS offset tracking [2] and from February 2009–April 2010 and January 2012–February 2013 using sub-pixel offset tracking [1], prior to the mitigation methods. Part of the reason for the success of dInSAR which was next to impossible to apply previously was that the mitigation measures resulted in a substantial portion of bare earth which had much higher phase coherence than the previously vegetated area. A comparison of the three methods are discussed alongside which one is best in different circumstances.

This work was partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL. We thank Space Catapult, Harwell space campus in general and Terri Freemantle, in particular, for arranging the provision of Cosmo-SkyMed data through the CORSAIR010 data grant.

[1] L. Sun and J.-P. Muller, “Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas,” Remote Sensing, 8, 25, doi: 10.3390/rs8080659

[2] L. Sun, J.-P. Muller, and J. Chen, “Time Series Analysis of Very Slow Landslides in the Three Gorges Region through Small Baseline SAR Offset Tracking ,” Remote Sensing, 9, 1314. doi: 10.3390/rs9121314

3D SAR Tomography (TomoSAR) [1-4] and 4D SAR Differential Tomography (Diff-TomoSAR) [8-14] can be used to exploit multi-baseline SAR data stacks to create an important new innovation of SAR Interferometry, to sense complex scenes with multiple scatterers mapped into the same SAR cell. In addition to the 3-D shape reconstruction and deformation solution in complex urban/infrastructure areas [2,4], and recent cryospheric ice investigations [5], emerging tomographic remote sensing applications include forest scenarios [3,6,7], e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, these scenes are often characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to extend characterisations of temporal decorrelation, orbital, tropospheric and ionospheric phase distortion.

We report here on 3D imaging (especially of vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China. The new TanDEM-X 12m DEM is being used to assist co - registration of all the data stacks first and has raised a number of unforeseen challenges, which will be described. Then, atmospheric correction is assessed using weather model data such as ERA-I and compared against GACOS in addition to ionospheric correction methods to remove ionospheric delay. Then the new TomoSAR method with the TanDEM-X 12m DEM is described to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract tomograms (imaging), their 3D positions and motion parameters (deformation). A progress report will be shown on these different aspects.

This work is partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL.

[1] A. Reigber, A. Moreira, “First Demonstration of Airborne SAR Tomography using Multibaseline L-band Data,” IEEE TGARS, 38(5), pp.2142-2152, 2000.

[2] G. Fornaro, F. Serafino, F. Soldovieri, “Three Dimensional Focusing With Multipass SAR Data,” IEEE TGARS, 41(3), pp. 507-517, 2003.

[3] M. Nannini, R. Scheiber, R. Horn, “Imaging of Targets Beneath Foliage with SAR Tomography,” EUSAR’2008.

[4] F. Lombardini, F. Cai, D. Pasculli, “Spaceborne 3-D SAR Tomography for Analyzing Garbled Urban Scenarios: Single-look Superresolution Advances and Experiments," IEEE JSTARS, 6(2), pp.960-968, 2013.

[5] L. Ferro-Famil, C. Leconte, F. Boutet, X. Phan, M. Gay, Y. Durand, “PoSAR: A VHR Tomographic GB-SAR System Application to Snow Cover 3-D Imaging at X and Ku Bands,” EuRAD’12.

[6] F. Lombardini, F. Cai, “3D Tomographic and Differential Tomographic Response to Partially Coherent Scenes,” IGARSS’08.

[7] M. Pardini, K. Papathanassiou, “Robust Estimation of the Vertical Structure of Forest with Coherence Tomography,” ESA PolInSAR ’11 Workshop.

[8] F. Lombardini, F. Cai, “Evolutions of Diff-Tomo for Sensing Subcanopy Deformations and Height-varying Temporal Coherence,” ESA Fringe’11 Workshop.

[9] F. Lombardini, “Differential Tomography: A New Framework for SAR Interferometry”, IEEE TGARS, 43(1), pp.37-44, 2005.

[10] Xiang, Zhu Xiao, and Richard Bamler. "Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm." In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp. 17-20. IEEE, 2010.

[11] F. Lombardini, M. Pardini, “Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data,” IEEE TGARS, 50(4), pp.1117-1129, 2012.

[12] F. Lombardini, F. Viviani, F. Cai, F. Dini, “Forest Temporal Decorrelation: 3D Analyses and Processing in the Diff-Tomo Framework,” IGARSS’13.

[13] Tebaldini, S., & Rocca, F. (2012). Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 232-246.

[14] Huang, Y., Ferro-Famil, L., & Reigber, A. (2012). Under-foliage object imaging using SAR tomography and polarimetric spectral estimators. IEEE transactions on geoscience and remote sensing, 50(6), 2213-2225.

On 3 September 2017, North Korea (Democratic People's Republic of Korea, DPRK) claimed it has successfully tested a hydrogen bomb that could be loaded on to a long-range missile. Seismic readings of 6.3 indicated the test was bigger than any other that has been conducted. Punggye-ri Nuclear Test Site is the only known nuclear test site of North Korea. During the past 12 years, nuclear tests were conducted at the site in October 2006, May 2009, February 2013, January 2016, September 2016, and September 2017. Because of political and other complex factors, it is impossible to obtain any GPS, geology, and field surveying data for direct monitoring and research. InSAR provides a new inspiring research method for underground nuclear deformation monitoring. Here, we use multiple spaceborne SAR data that are ALOS-2, Sentinel-1 and TerraSAR-X to retrieve surface displacement caused by the latest 3 events. The results show that InSAR provides an independent tool to locate and retrieve surface displacement of nuclear tests in North Korea as a supplement of seismic and other methods.

Punggye-ri Nuclear Test Site is located in the northern part of DPRK with complicated land cover, high altitude and mountainous terrain. To achieve homogeneous and reliable measurements in the nuclear test site based on InSAR is really challenging. In mountainous regions, the atmospheric phase screen (APS) can cause serious problems in InSAR observation. From the images we have processed, it is obviously to distinguish atmospheric phase delay. Hence, we conduct APS correction based on WRF (Weather Research and Forecasting) and ECMWF (European Center for Medium range Weather Forecasting) to reduce the APS in D-InSAR processing. Second, the coherence of InSAR interferometric pairs is affected by many factors such as spatial-temporal baseline, wavelength and land cover. We selected multiple interferometric combinations and compared the performance of C-band Sentinel-1, L-band ALOS-2 and X-band TerraSAR-X in InSAR deformation monitoring. The results show that the L-band ALOS-2 data are generally more coherent therefore can provide effective information for surface deformation monitoring. Finally, due to the lack of external data to verify the reliability of InSAR results, we cross-validated the monitoring results of multi-source SAR data with different wavelengths, incident angles, and spatial resolutions aiming to get the robust and trustable result.

Key words：InSAR；Underground nuclear test；Surface deformations；Multiple SAR data；North Korea

Land cover classification is one of the important applications of polarimetric SAR (PolSAR) data. With the development of PolSAR techniques and the increasing demand for PolSAR data in applications, many SAR satellites with full-polarization mode have been successively launched, such as the widely used Japanese ALOS-2 PALSAR-2 (ALOS-2) and The Canadian RADARSAT-2 (R-2) data. China also successfully launched the first civilian SAR satellite with full-polarization in January 2017 - GF-3. However, due to the parameter differences in different SAR sensors, the resolution difference and difference in observation incidence, although in the same area there may be different land cover classification result obtained from different SAR images and the feature selection for classification may be different.

The aim of our study is to improve the land classification accuracy using GF-3, R-2 and ALOS-2 polarimetric SAR data. In this study, we used polarimetric decomposition results including Pauli decomposition H-α-A decomposition, and Yamaguchi decomposition as classification features and analyzed their distributions for different land cover types. After that, we selected the optimal combination of decomposition features as classification parameter for GF-3, R-2 and ALOS-2 respectively, and then carried out the experiments of land cover classification in Beijing. The results showed that for GF-3, using the components of Yamaguchi decomposition as feature parameters performs best, but for R-2 and ALOS-2, using the components of H-α-A decomposition as feature parameters performs best. Moreover, ALOS-2 has the highest classification accuracy (80%), but GF-3 and R-2 have similar classification accuracy (77%). Our study gives some references for the application of GF-3 PolSAR data.

Monitoring Anthropogenic Surface Deformation in Tibetan Plateau Using Sentinel-1 Data

Yunfeng Tian, Jingfa Zhang, Yi Luo, Yongsheng Li

Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration

1 Anningzhuang Road, Haidian, Beijing 100085, China

tel(O) 86-10-62842646 zhangjingfa@hotmail.com

1. INTRODUCTION

InSAR has been one of the key techniques for crustal deformation study. However, attentions should be paid to various nontectonic surface deformation which can also be captured by InSAR, for example, the ground subsidence due to extraction of underground water, which is common nowadays for densely populated urban regions. The presence of localized deformation arising from anthropogenic activities often obscures the movement of the Earth’s upper crust layer; and thus introduces bias in quantifying slip rates of active faults or motion of crustal blocks. In this work, we focus on the deformation related to human activities in Tibetan plateau, with the help the high-resolution Sentinel-1 C-band SAR data collected from late 2014 to early 2018, aiming to figure out various signals in the InSAR deformation map.

2. DATA & ANALYSIS

We used both ascending and descending orbital data of Sentinel-1 A/B satellites which serve as a validation of the signals we observed. The observation interval was 24 days from late 2014 to early 2017 and 12 days since middle 2017.

We processed the data using the GMTSAR software package (Sandwell et al., 2011). We first aligned all other acquisitions to the super master scene that we manually specified; and then generated interferograms for each acquisition pair. Strong decorrelation during the interferometric processing is rare due to the improved orbits of Senetinel-1 satellites and dry climate on the highland of Tibetan plateau, except for areas with strong seasonal frost deformation. The LOS displacement time series were generated using the coherence-based SBAS method which assigns small weights to pixels with lower coherence and produces a continuous deformation map, compared to traditional methods. Finally, the velocity was derived by fitting a straight line to the displacement time series.

3. RESULTS

(1). Ground subsidence due to mining

The Sentinel-1 data captured clearly the ground subsidence due to the mining activity at Zhaxikang (Figure 1), a town located right at the eastern fault trace of the Sangri-Cuona rift in southern Tibet. The maximum subsiding rate reaches ~10 mm/yr during the data period. Locations of construction sites and buildings were identified from the high-resolution multi-spectral images in Google Earth; and they were in good accordance with the distribution of the subsidence area in InSAR LOS rate map.

Figure 1. InSAR LOS rates (descending orbit) for Zhaxikang Mine in Sangri-Cuona Rift. (a) Location map. (b) Rate profile. The width of buffer zone is 5 km at both sides of the profile line. The color of symbol in profile plot represents the distance to the profile line.

(2). Ground uplift due to oil-drilling activity

There are several oil fields along the Mangya-Huatugou thrust fault zone in Qinghai province, China. The oil-drilling work usually involves injecting water down to the deep after extracting underground oil out, to maintain a certain level of pressure. We observed, using Sentinel-1 InSAR time series analysis, several localized uplifting areas in Qinghai province (Figure 2). The maximum uplifting rate can be > 10 mm/yr in the LOS direction.

Figure 2. InSAR LOS rates (descending orbit) for oil field north of Huatugou Town, Qinghai province, China. (a) Location map. (b) Rate profile.

(3) Other types of small-scale deformation or bias

The ground deformation can be also caused by other human activities, such as the extraction of underground water for agricultural irrigation or drinking in urban area. The cause of such subsidence can usually be investigated by checking the locations of villages or towns where high demand of water supply is often needed.

There are also some subsidence places where no obvious anthropogenic activities are presented. These regions often locate in the river basin or in valley between mountain peaks, and also along certain active fault zones. It is difficult to discern the cause of such deformation without help of other sources. Therefore, attentions should be paid when deriving the contemporary fault slip rate of such active fault.

Moreover, subsidence or uplift trend can also be fake deformation signal, especially in mountainous regions with high and steep topography. The situation might get worse, sometimes, in thrust faulting zone where both crustal uplifting and large topographic errors concur.

4. CONCLUSIONS

Our recent work using the latest spaceborne C-band SAR satellites (Sentinel-1 A/B) data demonstrated that InSAR technique nowadays is capable of measuring the crustal deformation at the millimeter level accuracy. Ground deformation related to anthropogenic activity, either subsidence or uplift, can be detected with sufficient confidence for the broad area in Tibetan plateau. However, there is also regional deformation whose origin is unknown or difficult to investigate. We should prefer to not make conclusions on geological issues before figuring out the origins of such observed deformation.

ACKNOWLEDGEMENTS

This work is supported jointly by National Science Foundation of China (41104001), China Earthquake Administration (Y201711), and Institute of Crustal Dynamics (ZDJ2017-29).

REFERENCES

Sandwell, D., R. Mellors, X. Tong, M. Wei, and P. Wessel (2011). Open radar interferometry software for mapping surface deformation, Eos Trans. AGU 92(28) 234, doi:10.1029/2011EO280002.

Landslides are one of the most damaging hazards for human beings and can be affected by multiple factors, including the natural environment and human activities. Since the Three Gorges Dam on the Yangtze River was completed in 2003, detecting and monitoring the landslides in the upstream area has become more important in order to protect human lives and properties. Compared to conventional in situ measurements, various remote sensing techniques have been carried out and found capable of monitoring landslides in difficult terrain over a large area.

This study focuses on monitoring landslides in the Three Gorges Region (TGR), which is characterised by the high humidity, dense vegetation, and steep slopes. Shuping with centre coordinates of 30.996◦N, 110.609◦E and Tanjiahe with centre coordinates of 31.030◦N, 110.509◦E are the two selected study sites. Synthetic aperture radar (SAR) techniques are applied to monitor landslides in these study areas and mitigation works performed to reduce the risks of landsldies in unstable areas. To assess the accuracy of digital elevation models (DEMs) derived from interferometric SAR data, TLS data was acquired by Zhang and co-workers and this is compared with the post-mitigation 6 m TDX CoSSC DEMs, SRTM and ASTER DEMs and DEMs derived from Cosmo-Skymed Spotlight data. The assessment of mitigation is also carried out by comparing two sets of Terrestrial Laser Scanning (TLS) data of the study sites before and after remediation.

The potential and limitations of using different SAR data, especially Sentinel-1 to identify unstable regions for follow-up acquisitions of TerraSAR-X Staring Spotlight and Cosmo-Skymed Spotlight data are described. The potential of TLS techniques which have not been widely used in previous studies will also be evaluated. Furthermore, the effect of mitigation in landslide area is also going to be assessed.

Acknowledgments: We thank Prof. J. Zhang, Dr. Q. Jiao, and Dr. T. Xue from the China Earthquake Administration for their support on our fieldwork.

Together with SAR interferometry (InSAR), Ground-Based Synthetic Aperture Radar (GBSAR) has proven to be a powerful field-based remote sensing tool for deformation monitoring. This work proposes two complete GBSAR data processing chains developed on the basis of advanced InSAR time series analysis algorithms including the Small Baseline Subset (SBAS) concept and the Persistent Scatterer Interferometry (PSI) for continuous deformation monitoring. The developed SBAS chain exploits redundant interferograms and processes consecutive GBSAR imagery unit by unit, which allows the opportunity to investigate temporarily coherent targets and reduces the requirement of computation memory. Contrarily, the PSI chain is more computationally sufficient and is developed to support early warning and rapid decision-making in urgent situations. Two practical applications are given in this work to demonstrate the feasibility of the developed GBSAR data processing chains for continuous deformation monitoring.

The magnitude 7.0 Jiuzhaigou earthquake occurred in August 8, 2017 resulted in a large number of landslides near the Jiuzhaigou panda sea, causing road congestion and seriously affecting the earthquake emergency rescue progress. The landslide caused by earthquake has the characteristics of wide distribution and large quantity. Because of the urgency of the disaster and high resolution of unmanned aerial vehicle (UAV) images the traditional artificial visual interpretation model cannot meet the needs of earthquake emergency response. Therefore, it is necessary to provide an automatic information identification method. Thus, the distribution range of landslide can be identified quickly and accurately.

Based on the deep analysis of the features of remote sensing images of landslide, an automatic information identification model for object oriented analysis is constructed. Firstly, the remote sensing images are segmented at different scales to obtain different levels of image objects according to different types and scales of land objects. Then, SEath algorithm is used to construct feature rule set automatically by comprehensive utilization of the information of spectrum, texture and shape of object at every level, and the distribution of earthquake-induced landslides is identified. After that, taking artificial visual interpretation as a reference, the recognition accuracy and efficiency are evaluated. Finally, the spatial distribution features of landslide body in topographic factor and fracture distribution layer are analyzed by statistical analysis. The overall accuracy is 94.8%, and the Kappa coefficient is 0.827. At the same time, on the basis of the same configuration of the computer, the present method is twice as efficient as that of the artificial visual interpretation method.

The paper also analyzes the earthquake-induced landslide distribution features in elevation, slope, aspect, fault distance and other factors. The correlation between landslide and topographic factors is found. It is concluded that the earthquake-induced landslide in the study area is mainly controlled by the Tazang fault. The spatial distribution rule can provide information support for landslide risk assessment, disaster investigation, prediction and prevention. There are obvious fault effects in the distribution of landslide.

The geologic observations presented above suggest that conjugate strike-slip faults are significant structures along the Bangong-Nujiang suture zone in central Tibet. However, some small fault zones located inside the Qinghai Xizang Plateau, especially in the secondary blocks, have not attracted enough attention. For example, a series of V-shaped conjugate strike slip fault systems between Lhasa block and Qiangtang block. The V-shaped conjugate strike slip fault zone is composed of a series of small fault zones with oblique lines. It is an important product of the neotectonic movement in the Qinghai Tibet Plateau. It plays an important role in the deformation of the East-West extensional tectonic deformation in the Qinghai Tibet Plateau. This study will use InSAR technology to obtain the surface deformation information of conjugate strike-slip faults(Bengco Fault and Dongqiao Fault). The two faults are nearly 300 km in length. Therefore, the wide range SAR data should be selected (for example, Sentinel-1 IW mode SAR width is 250km) and used to obtain the active fault deformation signal in the whole conjugate strike slip fault at one time, which will help the overall analysis of the fault distribution. We will analysis the whole motion characteristics of conjugate strike-slip faults，investigate the strain accumulation of tectonic deformation in time and space. It is helpful to understand the characteristics of a series of conjugate strike slip faults developed in the middle part of the Qinghai Tibet Plateau.

The tremendous development of InSAR missions (e.g., Sentinel-1A/1B, ALOS-2, TerraSAR-X/TanDEM-X, COSMO-SkyMED, RADARSAT-2, and Gaofen-3) in recent years facilitates the study of smaller amplitude geohazard deformation monitoring using longer time series and over greater spatial scale, and this trend is set to continue with Sentinel-1C/D, Gaofen-3B/C, RADARSAT Constellation planned for launch during 2018-2025. This poses more challenges for correcting interferograms for atmospheric (tropospheric) effects since the spatial and temporal variations of tropospheric delay may dominate over large scales and can cause errors comparable in magnitude to those associated with crustal deformation (e.g. landslides, city subsidence and so on). In previous attempts, observations from Global Navigation Satellite System (GNSS) and Numerical Weather Models (NWM) have been used to reduce atmospheric effects on InSAR measurements, but GNSS-based correction models are limited by the availability (and distribution) of GNSS stations, and for NWM-based correction models, there might be a time difference between NWM and radar observations.

To overcome this, we have developed a generic InSAR atmospheric correction model whose notable features comprise: (i) global coverage, (ii) all weather, all time useability, (iii) correction maps available in near real-time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high resolution ECMWF data (0.125-degree grid, 137 vertical levels, 6-hour interval) and continuous GPS tropospheric delay estimates (every 5 minutes) using an iterative tropospheric decomposition model. The model’s performance for InSAR atmospheric correction was tested using globally-distributed interferograms, encompassing both flat and mountainous topographies, mid-latitude and near polar regions, monsoon and oceanic climate systems, achieving a phase precision and displacement accuracy of approximately 1 cm for the corrected interferograms. Indicators describing the model’s performance including (i) GPS network and ECMWF cross-RMS, (ii) phase-delay correlations, (iii) ECMWF time differences, and (iv) topography variations, were developed to provide quality control for subsequent automatic processing, and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.

We have released the Generic Atmospheric Correction Online Service (GACOS) based on the proposed model (http://ceg-research.ncl.ac.uk/v2/gacos/). This service aims to provide InSAR atmospheric correction maps in a convenient way with all features discussed above. The website was released on 6th June 2017 and has received over 10 thousand requests from all over the world. Given the convenience and the real time availability, the website has rapidly responded to recent events such as the Maoxian Landslide (24 June 2017) and the Xinjiang Earthquake (8 August 2017) by providing the atmospheric corrections used in the generation of near real time deformation fields to identify surface damages and contribute to rescue and recovery operations, which have been reported and highlighted by over 20 social medias and organizations.

Seismic indirect economic loss assessment not only has a major impact on regional economic recovery policies, but also it is related to the economic assistance at the national level. However, due to the Cross-regional economic activities and the difficulty of obtaining data, the seismic indirect economic loss are often predicted based on the direct loss of buildings and life lines. Although this method takes into account the impact of production factor stock on economic flows, the effects of disasters on economic activity are neglected and the economic losses in the tertiary industry are seriously underestimated.

The Defense Meteorological Satellite Program (DMSP) provides global images of 4 periods which from morning to night. Since the Operational Linescan System of DMSP (DMSP-OLS) can observe the city night light, it was widely used in population distribution analysis, economic development monitoring and so on. This paper took Sichuan Province as an example to evaluate the impact of earthquake on economic activities on large spatial scale based on DMSP/OLS, and then estimated the recovery of the economy in the disaster area on the view of time and space by analyzing a series of data from pre-event 5 years to post-event 5 years. First, the county economic evaluation model is established. Upon image registration and correction, the nighttime light images are clipped by the county boundaries. Afterwards, counting the nightlight index of all counties, comparing with Sichuan Statistical yearbook, the corresponding relations between nightlight index and economic activities was finally established. Second, a seismic indirect loss Assessment method are presented. Through the analysis of the area and spatial distribution of night-time light around 2008, the spatial migration and change characteristics of economic activities were summarized, which were caused by Wenchuan earthquake. Then a functional relationship between seismic indirect economic loss and night-time light changes of post-earthquake was established. Third, the economic recovery of affected areas was evaluated. The economic recovery of Sichuan Province was evaluated in time and space by comparing with the cumulative growth of night-time light within the 5 years from 2009 to 2013 and the value of per-earthquake.

In this paper, more attention should be paid to the impact of earthquake on social economic activities. Especially in some areas dominated by the service industry, indirect economic losses can better reflect the impact of the disaster on the area. At the same time, it is also hoped that the application of night-time light data in the evaluation of earthquake disaster damage and restoration will also help the government to formulate a policy on regional economic assistance.

Abstract: In November 2017, a strong Mw7.3 earthquake occurred at the Iran Iraq border. The earthquake caused the surface to rise and settlement on both sides of the fault zone, and the maximum displacement of LOS was about 0.85m. The fault rupture begins in the northwest and continues along the fault to the southeast. The coseismic deformation field is retrieved based on ALOS-2 satellite data and Sentinel-1 satellite data. Using the two step inversion algorithm to do the seismic source mechanism inversion, the inversion results are compared with the USGS results and both of them have good coincidence degree, and the inversion of the seismic source mechanism is more fine. It can better analyze and describe the earthquake. The seismogenic structure laid the foundation for studying the fault structure in the area.

Keywords: Iran Iraq earthquake, D-InSAR, Seismic source mechanism inversion

1. research status

In November 12, 2017, a strong earthquake of magnitude Mw7.3 occurred on the Iraqi border in Iran. The epicenter was located at (34.886°N, 45.941° E) and the focal depth was 19km. The earthquake caused more than 500 deaths, thousands of injured, more than 7000 homeless and thousands of houses collapsing, causing huge economic losses and casualties to the local people.

The earthquake occurred at the front of the collision zone between the two large plates - the Arabia plate and the Eurasian plate, along the Iran and Iraq border in the northwest of the Zagros belt. The Zagros thrust belt is a long 1500km fold thrust belt which extends to the west of Iran and extends to northern Iraq. Although Iran and Iraq are earthquake prone areas, there has not been an earthquake above Mw5.0 for many years. The earthquake damage was relatively light on November 12 of 2017, because before the occurrence of the Mw7.3 earthquake, the region had 4.4 levels of pre-earthquake, and most of the people moved to the relatively safe area after the occurrence of the pre-earthquake.

After the earthquake, by collecting the SAR data before and after the earthquake, the coseismic deformation field can be analyzed and processed. Because the acquired SAR data can cover the focal area completely, so the differential interference measurement technique is used to deal with the very clear deformation field after the earthquake. Through the analysis of the coseismic deformation field, it can be seen that the earthquake caused a relative decline of the upper plate and uplifting of the footwall on both sides of the fault, and the maximum displacement of the satellite's flight direction is up to 0.85m.

The two step inversion algorithm is used to estimate the fracture set parameters and the slip distribution of the fault under the constraint of the InSAR result. Firstly, the fault is assumed to be a homogeneous fault model, and the geometric parameters of the fault are calculated. Then the distributed fault model is used to calculate the distributed slip on the fault surface. Using PSOKINV software to inverse the source parameters, the software uses an improved group cooperative stochastic search particle swarm optimization (Particle Swarm Optimization, PSO) algorithm, which mainly solves the optimal solution through a group of random solutions by iterative method.

2. research significance

The Iraq Iran border is located in the collision zone between the Arabia plate and the Eurasian continent plate. The energy of collisions is cumulative and released and then resulting the earthquakes. This area is a shallow source area at most time. Due to frequent devastating earthquake, the Iran government has formulated corresponding building regulations to ensure the safety of the lives and property of the residents. The earthquake magnitude is relatively large, but the casualties are relatively not very serious. It also indicates the necessity of the construction of earthquake resistant buildings and the study at the same time. The seismogenic background and fault structure of the area have important research significance for earthquake disaster prevention in this area.

One of the largest inland earthquakes in Taiwan happened on 21 September 1999, the Mw 7.6 Chi-Chi event. It struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. An improved study of this earthquake will allow better understanding of regional fault properties.

Six ERS images from the descending track 232 and covering the period from 21 January 1999 to 25 May 2000 were processed to investigate the co-seismic deformation. The Interferometric Synthetic Aperture Radar (InSAR) technique was used and via the ESA open-source software SNAP. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experiences the main deformation, is densely vegetated resulting in very low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes which is equivalent to a displacement variation of approximately 30 cm.

We used PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, to determine the fault geometry and the slip distribution. First, the non-linear problem is to use the Particle Swarm Optimization (PSO) for geodetic modelling with the assumption of a uniform slip on a rectangular fault. Second, a joint inversion of InSAR and geodetic data (GNSS and levelling) is realised. The GNSS enables us to get information about the hanging-wall of the fault and to improve the modelling. The slip distribution is determined as a linear problem, optimally-smoothed parameters are obtained.

Sub-pixel offset tracking has been used in various applications, including measurements of volcanic activities, glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging
areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de-correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data. With sufficient point density, we estimate the precision of the SBAS offset
tracking approach to be 2–3 cm on average. The results are demonstrated accord well with corresponding GPS measurements.

The key point of earthquake emergency is to quickly grasp the disaster, that is, earthquake damage assessment, in which the seismic hazard assessment of buildings is closely related to human life and property, which is the main content of seismic hazard assessment. Bad weather will generally follow the earthquake, the polarization of synthetic aperture radar (PolSAR) which is an active microwave radiation source, can penetrate many materials such as the rain,clouds,fogs,etc, thus it can imaging for the disaster areas in all weather and in all time, withal, the acquisition of target polarization scattering characteristic is relevant to the shape and physical property of the ground target, which benefits to ground-object identification, therefore PolSAR is widely applied in earthquake emergency. Compared with early single-polarization and multi-polarization SAR, full polarization SAR obtain the best effect of observation through flexible change of polarization state, it gets more complete polarization information, more abundant measurement information data, stronger performance for features classification. Earthquake damage buildings extraction can be divided into two kinds of methods: using multi-temporal change detection method and single phase post-earthquake image extraction method. The former one does polarization target classification firstly, then constructs seismic difference map to extract the earthquake damage buildings. Its core is to construct the difference graph, common methods such as establishing the polarization likelihood ratio model, defining polarization difference degree through combining scattering difference and power difference, Whishart distance change detection method etc. There is a difference of scattering mechanism between the collapsed buildings and intact buildings in the fully polarimetric SAR image after the earthquake, which is the theoretical basis for the single phase post-earthquake image extraction.
The current methods include: Polarization classification combined with the minimum heterogeneity criteria aggregation of hierarchical clustering algorithm, and template matching based on feature of image retrieval, the introduction of polarization orientation Angle compensation mechanism to improve and complete the structure of the the collapsed buildings and intact buildings.

On 24^th June 2017, a catastrophic landslide suddenly buried the Xinmo village (in Sichuan province, south-western China), resulting in heavy causalities. After the failure, the disaster assessment was in urgent need for the rescue and relief work. Except the field observation or UAV, spaceborn SAR data could provide valuable information to the disaster assessment.

In this study, we proposed a method that used the SAR interferometry coherence map to identify the landslide boundary and source area. With use of Sentinel-1 SAR images acquired on 12^th, June 2017 and 24^th June, 2017 (13 hours after the failure), the landslide boundary and source area were mapped by this method. It was revealed that the source area of this landslide was not at the top of the mountain. Compared with the UAV image acquired on 26^th June 2017, the location of the landslide boundary and source area were consistent.

This results show that, this first Sentinel-1 interferogram, together with its corresponding coherence and amplitude maps, not only helped us identify the source area of this massive landslide, but also assisted with mapping the landslide boundary. Spaceborn SAR data could help the disaster assessment to some degree.

Abstract: The new developments of satellite series in China and Europe are bringing new opportunities to advance the agricultural monitoring with abundant satellite data. The Sentinel and GF are both quite similar high resolution satellite series onboard European and Chinese satellites, respectively. The Proba-V and FY3-MERSI both have quite similar channels and their own advantages in the medium to low resolution satellite.

This project is going to focus on the crop mapping, crop condition monitoring and crop drought monitoring with both satellite data. The Ningxia Hui autonomous region, one of small size provinces in China, was selected as the study area for the crop mapping study with GF and Sentinel optical satellite data. The field survey was conducted in June, 2016 and 2017 as well as in June/July this year. Sen2Agri, an open source system has been developed and demonstrated in various continents and is now considered as an operational system enabling the delivery in near real time of four products for any region in the world. The GF satellite data were also collected as much as possible for the coverage of Ningxia in the growing season. The processing method of GF data is now developing in order to automatically ingest large volume data. Based on the Sen2Agri system, the 2017 cropland product is already quite promising, with an overall accuracy of 86%. The compatibility of GF data need to be evaluated and combined with Sentinel-2 data in order to improve the classification accuracy.

Another two major agricultural production areas in China, North China Plain and Northeast China Plain were also selected for the crop monitoring and crop mapping with both medium to low resolution satellite data. The field surveys were conducted in summer 2016 and spring in 2017. The relevant Proba-V satellite data have been downloaded and a processing code was developed to extract the Proba-V data for the area of interesting. The FY-MERSI process chain has been developed in the past. The classification approach was integrated with Radom Forest, Support Vector Machine and Neural Net. Hopefully the preliminary results may be reported at the symposium.

Keywords: Crop Mapping; Classification; GF; Sentinel, Sen2Agri

Sentinel-2 for Agriculture system for crop mapping along the season in the Ningxia Hui Autonomous region

Mathilde De Vroey¹, Jinlong Fan², Nicolas Bellemans¹, Xiaoyu Zhang³ ,Lei Zhang³, Qi Xu^2,4,QiLiang Li^2,4, Hao Gao², Sophie Bontemps¹ and Pierre Defourny¹

¹ Earth and Life Institue, Université catholique de Louvain, Louvain-la-Neuve, Belgium

² National Satellite Meteorological Center, China

³ Ningxia Meteorological Science Institute, China

⁴ Shanxi Agricultural University, China

Abstract: Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data, such as Sentinel-2A and B, constitute a major asset for this kind of application. The flows of observation data provided by these new sensors introduced new conceptual and processing challenges. The development of the Sentinel-2 for Agriculture system (www.esa-sen2agri.org) was supported by the European Space Agency to facilitate the Sentinel-2 and Landsat-8 time series exploitation for agriculture monitoring in most agricultural systems across the globe.

This open source system has been developed and demonstrated in various continents. Sen2Agri is now considered as an operational system enabling the delivery in near real time of four products for any region in the world, namely (1) a monthly cloud free surface reflectance composite at 10-20m, (2) a binary map identifying annually cultivated land at 10m updated every month, (3) a crop type map at 10m (provided twice along the season) for the main regional crops type and (4) an NDVI and LAI maps at 10m describing the vegetative development of crops on a 5 to 10 day basis. This Sen2Agri system can still be improved and further research is needed to optimize the use of the available processing chains and adapt them to the diversity of agricultural landscapes and biophysical environments.

In the context of a Dragon 4 project, this research aims to validate the system for the Ningxia Hui Autonomous Region in China and to evaluate the precision and accuracy of the crop mask and crop type products (L4A and L4B respectively) obtained from Sentinel-2A and Sentinel_2B. In 2017 a field campaign allowed collecting calibration and validation for the whole irrigated floodplain. The ground dataset have been complemented by delineating additional non cropland samples to cover the whole range of the landscape diversity. The whole study site covers an area of 66500 km² corresponding to 6 Sentinel-2 tiles. The Sentinel-2 images of the same season have been downloaded and pre-processed automatically by Sen2Agri system. The Sentinel-2 surface reflectance time series was then processed to generate a crop mask and then a crop type map from the ground truth data provided by a field campaign in 2017. The 2017 cropland product is already quite promising, with an overall accuracy of 86%. Secondly, the Sen2Agri system generates using a random forest classifier a very accurate and precise classification for the main crop types of the region. Nevertheless, several issues were brought to light. Firstly, Sen2Agri tends to neglect the marginal classes, which are much less represented in the training dataset. Secondly, the crop mask which should be generated without any in situ data, i.e. using ESA’s CCI Land Cover 2010 as default base map, needs be improved either by using the ESA’s CCI Land Cover 2015 or by alternative processing strategies. Based on the crop calendars, the timeliness of the products is still to be discussed to understand how long before harvesting an accurate crop type classification can be obtained.

In addition, this study aims to evaluate the potential contribution of GF images to crop mapping in combination with Sentinel-2. First of all the compatibility of GF data need to be evaluated and combined with Sentinel-2 data. Then the complementarity of both data sources will be assessed in terms of accuracy and timeliness.

Keywords: Sen2Agri; Crop Mapping; Classification; GF; Sentinel

Abstract: Identifying crop type with remotely sensed image is the fundamental step for calculating crop area and monitoring crop growth as well as estimating crop yield in the context of agricultural remote sensing. At present, the method of identifying single or two crops among the major staple crops, such as corn, rice and wheat, was well investigated by researchers, however, the identification of all crop types at the same image is very difficult and needs to be further improved.This study intends to use three kinds of classifiers, such as RF, SVM and NN with the Chinese High Resolution satellite (GF) data, to map the crop types in Ningxia. The crop types are recognized as rice, corn, wheat, clover, grapes, alfalfa, vegetables and greenhouse which are planted in the crop land. The Chinese High Resolution satellite (GF) data in 16m spatial resolution covering the entire Ningxia within the growing season was collected as much as possible. Around 1700 ground truth sample data were also collected In June 2017.

The main steps of the study are as (1) randomly dividing all field sample points into 70% training samples and 30% validation samples; further training more samples with the support of Google Earth image taking the crop phenology into account; adding more samples for non-crop area (Water, Built-up, Bareland, Forest, SolarPanel), and finally the best training sample datasets were obtained after the preliminary classification, self-test, and correction of training samples;(2) three classifiers are tuned to get the optimal classification model. The optimal NN activation function is Hyperbolic; The SVM optimal function is Polynomial with the Degree of Kernel Polynomial and Probability Threshold of 6,0.2 respectively; Number of trees and Number of features for RF were set as 1000 and 4 respectively;(3) the classification accuracy and the efficiency of the three classifiers were compared and evaluated. The accuracy evaluation indexes include Overall Accuracy, Producer accuracy, user accuracy, Kappa and F1 Score. The classification results show that NN>RF>SVM for the efficiency, RF>SVM>NN for the classification accuracy;(4) finally, the crop type map was created. The parameters for the Classifiers applied in this study were tuned specially with the training samples. It needs to be further investigated if those parameters may be extended to other areas and training samples.

Keywords: Classification; Crop type mapping; GF; RF;SVM;NN

Abstract: Crop and land cover classification requires a large amount of ground sample data with the location information in support of the supervised classification of remote sensing images and the accuracy evaluation. Due to the limitation of operating efficiency and cost, the traditional sampling method is not sufficient to support the crop classification at large scale. This study proposed an approach of retrieving the ground truth data from GPS photos taken as the vehicle is moving. The key technical aspects in the study include checking and restoring the photo location information; determining the observing azimuth; shifting the photo taken location to the object location; and interpreting the photos and outputting the data set with the crop type, code and the position information. (1) Checking and restoring the photo location information; Due to the failure connecting to the GPS signal, the GPS camera sometimes was not able to record the position information in the photo file. Another set of GPS recorder may be used to record the position as a complementary. The photos without GPS position may be added the position information later on. The photo and GPS records may be matched by the time but the time difference of two sets of equipment should be taken into account. The time difference may be calculated using the photos with the position information. In case that all photos do not have the position information, a few of typical photos should be checked and identified the position with the Google Earth image and then matched with GPS recorder data. An averaged time difference was further calculated and used as an offset to match both photos and the GPS recorder data. (2) Determining the observing azimuth. Many GPS cameras cannot record the observing azimuth. The observing azimuth may be 0-360 degree for one single sample point. When there are two sample points, the moving direction can be determined by the positions of two points. Adding the angle between the moving direction and the observing direction (close to 90 degree) to the azimuth of moving, the observing azimuth is available. The observation direction, left or right should be recorded as well. (3) Shifting the photo taken location to the object location; the position of the photo file is recorded as the position of photo taken and not the position of the object in the photo. The difference of the position should be compensated when the ground truth data is retrieving. The observing azimuth is available after the previous steps, and then the offset may be calculated with an estimated the distance between the photo taken and the position of the object in the photo. (4) Interpreting the photos and outputting the data set with the crop type, code and the position information. The software was developed to display the photo and select the preset crop types and the crop code. And finally, a text file with all these information was output as the ground truth data set. This approach and the software has been demonstrated for several case studies.

Keywords: Sample; GPS photo; GPS

In this paper, we review the main research work and results in the first phase of our dragon-4 project from kick-off to the mid-term. The contents of this paper include the following three parts: 1) multi-source altimetry data fusion and marine application, 2) sea ice freeboard retrieval by Cryosat-2, 3) Sea surface salinity algorithm based on combined active/passive microwave imagers.

In altimetry marine application, a multi-source satellite crossover data comparison of Sentinel-3 SRAL, HY-2A RA and Jason-2 altimeter were conducted, and the accuracy of the sea surface height of Sentinel-3 SRAL altimeter was analyzed. For the capabilities of the new satellite altimeter data to detect mesoscale eddies, the data fusion of multi-source satellite altimetry including Sentinel-3 and Jason-2/3 and the comparison of mesoscale eddies detection using these fusion data are carried out for the different satellite combinations. The mesoscale eddies observation abilities of Sentinel-3 SRAL were summarized.

In sea ice freeboard retrieval, a new method called Bézier curve fitting (BCF) that can simulate the CryoSat-2 SAR-mode waveform is developed for the retrieval of surface elevation of both sea ice and leads. We apply this method for optimizing the retracking procedure. The results of the retracking procedure for the algorithm was validated using data of the Operation IceBridge (OIB) airborne mission. The mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.5 and 13.8 cm when using the proposed method. This suggests that the sea ice freeboard data obtained from our proposed BCF method has a high accuracy.

In the study of SSS retrieval, based on the combined active/passive observations of the L-band microwave radiometer and scatterometer onboard Aquarius, a method to retrieval the sea surface salinity under the rainy conditions is developed and validated. The L-band GMFs (Geophysical Model Functions) are developed and the radiation feature of the rough sea surface is analyzed. The dependence of the sea surface emissivity (sensitive to both roughness and freshening) on the backscatter (only sensitive to roughness) is obtained and the rain-induced roughness is corrected. The method is applied to the salinity retrieval under rain. The retrieval results (SSS_rc) are compared with HYCOM data corrected by RIM (Rain Impact Model). The standard deviation of SSS_rc is about 0.5 psu and the bias of SSS_rc shows no clear dependence on the rain rate.

Ocean mesoscale eddies transport properties such as heat, salt and nutrients around the ocean with typical horizontal scales of less than 100 km and timescales on the order of a month. Eddies are important in supplying nutrients to coastal zones and the surface ocean where plankton blooms may result.

Mesoscale eddies can be detected through satellite altimetry technique due to depressions formed as they spin. Traditionally, those measurements have been retrieved through satellite with the Low Resolution Mode (LRM) which allowed a limited resolution and distance to the coast. Now thanks to the constant advance, those limitations have been reduced, allowing a better resolution and consequently obtaining data where before it was not possible, thanks to the new satellites generation (Cryosat-2, Sentinel-3 and Sentinel-6) with Synthetic Aperture Radar (SAR) mode.

This presentation will show the results obtained with the in-house isardSAT SAR ocean retracker [RD-1 and RD-2], using CryoSat-2 L1B data over Bohai Sea region. To do so, an analysis of the precision has been carried out on the geophysical retrievals (Sea Surface Height, Sea Wave Height and sigma0) obtained against the ones on ESA L2. This retracker is able to fit ocean-like surfaces as well as more specular-like responses, expected when getting close to the coast, thanks to an additional fitting parameter related to the surface roughness (Mean-Squared Slopes). Some pre-processing stage is required to choose the proper portion of the waveform related to the surface beneath the track, especially when getting close to the coast due to land contamination. DEM/geoid supported retracking operation is exploited in this case.

On further stages the same analysis will be repeated with Sentinel-3 data since the 1.5 years data only became available recently.

References:

[RD-1] E. Makhoul, M. Roca, C. Ray, R. Escolà, and A. Garcia-Mondéjar, “Evaluation of the precision of different Delay-Doppler Processor (DDP) algorithms using CryoSat-2 data over open ocean”, accepted for publication in Advances in Space Research.

[RD-2] Q. Gao, E. Makhoul, M. J. Escorihuela, M. Zribi, and P. Quintana-Segui, “Comparision of Retrackers’ performances over inland water bodies”, in Geophysical Research Abstracts, vol. 20, EGU2018-14298, 2018, EGU General Assembly 2018.

Oceanic mesoscale eddy is an important mesoscale dynamic process in the global ocean, and it is one of the research hotspots in physical oceanography. Mesoscale eddies play an important role in ocean circulation, material and energy transport and other marine dynamics and marine biochemical processes in the global ocean. Mesoscale eddies usually have a spatial scale of tens to hundreds of kilometers and a time scale of more than ten days to several months. Conventional in situ observations make it difficult to achieve complete observations of mesoscale eddies. Satellite altimetry is the important means of mesoscale eddies detection. Multi-source satellite altimetry data fusion provides abundant data for the global mesoscale eddies detection. ESA launched sentinel-3 satellites equipped with Synthetic Aperture Radar Altimeters (SRAL) on February 16, 2016, which provides new data sources for the detection of mesoscale eddies in global ocean.

In this study, the northwestern Pacific Ocean of Kuroshio region is selected as the experimental area and the mesoscale eddies observation abilities of Sentinel-3 SRAL are analyzed, including the independent detection abilities of Sentinel-3 SRAL and the improvement of mesoscale eddies detection abilities by data fusion with other satellite altimetry data. Firstly, Jason-2 altimeter is taken as the reference and Sentinel-3 SRAL data are compared with Jason-2 at the crossover of each other. Then the data of Sentinel-3 SRAL are corrected and uniformed based on their comparisons at the crossovers. The uniformed Sentinel-3 SRAL data are mapped by the spatial-temporal objective analysis method to the sea level anomaly grid data. The mapping errors are analyzed by the comparisons between the grid data and the Jason-2 along track data. The independent detection abilities of Sentinel-3 SRAL are analyzed by the comparison between the grid data and the AVISO MSLA data. On the other hand, through the multi-satellite data fusion of different combinations of Sentinel-3 altimeter and other satellite altimeter such as Jason-2/3, the mesoscale eddies detection was performed based on the merged sea level anomaly data, and the addition of Sentinel-3 SRAL data for the improvements of mesoscale eddies detection abilities by multi-satellite altimeters are concluded. Based on the above analysis, the mesoscale eddies observation abilities of Sentinel-3 SRAL are summarized.

During the last decade the radar altimetry has entered in its golden age as demonstrated by the different number of missions (Jason-2/-3, CryoSat-2, Saral/Altika, Sentinel-3) currently operating and the forthcoming ones (Sentinel-6). The relatively new operational synthetic aperture radar (SAR) mode in CryoSat-2 and Sentinel-3 missions, opens a new paradigm in the capabilities that can offer an altimetric radar mission. In this line, a scientific proposal within the DRAGON-4 tries to exploit the lessons learned from classical 2-D SAR focusing to evaluate the imaging-like capability of delay-Doppler altimetric radar mounted on Sentinel-3 over coastal areas. In this way, the altimetric product gets closer to the conventional SAR imaging data, but in the altimeter case a “strip-like” image is obtained compared to the classical 2D SAR image.

Conventional delay-Doppler processor (DDP) coherently integrates a series of pulses to provide specific Doppler beams focused to a specific location, which after being correctly aligned (compensating for the slant-range variation, among others) provide several looks that can be incoherently averaged, increasing the performance in terms of geophysical retrieval (increasing the signal-to-noise ratio-SNR). The fully focused DDP moves one step ahead and intends to coherently integrate such information to get an even higher along-track resolution with an improved SNR and the available number of beams.

In order to achieve such imaging capability, the azimuth or along-track phase modulation needs to be compensated for. The relative movement between the scene and the satellite creates a chirp-like modulation in the along-track dimension (quadratic phase response), and so an azimuth compression needs to be performed (once range migration has been compensated) to obtain a fully focused SAR strip, analogous to the well-known range compression (where a specific chirp pulse is compressed).

The main objective of the scientific proposal within the DRAGON-4 is to evaluate the potential capabilities offered by the state-of-the-art Sentinel-3 operational synthetic aperture radar (SAR) mode, when extending the delay-Doppler processing (DDP) to a fully focused DDP (FF-DDP) altimetric operation. This will confer the SAR altimetric product a very high resolution (in the order of 0.6 m) of great interest for Coastal Altimetry (being able to get closer to the coastline), providing much higher number of looks that can be averaged to improve the altimetric performance as anticipated by Raney in [RD-1]:

• Development of an efficient fully focused SAR altimetric processor

• Validation of the processor’s chains using point-like target (transponder)

• Evaluation of the capabilities of the fully focused SAR over coastal regions in Chinese seas

The core of this presentation is to show the preliminary investigations carried out in the development of such innovative processor (FF-DDP), pointing out the specificities of such processing compared to the conventional DDP. The initial implemented processing chain will be described, showing preliminary tests on simulated point-targets. ESA Sentinel-6 simulated data will be exploited as testbed, since the flexibility of the Sentinel-6 interleaved mode allows to emulate different acquisition configurations (potentially simulating a closed burst operation, similar to Sentinel-3 or CryoSat-2 modes) and how this may impact the final results.

References:

[RD- 1] Curlander, John C., and Robert N. McDonough. Synthetic aperture radar. New York, NY, USA: John Wiley & Sons, 1991.

This paper presents two work we developed in the past two years. The first is sea ice freeboard retrieval by Cryosat-2 data; and the second is sea ice drift detection by Sentinel-1 SAR data.

For sea ice freeboard retrieval, a new method called Bézier curve fitting (BCF) that can simulate the CryoSat-2 (CS-2) SAR-mode waveform is developed for the retrieval of surface elevation of both sea ice and leads. We apply this method for optimizing the retracking procedure. Retracking points are fixed on positions at which the rise reaches 70% of the Bézier curve peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracker algorithm we compare it to other methods currently reported in the literature, namely the Threshold-First-Maximum-Retracker-Algorithm and the ESA CS-2 L2I. The results of the retracking procedure for the different algorithms are validated using data of the Operation IceBridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.5 and 13.8 cm when using the BCF method, 11.4 cm and 15.6 cm for TFMRA, and 14.5 cm and 15.5 cm for L2I. This suggests that the sea ice freeboard data obtained from our proposed BCF method has a high accuracy.

For sea ice drift detection, in order to solve the problem of high error rate of sea ice drift retrieval that caused by SAR sea ice images have similarities in many areas. And for the purpose of improving the computational efficiency of SAR sea ice drift detection method, multi-scale fast sea ice drift detection method based on principal direction constraint was proposed. Firstly, a pair of full low-resolution SAR image pairs is divided into several sub-image pairs using SAR sea ice image segmentation method based on image matching, and then the main direction of sea ice drift is extracted. Finally, the main direction is used to limit the matching search area of the feature point of SURF algorithm to more accurately extract sea ice drift information of the original resolution SAR. To verify the performance of the fast SURF algorithm based on the main direction constraint. The method is compared with the classic sea ice drift retrieval method. The measured data results show that compared with the traditional SURF algorithm, the matching ratio of feature points is improved by about 10 times, and the calculation efficiency can be increased by about 1 times. Compared with the NCC algorithm, the computational efficiency of this method is dozens of times faster than NCC method, and the image matching accuracy is still higher than that of the NCC method.

Optical remote sensing is one of the most important methods for large-scale observation of ocean internal wave, which has the advantages of wide width and high temporal resolution. However, the optical remote sensing image is affected by cloud, sea condition and imaging angle, which brings difficulty to extract and retrieve ocean internal wave information from the optical remote sensing image. Currently, parameter inversion of internal solitary wave on optical remote sensing image is still based on the inversion model of SAR image. Therefore, a new approach is proposed to establish an experimental system of optical remote sensing to detect internal solitary wave in the laboratory, which aims to explore the response characteristics of optical remote sensing images caused by internal solitary waves. An experimental platform for detecting internal solitary wave by optical remote sensing is constructed by a 3D internal wave flume, a LED light source, CCD cameras and an air blower. The imaging principle of internal waves on optical remote sensing images is quasi-mirror reflection, and LED simulates the parallel incident of sunlight. The method of gravity collapse is used to generate internal waves in the flume of two-layer water. Internal solitary waves with different amplitudes are generated by different collapse heights. Two CCD cameras are used to synchronously observe the surface optical remote sensing images and vertical internal wave images caused by the propagation of internal solitary waves in the same field of view. The mechanism of the internal solitary waves detected on optical remote sensing is compared and analyzed by changing the parameters such as the collapse height, the zenith angle of the sun and the receiving angle of CCD in turn. The experimental results show that the higher the collapse height brings the larger the amplitude of the internal solitary wave. To be more precise, the amplitude is proportional to the collapse height in a certain range. During the process of internal solitary wave propagation, the surface mirror elements are inclined, and the response of the optical remote sensing image corresponds to the vertical displacement of the internal solitary wave one by one. At the same time, stripes are detected on the surface of water by optical remote sensing, which result in the change of gray scale. The relative gray value difference is positively correlated with the amplitude of the internal solitary wave. The larger the amplitude of the internal solitary wave leads to the larger slope of the surface, and finally the greater the change of the light intensity is received by the optical sensor. The research provides a useful reference for quantitative inversion of internal wave parameters on optical remote sensing image.

Keywords: optical remote sensing, internal solitary wave, surface response, relative gray value difference

Mesoscale eddies are rotating coherent structures of ocean currents, which generally refer to ocean signals with spatial scales from tens to hundreds of kilometers and time scales from days to months. Eddies can be found nearly everywhere in the world ocean, and dominate the ocean’s kinetic energy. Over the recent decades, with the advancements in remote sensing satellites and the abundance of in-situ observations data, people find that mesoscale eddies can transport water, heat, salt, and energy as they propagate in the ocean. By combining satellite altimetry and Argo profiling float data, the analysis of eddy three-dimensional structure becomes an important part of studying the oceanic eddy.

The Bay of Bengal, the largest bay in the world, forms the northeastern part of the Indian Ocean. It connects with the South China Sea through the Andaman Sea and the Strait of Malacca. The bathymetric contour of the Bay of Bengal is oriented east-west and the bay presents “n” pattern. As these bathymetric constraints, the local ocean dynamics is complex, with a broad spectrum of processes, from a seasonal reversing monsoon, cyclonic storms, small-scale river plumes, instabilities generated near the continental slope, eddies and large-scale circulation. The Bay of Bengal is a region abundant of mesoscale eddies. In this paper, we analyzed statistical characteristics of mesoscale eddies in the Bay of Bengal based on merged satellite altimetry data as well as Argo profile data.

Firstly, based on satellite altimeter data, the automatic identification method was used to extract the position and shape information of the mesoscale vortices. A series of statistical analysis methods were used to study the statistical characteristics of the mesoscale eddies in the region, e.g., eddy number and lifetime, geographical distribution of eddies, and evolution of eddy properties. Then, based on Argo profile data and climatology data, the eddy synthesis method was used to construct the three-dimensional temperature and salt structure of the eddy in this area.

The Bohai Sea is the southernmost frozen sea in the Northern Hemisphere. The sea ice is a major marine disaster to the Bohai Sea in the winter, which seriously impacts the marine transportation, oil and gas exploitation etc., leading to the great loss to the economical circle surrounding the Bohai Sea. It is very important to evaluate the damaging effects of the sea ice on the marine transportation and offshore constructions (e.g. the oil platform) quantitatively, which has not been studied and analyzed systematically using long-term data so far. In this paper, the quantitative evaluation of the sea-ice disaster in the Bohai Sea will be studied based on the GOCI and Sentinel-1 data. GOCI (Geostationary Ocean Color Imager), to be a payload of COMS satellite launched in Korea in 2010, is the first geostationary sensor in the world, which covers the whole Bohai Sea completely with a spatial resolution of about 500 m of 8 images for one daytime. The Sentinel-1 consists of two satellites (AB) loading C band SAR, which provides single- and dual-polarization data.

The different sea-ice-disaster indexes should be defined for different disaster-bearing bodies. For the marine transportation, its sea-ice-disaster index is equal to multiplying the sea-ice concentration (C_i) by the sea-ice thickness (H_i), which is represented by I₁, that is I₁= C_i × H_i (unit: %∙cm), indicating the sea-ice mass per unit area in physics, and a bigger value means harder breaking ice and less navigable; For the offshore constructions (e.g. the oil platform), its sea-ice-disaster index is equal to multiplying I₁ by the sea-ice velocity (V_i), which is represented by I₂ , that is I₂= I₁ × V_i = C_i × H_i × V_i (unit: %∙cm²∙s⁻¹), indicating the sea-ice momentum per unit area in physics, and a bigger value means a higher extruding pressure and impulse force imposed by the sea ice. In the paper, based on the GOCI and Sentinel-1 data, the sea ice and the sea water are recognized through combining the sea-ice optical and microwave features, which is used to calculate the sea-ice concentration; the sea-ice thickness is retrieved using the sea-ice optical information of GOCI; the sea-ice velocity is extracted through the GOCI geostationary characteristics and the maximum cross correlation method (MCC); based on the sea-ice parameters of the sea-ice concentration, thickness, and velocity, the two types of the sea-ice-disaster indexes I₁ and I₂ can be calculated, which are used to evaluate quantitatively the spatial distribution features and the interannual variations of the sea-ice disaster in the Bohai Sea in the period from 2011 to 2018. The research results will quantitatively shows that the period from 2011 to 2018 is conventional ice condition, which is relatively heavy in 2011 and 2013. The sea-ice-disaster indexes I₁ and I₂ will quantitatively illustrate the space-time distribution features of the sea-ice disaster for the marine transportation and the offshore construction, which can satisfy the request of the sea-ice disaster prevention and reduction and provide the reference of the monitoring and research on the sea-ice disaster.

Bohai sea is located in the northern latitude 37 ° 07 '- 41 ° 0', eastern longitude 117 ° 35 '-121 ° 10', the Bohai sea and its surrounding their rich oil and gas resources, there are a number of important large fields. However, due to the Accumulated ice that drift ice accumulates and accumulates will cause various degrees of impacts on shipping traffic, marine structures, and fishery production in Bohai. It may even cause serious disasters and bring incalculable losses to China's economy. There is an urgent need for studies related to sea ice drift monitoring. The daily drift of sea ice in Bohai sea is changing rapidly, The daily drift of sea ice in Bohai sea is changing rapidly, and the revisit period of microwave scatterometer, microwave radiometer and SAR is longer, and it cannot meet the demand for sea ice drift monitoring in Bohai Sea. The "GF-4" satellite is China's first high resolution geostationary optical remote sensing satellite. It has the unique advantages of short imaging time interval (20s) and high resolution (50m), and is more suitable for sea ice drift tracking. However, the effect of GF4 satellite image product's own error on sea ice drift is rarely researched at home and abroad. Therefore, it is necessary to carry out error analysis of sea ice drift tracking of GF4 satellite imagery.

This paper mainly uses GF4 satellite imagery to carry out the sea ice drift monitoring error analysis with time intervals of 1 minute, 3 hours, 4 hours, and 24 hours. Firstly, the orthorectification of the 28 image data available from August 2016 to March 2018 in the Bohai Sea area was carried out. Then we select the sea-land edge points as control points, and registration of two images which have the same time interval. Next, we recorded the marked same name points which searched from the bottom of Liaodong bay, east of Liaodong bay and west of Liaodong bay respectlly. Statistics the direction and frequency of land point offset sub-regionally and created the rose plots. And maked histogram of the offset and offset angle of land point.

The results show that, when the time interval is 4 hours and 24 hours, the dominant migration direction in the three regions in Liaodong bay is east; when the time interval is 1 minute, the dominant migration direction in Liaodong Bay bottom and Liaodong Bay west coast land is south, Followed by east and southeast respectively; the dominant migration in Liaodong Bay East Coast is north, followed by east; When the time interval is 3 hours, the dominant migration direction in west of Liaodong Bay, bottom of Liaodong Bay and east of Liaodong bay are east, west and south respectively, followed by southeast, east, southeast respectively. The land offset in three regions is major centralized distribution in a range which is from 60m to 80m. That is to say, the offset of land is basically equal to 1.2 times of pixels, and the maximum land offset is less than 2 times of pixels. Through statistical analysis, it can be seen that with the increase of time interval, the land offset will not change much. This study also paves the way for the study of the drift of sea ice.

Iceberg is a potential threat to maritime transport, drilling platforms and shore facilities in high latitude. In existing research iceberg is mainly detected by Constant False Alarm Ratio(CFAR) according to brightness variation between icebergs and background in Synthetic Aperture Radar image. The performance of iceberg detection strongly depends on the accurate statistical modeling of local background clutter measurements, which is also focused on in existing research. In order to accurately detecting iceberg especially iceberg edge, an iceberg detection method combining image segmentation and CFAR algorithm is proposed in this paper. The image is firstly segmented by watershed algorithm which can accurately determine edge of iceberg，the segmentation areas (aggregation of similar pixels) are used for subsequent processing instead of pixels to reduce speckle noise and improve operational efficiency. The statistical characterization of local background including sea ice and water is modeled accurately and the iceberg is finally detected by CFAR.

The Bohai Rim Region is an important economic circle in China. In winter the freezing of sea ice in the Bohai Sea has caused serious impacts on sea shipping and sea-related production, resulting in accidents such as channel blockage, ship damage, and oil platform collapse. The monitoring of sea ice in Bohai Sea is of great significance and has now become the routine operational work of the marine management department. The first geostationary orbit satellite launched by China on December 29, 2015—the High Resolution Four Satellite (GF-4), with an orbit altitude of 36,000 kilometers, equipped with a visible light sensor with 50 m resolution, 400 M-resolution mid-infrared sensors, and gaze cameras with a width greater than 400 km. It can perform a wide range of observations on about one-third of the Earth's surface and can obtain multiple observations within a day. The spatial resolution of the GF-4 is an order of magnitude better than that of the existing geostationary-satellite GOCI. At the same time, it has the characteristics of high temporal resolution of geostationary satellites. It is very advantageous to detect changes in the ice conditions of sea ice in the Bohai Sea. Within one hour, the drift and change of sea ice in the Bohai Sea are relatively fast. Therefore, the better spatial resolution of GF-4 is very suitable for Bohai Sea ice monitoring, play an important role in monitoring and forecasting sea ice conditions in the Bohai Sea.

This article based on the GF-4 Bohai Sea ice imagery studied the optimal wavebands for the identification of sea ice and seawater: use the 29 pictures remote sensing images of the Bohai Sea between 2017 and 2018 obtained from the GF-4 to extracted 377 samples of sea ice and seawater samples respectively, and normalize the spectral values of the five bands of sea ice and seawater samples respectively; There are a total of 57 species band combinations that single band, two bands combination(adding, subtracting, dividing) ,Band 2 (B2) and band 4 (B4) and band 5 (B5) three bands combination(only analysis of 208 sea ice and sea water samples in 2017: the recognition of sea ice and seawater in single band is relatively good, with B2, B4 and B5). Using graphic method and feature distance method to analyze the ability of these band combinations to identify sea ice and seawater. The graphic method is to display the spectral values of sea ice and seawater corresponding to each band combination in a scatter plot, by visual interpretation of scatter plots, qualitative analysis of sea ice and seawater aliasing (total number of mixed sea ice and seawater samples/samples total 377) is less than 10%, think this band can identify of sea ice and seawater; The feature distance method selects the Bahman distance and the Euclidean distance for quantitative analysis of the ability of each band combination to identify sea ice and seawater . Research results show that, In the graphic method, the B2/B4/B5 has the lowest rate of aliasing, which is 5.31%; In the feature distance method, the feature distance of B2/B4/B5 has the largest calculation result, the Euclidean distance calculation result is 8.89336, and the Bahrain distance calculation result is 91.84793; Shows that the analysis results of the two methods are consistent ,The conclusion is that the qualitative and quantitative analysis of the band results is consistent, B2/B4/B5 is the optimal band combination for GF-4 sea ice and seawater identification. The conclusions obtained in this paper have important significance and reference value for GF-4 sea ice monitoring.

Snow depth derived from passive microwave (PMW) with spatial resolution of 25 km is difficult to describe the snow condition and has been generally overestimated in the Qinghai-Tibetan plateau (QTP) which is characterized by patchy snow cover. The main reason for such overestimation is the contribution of low-temperate snow-free ground to the brightness temperature difference between K and Ka bands (TBD). Therefore, in this study, a new method combining MODIS snow cover fraction (SCF) and land surface temperature (LST) and PMW brightness temperature data is developed to derive snow depth at cell size of 0.005^o. MODIS’S SCF is used to identify the snow cover portion of a PMW pixel, and its LST is applied to calculate the TBD contributed from snow-free portion of the PMW pixel. Result shows that after removing such contribution, the TBD value of the PMW pixel is more reasonable and the derived snow depth exhibits relatively smaller errors. The bias and RMSE are 23.4% and 37.3%, respectively, as compared with the 48.5% and 60.5% before such contribution was removed, when using meteorological station observations (2003-2010) as reference. They are 22.5% and 76.1%, respectively, compared with 54.9% and 107.0%, when using field observations (March 2014) as reference. The remaining bias (i.e., overestimation) is mostly due to the TBD contribution (up to 10K) from the low temperature of frozen ground underlying the thin and patchy snow cover (or area). This phenomenon also exists in other cold areas, such as eastern Russia, although not as obvious as on the QTP, because the overall thin and patchy snow cover in QTP could not shield the underling soil from the impact of low air temperature.

Meltwater from rock glaciers could provide a relevant contribution to water supply especially in dry regions. Moreover, rock glaciers could have serious hazard potentials when located at or above steep slopes or when damming lakes. Existing investigations about rock glaciers in High Mountain Asia indicate that the landforms are abundant but information is rare for the Tibetan Plateau and the northern slopes of the Himalaya.

We compiled a rock glacier inventory for the Poiqu region (28° 17´N, 85°58´E) – Central Himalaya/Tibet. The mapping was mainly based on optical images from Sentinel 2 and Google Earth. In addition, we used a hillshade calculated from the new 8 m High Mountain Asia DEM where we filled existing gaps with the 12 m TanDEM-X DEM. Rock glaciers were identified based on their characteristic shape and surface structure. Additional information on the occurrence and activity of the rock glaciers was provided by the InSAR technique using ALOS-1 data.

The preliminary results of the inventory reveal 362 rock glaciers covering an area of about 42 km². The largest one has an area of 2 km² and four have an area of around 1 km². The rock glaciers are located between ~4100 m and ~ 5700 m with a mean altitude of ~5040 m a.s.l.. The mean slope of all rock glaciers is close to 20° (min. 8°, max. 35°). Most of the rock glaciers face towards the Northeast (19%) and West (18.5%). Our study indicates that 158 rock glaciers can be classified as active. We also found rock glaciers damming lakes and infrastructure (streets), which could be threatened by the instability from rock glaciers above.

Future work will concentrate on additional datasets like Sentinel 1 for the improvement of the rock glacier inventory in the Poiqu region.

A group of tongue-shaped periglacial landforms near Jingxian Valley (35°40´N, 94° 00´E) in eastern Kunlun Shan have been reported 20 years ago and classified as “Kunlun-type” rock glaciers due to their unique morphology and slow creeping rates. However, the nature of the northern slopes has remained contentious and later been interpreted as gelifluction deposits. Moreover, the kinematic features of the landform had not been fully investigated or understood. Here, we use satellite SAR interferometry to quantitatively characterize the spatial and temporal changes of the surface movement of these landforms. Five ALOS-1 PALSAR images acquired between 2008 to 2009 over eastern Kunlun Shan area have been used to generate three interferograms to measure the surface movement velocities of the landform. One interferogram records the kinematic information during winter/early spring and the other two are averaged to represent the surface movement during summer. We observe that: (1) the eastern slope is also active with a summer velocity of 20­–60 cm/yr (in the satellite line-of-sight direction, same for all the velocities reported here); (2) the northern lobes moved at 20 to 50 cm/yr in summer, which are much larger than the field measured velocities of less than 3 cm/yr near the front as reported in a previous study conducted from 1980 to 1982; and (3) both the northern lobes and eastern slope are inactive during winter.

The seasonal acceleration in movement of rock glaciers during summer have been observed and, in some cases, no movement can be detected in winter. Gelifluction processes can also trigger seasonal velocity variations. However, creeping rates during summer are typically smaller than 20 cm/yr in cold and dry climate conditions such as Jingxian Valley. Several key pieces of evidence, such as (1) the widespread and relatively fast movement and (2) the large-scale tongue-shaped morphology, suggest that the northern lobes are rockglaciers. The lack of oversteepened fronts presumably results from gelifluction processes of the fine-grained deposits covering the slopes, which smooths out the surface of the landform. The eastern slope shows a similar pattern of seasonal surface kinematic variations to the northern lobes. However, the different morphologic characteristics of the two groups of targets indicate different types of periglacial landforms. With a relatively high surface moving speed and large geometry scale, the northern lobes are unique parts of the alpine permafrost in Eastern Kunlun Shan, representing a mixed type of rock glaciers and gelifluction deposits.

There are approximately 1200 lakes whose area is greater than 1 km² on the Tibetan Plateau (TP), the highest plateau of the world. These lakes are important indicators of environment change because they integrate the basin-wide variations of climate, cryosphere and ecosystems. Previous work on lake changes on the TP during the last several decades have focused on surface area because volume variations need information about lake levels — either in-situ or by satellite altimetry data. However, in-situ measurements are very limited and altimetry data such as ICESat-1 and CryoSat-2 are available at a short term. Here, we present an innovative and robust method that combines digital elevation data and multispectral images to estimate water volume changes for the two largest lakes on the TP, Selin Co and Nam Co from the 1970s to 2015. In addition, the contribution of glacier mass changes to lake volume change between 2000 and 2015 is examined at lake-basin scale using existing estimates based on ICESat and ASTER DEM data. The lake storage changes for Selin Co and Nam Co between 1970s and 2015 are 18.8 and 7.0 Gt. Combining with previous studies of glacier mass balances, the lake volume increase from glacier contribution for two largest lakes, Selin Co and Nam Co, are approximately 28% and 8%, respectively. The future research will extend the estimates of glacier contribution to early 1970s combining declassified satellite data, SRTM and TanDEM-X DTMs and other data sources.

Rock glaciers are important periglacial phenomena in high mountain regions. The Yarlung Tsangpo River basin in the Tibet Autonomous Region of China, the distribution of rock glaciers and their hydrological and environmental effects are poorly understood in the basin. We have produced the first comprehensive inventory of rock glaciers in the Yarlung Tsangpo River basin through the fine spatial resolution satellite data that is freely available on Google Earth, we identified 372 rock glaciers based on their morphological features. We then generated attributes of these rock glaciers including the average length, width, slope, orientation, average elevations of the upper and lower limits, their average elevation and median elevation, as well as hypsometry of each glacier. Through statistical analysis, we show that rock glaciers are situated between 4307 and 5814m a.s.l, with the mean minimum elevation at the front estimated to be 4427 m a.s.l, and the mean maximum elevation at the front estimated to be 5731 m a.s.l. The majority (53%) were found to have a northerly aspect (NE, N, and NW).It provided an important basis for our further understanding of the rock glacier in the Yarlung Tsangpo River basin.

Glacier mass balance, as a direct indicator of climate change, attracted increasing attention in the field of cryosphere. Measuring the region-wide glacier mass balance plays a significant role in understanding the response of glaciers to climate change and their influence on water resources and glacial hazards. In this paper, we derived the mass changes of glaciers according to the geodetic method based on three DEMs representing status of glaciers in different years. These DEMs were generated from declassified Hexagon images (1973-1980), SRTM DEM with 30 m resolution (2000) and TerraSAR-X/TanDEM-X data (2012). All DEMs were co-registered by eliminating errors resulted from horizontal difference and removal of the elevation anomalies. We also took into account errors in the glacier boundary delineation, the seasonal fluctuation in surface elevation, snow and ice density and penetration depth of radar beam. Our expected result is that glaciers mass budgets are negative in the Mt. Xixiabangma during the past period.

This study is aimed to studying electromagnetic anomalies before main shock and aftershocks of Jiuzhaigou earthquake (M=7.0, August 8, 2017) and comparing the phenomana observed by the ground-based CSELF network and by the SWARM satellites. The Jiuzhaigou earthquake (M=7.0, 13:19:46, August 8, 2017, UTC) occurred in the Sichuan province. The CSELF network consists of 30 stations across two main seismic belts in China, in which 15 stations are located in Sichuan and Yunnan provinces. Each station records five alternate EM filed components (Ex, Ey, Hx, Hy, Hz) in a frequency band of 0.001-1000Hz. The data have been recording for about 3 years using the network. In the study on the EM anomalies before earthquakes, the following steps are involved. The first step is to choose the quality data from huge amount of the observed data. Secondly Top-Down Level analysis is carried out for identifying and catching anomalies in the data based on the different time and different frequencies either for Network data or for SWARM data. The final step is to investigate the relationship of anomalies with earthquake events.

Through analysis on the huge amount of Network data, the time series from August 6^th to 12^th is good meaning on obvious disturbance noise existing in the data. But some anomalous phenomena appeared before main shock and successive 18 mid-strong aftershocks. Except for three aftershocks the anomalies are featured as (1) anomalous pulsating clustering of EM fields appeared simultaneously at several stations, e.g., at the station of JianGe in Sichuan, at the LiJiang and JingGu stations in Yunnan with 205km, 770km and 1110km distances to the epicenter, respectively. The smooth variation of EM fields appeared between adjacent clusterings. (2) The pulsating clustering started at about 12-13 minutes before the earthquake and lasted for about 10 minutes and recovered at about 3 minutes before the shock. (3) Individual pulse in the clustering has a period of about 60-80s. (4) The amplitude of maximum pulse in the clustering is about 70% higher than the background value of corresponding EM component. The anomalous pulses seem to be decreased with the distance to epicenter. The clustering form is similar to those of the Pc3-Pc5 pulse clustering, but the observed anomalies by SWARM appeared in the different time section. The clustering is also not caused by co-seismic waves (P and S waves). It is postulated that the anomalies before each shocks may be caused by the shocks during the process of earthquake generation.

Acknowledgement: Tang J, Chen X, Zhan Y, Xiao Q, etc. from IGCEA joined the CSELF observation. The study is supported by NDICC (15212Z0000001) and NSFC (41374077）.

Yaxin Bi¹, Vyron Christodoulou¹, George Wilkie¹, Zhao Guoze², Ming Huang¹ and Han Bing²

1) Faculty of Computing, Engineering and the Built Environment, University of Ulster, Co Antrim, United Kingdom

2) Institute of Geology, China Earthquake Administration, Beijing, China

Email: y.bi@ulster.ac.uk

Electromagnetic (EM) field is sensitive to the stress of plate tectonics, and changes in the duration of earthquake preparation, the changes would cause electromagnetic emission to transmit into ionosphere, which could be observed by satellites. There were a number of studies conducted on DEMETER satellite data, the results shown that precursory phenomena were captured before earthquakes. Piša D, et al. (2013) carried out a rigorous statistic analysis on the 8400 earthquakes that have a magnitude of 5 or greater than 5 and electromagnetic perturbations within 440 kilometers of the earthquake epicenters, the results revealed that the probability of electromagnetic attenuation was very high before 0-4 hours of the events. Le et al. (2015) conducted a survey on studies of ionospheric abnormal behaviors before some great earthquakes and reported ionospheric disturbance to different extent.

This study reports the progress of development of anomaly detection algorithms and their application to analysing the SWARM satellites data and discovering precursory phenomena before large earthquakes. The study selects three earthquakes, i.e. the Ludian earthquake with a magnitude 6.2 occurred on 3 August 2014 in Yunnan, China, the Peloponnese earthquake with a 5.7 magnitude occurred in southern Greece on 29 August 2014 and the Eketahuna earthquake with a 6.8 magnitude occurred in Peru on 20 January 2014. For each earthquake, a 1000kmx1000km study area is defined and divided into 9 grids. For each grid a time series data is generated, as a result each area has 9 sets of time series data. The duration of the selected data is from 25th March 2014 to 24 January 2015, which were recorded by the Vector Field Magnetometer (VFM).

Four different methods are used to generate time series data, i.e. first day, middle, predefined and average points in order to investigate artificial anomalies introduced when generating time series data. The three detection algorithms of CUSUM-EWMA, Fuzzy-inspired and Hot-SAX are specifically selected to address the unknown nature of the EM signals with respect to their duration, their amplitude and frequency changes, they are applied to analyse 27 sets of time series data in order to detect anomalous phenomena before these three earthquakes. The detected results show various phenomena, and no specific patterns can be discovered, which are closely related to the times of occurrence of these earthquakes. From this studying results, the interesting points are observed as follows:

• the algorithms are capable of detecting anomalies, the CUSUM-EWMA provides good anomaly detection, but it struggles in different anomaly cases.
• the satellites observe the whole earth, their revisit time and orbit reveal a serious constraint in generating sufficient and high quality time series data for earthquakes.
• difficulties appear in selecting the fuzzy membership functions (MF) that depend a lot on the form of the input signals

References:

1. Piša D, Němec F, Santolik O, et al. 2013. Additional attenuation of natural VLF electromagnetic waves observed by the DEMETER spacecraft resulting from preseismic activity. J Geophys Res, 118: 5286–5295.
2. Huijun Le, Jing Liu, Biqiang Zhao, Libo Liu. Recent progress in ionospheric earthquake precursor study in China: A brief review, Journal of Asian Earth Sciences. Volume 114, Part 2, Pages 420-430.

In this work we report the development of a system pipeline for the analysis of the Swam satellite electromagnetic data. Our objective is to provide a streamlined functional tool for analyzing electromagnetic data over regions and investigate the relationship of precursory electromagnetic signals to seismic events. The process of the system pipeline consists of three stages of data extraction, data pre-processing and anomaly detection. The first stage provides an interactive interface, allowing users to define study regions and periods of seismic events, and then extract data from the Swarm CDF data archive. The second stage consists of four different pre-processing methods, including the first arrival sampling within regions, middle points and average value, which address the data sparsity problem and the cause of artificial anomalies in a defined region. The last stage offers a range anomaly detection functions underpinned with a variant of the basic CUSUM-EWMA statistical algorithm, fuzzy-logics inspired method, and HOT-SAX method, etc. To demonstrate the potentials of the tool in applying different kinds of algorithms under an anomaly detection scope of electromagnetic sequential time series data, we select a seismic event under scrutiny is in Ludian, China and occurred on 03/08/2014, and present the usefulness of our approach and pinpoint some critical problems regarding satellite data that were identified.

With the support the Wireless Electro-Magnetic Method (WEM) project, we built the first Control Source Extremely Low Frequency (CSELF) continuous observation network which include 30 electromagnetic stations in Beijing Capital Area (BCA) and Southern Section of North-South Seismic Belt in China for the artificial and nature source singles recording. The instruments collect the data 16 seconds every ten minutes with sample rate of 256Hz and then the whole day’s data was analyzed with the method of Flourier transformation and the FFT length was set as 4096. After that we can get the spectrum with the frequency range from 3Hz to 48Hz and the Schumann resonance and six harmonic frequencies can be observed clearly, however, the peak frequency of Schumann resonance are slightly different due to the stations’ location and other factors.

By comparing the long-term observation data of the same station, we can see that 1.The annual variation of the spectrum in Schumann resonance frequency is basically the same as that of other frequency bands. the intensity of the magnetic field is strong in summer, low in winter and the law of long term change conforms to the half cycle sine wave form. From January to July, the power spectral density is increasing, while from July to December, the spectral density of the vibration amplitude decreases.2. The power spectrum of Schumann resonance frequency is smaller than that of surrounding frequency, that is, its variation is more concentrated. 3.For one station the peak frequency of Schumann resonance shift during time. Take Lijiang as an example, and the peak frequency of the first Schumann resonance frequency of the north to south magnetic field component in one year is between 7.5Hz and 7.9Hz, and tends to low frequency in winter and summer, and to high frequency in spring and autumn.

Accurate and recursive maps of crops at the field scale is of great interest for the farmers to optimize the agronomical practices by minimizing the intra-field yield variability. Sentinel 2 and Venμs free available multispectral satellite imagery, with a spectral configuration optimized for vegetation and a revisit time less than 5 days, opens up new perspectives in the framework of precision agriculture. These satellite data can lead to the development of higher level products both at the farm and field scale such as yield estimation and prediction maps, crop nitrogen (N) balance assessment, weed patch detection and bare soil properties estimation (e.g. soil texture and organic matter).

The objective of the study, which was conducted in the framework of the Topic1 of the Dragon4 #32275 program, is to carry out a systematic work to explore the optimal configurations and possible alternative set-ups of algorithms allowing to exploit the full potential of S-2 and Venμs sensors in terms of their spectral and spatial resolutions.

To this aim, the Maccarese farm located in Central Italy, which is the second largest Italian private farm with about 3500 ha of agricultural fields (typically 10 ha or larger) was selected as study area. This because the farmers were equipped of yield maps machinery and in 2018 Venμs new generation satellite started programmed acquisitions on this study area (ADEPAMAC project).

Freely available toolboxes such as BV-Net (Baret et al., 2007), ARTMO or SNAP (ESA) were used for semi-automated retrieval of biophysical parameters through radiative transfer model inversion, i.e. by optimizing LUT-based inversions.

The biophysical canopy variables, expressing the crop ability to intercept and convert solar radiation also reflecting the vigour of the plant canopy, were retrieved using both S2 and Venms sensors when near acquisitions occurred. In particular, LAI and Chl were retrieved and compared in terms of accuracy with respect to the ground truths (LAI measured with LAI2000 and Chlorophyll with Dualex) acquired during 4 different field campaigns in the winter wheat growing season in the study area.

Moreover, these analyses were coupled with the analysis of different spectral indexes/procedures in order to assess the capability of the red-edge bands in retrieving leaf/plant pigments (i.e. chlorophyll, carotenoids) and the Leaf Area Index (LAI).

The results show that both S2 and Venμs satellite sensors are able to retrieve with a good accuracy crop biophysical variables such as LAI and chlorophyll, both by using retrieving algorithms from RT codes or spectral indexes/procedures. Moreover, the few available experimental results suggest that the use of multi-temporal remote sensing data can significantly improve estimation of canopy biophysical variables.

Introduction

Yellow rust (Puccinia striiformis) is one of the most severe epidemic diseases for winter wheat in China, annual affected area of yellow rust on winter wheat is greater than 6.7 million ha during 2000-2016. Pathologically, the development of yellow rust comprises five spore stages, including uredospores, appressorium, basidiospores, spermatia, and aeciospores, and the foliar biophysical variations are critical indicators for tracking the progressive host-pathogen interactions at different stage. The interaction of electromagnetic radiation with plant leaves is governed by their biophysical constituents, and response to infestations. However, current researches for agricultural pests and diseases monitoring generally are premised on a given infestation stage. Hyperspectral- and multispectral- continuum observations permit the acquiring of the host-pathogen processes within entire epidemic stages of rust on wheat. Tracking the progressive infestation is complicated by the following aspects: 1) the pre-existing VIs are not disease-specific, 2) these VIs nonlinearly varying as the increase of pathogen attack hard to express progressive spectral variations caused by the infestation process, 3) spatial and spectral redundancy have to be taken into account. The continuous wavelet transformation (CWT) have been proven to be a promising tool to capture subtle spectral absorption characteristics in detection of foliar constituents. The CWT-derived wavelet features are capable of decomposing raw spectral data into different amplitudes and scales (frequencies) in order to facilitate the recognition of subtle variation (or signals) and held the potential on retrieving foliar constituents.

Objective

The contributions of this paper are: 1) to identify a wavelet-based rust sensitive feature set (WRSFs) for characterizing the spectral changes caused by rust infestation at different stages, 2) to provide insight of the proposed WRSFs into specific leaf biophysical variations in the rust development progress, 3) to evaluate the performance of the proposed WRSFs as input feature space for tracking rust progress and retrieving rust severities on hyperspectral and multispectral images, such as sentinel-2. These continuous goals depend on a multi-temporal hyperspectral observation which covered entire circle of rust infestation.

Study Area

A series of in-situ observations were conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science (39°30’40’’N, 116°36’20’’E) in Langfang, Hebei province, China, from jointing season (20^th April) to milk-ripe season (25^th May) of winter wheat in the 2017. We selected a cultivar, ‘Mingxian 169’, due to their susceptibility to yellow rust infestation, which were inoculated with yellow rust by spore inoculation in 13^th April. The concentration levels of 9 mg 100^-1 ml^-1 spores solution was implemented to naturally generate infestation levels (all treatments applied 200 kg ha^-1 nitrogen and 450 m³ ha^-1 water). Each treatment and repeat occupied 220m² of field campaigns. The makeup of topsoil nutrients (0 ~ 30 cm deep) in the experiment sites were as follows: soil organic matter 1.41~1.47%, nitrogen 0.07~0.11%, available phosphorus content 20.5~55.8 mg kg^–1, and rapidly available potassium 116.6~128.1 mg kg^–1.

Methodology

A wavelet-based technique for extracting the shape-based reflectance spectral feature was proposed based on the implementation of continuous wavelet transform (CWT), which provides a powerful method for detecting and analyzing weak signals at various scales and resolutions, and for analyzing multidimensional hyperspectral signals across a continuum of scales

A total of 9 hyperspectral VIs that have been reported as the rust-related proxies in relevant researches were selected and compared with the extracted WRSFs for disease detection. These adopted VIs have proved to (1) sensitive to crop growth: modified simple ratio (MSR); (2) pigment variation: structural independent pigment index (SIPI), normalized pigment chlorophyll index (NPCI), anthocyanin reflectance index (ARI), and modified chlorophyll absorption reflectance index (MCARI); (3) water and nitrogen content: Ratio Vegetation Structure Index (RVSI), (4) photosynthetic activity: photosynthetic radiation index (PRI), physiological reflectance index (PHRI); and (5) crop disease: yellow rust-index (YRI), aphid index (AI), and powdery mildew-index (PMI),

In the past, various supervised classification frames have been developed to detect plant stresses from remotely sensed observation, such as Artificial Neural Network (ANN), Decision Trees (DT), and Support Vector Machines (SVM). In this section, linear discrimination analysis (LDA) model and SVM model were used as the example frames for testing and comparison of the performance of WRSFs and VIs on detecting the progressive rust development under the linear and non-linear conditions, respectively.

Conclusion

This study proposed a new shape-based WRSFs from the wavelet transformed reflectance spectra of winter wheat leaves inoculated with yellow rust. The identified wavelet features in WRSFs is capable of capturing and tracking rust related biophysical indices (CHL, ANTH, NBI, and PDM) in progressive host-pathogen interaction. The performance of WRSFs as input feature space for DR estimation and lesions detection of rust was evaluated and compared with traditional VIs that sensitive to disease infestation. Our findings suggest that the WRSFs-PLSR model provide insight into specific host-pathogen interaction during rust development progress, which is more effective than VIs-PLSR model in DR estimation. For the rust lesion detection, the WRSFs-based feature space performed best for both LDA and SVM classification frame. Unlike the traditional techniques, the CWT based technique for WRSFs extraction is simple and straightforward to reflectance spectral signals. No predetermination of wavelength delimitation or other parameterization is needed. The practical WRSFs has great robustness for better understanding the pathological progress in tracking the rust development with hyperspectral data from various sensors. This method may be even applicable to others plan-pathogen systems.

Wheat powdery mildew is one of the serious crop diseases which affect the food safety of China. Integrating multi-source information (Earth Observation-EO, meteorological, etc.) to support decision making in the sustainable management of wheat powdery mildew in agriculture is demanded. With the development of satellite and sensor, the amount of available remote sensing data has increased dramatically. However, the high cost of filed survey data in regional level causes the inconsistency between the number of filed survey samples and the amount of remote sensing data, thus affecting the accuracy of crop monitoring model. In this study, a framework of transfer learning, TrAdaBoost, was used to monitoring the distribution of wheat powdery mildew in study area using the auxiliary field data from another region. This study was carried out in western Guanzhong Plain, Shaanxi province and the auxiliary field survey samples were acquired from south-central part of Hebei province. The Landsat-8 OLI images were used to extract vegetation indices which could indicate the growth status of wheat and meteorological data including Climate Hazards Group InfraRed Precipitation with Station data and the MODIS/Terra Land Surface Temperature and Emissivity (LST/E) product were used to describe the environmental conditions of wheat from booting stage to grain filling stage. With these features, TrAdaBoost with weak learner of Support Vector Machines was used to develop the wheat powdery mildew monitoring model. To evaluate the effect of auxiliary data, a referenced model which only used the samples available in study area was developed using Support Vector Machine. The experimental results suggested that two models provided similar disease distribution patterns over the study area while TrAdaBoost had significant higher accuracy than Support Vector Machine when too few samples available in study area and it could give better or comparative performance with the increase of available samples. When all the samples became available, TrAdaBoost had a higher overall accuracy (80%) and kappa coefficient (0.66) than Support Vector Machine (overall accuracy was 75% and kappa coefficient was 0.59). All these results reveal that transfer learning could be used to monitor the occurrence of wheat powdery mildew.

Key words: wheat powdery mildew; transfer learning; TrAdaBoost; disease monitoring;

Biophysical variables such as Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are of crucial importance for a range of agricultural, forestry and ecological applications. Many approaches have been developed to extract these variables from satellite images. Broadly, these methods can be categorized as Statistical (Parametric and Non-Parametric), based on Radiative Transfer Physical Models and Hybrid approach. Recent studies show that hybrid methods, which combine Radiative transfer modeling (RTM) with intelligent Machine Learning (ML) algorithms may overcome many of the disadvantages of the other methods, for example, by being fast, more robust and having higher generalization capabilities. Different ML methods have been used with data simulated from RTM to extract LAI and LCC, but only Neural Networks (NN) have been successful to reach to the operational use. Although, the retrieval of biophysical variables using NN has been widely applied, the algorithm has some drawbacks: 1) low accuracy at higher values of LAI due to the saturation effect in RTM simulation and NN inversion algorithm and 2) unpredictable results, if training and test data are deviating from each other, even slightly. In the recent years, a suite of kernel-based algorithms have been explored to estimate LAI and LCC from satellite images, and have been shown to be a valid alternative to NN. For example, Kernel Ridge Regression (KRR) algorithm is simple for training and it provides competitive accuracy as compared to NN. Gaussian Processes Regression (GPR), performs well in terms of computational costs and speed, it provides higher accuracies, and uncertainty intervals. However, if trained on large simulated datasets from RTM, these algorithms are computationally expensive and this limits their operational use. Active Learning (AL) techniques have been proposed to reduce the size of the input training data generated from RTM, as they only selects the most informative cases from a large dataset, based on either the uncertainty or diversity of the data points.

In this work a study is presented on the comparison of different hybrid approaches for the retrieval of biophysical variables from Sentinel-2 data, based on the training of kernel-based ML algorithms with simulations from RTM. As a benchmark, the results obtained from these methods are compared to those from the biophysical processor implemented into the ESA Sentinel Application Platform (SNAP) software, which relies on the training of NN with PROSAIL simulations. The same simulated training set, sampled and optimized using active learning techniques is tested with different kernel based machine learning algorithms for the retrieval of biophysical variables from Sentinel-2 images acquired over European and Chinese test sites. Ground data measurement campaigns, on the wheat crop, have been carried out in Maccarese (Italy) and Shunyi (Beijing, China) in correspondence with Sentinel-2 acquisitions, to verify the accuracy of the algorithms. The results of this comparison study allow to obtain useful information in terms of quantitative statistical assessment, as well as of the practicality, computational time and cost of emerging hybrid approaches.

The productivity of wheat, including grain yield and quality, directly determines its market price and related agriculture policies. Currently, most prediction models of wheat yield and grain protein content (GPC), one parameter of grain quality, by remote sensing are a little mechanism and difficult to expand at interannual and regional scales. The objective of this study is to use Hierarchical Linear Model (HLM) integrating hyperspectral data at anthesis and environmental data to achieve yield and GPC prediction at interannual scales. Eight experiments during seven growing seasons, during 2008/2009, 2010/2011, and 2012-2017, were carried out. Fifteen spectral indices from hyperspectral data correlated with GPC at anthesis were calculated, and environmental information including daily radiation, maximum and minimum temperature, and rainfall was mean counted one month before anthesis at each growing season. Results suggested that Standardized leaf area index determining index (sLAIDI) and spectral polygon vegetation index (SPVI) showed the best correlation with yield (r = 0.77) and GPC (r = 0.38), respectively. The estimation of yield and GPC based HLM model considering environmental variations showed higher accuracy (Yield: R² = 0.75 and RMSE = 0.96; GPC: R² = 0.58 and RMSE = 1.21%) than the simple linear models (Yield: R² = 0.60 and RMSE = 0.97; GPC: R² = 0.13 and RMSE = 1.73%). A high consistency between the predicted values and the measured values with HLM method was shown at different years. Overall, these results in this study have demonstrated the potential applicability of HLM model for yield and GPC prediction at various years.

This study estimated wheat aboveground biomass (AGB) based on GF-3 synthetic aperture radar (SAR) data. In the Gaocheng research area, We collected ground. Including: biomass data (aboveground fresh biomass, aboveground dry biomass, fresh ear biomass, dry ear biomass) and soil moisture data. The collected ground samples data and the corresponding SAR data were used to establish biomass estimated models, that were water cloud model and polarized water cloud model. Finally, the effects of different biomass types, ROI window sizes and location accuracy about the biomass estimation result were analyzed. The result shows that the water cloud model is the best wheat biomass estimation model. However, the polarized water cloud model can replace the water cloud model for wheat biomass estimation when there is no soil moisture data. The final results provide a reference for estimating wheat biomass based on GF-3 data.

Introduction

In this paper an approach towards the automatic olive tree extraction from satellite imagery is presented. Automatic olive trees detection at a large geographical scale and their health status evaluation are necessary in order to provide an inventory map that may help in a better planning of the management activities and for predicting the olive production. The olive planted areas can be determined more precisely and in a short time through high resolution satellite images at low cost.

In a previous paper the possibility to detect olive groves affected by xylella was demonstrated by considering several fields and observing the behavior of the annual variation of the NDVI. Therefore, unchanged and changed (eradicated) olive groves show a distinctive behavior due to the change in the presence of olive trees as consequence of the trees eradication requested to stop the spread of the disease. In the plot where the olive trees have been eradicated the NDVI standard deviation (STD) increases significantly due to the reduced importance of the evergreen olive trees in determining the behavior of NDVI with respect to the background characterized by the presence of grass or shrub. However, this analysis was carried out on several plots selected taking into account the sites where the presence of the disease was evaluated with different results. In some cases it was not necessary to remove the plants, in other cases the olive plants were eradicated. The delineation of the plots was carried out manually by visual inspection of the image. In fact, the use of polygons of olive groves taken from the 2012 Corine Land Cover map (CLC 223) was not effective due to the variability of the surface cover types within such polygons. Thus, we decided to devote this paper to develop a technique suitable to identify olive groves with higher accuracy than CLC. Since olive groves are characterized by a significant variability in terms of tree density our classification introduce also a way to assess the olive trees surface cover fraction. A vegetation classification method based on plant biochemical composition and phenological development through the year is presented.

Objectives

The enhanced classification of olive groves has been achieved by utilizing EO data, developing new algorithms, and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information. Concerning this last point. The research activity follows two main approaches:

- Improving the classification of the agricultural areas devoted to olive trees, starting from what has been made available from the Corine Land Cover initiative;

- Developing an approach suitable to be automated for counting trees by using very high spatial resolution images in areas at high risk of infection.

The analysis starts from the following observations and hypothesis:

- the CLC polygons, corresponding to the class 223, outline areas containing fields characterized by different distribution density of olive trees;

- an accurate classification of the olive groves is required for applying further analysis aiming at the assessment of the plant tress status potentially due to diseases;

- the assessment of the olive groves status is based on the analysis of a temporal series of NDVI (Normalized Difference Vegetation Index) taking into account that olive trees exhibit values almost constant of NDVI during the year.

Data and study Area

The study area corresponds to the Province of Lecce, located in the Southern part of the Apulia Region. 77 Sentinel-2 (MSI) cloud free images of the area of interest covering the period February 2015 – July 2017, were found in the ESA database (tale T33XE). The research activity covers the Province of Lecce, that is the Italian area most affected by the Xylella fastidiosa disease causing a rapid decline in olive plantations, the so-called olive quick decline syndrome (OQDS, in Italian: complesso del disseccamento rapido dell'olivo). By the beginning of 2015 it had infected up to a million trees in the southern region of Apulia (Lecce Province).

Methodology

The tree density in the olive groves varies significantly and significantly influences our ability to detect them. The lower is the density, the greater the contribution of the underlying and surrounding vegetation to the detected spectral signature. The changes of leaf Carotenoid (Car) content and their proportion to Chlorophyll (Chl) are widely used for monitoring the physiological state of plants during development, senescence, acclimation and adaptation to different environments and stresses.

Then we developed an automatic olive tree detection technique based on tracking the NDVI and CRI2 (Carotenoid Reflectance Index 2) indexes development during the year. The chlorophyll/carotenoid index CRI2 was specifically implemented to help detecting sparsely populated olive orchards. Decisional rules selected on the basis of NDVI and CRI2 characteristics, retrieved over different test, sites were implemented to carry out the classification. The first objective was to clean the CLC 223 polygons removing the areas that shows no olive coverage at all; the second objectives consisted of adding new olive areas not previously classified to the CLC 223 polygons.

Then, the segmentation of the classified areas has been carried out by using NDVI maps of the area of interest and a mathematical morphology approach. The processing procedure has been implemented in Matlab. The procedure to estimate the fractional cover of olive trees within the previously classified areas foresees the following steps:

1. apply a segmentation function (gradient filter) to each CLC polygon;
2. Compute foreground markers (morphologic operators). These are connected blobs of pixels within each of the objects.
3. Compute max, min, average and standard deviation of NDVI in each polygon;
4. Estimate FCOV (Fraction of trees in each field).

Based on the above described procedure, the accurate olive tree distribution is retrieved for the area of interest. An automatic and continuous olive groves monitoring system is currently under development; this system will be capable of tracking the olive groves status, detecting and evaluating the presence and effects of stressing factors such as pests infestation, specific disease affecting olive plants, and general adverse environmental conditions due to climate changes.

Abstract: Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. The combination of REDSI and the optimized thresholding method proved to be a powerful method for detecting YR infection in winter wheat at regional scales. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.

Keywords: yellow rust; Sentinel-2 MSI; red edge disease stress index (REDSI); winter wheat; detection

Objective

The aims of this study were to: (1) select the most sensitive bands of multispectral data (Sentinel-2) for identifying healthy wheat and both slight and severe yellow rust infection in winter wheat; (2) propose a new red-edge multispectral vegetation index for discriminating yellow-rust-infected winter wheat from healthy wheat; and (3) map yellow rust infection using realistic Sentinel-2 satellite imagery at regional scales.

Data and study Area

A series of in-situ canopy hyperspectral observations were conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science (39°30’40’’N, 116°36’20’’E) in Langfang, Hebei province, China, at grain filling stage on 15, 18, and 25 May 2017. The winter wheat cultivar known as ‘Mingxian 169’ was selected, the yellow rust pathogens infected the winter wheat through an inoculation process (spore solution concentration of 9 mg 100⁻¹ mL⁻¹) according to the National Plant Protection Standard (NPPS) on 13 April 2017.

Field surveys of wheat yellow rust infection were conducted in Chuzhou and Hefei, Anhui Province, China (32°6.36′–32°38.02′ N, 117°6.09′–117°49.10′ E) on 9 May 2017 at grain filling stage, where winter wheat is considered to be one of the area’s major crops. Two simultaneous Sentinel-2 multispectral images were acquired on 12 May 2017, from https://scihub.copernicus.eu/, and the full coverage image was mosaicked by two images acquired simultaneously.

Methodology

We integrated the field canopy hyperspectral data based on the sensor’s RSR function to simulate the multispectral reflectance of Sentinel-2 to assess its potential for winter wheat yellow rust monitoring and detection. B4 (red), B7 (Re3), and B5 (Re1) were the most sensitive bands for identifying winter wheat infected with yellow rust through random forest model. Consisting of these three sensitive bands, Red Edge Disease Stress Index (REDSI) was proposed.

REDSI and nine SVIs (NDVI, VARI_green, EVI, RGR, NDVIre1, NREDI1, NREDI2, NREDI3, and PSRI) were selected for testing and comparison of their performances on detecting the wheat yellow rust at the canopy and the regional scales, respectively. The overall accuracy (OA) and kappa coefficient were used to evaluate the classification and discrimination performance of FLDA.

Conclusion

In this study, we developed a new index, REDSI (consisting of Red, Re1, and Re3 bands), for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale. Compared with other common spectral vegetation indexes, REDSI has excellent performance in detecting and monitoring yellow rust in winter wheat at the canopy and regional scale, with the overall accuracy of 84.1% and 85.2%, respectively. Furthermore, the index had to be continually validated with other diseases and other cultivars to guide agriculture precision management.

Introduction

Powdery mildew (Blumeria graminis) is one of the most destructive foliar diseases of winter wheat and occurs in areas with cool or maritime climates. The infection of this disease results in a reduction of yield and quality of wheat. According to the statistics of National Agricultural Technology Extension and Service Center (NATESC) of China, the average outbreak area of powdery mildew was recorded to be as high as 10 million ha in the last 17 years. Powdery mildew can infect winter wheat in the whole growth period. Generally, powdery mildew hypha recovers growth in the first decade of February, the beginning development period of the disease is in March and the disease occurs generally in April and greatly in May. However, the current studies on crop diseases were mostly based on one single growth phase image in late stage of disease development, did not consider the temporal change characteristics of diseased crops. Otherwise, remote sensing-based time series were successfully used for crop phenology detection and, crop classification, crop area estimation, etc.

Objective

the objectives of this study were: (1) to analyze the relationship between normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series and winter wheat powdery mildew, (2) to monitor the occurrence severity of winter wheat powdery mildew through NDVI and EVI time series, (3) to map the spatial distribution of winter wheat powdery mildew occurrence severity, and (4) to assess the performance of the proposed disease monitor models.

Data and study Area

A total of 42 field survey points were collected in 10th May 2014 in western Guanzhong plain in Shaanxi Province, China, which area is a commonly occurred area of winter wheat powdery mildew. In order to field investigation of diseases occurrence match with the spatial resolution of the remotely sensed image, five 1m×1m representative ranges were relatively uniformly selected in a 30m×30m spatial extent. The central latitude and longitude of each point were recorded by sub-meter differential GPS. The specific survey included wheat growth condition, height and occurrence severity. The occurrence severity was reclassified there levels which include normal, slight and severe to reduce the difficulty of monitoring.

Methodology

A monitoring model for monitoring of powdery mildew occurrence severity based on the NDVI and EVI time series was established. The model almost contained all the critical disease infected information in whole growth period of winter wheat.

Totally, 18 remote sensing images were acquired, for the period from 16^th November 2013 to 9^th April 2014. In order to reduce the impact of cloud cover, three sensors’ data (include WFV sensor data of Gaofen-1 satellite, CCD sensor data of the environment and disaster reduction small satellites and the OLI sensor data of Landsat-8) were chosen. The NDVI and EVI which sensitive to green vegetation and is often used to calculate the quantity and viability of surface vegetation and adverse effects of environmental factors such as atmospheric conditions and soil background were selected to develop time series for disease monitoring, and compared the performance of models with NDVI and EVI time series, respectively.

A significant level of noise was present in the temporal signatures due to clouds, aerosols and snow, etc. Hence, in order to ensure the quality, the NDVI and EVI time series need to be smoothed by discrete wavelet transformation (DWT) before being used, which is an orthogonal function which can be applied to finite group of data and has been widely used in the fields of signal processing and image compression. Support vector machines (SVM) exhibits many unique advantages in solving small sample, non-linear and high-dimensional pattern recognition problems and largely overcomes the problems of dimensionality disaster and over-study. And SVM has been widely used in text recognition, face recognition, gene classification, time series prediction, risk assessment, image classification, etc. In this study, SVM was used to construct monitor model with NDVI and EVI time series, and a leave-one-out cross validation method was used to testing and evaluate the performance of NDVI and EVI time series on monitoring the disease occurrence severity due to the small total sample size.

Conclusion

This study developed a monitoring model of disease occurrence severity based on NDVI and EVI time series features. The difference between NDVI and EVI time series curves of winter wheat infected with different disease severities was obvious. The NDVI and EVI time series were both able to discriminate the disease severities. Both the accuracies of the NDVI and EVI time series models suggested that the NDVI and EVI time series preformed good in quantifying disease severity. Compared the NDVI time series models, the EVI time series achieved a higher monitor accuracy for powdery mildew occurrence severity on winter wheat. Furthermore, the monitoring models with NDVI and EVI time series de-noised by DWT outperformed the models with original NDVI and EVI time series. These results reveal that the disease severity monitoring models based on satellite image time series can be a reference for field disease management.

Crop counting is of great importance for harvest yields estimating, to detect crop stress emergencies, to locate plants and tree species, among others. At the same time, in case of commercial orchards the tree identification is essential for the subsidies given by the European Union. Lately, and thanks the easy accessibility to remote sensing datasets provided by an increasing number of Earth Observation satellites (as Landsat, Sentinel, Planet, or GeoFen constellations) and Unmanned Aerial Vehicles (UAVs), the possibility to include these datasets to retrieve value added information, reducing time and simultaneously covering wide areas is a reality.

In particular forest management applications, and those related to tree identification, give an essential input in order to increase the efficiency of orchards management and the potential detection of pest outbreaks scenarios.

During last years, several projects have been carried out focused on forest monitoring, and some of them in particular related to analyze and control the diffusion of pests, as the case of Xyllela Fastidiosa (Xf), which is one of the most dangerous plant bacteria worldwide, causing a variety of diseases, with huge economic impact for forestry and the environment. In particular in Italy, more than 30,000 trees are under monitoring, and almost 2% of these resulted positive for Xf. And in this context the development of methodologies for tree detection, and a rapid anomaly detection is one of the main challenges, whose outcomes will support further surveys and inspections.

Among the most important international initiatives recently carried out, we could mention the Xf-Actors and POnTE projects, funded by the European Union within the Horizon 2020 EU Framework. Also in this context, the AMEOS project, sponsored by an ESA-Dragon agreement, aims to bring together cutting edge research to provide pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation-EO, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture. In particular the project team also explores the possibility of using remote sensing images to assess the evolution of diseases on permanent crops (olive groves, vineyards).

For the tests carried out in the present work the area of interest is located in Puglia region, Italy, widely infected with Xf. The region has been divided by the regional authorities in Infected, Containment and Bearing areas. The surveillance of big areas requires the assistance of a remote sensing approach, that has proved its effectiveness in detecting infected trees. For each of these areas, in this work a comprehensive imagery dataset taking into account different spatial and spectral resolutions have been processed. The algorithm has been tested with a set of satellite images (Landsat8-OLI, Sentinel-2, QuickBird, Planet, Gaofen-1), and imagery acquired with the MicaSense-RedEdge sensor on board a UAV SkyRobotics-VTOL-SF6 platform.

Crop counting complexity depends on the quality and resolution of the image, the spacing between trees and the algorithm implemented. In this case, FX (Feature Extraction) algorithm performance has been tested in a wide range of scenarios ingesting the procedures with images of spatial resolutions from 30 m. to less than 5 cm, and spacing trees from 4m. to 10m. The algorithm can be summarized in the following main subtasks: Image calibration, Morphological Filtering, Binary thresholding, Rule based Segmentation, Regionalization, Crop Counting and Geodatabase ingestion.

While with Landsat-8 and Sentinel-2 imagery feature extraction (FX) algorithms are able to detect, extract and count the number of trees in most of aged well point-distributed orchards, FX algorithms applied on QuickBird and UAV imagery are capable to achieve the main goal with a high level of effectiveness, and in case of UAV imagery even in recently planted fields, where the dimension of the objects is within the centimeter scale.

An orchard database automatically enriched with the geo-location of detected trees, will be a valuable resource to update existing orchards monitoring systems, essential to detect unexpected anomalies with the assistance of information extracted from other sources (i.e. on-field sensors, meteorological station, or plant-water-transport sensors.).

This study estimated wheat Aboveground biomass (AGB) based on GF-3 synthetic aperture radar (SAR) data. In the Gaocheng research area, and 40 ground samples data were collected. Including: biomass data (aboveground fresh biomass, aboveground dry biomass, fresh ear biomass, dry ear biomass) and soil moisture data. The collected ground samples data and the corresponding SAR data were used to establish biomass estimated models in the Gaocheng Research Area, that were water cloud model and polarized water cloud model. Finally, the effects of different biomass types, ROI window sizes and location accuracy on the biomass estimation result were analyzed.
The result shows that the water cloud model is the best wheat biomass estimation model. However, the polarized water cloud model can replace the water cloud model for wheat biomass estimation under the situation of without soil moisture data. The final results provide a reference for estimating wheat biomass based on GF-3 satellites.

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