Conference Agenda

In this project we study air quality over China using satellite observations, especially their spatial and temporal variability. The latest years of satellite observations of NO2 and SO2 concentrations have been used to infer NOx and SO2 emissions to augment our earlier trend analysis.
In addition, a new SO2 inversion algorithm for use with TROPOMI SO2 observations is under development. Since February 2018 NO2 observations became routinely available from the TROPOMI instrument on S5p. For this a snow-cloud differentiation method has been developed to drastically increase the number of observations over snow surfaces, like Norther China in winter and regions in the Himalaya mountains.
Both the SO2 and NOx 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.

Solar Global Radiation And Its Variation Mechanism At A Subtropical Site In China

Jianhui Bai¹ Gerrit De Leeuw² Larisa Sogacheva² Ronald Van Der A³ Yimei Wu¹ Xiaowei Wan¹

1. LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

2. Finnish Meteorological Institute, Climate Research Unit, Helsinki, Finland

3. Royal Netherlands Meteorological Institute, De Bilt, The Netherlands

Abstract Measurements Of Solar Radiation And Meteorological Parameters Were Carried Out At A Subtropical Pinus Forest Site In China From May, 2013 To December, 2016. An Empirical Model Of Solar Global Radiation Has Been Developed For Different Atmospheric Conditions And The Calculated Solar Global Radiation Is In Agreement With The Observed. This Empirical Model Was Used To Calculate The Attenuation Of Solar Global Irradiance In The Atmosphere Caused By Absorbing And Scattering Substances. Sensitivity Analysis Shows That Solar Global Radiation Is More Sensitive To Changes In Water Vapor Absorption Than To Changes In Scattering Factors, S/Q (S And Q Are Solar Direct And Global Radiation, Respectively). Aerosol Optical Depth (AOD) Is An Important Parameter To Improve The Understanding Of The Scattering Of Solar Radiation. The Relationship Between The Attenuation Factor (AF) And AOD Was Determined And Used To Estimate AOD.

Key Words: Solar Global Radiation, Absorbing And Scattering Factors, Energy, AOD, Climate.

Sentinel 5 Precursor (S5P) satellite was launched into a polar orbit in October 2017, carrying the
TROPOMI instrument. S5P has sun synchronous orbit with an equator crossing time of 13:30 LT.
TROPOMI achieves an almost daily coverage, due to the wide swath width of 2600 km.
The near-real-time (NRTI) ozone total column is based on the two step DOAS approach, consisting of a
slant column retrieval and an iterative AMF-calculation. In this study we present tropospheric ozone
columns based on total column from S5P and stratospheric column based on 4D-Var assimilation of
Aura MLS ozone profiles. To avoid potential errors we use d only cloud free TROPOMI data.
After a brief introduction of the methods the data will be compared to other tropospheric data
satellite data i.e. HCHO and NO2 S5P. The study will focus be on the first year of TROPOMI
observation over East Asia.

Dose-Response Functions (DRFs) are a very important model 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. In current international literature, available DRFs use ground-based air pollution and climatological data to model materials’ deterioration. This limits DRFs use only to areas where the necessary ground based data, is available. In a previous study were presented the results of the attempt to develop a new type of DRFs that modeled the deterioration – degradation of materials, especially carbon steel and limestone, using only satellite data. The term “Satellite Sensed Data-Dose Response Functions (SSD-DRFs)” was proposed for this new kind of DRFs. This study presents the preliminary results of the attempt on the development of SSD-DRFs to assess the deterioration – degradation of zinc and modern glass materials. Modern glass is used to monitor materials surface soiling rather than as structural material. The development of SSD-DRFs provides the opportunity to monitor cultural heritage monuments in areas where ground-based data are not available, extends the use of satellite data by introducing a completely new field of implementation and is largely in line with the European Commission’s effort supporting the Cultural Heritage preservation and management by using Copernicus data and services.

Analysis of PM_2.5 readings taken at the US embassy in Beijing since 2009 reveals that winter haze over North China Plain (NCP) peaked in 2012 and 2013 and there was an improvement in air quality until 2016. The variation of wintertime PM_2.5 from 2009 to 2016 is influenced by both emission changes and meteorology conditions, and in this study we quantified the relative contributions from these two aspects. The sensitivity simulation by the GEOS-Chem model suggested that the emission reductions over NCP in 2013-2017 caused a 10% decrease of the regional mean PM_2.5 concentration in 2016 winter compared to the 2012 winter level. We removed the emission influence on PM_2.5 concentration to get the PM_2.5 that influenced by meteorology (met-influenced PM_2.5). For the met-influenced PM_2.5, compared to the original observation, the percentage of clean days (daily PM_2.5 concentration less than 75 μg/m³) decreases while that of the polluted (daily PM_2.5 concentration between 75 μg/m³ and150 μg/m³) and heavily polluted (daily PM_2.5 concentration between 150 μg/m³and250 μg/m³) days increases. However, proportion of the extremely polluted (daily PM_2.5 concentration exceeds 250 μg/m³) days stays unchanged, even if the emission reduction is doubled, indicating that the extremely polluted situation over NCP is dominated by the meteorological conditions, and the emission control from 2013 to 2017 has little effects on the extremely polluted days. We developed an effective haze day index (HDI) to represent the weather conditions conducive to haze days. HDI is constructed based on the normalized near surface meridional wind (V850), temperature difference (δT) between near surface (850hPa) and upper atmosphere (250hPa), and the relative humidity on 1000hPa (RH1000). HDI correlates well with daily PM_2.5 with the correlation coefficient of 0.65, and is skillful to detect 72% of the severe haze days (daily PM_2.5 concentration exceeds 150 μg/m³), ranging 48% in 2014 winter to 94% in 2012 winter. The components of HDI can also reveal the relative importance of the three meteorological variables in haze days. On average, the anomalously high meridional winds is the main cause of severe haze these years, while in 2012 winter, the relative humidity favorable for secondary aerosols formation is the largest contributor to haze.

In this poster, we present ground based remote sensing activities at Hefei, China. It includes site report for both TCCON and NDACC-IRWG observations and some research activities based on these observations.

Abstract: A ground-based low-resolution (0.5 cm⁻¹) Fourier Transform Spectrometer (FTS), the EM27/SUN, is used for determining the total column XCO2 and XCH4 of the atmosphere by analysing direct solar radiation. A ground-based high-resolution Fourier transform spectrometer (FTS) station has been established in Hefei, China to remotely measure CO2, CO and other greenhouse gases based on near-infrared solar absorption spectra. The observations of low-resolution FTS were compared with the temporally coinciding on-site measurements taken with a high-resolution FTIR spectrometer. EM27 captures the seasonal variation of CO2 and CH4.Also, there is a offset between the values of EM27 and FTS, ranging from about 1.35ppm to 1.55ppm for XCO2, and about 7.01ppb to 9.74ppb for XCH4, respectively. The observation results demonstrate the ability of the portable EM27 spectrometer as a promising addition to the TCCON FTIR sites, suitable for remote areas with low infrastructure.

In this poster, the seasonal evolution of O3 and its photochemical production regime in a polluted region of eastern China between 2014 and 2017 has been investigated. We used tropospheric ozone (O3), carbon monoxide (CO) and formaldehyde (HCHO, a marker of VOCs (volatile organic compounds)) partial columns derived from high resolution Fourier transform spectrometry (FTS), tropospheric nitrogen dioxide (NO2, a marker of NOx (nitrogen oxides)) partial column deduced from Ozone Monitoring Instrument (OMI), surface meteorological data, and a back trajectory cluster analysis technique.

The multi-field-of-view solar photometer was developed by Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences which consists of three wavelengths (442 nm, 670 nm, 880 nm) and three fields of view (0.8°, 2°, 5°) for direct sunlight. In this paper, we explores the possibility to retrieve the cloud optical thickness and effective particle radius of thin cirrus clouds by using forward scatter radiation in small angles in terms of model simulation. The radiant fluxes of different wavelengths in different fields of view are calculated based on the ice cloud characters of scattering phase function and single scattering albedo of nine ice crystal particles provided by Yang (2013) by using libRadtran software and their trends varying with the cloud optical thickness and the effective particle radius are analyzed. It is obvious that the radiant fluxes change with the cloud optical thickness for larger cloud optical thickness means longer the ice water path and more portion of the radiation is scattered. They change much more slowly with the effective particle radius and only at a small cloud optical thickness they increase with the increasing of the effective particle radius. Therefore, it is possible to retrieve the cloud optical thickness and the effective particle radius of the thin cirrus (namely the cloud optical thickness is small) simultaneously by using small angle scattering. At the same time, we calculate the difference of the radiant flux between two different fields of view, no matter which two field of view are used, the radiance flux difference is not sensitive to the effective particle radius, but sensitive to the cloud optical thickness. With the increasing of cloud optical thickness, the radiant flux difference becomes smaller gradually. This feature can also be used to retrieve the cloud optical thickness. The radiant flux ratios for arbitrary different fields of view show a good tendency to decrease gradually for droxtal particles as the effective particle radius increases,but it is not sensitive to other particle habits. Fortunately there will be a significant improvement with the effective particle radius when we find the ratio of the radiant flux difference, especially for plates. The ratios of the radiant flux difference increase gradually with the increasing of the effective particle radius. It can be seen that the inversion of the effective particle radius needs to be used several values in combination with each other. In summary, the radiant flux difference between two field of view to retrieve the cloud optical thickness and the ratio or difference ratio to retrieve the effective particle radius can be useful to ensure that it is not sensitive to another factor while doing retrievals of one factor. But this is only the analysis result of theoretical simulation calculation and We still need to do a lot of work for experimental observation and combine them together for more detailed analysis in the future.

Over a decade of OMI observations provide insight into the rapidly changing air quality levels in China. Global documentation of key atmospheric pollutant gases such as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) allow the study of anthropogenic and natural emissions on different spatial scales.

Based on bottom-up emissions inventories, Chinese SO₂ emissions were the world’s largest, particularly over the North China Plain. SO₂ sources are related to major coal-fired power plants and industrial activities such as oil and gas refining, and metal smelting. Similarly, the highest NOx emissions are observed over the world’s most populated (increased mobile sources), highly urbanized and industrialized regions.

Despite the growth of the economy in the rapidly developing China over the past two decades, a substantial overall decrease in the SO₂ and NOx emissions has been observed with different patterns between the species. We investigate the spatial variability of these trends and we identity their origin. The differences between the spatial distributions of SO₂ and NOx emissions over the Chinese domain are related to differences in economic and technological activity, and regional environmental policies.

Government efforts to restrain emissions from power plants and industrial sectors (e.g. installation of de-sulfurization devices) have resulted in decreasing SO₂ and NOx emissions since approximately 2007 and 2011, respectively. We use the SO₂/NOx ratio to locate and characterize the emissions sources since, to some extent, it reflects the level of the regional modernization and helps us identify the source sector. For instance, the megacity of Shanghai and the areas around it are highly populated with cleaner power plants compared to other regions, therefore a relatively low SO₂/NOx ratio is observed.

Tropospheric ozone plays an important role in atmospheric processes. Hence, the acquisition of tropospheric ozone content from satellite observations is a crucial challenge for atmospheric pollution research. In this study, tropospheric ozone columns over China were retrieved from total ozone columns measured by the Ozone Monitoring Instrument (OMI) and ozone profile of the Microwave Limb Scanner (MLS). Inversion results were then compared and validated with Electrochemical Concentration Cell (ECC) ozonesonde observations, ground-based surface ozone measurements, and simulation results from the Regional Atmospheric Modeling System – Community Multiscale Air Quality Model (RAMS-CMAQ). Validation results in China during 2005–2014 showed a correlation coefficient of 0.67 between the OMI-MLS tropospheric ozone and ozonesonde data, although lower correlations (~0.36) were found over northeastern China, which were attributed to satellite observation errors at high latitudes. Comparisons between ground-based surface data and RAMS-CMAQ simulated results also demonstrated high correlations. Except for in Northeast and South China, the OMI-MLS tropospheric ozone correlates well with ground-based data (0.63) and RAMS-CMAQ simulated results (0.71). The weak correlation in South China was likely caused by the presence of ozone-generating mechanisms or sources in the upper troposphere, in addition to anthropogenic surface emissions of ozone precursors. Moreover, long-term seasonal and spatial distribution characteristics of tropospheric ozone over China were also determined, and the results show that the OMI-MLS tropospheric ozone has a trend consistent with that of ground-based and model data, accurately reflecting the seasonal changes of tropospheric ozone in China. Thus, this study demonstrates that OMI-MLS tropospheric ozone can accurately indicate changes of surface ozone concentrations. Satellite remote sensing can compensate for ground-based surface ozone observation shortages and improve the spatiotemporal coverage of near-surface ozone monitoring.

It is presented the recent progresses of ocean surface winds and typhoons 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) Sea Surface Wind Speed Retrieval and Validation of the Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory; (6) Top cloud motion field of Typhoon Megi–2016 revealed by GF-4 Images.

Thanks to the Satellite Hurricane Observation Campaign (SHOC) initiative, the ESA Sentinel-1 mission planning team allows acquisitions over Tropical Cyclone since 2016. This data collection yielded to a catalogue of about 100 hits over Tropical Cyclone (TC) eyes. In parallel, the hurricane watch program from CSA also organises acquisitions over TC eyes. This study co-analyses data from the two missions and presents performances of our algorithm for ocean surface wind field retrieval at high resolution. As a first step the quality of the Normalized Radar Cross Section (NRCS) for both polarization and sensors is compared and found to be very consistent. The relationship between NRCS, wind speed and direction is analyzed for extreme cases. Then, the wind speed performances are compared to other satellite remote sensing data, airplane measurements and analysis from experts in TC centers (tracks). The impact of rain on the ocean wind measurement is discussed. Finally, to complement the NRCS, other radar parameters such as Doppler Centroid and the energy of the MeAn Cross-Spectra (MACS) high frequency part are also analyzed. In particular, we show how MACS could be used to constrain the wind retrieval.

A new space-borne dataset of global ocean swell, called Environment Monitoring Services (CMEMS) “fireworks” Level-3 product, derived from Sentinel-1A/B Level-2 ocean swell spectra is presented and its performances are assessed. The Level-2 swells inverted from synthetic aperture radars (SAR) images are retro-propagated along the great circle and refocused at their remote origins (coinciding strong storms), producing a higher level product to describe the swell temporal and spatial evolution from origin until land across the oceans. The Level-3 “fireworks” are now operationally produced by Copernicus CMEMS.

Here, we assess their performances using sentinel-1A/B wave mode data for the period from July 2016 to Nov. 2018, based on the “virtual” buoy concept. Reference data are in situ directional wave measurements from two different buoy networks:National Data Buoy Centre (NDBC) and Coastal Data Information Program (CDIP). Comparison results show a good agreement between Sentinel-1 Level-3 swells and buoy measurements, with root mean square error of 48 cm, 45.66 m and 21.21° for swell height, peak wavelength and direction, respectively. Influence of buoy network on the validation results are also examined, revealing better wave directional measurement accuracy for Waverider buoys used in CDIP than in NDBC network.

Sentinel-1 A & B SAR constellation is collecting data in Wave Mode (WV) and ESA is routinely producing and delivering Level-2 Ocean products with ocean surface wind, waves and radial velocities. In particular, the wave component of this product contains the image cross spectra (real and imaginary parts) and the 2D ocean spectrum with associated waves partitions. Here, we focus on the wave measurements originating from Tropical Cyclones. As a first step, we developed a method to filter out the wave partitions with low quality. This quality control procedure is performed for each acquired track and relies on the expected swell consistency between successive acquisitions along any given track. This method is evaluated against model outputs (statistical analysis) and buoys (case study). Then, we analyze the waves properties (wavelength and wave propagation direction) with respect to the Tropical Cyclones properties. The impact of storm size, translation speed and intensity on the extended fetch and waves escaping from the storm source is illustrated and discussed.

Swells from strong storms can spread over very long distances. Ocean currents alter this propagation, with the possible formation of constructive or destructive interference. This effect, still neglected in current models of atmospheric, oceanic, and even wave prediction, is often traced in current measurements, altimetry or even scatterometry / radiometry at medium and high resolution.

Large-scale currents are indirectly measured by satellite. Since the small-scale currents are generally unknown, we propose to consider them as random in wave dynamics simulations. Specifically, the statistical spatial structure of these currents is inferred from large-scale currents through self-similar assumptions. The temporal correlations of the small-scale currents is neglected due to the short-time wave-current interaction. The dispersion ratio is modified and becomes stochastic. From there, we can derive and simulate the random dynamics of wave group the rays. Analytic and semi-analytic solutions have also been derived for simple – though realistic – cases.

Our results not only improve wave simulation capabilities, but also bring new insights about the large wave’s developments at small scales and the wave-current effects on satellite measurements. At longer term, those type of random dynamics will bring new data assimilation procedures for joint wave-current estimations from space.

In this paper, snow depth is derived using GPS Interferometric Reflectometry (GNSS-IR) and a method is presented to derive snow water equivalent (SWE) by using refracted GPS signals (GPS refractometry) from an antenna buried underneath the snow pack. The GPS monitoring system is installed at the narrow Grimsel mountain pass located in the Swiss Alps and is surrounded by high mountains. The GNSS-IR retrieved snow depth shows a certain correlation to the reference snow depth. The terrain influences thereby the precision of the retrieved snow depth seriously. GPS refractometry is able to correct the influence of the snow pack above the buried antenna. The systematic and stochastic snow induced effects in the GPS residuals are significantly reduced by estimating the SWE above the antenna. The method is thus able to estimate the SWE. Results of refractometric determination of the SWE show a very high correspondence within less than 5% with the results of conventional SWE determinations. This has be shown over three consecutive winter seasons.

Soil moisture plays an important role in water cycle study. Modern remote sensing technique has demonstrated that L-band is very sensitive to soil moisture variation. With the design and implementation of the Global Navigation Satellite System (GNSS) which working on L-band as well, remote sensing using navigation signal of opportunity gained wide interests. With two decades’ development, two technique based on signal reflection have been proposed including GNSS-R (GNSS-Reflectometry) and GNSS-IR (GNSS-Interferometric Reflectometry). More recently, some researchers tried to utilize the penetrating signal to measure soil moisture (Franziska Koch et al., 2016) and snow water equivalent (Franziska Koch et al., 2014 and Ladina Steiner et al,. 2018). For the soil moisture measurement, the investigation of the penetrating signal leads to better understanding of the sensing depth of the reflected signal, which is related to estimating the Root-Zone soil moisture and Field Capability.

We are going to study how different soil moisture affect the signal attenuation in the soil and the penetration depth of the signal under different soil moisture condition. We hope to predict the reflection caused by the soil based on the above analysis. Finally, we want to analyze the sensing depth of the GNSS signal which is defined as the maximum depth from where the signal reflected off can be received under certain receiving sensitivity. A long term experiment is carried out along with this study. Two identical antennas are used with one placed in the air and the other is placed at the bottom of a big plastic bag filled with soil of different thickness. At the same time, three FDR soil moisture probes are evenly buried at vertical direction with one probe always stay at the bottom of the bag. The increment of soil thickness is about 2 cm with its initial depth being 2 cm. Different navigation system will be investigated such as GALILEO and BEIDOU. Particularly, the BEIDOU System contains different kinds of satellite orbits including GEO, IGSO, and MEO. The GEO satellite can give quasi-static measurements, while the IGSO and MEO can give dynamic measurements.

Modern SAR technology offers various approaches for processing stacks of interferometric SAR data. For surface motion estimations in urban areas, normally short wavelength data, like X- or C-band, is preferred. For applications in urban areas and infrastructure monitoring. Long wavelength data offers a certain amount of penetration capability and they are less sensitive to temporal decorrelation.

PS-InSAR is a widely used method for surface motion estimation from interferometric SAR data stacks. It is used in commercial applications and also in many projects in the Dragon program, starting from Dragon-1 until today. We consider it a stable technique, proven to successfully and reliably offer surface motion estimations in numerous projects. We used PS-InSAR in Shanghai and Wuhan for estimating urban subsidence and infrastructure stability. With the availability of Sentinel-1 and the large global SAR archive, it is nowadays possible to process PS-InSAR and estimate subsidence in regions of interest all over the world, opening this field up to the public even further.

SAR tomography with long wavelength SAR data, preferably with P-band data, allows foliage penetration and the true 3D reconstruction of the SAR signal under the foliage. This can be used for various applications, e.g. for the estimation of above ground forest biomass. SAR tomography here allows to measure the biomass, instead of estimating it based on tree canopy heights, on a global level. ESA will use this with the upcoming BIOMASS mission.

There are still several problems to be solved though. On problem is the temporal decorrelation. Although P-band is less sensitive to temporal decorrelation, it is still not immune to it. Especially changes in rainfall and canopy water content / water content layers, can cause problems in the 3D reconstruction. With one of the main areas of interest along the tropical rainforest, rain and changes in the rainfall patterns are to be expected though. Minimizing the amount of data necessary for a tomographic inversion is therefore important to allow a good biomass estimation with few acquisitions.

In Dragon-4 we are working closely together towards these goals, continuing our research on PS-InSAR and related techniques, but also extending towards the 3D reconstruction using SAR tomography with long wavelength SAR data.

In recent years, advanced techniques such as polarimetric SAR interferometry (Pol-InSAR) and SAR tomography (TomoSAR) have been widely used to retrieve forest parameters by means of SAR measurements. Pol-InSAR was developed based on the Random Volume over Ground (RVoG) model, which assumes a penetrable volume layer consisting of randomly oriented particles over an underlying rough surface. On this basis, Cloude and Papathanassiou proposed a parametric inversion scheme to retrieve forest height, which has been successfully applied for a variety of forest sites at different frequency bands.

SAR tomography is instead an imaging technique based on the collection of multiple flight lines. It allows focusing the received signal not only in the range/azimuth plane, as in conventional 2-D SAR imaging, but also in elevation, hence providing 3-D resolution capabilities. The retrieval of canopy height using SAR tomography has been considered since the early experiments. Indeed, wave scattering from forested areas is bound to occur between the terrain and the top of the canopy. Hence, canopy height can be retrieved, at least in principle, by tracing the upper envelope in tomographic sections.

In this paper, we aim at presenting an experimental assessment of vegetation height retrieval in tropical forests based on P-band SAR acquisitions. Two approaches are considered: i) parametric height estimation under the assumption of the Random Volume over Ground (RVoG) model, and ii) thresholding the vertical backscattering profiles that are focused by SAR tomography. The data-set under analysis is from the ESA AfriSAR campaign that was flown over Gabon in 2016. Results show that both of the two approaches are able to retrieve forest height to within an accuracy of about 3 m or better over the interval of forest height between 30 m to 50 m at a resolution of 25 m × 25 m

Several upcoming SAR satellite constellations, are going to be operated in bi-static mode, like TanDEM-L or TwinSAR-L, or may have a bi-static companion, like Sentinel-CS. Until now, bi-static data is mainly used for DSM generation, as in the TanDEM mission. In the future, the goal is to use such data also for surface motion estimation. However, current multi-baseline D-InSAR approaches are not well suited for processing this data and need to be adjusted.

The main advantage of a bi-static operation is the minimization of the temporal decorrelation and the atmospheric influence. But, a temporal difference close to zero between the acquisitions also means that ground deformations cannot be measured. Motion related phase components will only appear with a significant time difference between the acquisitions. By acquiring several image pairs over the same area, bi-static missions can deliver such repeat-pass acquisitions with a required temporal baseline, but these interferograms will again suffer from temporal decorrelation and atmospheric effects like the standard acquisitions.

In terms of PSInSAR or related processing methods, that is to say that we would expect an improved estimation of PS point height, but not necessary a better estimation of the deformation phase components, as the most severe problems still occur.

Even more, standard processing chains for PSInSAR will not work well, or at all, with such stacks. In our experiments, we used pursuit mono-static data from the TanDEM-X science phase. The along track baseline is extended to 10 seconds between the satellites, allowing both satellites to transmit and receive data undisturbed from each other. The data is therefore not bi-static and generally suitable for standard InSAR and PSInSAR processing. However, the very small temporal baseline of 10 seconds compared to the 11 days repeat-pass baseline can cause numerical problems in the estimation of the deformation phase.

To avoid this, we separated the estimation of the topographic phase from the estimation of the deformation phase component and use different image pairs in both cases. We estimate the topographic phase only from the 10s pairs. Based on the estimated heights from this first step, we process the deformation phase using the repeat-pass images. Having two images per time can reduce the noise, however we found no significant difference in the performance from this. In areas with high skyscrapers, like our testing area in Guangzhou, China, the deformation estimation can vastly benefit from the much better height estimation of this approach. However, unfortunately, the amount of data available is currently very limited, so that we can only present preliminary results for deformation estimation, showing only slight improvements in this regard.

In synthetic aperture radar (SAR) remote sensing, Differential SAR Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography, producing advanced 4D (3D+Time) SAR imaging capabilities extensively applied to urban deformation monitoring.

More recently, it has been shown that through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating (forest) scatterers, the important decorrelation-robust forest Tomography functionality is obtained.

To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an advanced adaptive, just semi-parametric, generalized-Capon Diff-Tomo method conceived and developed at University of Pisa (UniPi) for extraction of height and dynamical information of natural distributed (volumetric) scatterers. In addition to robust Tomography, particular reference is to separation of decorrelation mechanisms in forest layers.

Simulated and P-band results are shown. A review of other advanced Diff-Tomo tools developed at UniPi for information extraction in decorrelating forest scenarios is also presented.

Ack.: the Authors thanks Dr. Francesco Cai, formerly at UniPi and now with Leonardo Company, for his support in the SW development.

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Huang, Y., Ferro-Famil, L., Reigber, A.: ‘Under-foliage object imaging using SAR tomography and polarimetric spectral estimators,’ IEEE Trans. Geosci. Remote Sens., 2012, 50, (6), pp. 2213-2225

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In this paper, the GPUs are used to accelerate the processing efficiencies in time domain (TD) SAR simulation and time domain back-projection (TDBP) focusing. The raw data simulation and back-projection reconstruction are both implemented in the time domain for handling the scenarios of highly non-linear trajectories. The processing inefficiencies, however prevent extensive applications of TD SAR simulation and TDBP focusing. Thus, we utilize the massive parallelism of GPUs to enhance the processing efficiencies. In this contribution, we develop an optimized time-domain SAR simulation algorithm with complexity O(n³). We also discuss the drawback of the optimized simulation method and our contributions to mitigate this problem. Furthermore, both parallel simulation and back-projection focusing algorithms are fully optimized under the NVIDIA’s Compute Unified Device Architecture (CUDA) framework to guarantee a relevant acceleration compared with CPU counterparts. As a result, the GPU-based TD SAR simulation gains a 78x speed-up factor over the CPU serial version. The GPU based TDBP implementation achieve an over 100x speed-up factor compared with the CPU counterpart. To demonstrating the validity of our methods, we apply our GPU based TDBP focusing methods in simulated SAR raw data from highly deviated trajectory and circular trajectory.

Forest above ground biomass (AGB) retrieval by P-band Synthetic Aperture Radar (SAR) tomography has been extensively studied in recent years in the context of the forthcoming spaceborne mission BIOMASS. Most studies made use of airborne data collected in a single day, for which temporal decorrelation could be neglected. This fortunate situation will clearly not be repeatable in the case of BIOMASS, for which the revisit time will be of 3 days. The impact of temporal decorrelation on tomographic observables was analyzed in previous studies using data from the ground-based experiment TropiSCAT, which provided continuous tomographic observations at the expense of covering a small area and providing no azimuth resolution. This paper is meant to complement those studies by investigating the effect of temporal decorrelation on forest canopies over large areas, based on the airborne data-set acquired by DLR during the AfriSAR campaign. The analysis is carried out based on the recently proposed ground-notching technique, which is used to single out volume scattering based on single-baseline acquisitions gathered at a time lag of 4, 5, and 9 days. Results show that volume temporal coherence is consistently between 0.6 and 0.85 when forest height is larger than about 25 m, whereas low vegetation areas appear to be significantly more affected by temporal decorrelation. As a result, the intensity of volume-only scattering is observed to vary to vary by less than 1 dB when ground notching is performed using acquisitions from different dates.

Desertification is one of the most important environmental problems in
China's arid regions, and the damage is very serious. Soil organic matter (SOM)
content is an effective indicator for effectively reflecting desertification status. Large
area monitoring of organic matter content is of great significance for mastering the
status and dynamics of desertification and formulating scientific and effective
prevention strategies. However, due to data limitations, vegetation signal interference,
etc., large-area acquisition of SOM content has always faced great difficulties. The
purpose of this paper is to estimate the soil surface SOM content in desertified land in
northern China by using Sentitle-2 data mainly based on the Google Earth Engine
(GEE ) platform. Two machine learning methods, classification and regression tree
(CART) and support vector machine (SVM) respectively, were employed with
different input variable combinations. The results show that the proposed approach
could estimate SOM in desertified land effectively. CART shows higher accuracy than
SVM, especially when Sentinel-2 band reflectance and ancillary factors (vegetation
index, elevation, annual average temperature, etc.) were totally selected as input
variables, the model’s R²could reach up to 0.73, RMSE can reach 0.31. Also, the
approach based on GEE has the advantage of time efficiency, less than1 hour is
needed to finish the estimation of the SOM at the 10 m spatial resolution for the entire
desertified land in northern China, which has great potential for high spatial resolution
SOM mapping at the large scale.

Observations show that in recent decades, a large area of China has been affected by drought and that frequent droughts have caused damage to the ecological environment and the economy. Because of the differences in data and methods, assessing regional droughts often leads to contradictory conclusions. The self-calibrated Palmer drought severity index (scPDSI) is based on multiple parameters, such as precipitation, temperature and soil properties, and it is considered regionally applicable and is widely used. However, some divergence has been observed in the results of drought in China using different scPDSI_PM (scPDSI based on the Penman-Monteith model) datasets. We establish an integrated scPSDI dataset (scPDSI_PM _INT) by averaging three scPDSI products through the equal-weighted method and analyze the temporal change in and spatial characteristics of drought in China from 1950 to 2009. The annual and seasonal drought intensities have increased in the past 60 years. The disturbed area has broadened significantly, especially in eastern China, which has become much drier. The intensity of most drought-prone regions is abnormally dry and moderate, while severe and extreme droughts occur mostly in the agro-pastoral zone and the Beijing-Tianjin-Hebei region. The vegetation activity of response to regional drought was analyzed in this research.

The objective of Dragon 4 Project 32396_2 aims at detecting land degradation in dry lands at a regional scale. The main achievements acquired during the last years could be summarized as follows:

(1) Assessment and monitoring of land degradation in dry lands of China: T With the development of remote sensing technology, long time series remote sensing data have been available for land degradation assessment and monitoring, and the vegetation indicators, such as the NDVI, NPP, Vegetation coverage and biomass were commonly used. However, time series vegetation index will fluctuate severely due to the impact of climate change, especially the fluctuation of annual precipitation, thereby the land production capacity could not be determined accurately. Therefore, to solve the problem, Xilin Gol League, Inner Mongolia Autonomous Region, China, where the land degradation is prevailing in the first decade of the 21st century was selected as the study area. Based on the annual NPP dataset estimated by 10-Day composite NDVI from Envisat-Meris data at 1.2km resolution during 2003 to 2013 and the same period meteorological raster dataset, a new Moisture-responded Net Primary Productivity (MNPP) method, for identifying areas of land degradation based on the change of annual NPP and MNPP over time and Moisture Index (MI) was developed. It was expected that provide technical support and scientific reference data for land degradation assessment and monitoring in study area, even in the whole drylands in China.

(2) Estimating Soil Organic Carbon Density in the Otindag Sandy Land, Inner Mongolia, China. Accurate quantitative estimates of soil organic carbon density (SOCD) can effectively represent regional carbon cycle processes and regulation mechanisms, and can serve as reference data when making land management decisions. Limited research, however, has been carried out in arid or desert zones covered with sparse vegetation, despite the fact that these cover wide areas of the earth and play a significant role in global carbon cycles. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) in the Inner Mongolia Autonomous Region of China was selected as the study area. The study introduces a useful technique for making high spatial coverage SOCD estimates for drylands by utilizing GF-1 WFV optical satellite images and a time series of MODIS satellite remote sensing datasets, and using these to optimize parameters for simulation models in conjunction with other technical procedures that are described. We expect this research to provide useful technical support and scientific reference data for land management and for land degradation/desertification assessments, for the study area monitored, as well as across the whole dryland area of China.

(3) Estimating Above Ground Biomass in the Otindag Sandy, Inner Mongolia, China by Using Sentinel-2 data. Above ground biomass (AGB) is an important measure of terrestrial ecosystem productivity, and it is used in quantifying the role of vegetation in the carbon cycle, the potential for energy production, and the carbon stock estimation for climate change modelling. Dryland AGB, also recommended as the indicator of land productivity by UNCCD in desertification assessment and monitoring, need to be quantitative assessed and evaluated. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) was set as the study area and a useful method for sparse vegetation aboveground biomass inversion in dryland was promoted. Firstly, the Sentinel-2 remote sensing data coved the whole area in growing season (May to September) during 2015 to 2018 and synchronous field survey data was collected and processed. Then, the estimation model was constructed by linear regression model, power function model, exponential model and machine learning model by taking band information, texture information and different vegetation index into consideration. In addition, total 2/3 field survey sampled AGB data were used for modelling, and the remaining 1/3 measured AGB data was set as the testing to evaluate the AGB estimation models. Finally, the AGB distribution of the OSLAIS was mapped and analysed based on the optimal model. This research is expected to provide technical support and scientific reference data for vegetation assessment and monitoring in the study area, and even across the entire dryland of northern China.

Land condition results from land use in a managed territory. However, the reverse holds too: only a subset of land uses can be applied given a state of land condition. Such reciprocal feedbacks can be of utmost importance for assessing the land management options of a territory.

This was the main hypothesis of this study. To test it, we explored associations and dependencies between land condition states and land cover classes in the China drylands. More precisely, the study area was the Potential Extent of Desertification in China (PEDC), determined after applying the FAO-UNEP aridity index to an archive of climatic surfaces. The study period was 2002-2012. Land condition states resulted from the application of the 2dRUE method to an archived time-series of Net Primary Productivity (NPP), derived from MERIS satellite data by the CASA algorithm. Such states describe ecological maturity in terms of aboveground vegetation biomass and turnover, and lend well to an ordinal scaling. Land cover classes resulted from the aggregation of thirty-eight classes of level II built for China for the year 2010 following the Land Cover Classification System of the FAO. Land uses were excluded from this preliminary run. The spatial resolution for all the analyses was of 4 km.

We performed two statistical tests on the described data set, stratified by aridity zones. First, associations between land condition states and land cover classes were determined by chi-square tests, using the Monte Carlo method. Wherever significant associations were found between these variables, we interpreted the standardized residuals to determine the significance and sign of individual combinations of the corresponding contingency table. The second test was a non-parametric ANOVA with unequal samples, using the Kruskal-Wallis and Median test. We also determined homogeneous groups of land cover classes (in terms of land condition) through non-comprehensive search of land condition differences in pairwise combinations of them.

The associations between land condition and land cover resulted significant for all the dryland aridity zones. In general, areas of low vegetation cover such as desert or bare soil were positively associated with more degraded states, whilst higher vegetation cover was positively associated with states of higher maturity and complexity. As for the second test, it was significant too and two homogeneous groups of land cover could be formed for all the aridity levels.

The results, as reported in this abstract, are somewhat limited because of the exclusion of proper land uses. The land cover classes used here were only five (Deserts and bare soils, Grasslands, Shrubs, Open forests and Forests) and these are likely to be controlled in balanced terms by physical gradients and human intervention. Still, 2dRUE detects land condition states after a climate correction, and both mature and reference states have been found in real deserts of North Africa, for example. This suggests that significant associations in this study between class Deserts and land degradation involves human management to some extent. In other words, this class might be a mixture of proper zonal deserts and desertified areas that were possibly with a denser vegetation cover in earlier times.

The approach will be repeated using a full classification of land uses. Meanwhile, we can preliminarily conclude that it produces interpretable results that will help determining interconversion pathways between land cover classes, which in turn supports the paradigm that land degradation can be defined as loss of management options.

The concentration of carbon dioxide (CO₂) in the atmosphere has been rapidly increasing since the 1750s, and CO₂ has been recognized as one of the most significant greenhouse gases responsible for global climate warming. To understand and mitigate anthropogenic CO₂ emissions, regional carbon flux estimation is required for identifying CO₂ sources and sinks. The first scientific experimental CO₂ satellite of China - Chinese carbon dioxide observation satellite (TanSat) was launched in 22 Dec, 2016. After on-broad test and calibration, TanSat has been measuring the backscattered sunlight in scientific earth observation mode and produces XCO₂ data for more than two years.

The inter-comparison study between UoL-FP and IAPCAS retrieval algorithm provide a valuable experiment and help to improvement on TanSat data retrieval and results accuracy. The TCCON validation indicate a well-agreed result that need to be further investigate the in future studies. The solar induced chlorophyll fluorescence (SIF) can be approached from clear solar lines from TanSat. The TanSat SIF product indicate the seasonal variations of vegetation growth. Aerosols significantly impact CO₂ retrieval precision by modifying the light path in hyperspectral measurements in the NIR/SWIR. After investigate the information contain in measurement, a new approach is proposed to optimize the aerosol model used in the TanSat CO₂ retrieval algorithm to reduce CO₂ uncertainties associated with aerosols. The TanSat preliminary results has been compared with GEOS-Chem model, and show a consistent picture. We also find a stronger North-South gradient in the satellite dataset compared to the model. The model also shows a shallower seasonal amplitude by as much as 2 ppm when compared to the satellite observation. The validation campaign in Beijing, inner Mongolia with multiple instrument coordinate measurement, incl. EM27/SUN, AirCore and POPS shows a preliminary result in Greenhouse satellite validations.

The Chinese scientist has been visiting the Sodankyla station that has been significant contribute to greenhouse gas measurement relative issues communities, join the AirCore experiment and visiting for a joint research on TanSat data retrieval that greenhouse gas data applications in the cooperation framework of Dragon programme among U.K., Finland and China.

The atmospheric carbon dioxide (CO₂) concentration has increased to more than 405 parts per million (ppm) in 2017 due to human activities such as deforestation, land-use change and burning of fossil fuels. Although there is broad scientific consensus on the damaging consequences of the change in climate associated with increasing concentrations of greenhouse gases, fossil CO₂ emissions have continued to increase in recent years mainly from rapidly developing economies and China is now the largest emitter of CO₂ generating about 30% of all emissions globally. To allow more reliable forecast of the future state of the carbon cycle and to support the efforts for mitigation greenhouse gas emissions, a better understanding of the global and regional carbon budget is needed. Space-based measurements of CO₂ can provide the necessary observations with dense coverage and sampling to provide improved constrains on of carbon fluxes and emissions.

The Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) was established by the National High Technology Research and Development Program of China with the main objective of monitoring atmospheric CO₂ and CO₂ fluxes at the regional and global scale. TanSat has been successfully launched in December 2016 to continue and extend space-based observations from the Japanese GOSAT and the NASA OCO-2 missions. In this presentation, we will focus on an evaluation of satellite observations of CO2 and CH4 from GOSAT over China and the wider Eastern Asia region. We will contrast GOSAT observations against state-of-the-art model calculations and ground-based validation data and we discuss how well we can observe signals from anthropogenic emissions.

In this presentation we give an overview of the recent recearch activities that have taken place at the Finnish Meteorological Institute related to remote sensing 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 satellite remote sensing of greenhouse gases using SWIR wavelengths is becoming more and more important method to understand the global distribution of methane and carbon dioxide concentrations. This is highlighted by the growing number of satellite missions targeted for greenhouse gases including GOSAT, OCO-2, TanSat, Sentinel 5P and most recently GOSAT-2 (2018) and OCO-3 with launch in April 2019 as well as ambitious plans like the proposed Copernicus high priority anthropogenic CO2 Monitoring mission. The satellite observations rely heavily on ground-based validation and bias correction and therefore, the stringent requirements on satellite observation are reflected also on the requirements for ground-based validation.

The FTIR instrument in Finnish Meteorological Institute’s premise in Sodankylä is one of the core high latitude sites for satellite validation as part of the Total Carbon Column Observing Network (TCCON) with regular observations from March till October since 2009. The AirCore balloon launches have been performed since 2013 to obtain accurate in-situ profiles of methane, carbon dioxide and carbon monoxide from troposphere to lower stratosphere. During summer 2018 a UAV version of AirCore system was tested. Sodankylä is also an ICOS site providing high quality in-situ concentration observations and eddy-covariance flux estimates at different altitudes in the new 25 m tower. These different GHG measurements together with additional measurements e.g. solar induced fluorescence provide valuable information for satellite validation. We present recent and on-going validation activities of OCO-2, TROPOMI and GOSAT and validation campaigns that have taken place in Sodankylä.

The improved data quality of the satellite observations facilitates further analysis of the global distribution of greenhouse gases and its variability. We have studied the seasonal variability and spatiotemporal distribution of greenhouse gases by analysing spatially/temporally OCO-2 and GOSAT data. The developed methods are applicable to TanSat data as well.

Carbon Dioxide (CO₂) is a main anthropogenic greenhouse gas whose concentration increase leads to heating of the troposphere and subsequently to global warming. The well-developed ground-based networks either using remote sensing or in-situ technology provide highly accurate reference measurement but their coverage is too coarse to inform reliable on regional carbon fluxes. Satellite measurement of the total column CO₂ from shortwave infrared hyperspectral measurement can provide highly accurate and precision measurements from space with sufficient coverage and resolution to improve the situation and help to advance our understanding of CO₂ and carbon fluxes.

The Chinese carbon dioxide observation satellite (TanSat), which is the first Chinese greenhouse gas monitoring satellite and a ESA third party mission and has been supported by the Ministry of Science and Technology of China, the Chinese Academy of Sciences, and the China Meteorological Administration, was launched on 22 Dec 2016, Following the European Space Agency (ESA) SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the ENVIronmental SATellite (ENVISAT), which is the first space-based instrument to provide SWIR CO₂ band hyperspectral detection, the next generation satellites GOSAT and OCO-2 launched in 2009 and 2014, respectively.

A hyperspectral grating spectrometer onboard the TanSat is monitoring the column-averaged CO₂ dry-air mixing ratio (XCO₂) over the globe. In-orbit calibration tests were completed in the summer of 2017, and the performance of the instrument has since been evaluated in test sessions. Subsequent to on-board testing and calibration, TanSat has been operationally measuring backscattered sunlight in its scientific earth observation mode and produces XCO₂ data for more than two years now.

In this study, we use two retrieval algorithms to approach the XCO₂ from TanSat hyperspectral measurements, (1) IAPCAS, Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS), is TanSat retrieval algorithm that has also been used for GOSAT (ATANGO) and OCO-2 retrieval studies. (2) UoL-FP, The University of Leicester ‘full physical’ algorithm, has been used for GOSAT retrieval and provide XCO₂ product to the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service. The retrieval accuracy and precision of both, the IAPCAS and UoL algorithm, has been well investigated by verifying them against TCCON measurement. The fitting residual has been analyzed, and PCA based analysis method and retrieval show an improvement on residual. The approaches for aerosol and cirrus treatment have been inter compared and indicate that the methods adopted by both algorithms are reasonable to deal with the particle scattering. Validation against TCCON measurement show a well agreement on both algorithms. The method used in this study and results can help to improve the XCO₂ retrieval from TanSat and subsequently the level-2 products.

Top-down flux inversions have been used to infer surface CO2 fluxes from the observed variations of atmospheric CO2 concentrations, which has led to substantial improvements in our understanding of the global carbon cycle. Most of top-down inversions rely on the in-situ observation network with sparse and unevenly distributed spatial coverage. As a result, the inferred surface fluxes have limited temporal and spatial resolutions, with large uncertainty over many regions critical to global carbon cycle.

Recently, space-based instruments such as the JAXA GOSAT, the NASA OCO-2 satellite and the Chinese TanSat,have been developed to measure column dry-air mole fraction of the targeted greenhouse gases (such as XCO₂ or XCH₄) with unprecedented precision. However top-down inversions based on space-borne observations can be comprised by varied observation coverage, and by small uncharacterized biases, reflecting the complexity in accurately modelling the radiative transfer in the atmosphere, particularly in the presence of cloud and aerosol scattering.

To examine observation constraints on CO₂ flux over China by the three different satellite measurements, we experimentally assimilated recent versions of the GOSAT, OCO-2 and TanSat XCO₂ retrievals over the same time periods. We compared the resulting fluxes with the prior estimates as well as with the fluxes inferred from the in-situ atmospheric CO₂ observations. We further validated our results by comparing the posterior model CO₂ simulations with independent in-situ observations.

The extensive burning of fossil fuels and cement production has increased the global mean carbon dioxide (CO₂) concentration from 280 ppm before the Industrial Revolution to 410 ppm today. More specifically, urban areas are responsible for 70% of global anthropogenic emissions, playing a key role in climate change. Of particular interest is China’s recent economic growth, which has resulted in the country becoming the largest emitter of CO₂,generating about 30% of all anthropogenic emissions. In this work, we aim to use satellite data to more precisely quantify urban CO₂ emissions.

NASA’s OCO-2 satellite launched in 2014 and TanSat from China Aerospace Science launched in 2016. These instruments have the capability to resolve CO₂ concentrations at a high spatial resolution over polluted areas, allowing emission sources to be observed for the first time from space. Using data from OCO-2, an estimation of regional enhancement of ΔXCO₂ over the city of Los Angeles has been quantified compared to the background area. However, from these measurements it is not possible to distinguish the origin of the measured CO₂, since satellite observations occur at a specific time and location. Because of this, column footprints of the air particles using the high-resolution Numerical Atmospheric-dispersion Modelling Environment (NAME) have been calculated in order to evaluate which part of an urban area contributed to the satellite observations. To ultimately estimate how much CO₂ is emitted from cities, a combination of these footprints along with fluxes from different emission inventories such as EDGAR are needed to quantify the enhancement of CO₂.

An estimation of the background CO₂ concentration also needs to be performed to understand its contribution to the satellite measurements. In this work, the background concentration of CO₂ will be estimated from the global chemistry transport model CarbonTracker and the NAME calculation of the air mass history, at the same resolution as the model.

The methodology presented in this work for Los Angeles serves as a test case for future analyses over Chinese cities in order to better quantify their contribution to global emissions.

Methane is the second most important greenhouse gas in the atmosphere. Globally, the largest natural source of methane is almost equal to the largest anthropogenic source of methane: a little over 30% of the total methane emissions are from agriculture and waste (largest anthropogenic source) and approximately 30% are from wetlands (largest natural source). Currently, most of the wetland methane emissions originate from the tropics but the amount and evolution of Arctic and subarctic methane emissions are highly uncertain and involve several open questions. These questions need to be answered in order to understand the role of the Arctic and subarctic regions in the changing climate.

The common characteristics for the Arctic and subarctic regions are high seasonal temperature variations and snow cover over frozen ground during winter. These are important properties for the wetland methane emissions, as the amount of emitted methane from a specific wetland depends on, for instance, soil moisture and the temperature of the ground. Frost and snow have both direct and indirect effects on how the wetlands acts as methane source or sinks, for example, during spring, after the snowmelt, methane flux from the ground increases when the soil temperature increases. These correlations between the seasonality of frost, snow and methane have been previously studied mainly based on in situ measurements. In situ measurements have spatial limitations, especially in the Arctic and subarctic areas: due to the remote locations and infrastructures, it is almost impossible to create a spatially comprehensive measurement network. To increase the spatial distribution of methane observations, the observations are increasingly made from satellites.

Here we investigate the seasonal variability of column-averaged methane and its correlation with the seasonality of soil frost and snow, using different Earth Observation data. We combine several different data sets and show the large-scale seasonal dependencies between methane and frost or snow, noting also special features in the different parts of the Arctic and subarctic regions.

To study the seasonal variability of methane, we use space-based column-averaged methane observations from the Greenhouse Gases Observing Satellite (GOSAT), from the Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor satellite and ground-based column-averaged methane retrievals from Fourier Transform Infrared Spectrometer (FTIR) measurements made at Sodankylä, Finland. The Sodankylä FTIR is part of the Total Carbon Column Observing Network (TCCON) that is a global network of ground-based observations of column-averaged greenhouse gases. The TCCON observations are the main validation source for space-based greenhouse gas observations. To detect the state of soil freezing, we use the Soil Freeze/Thaw product, which applies the observations from European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. The Soil Freeze/Thaw product is developed at Finnish Meteorological Institute (FMI). The seasonality of snow cover is studied with GlobSnow snow extent (SE) and snow water equivalent (SWE) products that are also developed at FMI. In addition, we show inverse model results for methane concentrations and fluxes from CarbonTracker Europe - CH4 data assimilation system, and compare the fluxes to the freezing periods estimated from space-based Soil Freeze/Thaw product.

Nearly ¾ of the world’s mega cities are by the sea and almost 80% of the global population live within 100km from the coast. These numbers make very clear that coastal regions worldwide are not only socially important but also economically critical with e.g. harbours, fish farms and exploitation sites for natural resources (oil/gas rigs or offshore wind farms). Moreover the seaside is important for recreational activities and as a natural habitat for local marine life. With respect to the extreme importance of the coastline, constant monitoring of this region is compulsory. Just like the diversity of perspectives in the coastal areas, the diversity of maritime information is complex. Many parameters and layers of information are needed to obtain a comprehensive picture for a given application.

Driven by the self-evident advantages of Earth observation methods to monitor large areas while keeping costs at a reasonable level, numerous methods have been developed and improved by the Chinese and German Dragon partners to extract maritime information from satellite-based sensors and will be outlined in the presentation. While most of the information is only available separately, the combination of different information layers is needed to generate a holistic maritime situation awareness. For this an integrated platform is needed to simultaneously visualize and generate a synopsis of different types of information, selected according to the respective application.

We present a prototype of a web-based near-real-time information platform to combine information such as sea state, wind information, AIS messages, SAR-based ship detections and sea ice information to obtain a thorough maritime situation awareness. The system can be expanded for oil spill detection and other relevant information and can thus serve as a powerful decision support system for national or international authorities e.g. in catastrophy or disaster management. The platform can help uncover exceptional conditions or behavior and represents an important constituent in sustainable coastal management.

Between 2008 and 2016, there were mass summer blooms of Enteromorpha in the Yellow Sea, China. It covered an area of thousands of square kilometers annually, lasting an average of 90 days. The blooms seriously affected the marine ecological environment and attracted considerable research attention. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the Enteromorpha blooms were used in this study of the national aquatic germ plasm resources of Qianliyan Island area. Underwater robots survey trepang, wrinkles abalone, and submarine ecological status. We found that the time taken by Enteromorpha to cover the national aquatic germ plasm resources of Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The Enteromorpha made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. Enteromorpha sedimentation during out-breaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of trepang and wrinkles abalone were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms.

The simulation of fully nonlinear steady-state large-amplitude internal solitary waves in continuously stratified fluids based on the 2D incompressible Euler equations with Boussinesq approximation is carried out with Gerris, an open source fluid dynamics software. The large-amplitude solitary wave structure and characteristic parameters simulated by the fully nonlinear Gerris implementation of Euler model and by the weakly nonlinear KdV model are compared. The results indicate that high-order nonlinear terms should not be neglected when large-amplitude are concerned for studying internal solitary wave. The results simulated by Gerris reveal that the wavelength of isopycnic surface of a fully nonlinear large-amplitude internal solitary wave varies with depth, which makes it doubtful to retrieve the internal wave amplitude using the distance between two extreme values of internal wave pattern extracted from a spaceborne SAR image based on the analytical solution of the KdV equation. Therefore, the retrieval method is necessary to be reassessed. The validity of the interal solitary wave modeling with Gerris is tested by two sets of in-situ measurements of internal waves.

The internal solitary wave (ISW) amplitude is one of the most important parameters of ISWs. Knowing its variation is helpful for understanding ISW energy transferring, dissipating and mixing processes. Synthetic aperture radar (SAR) has been considered as a powerful instrument for deriving ISW amplitude as it can provide a wide view of ISW evolution independent from daylight, cloud coverage, and weather conditions. However, the derivation of ISW amplitude by SAR images in a two-layer shallow water system is much sensitive to the upper layer thickness. So the accurate estimation of upper layer thickness is crucial for determining the ISW amplitude. In this paper, we present a novel method of finding the best-fit values of upper layer thickness within their reasonable ranges from consecutive SAR images based on the extended Korteweg–de Vries (eKdV) equation, to derive ISW amplitude. An ISW case observed twice by the Chinese C-band SAR GaoFen-3 (GF-3) and the German X-band SAR TerraSAR-X (TS-X) with temporal interval of approximately 11 minutes, in a shallow water depth of around 74 m at the southeast of Hainan Island, is used to demonstrate the method. Compared to the representative amplitude estimation of -4.43 m - 6.99 m derived by classic KdV equation in a continuously stratified ocean, the proposed method yields an amplitude of -4.67 m, which indicates the new method can provide reliable ISW amplitude estimation. To further illustrate its practicability in the case when there were no nearly synchronous in-situ measurements with the satellite observation, the typical climatological datasets World Ocean Atlas 2013 (WOA13) are used to perform the new method in the Hainan case, and the results show the new method has prominent advantages in amplitude estimation in shallow water than the conventional method based on classic KdV theory in a continuously stratified ocean.

The HY-2B satellite is the second dynamic environment satellite in China. It was successfully launched on October 25th, 2018 with a sun-synchronous orbit at an altitude of ~970km. Repeat cycles of 14 days are planned for the first two years with oceanographic purpose and 168 days geodetic cycles will follow for the third year of the mission. The satellite is equipped with a Ku/C bands altimeter and the orbit is determined thanks to SLR, GPS.

The HY-2B satellite altimeter provides sea surface height, significant wave height, sea surface wind speed, and polar ice sheet elevation. First of all, the description of instruments and the instrument parameters will be put forward briefly in this research. And then, the current status of the HY-2B products will be described in detail, including the measurement accuracy.

Comparing with Jason-2 and Jason-3 satellite radar altimeters and on-site buoys, the objects of comparison include significant wave height, sea surface wind speed, and sea level anomaly and so on. It is found that the precision of HY-2B satellite radar altimeter secondary products reaches the same kind of satellite radar altimeter products in the world, and some products are better than Jason-2 and Jason-3 standard products.

The response of glaciers to climate in the high elevation Tibetan Plateau (TP) is highly variable in space and time and strongly influenced by the monsoon, which affects both mass and energy fluxes. The contribution of mixed-phase precipitation events are rarely quantified in melt and mass balance models. Here we use a distributed energy balance model with new schemes for precipitation discrimination and albedo evolution, to understand the effect of dynamic modelling of monsoon precipitation on the summer mass balance of a glacier in the southeast TP. The main effect of modelling mixed-phase precipitation events in a dynamic way is to accumulate more high elevation snow and maintain higher albedo 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-wide mass balance is found to be 1.01 m w.e. (~72%) more negative over one ablation season. This is due to the fact that a static threshold for rain-snow events reduces total snow accumulation and promotes earlier retreat of the snowline altitude during the pre-monsoon season which heightens the dominance of net shortwave energy fluxes for most of the summer.

This paper presents a new water and energy budget-based glacier mass balance model. Enthalpy, rather than temperature, is used in the energy balance equations to simplify the computation of the energy transfers through the water phase change and the movement of liquid water in the snow. A new parameterization for albedo estimation and state-of-the-art parameterization schemes for rainfall/snowfall type identification and surface turbulent heat flux calculations are implemented in the model. This model was driven with meteorological data and evaluated using mass balance and turbulent flux data collected during a field experiment implemented in the ablation zone of the Parlung No. 4 Glacier on the Southeast Tibetan Plateau during 2009 and 2015–2016. The evaluation shows that the model can reproduce the observed glacier ablation depth, surface albedo, surface temperature, sensible heat flux, and latent heat flux with high accuracy. Comparing with a traditional energy budget-based glacier mass balance model, this enthalpy-based model shows a superior capacity in simulation accuracy. Therefore, this model can reasonably simulate the energy budget and mass balance of glacier melting in this region and be used as a component of land surface models and hydrological models.

The effect of monsoon on the mass balance of glaciers in High Mountain Asia, especially of those that are partly or fully covered by debris, has not yet been fully understood. Due to its insulating effect, debris strongly alters energy fluxes reaching the ice, and thus affects the rates and timing of melt. Monsoon conditions, dominated by persistent clouds, lower temperature ranges, high atmospheric water content, lower incoming shortwave radiation and higher receipts of incoming longwave radiation, can result in very distinct surface fluxes and mass balance of glaciers. The energy balance further changes under the presence of water within the debris, which controls conductive and latent heat fluxes, while another flux is added to the balance by rainfall. These effects have rarely been quantified, and to date only for single glaciers. In this study, we investigate how monsoon events influence the summer surface energy balance of debris-covered glaciers along the climatic gradient of High Mountain Asia, where monsoon dominates in the Eastern regions and progressively looses influence when moving westwards towards the Karakoram, where westerlies influence is predominant.

We use for this energy balance models (EB) developed to simulate melt of ice under debris, or debris energy balance (DEB) models, and Automatic Weather Stations (AWS) data. Most DEB models have been developed and tested for glaciers in temperate and arid climates, where the influence of water within the debris plays a less important role and many neglect or treat only simplistically the water content of the debris. We thus also evaluate the transferability of DEB models to monsoonal environments, and test distinct schemes to account for water content in the debris. We validate results against in-situ measurements, and describe how these events influence the summer surface energy balance of debris-covered glaciers. This work is fundamental to the development and optimization of more simplified approaches, such as the Debris-Enhanced Temperature Index (DETI) model, to distributed glacier melt modelling and as a result, to catchment-scale glacio-hydrological modelling.

Heihe River Basin (HRB), regarded as the second largest endorheic river basin in China, originates from the alpine region, flows through the Hexi Corridor, and ends at desert hinterland. The unique and various climatic and landscape types make the basin an ideal testbed for multi-disciplinary research, including hydrology, climatology, geography, ecology and so on. Over the past decades, extensive research was conducted over the basin, and fruitful new findings were obtained consequently. Based on these scientific bases, we have carried out two large-scale remote sensing experiments [1, 2] and Integrated research on the eco-hydrological process of the Heihe River Basin [3]. The main scientific contribution of the Heihe remote sensing experiments and integrated research can be summarized into: 1) a comprehensive watershed observing system was established [4] and a multi-scale dataset for understanding watershed ecohydrological processes was obtained [5], 2) a comprehensive modeling platform was designed and implemented for integrated hydrological simulation [6], and 3) a multivariate land data assimilation system was established [7]. These key progresses have been well documented and reported. Nevertheless, several new points have been observed in recent years, including: 1) developing an integrated watershed system model and closing hydrological cycle at watershed scale, 2) improving data assimilation algorithm and data assimilation system, and 3) developing key water cycle elements estimating algorithms and products. Here we focus on summarizing these recent progresses.

New modeling strategy and model platform for hydrological simulation were proposed. We proposed a new modeling framework to incorporate emerging knowledge into integrated models through data exchange interfaces to comprehensively understand complex watershed systems and to support integrated river basin management [6, 8]. 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 [9]. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012.

An updated data assimilation scheme was proposed and parallelized assimilation system was implemented. A 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 [10] was proposed recently, which 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 [11]. 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. [12] 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.

Key retrieval algorithms for hydrological elements have been witnessed progress. For instance, Li et al. [13] 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. [14] 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, ecohydrological research over HRB in terms of the hydrological observation, modeling and data assimilation has been witnessed huge progress. The Chinese Academy of Sciences is performing the Second Tibetan Plateau Scientific Expedition (STEP) Program. The Qilian mountain and other endorheic river basins are the key expedition regions. The scientific findings and practical experiences of HRB should and could provide very useful prior knowledge for the program. Simultaneously, the observing system design scheme, modeling idea and data assimilation systems can be extensively examined, extended and widely applied in a more generic scope.

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 and in some Italian river basin by using MOST, ESA and NASA multi-source satellite data coupled with distributed hydrological models.

In this year presentation, following the scheduled activities, results will be presented for the Chinese Heihe basin and for the Italian Capitanata irrigation district.

For both case studies, the FEST-EWB hydrological model will be used in a synergic way with satellite data. In particular, its 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 the land surface temperature (LST) as retrieved from operational remote sensing data (SENTINEL3, MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale. Vegetation information (LAI, NDVI, fractional cover) and albedo are obtained from satellite data (SENTINEL2, MODIS, LANDSAT) and used as input parameters to the hydrological model.

For the Heihe river basin, FEST-EWB model is run 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.

Evapotranspiration estimates are then compared at local scale with two eddy covariance data, showing good agreement, and at basin scale with the estimates from the Chinese ETMonitor, and also global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM, reporting a general agreement but with irregularities, due to the different models hypotheses and algorithms.

For the Capitanatairrigation district, the model is run at 30 m of spatial resolution using the SENTINEL2 and LANDSAT images for vegetation input data and LANDSAT data for land surface temperature hydrological model calibration and data assimilation. Good estimates are obtained at basin scale in terms of RET, but also at local scale in terms of evapotranspiration and soil moisture against eddy covariance stations.

The district is an intensive cultivation area, mainly devoted to wheat, tomatoes and fresh vegetables cultivation. Hence, distributed irrigation quantity maps are also estimated from the combined use of satellite data and hydrological modelling.

Hydrological model is a simplification of a real-world system that aids in understanding, predicting, and managing regional water resources. However, it required different kinds of data for model localization and simulation. Microwave remote sensing products could provide space-time continuous data for improvement of hydrological modelling. In this study, two typical monsoon basins, Red River Basin (RRB) with tropical monsoon climate and Luan River Basin(LRB) with temperate monsoon climate, were selected for comparison. By using two long-term driving forcing dataset: The Chinese Meterological Assimulation Driving Dataset for the SWAT model(CMADS) and Global Land Data Assimilation Systems (GLDAS) , as well as GPM IMERG V5B and TRMM precipitation products, we simulated the soil moisture,runoff and crop yields in both basins by using Soil Water Assessment Tool (SWAT) model. The simulated runoff by SWAT model is expected to be fit well with observations; The simulated soil moisture can be validated by local measurements and improved SMOS& SMAP soil moisture products (Stefan et al., 2019). We compared the soil evaporative efficiency by remote sensing and those by SWAT. Then, we simulated crop yields under different irrigation Scenario in the downstream Red River Delta and Luanhe Delta. Our study reveals the usefulness of remote sensing products for water resource simulation in monsoon climate basins.

The world s population density in flood-prone coastal zones and megacities is expected to grow up to 25% by 2050. Global sea-levels have risen during the 20th century, and they will rise by up to ~60 cm by 2100. 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), as well as frequently encountered natural hazards (such as storms and storm-surge) 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. The coastal vulnerability of Yangtze River Delta (YRD) and Pearl River Delta (PRD) is currently being amplified by the compounding effects of the time-dependent ground subsidence, the accelerated rate of sea level rise, and natural hazards. The provided examples of delta regions affected by the combination of sea-level rise, significant modifications over time, and natural hazards make clear the need of extended analyses for the understanding of the mechanisms at the base of the surface modifications of coastal areas, estimating of future regional sea level change, and evaluating the potential submerged land area [1]-[3].

In this project, the use of well-established remote sensing technologies, based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the advanced Differential Interferometric Synthetic Aperture (DInSAR) techniques [4]-[5], GPS/leveling campaigns aiming to perform sound and extended geophysical analyses, satellite altimeter data and tide gauge data, and the Coupled Model Inter-comparison Project Phase 5 (CMIP5) climate model projections are being employed for these purposes. The results obtained in this project represent 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. The main goals of the project are to provide a full characterization of the scene modifications over time and causes of the coastal delta region environments, to provide estimates of future regional sea level change, to derive coastal submerged area and wave field, and to provide suggestions for implementing coastal protection measures to adapt and mitigate the multi-factors induced coastal vulnerability.

The main achievements obtained during the years of the project will be summarized and discussed at the forthcoming D-4 conference, highlighting the scientific relevance and the expected added value of the project, itself.

References

[1] Yang S. L., Belkin I. M., Belkina A. I., Zhao Q. Y., Zhu J., Ding P. X. (2003) Delta response to decline in sediment supply from theYangtze River: evidence of the recent four decadesand expectations for the next half-century, Estuarine, Coastal and Shelf Sciences, 57, 689-699.

[2] Wang W., Liu H., Li Y., Su J. (2014) Development and managment of land reclamation in China, Ocean & Coastal Management, 102, 415-425.

[3] Zuo J, et al. 2013. Prediction of China’s submerged coastal areas by sea level rise due to climate change. Journal of Ocean University of China, 12(3): 327–334.

[4] Berardino P., Fornaro G.,Lanari R.,Sansosti E.(2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,IEEETransaction on Geoscience and Remote Sensing, 40, 11, 2375-2383.

[5] Zhao Q., Pepe A., Gao W., LuZ., 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

Shanghai is located at the midpoint of the north–south coastline of China. In order to solve the problem of land scarcity, several reclamation and siltation promotion projects have been implemented since 1995. Due to land reclamation, ground settlement as an inherent problem has arisen in the new lands area, which is responsible for serious damage to infrastructures.

Spaceborne Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an investigating technique capable of extracting line of sight (LOS) cumulative ground settlement measurements with millimeter or even sub-millimeter accuracy. However, the original deformation time-series is produced by single dataset with the using of MT-InSAR technique. Recent years, massive and different types of SAR data are available with the continuous launching of Synthetic Aperture Radar (SAR) satellites. Lei Yu and other scholars have combined 3 platforms’ deformation time-series to retrieve long-term displacement time-series in the ocean-reclaimed areas of Shanghai. In this study, five deformation time-series as well as deformation rates are derived by 5 independent SAR datasets respectively with the using of Small BAseline Subset (SBAS) algorithm. Then, we not only combined 3 platforms’ deformation time-series, we also combined 4 platforms’ deformation time-series. And the combinations were compared by us.

Specifically, five independent SAR datasets are used for this study. The first dataset consists of 35 images, collected by ENVISAT/ASAR(ENV) sensor operated at C band (Ascending, VV polarization) from February 2007 to September 2010. The second dataset consists of 11 images, collected by TerraSAR-X sensor operated at X band (TSX1, Ascending, HH polarization) from December 2009 to December 2010. The third dataset is also collected by TerraSAR-X (TSX2, Descending, VV polarization) from April 2013 to July 2015, consists of 11 images. The fourth dataset consists of 61 images, collected by COSMO-SkyMed(CSK) sensor operated at X band (Descending, HH polarization) from December 2013 to March 2016. The last dataset consist of 33 images, collected by Sentinel-1A(S1A) sensor operated at C band (Ascending, VV polarization) from February 2015 to April 2017. At the beginning, interferometric process is implemented in each dataset separately, and 91, 36, 66, 155, 368 better interference image pairs are sequentially selected. After removing the elevation phase by using ASTER elevation data (30m*30m), it is unwrapped by the Delaunay Minimum Cost Flow(MCF) method. Then, the time coherence coefficient is set to be greater than 0.65 for ENV and CSK, and the other three datasets are set to be greater than 0.55. After that, the deformation time-series and deformation rates of 5 time periods are obtained.

Since TSX1 and TSX2 do not have the overlapping area and they share common areas with other three SAR datasets respectively, we combined deformation time-series of time-overlapped datasets by using Singular Value Decomposition (SVD) method and combined non-time-overlapped datasets by using time-dependent geotechnical models. Three joint strategies, ENV+CSK+S1A, ENV+TSX1+CSK+S1A and ENV+TSX2+CSK+S1A, are implemented respectively. By analyzing the feature points, we found that the annual deformation rate difference between the three joint methods is less than 1mm/y in the area with small settlement. In the areas with obvious subsidence, such as the fourth and fifth runway of Pudong Airport, the annual deformation rate of the three combination fluctuate by ±2.5 mm/y. In terms of deformation time-series, all three combinations have consistent settlement trends.

Over the last decades, the use of Differential Synthetic Aperture Radar Interferometry (DInSAR) [1] technology has gained an increasing attention for its capability to investigate large-scale Earth’s surface deformation phenomena. The DInSAR technique allows the timely monitoring of displacement phenomena with dense grids of measurement points. The availability of measurements over dense spatial grids represents the typifying factor of the DInSAR technology with respect to other conventional approaches (e.g. GPS and levelling measurement campaigns), thus making nowadays DInSAR largely adopted both in scientific and operational frameworks. However, in regions where the density of coherent points is large, the use of dense grid of measurement points leads DInSAR being not very efficient from the computational point of view. Many solutions have been proposed to overcome such a problem. A role of particular importance is covered by the multiresolution/multi-grid [2] algorithms that not only improve computational efficiency but also allow performing a more comprehensive analysis of the deformation phenomena that characterize Earth’s
surface, which exhibit different characteristics at multiple scales of resolution [3].
Recently, DInSAR techniques have largely been used to analyze subsidence phenomena in coastal delta region like Yangtze and the Pearl River Deltas where man-made lands, reclaimed from the sea, are used to build airports, harbors, and industrial areas. In such a context, advanced DInSAR approaches, such as the Persistent Scatterer Interferometry (PSI) [4] and the Small BAseline Subset (SBAS) technique [5], [6], [7] , allow to produce spatially dense velocity maps as well as long-term displacement time-series (with millimeter accuracy even sub-millimeter) corresponding to coherent targets location.

In this study, we develop and discuss the potential of an adaptive quadtree-based decomposition method [8] applied to DInSAR data, which allows one to produce DInSAR deformation products at different scales of resolutions. The latter are adaptively chosen within the imaged scene to better analyze the on-going deformation signals. Specifically, the multi-grid algorithm exploits a multiresolution scheme for the phase unwrapping of sequences of DInSAR interferograms, and shares some similarities with [9]. The selection of the used multi-grids is based on the analysis of the statistical properties of a sequence of interferometric phase that allow to recognize major deformation areas where phase unwrapping operations can be performed more efficiently with a computational improvement and without losing significant information. The algorithm preserves details of deformation as much as possible, and achieves efficient data reduction. The area of interest analyzed is the Pearl River Delta (PRD) region, in particular the island of Hong Kong, which is characterized by subsidence phenomena. Pearl River Delta (PRD) is located on the southern coast of mainland China. It is the third largest delta in China and adjacent to the South China Sea from the north. In the past 50 years, reclaimed lands were merged into just over 100 enclosures protected by flood defenses. However, the coastal area has always been under threat from natural hazards, including river flood, waterlog, typhoon, and tidal flood. These hazards will no doubt be intensified by the predicted sea level rise.

The analysis relies on a set of 60 SAR data acquired by the Sentinel 1A/B radar sensor from December 2017 to January 2019. Starting from these data, we generated a stack of interferograms on which we have tested the new adaptive quadtree decomposition method. The goal of this present investigation is to prove that, at least in correspondence to the highly coherent targets on the ground, the deformation signals can be detected at different scales of resolutions using local, adaptive multilook factors (e.g., 2 x 10, 20 x 4, 40 x 8 and 80 x 16). The proposed method can be integrated with adaptive multi-looking noise filtering techniques [10], [11] to improve accuracy of estimated deformation. The preliminarily results will be presented and discussed at the next Dragon-IV meeting.

[1] D. Massonnet and K. L. Feigl, "Radar Interferometry and its application to changes in the earth's surface," Rev. Geophys., vol. 36, pp. 441-500, 1998.

[2] M. D. Pritt, "Phase Unwrapping by Means of Multigrid Techniques for Interferometric SAR," IEEE Transaction on Geoscience and Remote Sensing, vol. 34, no. 3, pp. 728-738, 1996.

[3] T. Kobayashi, Y. Morishita, H. Yarai and S. Fujiwara, "InSAR-derived Crustal Deformation and Reverse Fault Motion of the 2017 Iran-Iraq Earthquake in the Northwestern Part of the Zagros Orogenic Belt," Geospatial Information Authoriti of Japan, vol. 66, 2018.

[4] A. Ferretti, C. Prati and F. Rocca, "Permanent scatterers in SAR interferometry," IEEE Trans.Geoscience, vol. 39(1), pp. 8-20, 2001.

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

[6] R. Lanari, O. Mora , M. Manuta , J. J. Mallorqui, P. Berardino and E. Sansosti , "A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms," IEEE Transaction on Geoscience and Remote Sensing, no. 7, pp. 1377-1386, 2004.

[7] F. Falabella, A. Pepe, Q. Zhao, M. Guanyu, C. Serio and R. Lanari, "A hybrid multi-scale InSAR approach to study the 2014-2018 Surface Deformation of the Shanghai Coastal Region through Sequences of Time-Gapped Cosmo-SkyMed SAR acquisitions," Proceedings of Dragon 4 Programme Symposium, 19-22 June 2018, Xi'an, P.R. China.

[8] R. B. Lohman and M. Simons, "Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling," Geochem. Geophy. Geosy., vol. 6, no. 1, pp. Q01007-1-Q01007-12, 2005.

[9] C. Wang, X. Ding, Q. Li and M. Jiang , "Equation - Based InSAR Data Quadtree Downsampling for Earthquake Slip Distribution Inversion," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 12, pp. 2060-2064, 2014.

[10] P. Mastro and A. Pepe, "Adaptive Spatial Multi-looking of Differential SAR Interferograms Sequences using Circular Statistic," VDE, pp. 1-6, 2018.

[11] A. Ferretti et al., "A new algorithm for processing interferometric datastacks: SqueeSAR," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, p. 3460–3470, 2011.

Shanghai is situated at the mouth of Yangtze River, on the coast of the East China Sea. It is one of the 47 megacities in the world with a population of more than 10 million and is also the financial and science and technology center of China (Dsikowitzky et al. 2016; Kuang et al. 2014). Due to the explosive expanding of the population and economic, Shanghai has started large-scale exploitation of groundwater and infrastructure since the last century which leads to significant land subsidence. According to the records, the cumulative subsidence in the downtown area of Shanghai has exceeded two meters since have leveling data by the 1920s and land subsidence has become one of the most serious urban risks (Zhang et al. 2002). Although groundwater exploitation has been effectively controlled in recent years, the city is still suffering serious risks in view of global sea level rise and the continuous subsidence caused by large-scale infrastructure construction and land reclamation.

During the last century, the subsidence monitoring in Shanghai was mainly by leveling or GPS which was based on single-point survey and will produce large amount of cost. Fortunately, the development of InSAR technology in recent decades has made large-scale, high-frequency, low-cost urban deformation observation possible (Burgmann et al. 2000; Massonnet and Feigl 1998). Especially, MT-InSAR technology, can achieve the ground deformation monitoring with accuracy of millimeter-level, which can meet the needs of high accuracy urban deformation monitoring practice (Lanari et al. 2007; Solari et al. 2016).

Disaster risk assessments is an effective means to qualitatively describe the degree of disaster impact. There have been a large number of related studies in the fields of geology, urban flood risk, and drought, such as the geological risk assessment by fuzzy cluster-analysis methods(FCM) and the flood risk grading based on risk matrix (Efendiyev et al. 2016; Klein et al. 2013). Although MT-InSAR method have been effectively used for studying the ground subsidence in Shanghai in recent decades, these studies are mainly concentrate on the quantitative study of the surface deformation, which are lack of further grading the hazard risk with auxiliary data, such as economic losses, infrastructure vulnerability and land use/land cover data.

In this work, in order to make a relatively detailed assessment, Shanghai is divided into a regular grid matrix according to the size of 100m*100m. And we quantitative scoring each grid with the comprehensive risk assessment matrix, which was consists of deformation time series obtained by Small Baseline Subset (SBAS) algorithm and Sentinel-1A datasets acquired from 2016 to 2018, the land use/land cover data obtained by Landsat-8 images, and the major infrastructure data includes main buildings, flood control levees, and road networks of Shanghai. Subsequently, all of the grids are divided into three levels, including low risk, medium risk and high risk, with support vector machine. Finally, the land subsidence risk rating map of Shanghai was acquired, which will provide a useful reference for the urban risks assessment and the comprehensive management of relevant departments.

[1]Burgmann, R., Rosen, P.A., & Fielding, E.J. (2000). Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation. Annual Review of Earth and Planetary Sciences, 28, 169-209

[2]Dsikowitzky, L., Ferse, S., Schwarzbauer, J., Vogt, T.S., & Irianto, H.E. (2016). Impacts of megacities on tropical coastal ecosystems The case of Jakarta, Indonesia. Marine Pollution Bulletin, 110, 621-623

[3]Efendiyev, G.M., Mammadov, P.Z., Piriverdiyev, I.A., & Mammadov, V.N. (2016). Clustering of Geological Objects Using FCM-algorithm and Evaluation of the Rate of Lost Circulation. Procedia Computer Science, 102, 159-162

[4]Klein, J., Jarva, J., Frank-Kamenetsky, D., & Bogatyrev, I. (2013). Integrated geological risk mapping: a qualitative methodology applied in St. Petersburg, Russia. Environmental Earth Sciences, 70, 1629-1645

[5]Kuang, W., Chi, W., Lu, D., & Dou, Y. (2014). A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. Landscape and Urban Planning, 132, 121-135

[6]Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M., & Pepe, A. (2007). An overview of the small BAseline subset algorithm: A DInSAR technique for surface deformation analysis. Pure and Applied Geophysics, 164, 637-661

[7]Massonnet, D., & Feigl, K.L. (1998). Radar interferometry and its application to changes in the earth's surface. Reviews of Geophysics, 36, 441-500

[8]Solari, L., Ciampalini, A., Raspini, F., Bianchini, S., & Moretti, S. (2016). PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization. Remote Sensing, 8

[9]Zhang, W., Duan, Z., Zeng, Z., & Kang, Y. (2002). Feature of Shanghai Land Subsidence and Its Damage to Social-economic System. Journal of Tongji University, 30, 1129-1133,1151

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. Schistosomiasis is a parasitic disease that menaces human health. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Landsat 8 and Sentinel 2 had 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 this study used T-S Fuzzy RS model to establish a new suitable index membership function due to the different RS data, and a long time series dynamic monitoring of snail distribution in Dongting Lake were achieved. A comparative analysis had been performed to validate the predicted results against the field survey data. The results demonstrate the accuracy of the developed model in predicting distribution of snails.

Malaria has induced enormous public health problems worldwide, especially in the tropical and subtropical areas. The transmission of malaria parasites depend on mosquitoes’ biting on human beings. The ability of the mosquitoes to transmit malaria parasites is dependent upon a series of biological features generally referred to as vectorial capacity. The development of mosquitoes’ population as well as their biting behaviors are determined by a serial of environmental factors, especially the rainfall and temperature. In this study, remote sensing products from the high-resolution GF-1 images were utilized to develop the vectorial capacity model (VCAP), which was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. The developed model was implemented in Tengchong City in Yunnan Province, which is located at the China-Myanmar border area. The data of historical malaria infections, as well as the meteorological and hydrological records were collected to establish geographic information system database in terms of spatial distribution of malaria and mosquitoes．Then spatial pattern of mosquitoes’ vectorial capacity were mapped and the risky area for malaria transmission were identified, which will help to develop more sustainable strategies for malaria control and prevention．

Schistosomiasis is one of the most serious parasitic diseases due to the infection of schistosoma japonieum via the intermediate host snails, which have endangered to the safety of public health worldwide. In China, it remains endemic in lake and marshland regions including Anhui, Hubei, Hunan, Jiangxi, Jiangsu Provinces as well as mountains areas in Sichuan and Yunan Provinces. The transmission of schistosomiasis is closely related to environmental factors, such as vegetation, temperature, hydrology and soil. In order to enhance the capacity of schistosomiasis contro1 and prevention, TM images and high-resolution GF-1 images were utilized to identified snail habitats of based on geographic information system (GIS) and remote sensing(RS) technology. As a typical lake and marshland endemic regions, Yueyang City in Hunan Provice which is located near the Donting Lake, is selected as the study region. The data of historical infection records, as well as the meteorological, hydrological and land surface types were collected to establish geographic information system database of spatial distribution of schistosomiasis and snails．Then spatial pattern of schistosomiasis risks were mapped and factors associated with geographical variation in infection patterns were identified, which will help to develop more sustainable strategies for schistosomiasis surveillance．

The global wind profile has a significant impact on the atmospheric circulation, the atmospheric carbon cycle, marine–atmosphere circulation, and aerosol activities. On the other hand, aerosols in the whole troposphere play a key role in the climate change and the air quality because of its direct, semi-direct, and indirect effects on the radiation budget. ESA decided to implement the Atmospheric Dynamics Mission ADM-Aeolus and the Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) to provide global profiles of wind, clouds, aerosols, and properties together with derived radiative fluxes and heating rates. ADM-Aeolus carried the first wind lidar in space (ALADIN) and launched in August 2018. EarthCARE will carry cloud profiling radar, HSRL (High Spectral Resolution Lidar) and multispectral imager and is scheduled for launch in 2021. TROPOS developed several multiwavelengths and polarization Raman lidar systems (about 10 PollyXTs, MARTHA and BERTHA) and is using these systems at different continents. The recent and ongoing campaigns are atmospheric measurement at Cape Verde, the Central Asian Dust Experiment (CADEX), the Widefield Sky Scatterer Tomography by Lidar Anchor together with Technion Haifa, the Atlantic atmospheric observation experiment (OCEANET), and the Cyprus Clouds and Aerosol and precipitation experiment (CyCARE). The measurement results will support the CAL/VAL of the Aeolus. Ground-based WACAL (WAter vapor, Cloud and Aerosol Lidar) was developed by the lidar group at OUC (Ocean University of China) and deployed during several field campaigns, including the third Tibetan Plateau Experiment of Atmospheric Sciences (TIPEX III) in Naqu (31.5°N, 92.05°E) with a mean elevation of more than 4500 m above MSL in summer of 2014. HSRL and CDL (Coherent Doppler Wind Lidar) developed by OUC were also deployed in several field campaigns in the coastal zone and China Seas. These devices were deployed for the CAL/VAL field campaigns. The ground-based co-located and simultaneous measurements with lidars and sun photometer during overpasses of Aeolus are foreseen over Qingdao (for example: 17:37 LT on 18 November 2018 and 06:10 LT 19 November 2018). The CAL/VAL campaigns will contribute to exploit the Aeolus wind observations for the study of atmospheric dynamics. Lanzhou University (LZU) established a Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and conducted lidar observations of dust aerosol physical optical characteristics near the resource area in the northwest of China. To investigate the characterization of atmospheric bioaerosols along transported pathways of dust aerosols, a multi-channel lidar spectrometer system was developed to observe Mie, Raman scattering and laser-induced fluorescence excitation at 355 nm from the atmosphere. Long-range transport of Asian dust from the Taklimakan and Gobi deserts was studied based on CALIPSO lidar measurements. German Aerospace Center's (Deutsches Zentrum für Luft- und Raumfahrt; DLR) Institute of Atmospheric Physicsa (IAP) is a member of ESA´s ADM-Aeolus Mission Advisory Group, Head of ESA funded pre-launch campaign study and contributor to algorithm and processor studies for Aeolus data products. DLR-IAP conducted several flights in the Mediterranean area which aimed at aerosol (incl. Saharan dust) detection using the ALADIN Airborne Demonstrator (A2D). DLR leads the Aeolus Data Innovation and Science Cluster (DISC) after launch to provide recommendations for the Aeolus instrument operation, retrieval algorithms as well as the calibration and validation procedures. The campaigns including airborne rehearsal campaign, airborne CAL/VAL campaigns and Tropical airborne campaign are ongoing/scheduled for 2018-2020.

The project objective is to validate the ADM-Aeolus and EarthCARE wind, cloud and aerosol data products. Ground-based co-located measurements with PollyXT, BERTHA, WACAL, CDL and HSRL lidars during overpasses of Aeolus and EarthCARE are foreseen in China (Costal cities, China Seas, inland cities, Tibetan Plateau, Taklimakan desert) and in Central Europe. An overview of the field campaigns will be presented in this report together with observation results from the ongoing data analysis. As an outlook, combing the aerosol data from other atmospheric lidar mission with the wind/aerosol products from ADM-Aeolus, the comprehensive observations of vertical profiles of optical properties, flux and the deposition of dust during the long-range transport over continents of Europe and Asia can be implemented. Furthermore, by means of the back trajectories model, it is possible to determine the long range transportation of dust and to reveal its impact on marine ecosystem.

Launched on 22 August 2018, Aeolus is the first satellite mission to measure atmospheric wind profiles on a global scale. The wind observations contribute to the improvement in numerical weather prediction (NWP), as they help to close the gap in wind data coverage, especially over the oceans and in the tropics, which has been identified as one of the major deficiencies in the current Global Observing System. For this purpose, it provides profiles of one line-of-sight (LOS) component of the horizontal wind vector from ground throughout the troposphere up to the lower stratosphere with a vertical resolution of 0.25 km to 2 km depending on altitude and precision of 2 m/s to 4 m/s. The obtained near-real-time data allow for greater accuracy of the initial atmospheric state in NWP models and thus improve the quality of weather forecasts as well as the understanding of atmospheric dynamics and climate processes.

At the heart of Aeolus is the Atmospheric Laser Doppler Instrument, ALADIN, which is composed of a frequency-stabilized, ultraviolet laser, a 1.5 m-diameter telescope and a highly sensitive receiver. The revolutionary instrument works by emitting short, powerful laser pulses through the atmosphere and collecting the backscattered light from air molecules, particles and hydrometeors which move with the ambient wind. The wind speed is then derived from the frequency difference between emitted and backscattered pulses, which is caused by the Doppler effect, while the travel time of the pulses contains the altitude information. The algorithms and processors needed to derive wind profiles from ALADIN's raw data were developed by a European team of DLR institutes, the software company DoRIT as well as several European meteorological services (ECMWF, Météo-France and the Dutch weather service KNMI).

After a four-month commissioning phase dedicated to the initial in-orbit characterization and optimization of the instrument, its data processing, and as such the improvement of the wind data quality, the mission is currently in the transition to its operational phase. During this transition phase, data is released to an extended group of calibration/validation teams in order to receive important data quality assessments and further feedback, e.g. from co-located measurements. This allows for the necessary refinements of the data processing and mission operation in preparation of the official data release which is scheduled for 2019.

This presentation will provide an overview and status report of the Aeolus mission and present first impressive results obtained with the first wind lidar in space.

Since the successful launch of ESA’s Earth Explorer mission Aeolus in August 2018, atmospheric wind profiles from the ground to the lower stratosphere are being acquired on a global scale deploying the first-ever satellite-borne wind lidar system ALADIN (Atmospheric LAser Doppler INstrument). ALADIN provides one component of the wind vector along the instrument’s line-of-sight (LOS) with a vertical resolution of 0.25 km to 2 km depending on altitude, while the precision in wind speed is between 2 m/s to 4 m/s. The near-real-time wind observations contribute to improving the accuracy of numerical weather prediction and advance the understanding of tropical dynamics and processes relevant to climate variability.

Already several years before the satellite launch, an airborne prototype of the Aeolus payload, the ALADIN Airborne Demonstrator (A2D), was developed at DLR (German Aerospace Center). Due to its representative design and operating principle, the A2D has since delivered 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. Broad vertical and horizontal coverage across the troposphere is achieved thanks to the complementary design of the A2D receiver, which, like ALADIN, comprises a Rayleigh and Mie channel for analysing both molecular and particulate backscatter signals. In addition to the A2D, DLR’s research aircraft carries a well-established coherent Doppler wind lidar (2-µm DWL) which allows determining the wind vector with accuracy of better than 0.1 m/s and precision of better than 1 m/s. Hence, both instruments represent key instruments for the calibration and validation activities during the Aeolus mission.

Over the past years, the A2D and 2-µm DWL were deployed in several field experiments for the purpose of pre-launch validation of the satellite instrument and of performing wind lidar observations under various atmospheric conditions. In autumn of 2018, the first airborne campaign after the launch of Aeolus was carried out from the airbase in Oberpfaffenhofen, Germany. Aside from extending the existing dataset of wind observations, this field experiment aimed to perform several underflights of Aeolus in Central Europe in order to provide first comparative results between the two airborne wind lidars (A2D and 2-µm DWL) and the satellite instrument. At the same time, the campaign served to optimize the operational procedures, particularly in terms of flight planning, to be applied during the forthcoming Cal/Val campaigns in the operational phase of Aeolus. In particular, two campaigns are scheduled for May 2019 in Central and Southern Europe and September 2019 in the North Atlantic region.

This work will provide an overview of the most recent airborne wind lidar campaigns and present their results both from an instrument and a meteorological point-of-view. Furthermore, an outlook on the upcoming Aeolus Cal/Val campaigns will be presented.

During over the past 10 years, the massive green macro-algal bloom has regularly occurred in the Yellow Sea, the spatial coverage of which is mainly derived by the remote sensing community from satellite images with moderate/low resolution (30-m~1000-m), such as the 250-m-resolution MODIS (Moderate Resolution Imaging Spectroradiometer). In this paper, the MODIS estimates are compared for the first time with the concurrent high resolution (3-m) airborne Synthetic Aperture Radar (SAR) data. We find that the MODIS results are overestimated by more than a factor of 3 when each algae pixel is assumed to be pure (i.e. 100% algae cover), whereas the overestimation is significantly reduced to 1.14 when the pure pixel assumption is abandoned and the genuine (fractional) algae coverage is derived with the linear pixel un-mixing method. These results, together with the re-sampling processing of the high resolution images, indicate that the mixed pixel effect, that is inherent with images with moderate and low resolutions, is the key factor for the satellite extraction of the macro-algae coverage, and these findings are further confirmed by the satellite data with different resolutions. Besides, significant correlations (R²>0.9) are found between the macro-algae coverage from 3-m resolution SAR images and those from concurrent satellite images with various resolutions (30-m~1000-m) under the pure pixel assumption, which provides an alternative statistics-based method (in addition to the linear pixel un-mixing) for the accurate macro-algae coverage extraction from satellite images with coarse resolution (e.g. HJ-1 CCD, AQUA MODIS, COMS GOCI). This new method is independently validated with high resolution optical images, and applied to derive the annual maxima of the massive green macro-algal bloom areas (fractional coverage) in the Yellow Sea from 2007 to 2016, which ranges from 45.6~732.9-km² with an average of 247.9 ± 199.3-km².

High-resolution satellite earth observation data are available in large archives, as data collection increases the ability to inspect and investigate each scene becomes impossible due to the scale and quantity of observations. Computer assisted classification, segmentation and description of satellite data over aquatic bodies can provide invaluable information for focusing analysis to experts and the general public on everyday use of water resources.

Convolutional Neural Networks are capable of classifying and segmenting objects across thousands of images in a fraction of the time a human operator would require inspecting these images visually. These Deep Learning networks have previously been applied to classifying both land usage and land cover, they have been shown to be accurate using multi- or hyper-spectral data such as those collected by the Sentinel-2 MultiSpectral Instrument.

In this work, a training dataset consisting of coastal and in-land waters has been assembled from Sentinel-2 imagery covering multiple sites across North America, South Africa and China and extensively labelled to be compatible with Deep Learning methods. Convolutional Neural Networks developed and trained for natural image classification and segmentation have been extended and retrained through transfer learning to detect and segment the extents of Algal Blooms and River Plumes in the imagery.

Current Convolutional Neural Network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines.

Observation and modelling of the coupled energy and water balance is the key to understand hydrospheric and cryospheric processes at high elevation. In the Qinghai – Tibet Plateau (QTP) in – situ observations of liquid and solid precipitation are very sparse and studies on the mass balance of glaciers and the water balance of catchments are hampered by this gap. We are exploring the potential of using model forecasts of precipitation at high spatial resolution to replace or complement in-situ observations. A first experiment on applying WRF to model an extensive snowfall event on the entire QTP was completed and the results are very encouraging. In this study in – situ observations of air temperature, snow depth and snow water equivalent were used to evaluate model performance and particularly alternate model configurations. Our experiments did show that the WRF configurations with advanced land surface physics schemes captured better the spatial distribution of the snow event, but overestimated the intensity and extent of SD and SWE. Next, we focused on areas at lower elevation to carry out experiments with a coupled energy and water balance model of a catchment using again model output on precipitation.

A second set of experiments with WRF targeted the evaluation of model precipitation and other at-surface fields, e.g. air temperature and wind speed, for individual glaciers. This approach can potentially overcome a major challenge in energy and mass balance of glaciers, i.e. the lack of spatially distributed forcing data at high spatial resolution.

The energy and mass balance of glaciers was also analysed using a suite of earth observation data. The trend in glacier thickness at very high spatial resolution was determined for several glaciers using multi – temporal DEM-s generated with ZY – 3 stereo-image data. This study determined changes in glacier surface elevation separately for the accumulation and ablation zone. For the same glaciers, accurate surface velocity fields were retrieved by staking L-TM, L8-OLI and S2-MSI images.

Quantitative information on water losses is important to understanding the global terrestrial water cycle and land – atmosphere interactions. However, land surface water loss (evapotranspiraiton, ET) estiamted by land surface models usually neglects the sub-grid heterogeneity of landatmosphere parameters, and it will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. To evaluatethe aggregation bias, a numerical analysis was conducted to compare the aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the Probability Distribution Function (PDF), which complies with energy conservation. Four types of PDF were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces.Overall, the numerical experiments indicate that impacts on tile ET related to LAI are smaller than the ones related to SSM. Different meteorological conditions combined with the nonlinear dependence of ET on SSM/LAI may lead to large changes in the aggregation bias, even from underestimates to overestimates or conversely. In climate conditions with larger atmospheric water demand, enhancing evaporation, underestimation is more likely, and vice versa. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET in semi-arid areas. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0). The relation between aggregation bias and meteorology found in this study has the potential to identify or even as a starting point to correct the possible serious underestimations and overestimations in applications.

Meanwhile, to achieve a better water loss products, the ETMonitor algorithm was further improved following the former study of last year, to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature - vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d-1, which showed better accuracy than the former ET products, especially for the forest sites. The improved algorithm was further applied based on ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product, and ET products in the northeastern Thailand from 2001 to 2015 was achieved and analyzed.

This project aims at developing new algorithms and finding new synergies between different remote sensing products in order to better monitor water resources in the Red River basin and in the Luan River basin, namely by combining water level, soil moisture (SM) and runoff products. Over the Red River, water balance and water management can prove quite challenging, for a number of different reasons: it has a complex topography, with a high drop of 2574 m, and since it is a transboundary river, there is a lack of information on reservoir management. The Luan River is characterized by steep hills and deep ravines at its upper reaches, leading to it overflowing during the rainy season. On the contrary, the water flow is much reduced in winter, with the river being icebound for some months. All these features amount to difficulties in getting the information on time for flood or drought early warning systems. Nevertheless, by using remote sensing data, the aim is to tackle these difficulties and obtain a better estimation of water resources, leading to a better management.

In this respect, SM products can be a powerful asset. Currently the spatial resolution of satellite SM products is quite coarse, ranging from 36 km for SMAP (Soil Moisture Active Passive) to 40 km for SMOS (Soil Moisture and Ocean Salinity). However, in some cases, higher resolutions are required. In this respect, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) is an algorithm that downscales the SMOS and/or SMAP SM data by using MODIS (Moderate Resolution Imaging Spectroradiometer) or Sentinel-3 land surface temperature (LST) and vegetation cover data, along with a self-calibrated evaporation model. The algorithm estimates the SM variability at a 1km resolution within a low resolution pixel by relying on the self-calibrated evaporation model. More specifically, it derives a term, called soil evaporative efficiency (SEE), defined as the ratio of actual to potential evaporation, from LST and vegetation cover data. By taking into account the instantaneous spatial link between SEE and SM, it then distributes the high resolution SM around the low resolution observed mean values. Previous results obtained over the Red River basin and derived from SMOS needed further investigation due to Radio Frequency Interference (RFI) detected over the area. Since then, work has been undergone to filter the RFI from the SMOS-derived 1 km SM products. For this study, 1 km SM products have been produced over the entire Red River basin and the Luan River basin for the 2015-2018 time period, derived from both SMOS and SMAP. Preliminary results show to be promising, with an improvement with respect to the SMOS-derived products, thanks to the RFI filtering. SMAP-derived SM products have also shown promising results over the area.

By combining these enhanced 1 km soil moisture products over the Red River basin and the Luan River with water level products, the hydrological model estimations can be further improved (Li et al. 2019).

Land surface temperature (LST) plays an important role in the processes controlling energy and water exchanges at the surface-atmosphere boundary. It has been widely used in studies such as hydrology, ecology, meteorology, and climatology. Satellite remote sensing makes it possible to retrieve LST at relatively dense and regular spatial sampling intervals over large areas. Over the past three decades, satellite thermal infrared (TIR) remote sensing has become one of the most important approaches to estimate LST.

However, a major shortcomingof satellite TIR remote sensing for LST estimation is its extremely low tolerance to clouds. Clouds not only reduce the spatial coverage of the TIR LST but also decreases the actual temporal resolution. The evidence has been reported for current satellite TIR LST products over different areas in previous studies. Therefore, the performance of current satellite TIR LST product is greatly limited in many applications, especially for those requiring LST with both high temporal resolution and dense spatial coverage. In contrast, passive microwave (MW) remote sensing is insensitive to clouds: thus, it is an important independent source for LST estimation complementing the available TIR LST. Merging TIR and MW observations is able to overcome shortcomings of single-source remote sensing to derive such a LST, in which how to efficiently improve the spatial resolution of MW LST to the same level as the TIR LST is a crucial link. However, in current merging methods, models adopted for downscaling MW LST fails to quantify the effect of temporal variation of LST. Thus, the accuracy and the image quality of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas.

In this context, this study proposes a practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: the annual temperature cycle component (ATC), diurnal temperature cycle component (ΔDTC) as prescribed by solar geometry and weather temperature component (WTC) driven by weather change. For each component, a dedicated algorithm was applied to improve its spatial resolution or optimize its accuracy according to its thermal properties. The merged LST can be obtained by combining the improved components together,

The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over northeast China: the resulting merged LST have a standard deviation of error (STD) of 1.29-2.71 K compared to the 1-km MODIS LST (MYD11A1) and successfully fill missing pixels due to clouds. Validated against in-situLST from three ground sites with diverse land cover types, the merged LST have a root mean square of error (RMSE) of 1.20-2.75 K, which is comparable to MODIS LST; besides, no obvious differences in the accuracy of the merged LST were found between daytime and nighttime, or under clear sky and unclear sky conditions.

The generated all-weather LST was also compared with the 1-km AATSR LST from the European GlobTemperature project. Good agreement between these two products was also found: the mean bias error (MBE) was from -0.04 to 0.14 K and the STD was from1.02 to 1.61 K.

Compared to a 1-km all-weather LST from a previous method, the merged LST derived from this study performs better in both accuracy and image quality, indicating the proposed method has an improved capability to generate 1-km all-weather LST data. The method was further applied to generate the daily all-weather LST during 2003-2017 for the Tibetan Plateau and its surrounding areas. This dataset is now being utilized in the modelling of water cycle over the Tibetan Plateau.

In this study, a time series ground-based active and passive microwave experiment for snow is presented. The experiment is carried out in 2017-2018 and 2018-2019 winter in Xinjiang, China. In the experiment of 2017-2018 winter, ground based SAR and microwave radiometers are used to measure the multiple frequency and multiple polarization backscattering coefficient and brightness temperature of snow covered soil. In 2018-2019 winter experiment, precise phase measurement is emphasized in the SAR observations to study the phase change due to snow accumulation and co-polar phase difference of terrestrial snow. Different microwave scattering and emission models of snow are used to study the measurement results, and the microwave signature of snow are studied by model simulations. The application of backscattering coefficient, brightness temperature, phase change and co-polar phase difference in snow water equivalent retrieval will be discussed.

Mountain glaciers can directly reflect local climate change and play a crucial role in the terrestrial water cycle and food security of local people. Nyainqentanglha Mountains (NM) have about 9600 km² glaciers, which account for 18.47% of the Tibetan Plateau (TP) and are the major water resources of rivers, lakes and human activities as well. The field observation is difficult to implement because of the high altitude and risk, therefore, many different experimental remote sensing techniques have been applied to estimate the glacier mass balance by several authors. Although the spaceborne optical photogrammetry is one of the promising ways to capture the glacier mass balance, the High Resolution TLA stereo images have been used less frequently. And the glacier mass balance patterns in the EM need to be further explored.

In this study, we used Zi Yuan-3 (ZY-3) Three-Line-Array (TLA) stereo images to extract the glacier mass balance in two study sites during 2000–2017. One is located in the western of the NM (WNM), a moderately complex terrain. The other one lies in the eastern end of the NM in the southeastern TP (ENM), where the topography is more complex than in the WNM. The glaciers in the WNM and ENM are of a subcontinental and maritime type, which provides an opportunity to compare and analyze the glacier mass balances of different glacier types during a decade.

The results showed that the glaciers in the WNM and ENM experienced mass loss in the 2000-2017, and the glacier thinning rates in the ablation regions were apparently larger than in the accumulation regions. In the WNM, the mean glacier elevation change and mass balance were -0.31 m a^-1 and -0.26±0.18 m w.e. a^-1, while the glaciers in the ENM obviously melted faster than the WNM, and these two values became -0.92 m a^-1 and -0.78±0.12 m w.e. a^-1, respectively. In the WNM and ENM, the glacier mass balances in the ablation zones were -0.57±0.18 m w.e. a^-1 and -1.02±0.12 m w.e. a^-1, while both values in the accumulation zones were 0.16±0.02 m w.e. a^-1 and -0.08±0.12 m w.e. a^-1.

Multi-source remote sensing data, from visible to thermal infrared are used for forcing, calibration, validation and data assimilation of/into basin scale hydrological models.

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 showing an overall agreement between the estimated and measured data, as certified by numerous statistical indexes.

Then, at basin scale the modelled Evapotranspiration has been compared with a number of global products: the Chinese ETMonitor, and with global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM. At basin scale, the agreement between the model and the ET data is consistent but presents some irregularities, as a consequence of each ET product’s own foundational hypotheses and algorithms.

The objective of this study is the evaluation of the potential of two Land Surface Temperature downscaling algorithms with respect to high resolution LST from LANDSAT 7 ETM+ and resampled MODIS LST (MOD11A1).

Four different LST sources have been compared over the Heihe river basin, an endorheic basin in China, characterized by a wide variety of ecosystems, from desert oases to irrigated croplands and wooded mountain ridges.

The first two LST sources are measurements provided by the ETM+ instruments (aboard satellite Landsat-7) at 30m spatial resolution every 16 days, and MODIS (aboard Terra at the lower resolution of 1000m. The other two sources are products of downscaling algorithms. The STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm merges ETM+ and MODIS images to obtain LST data with the spatial resolution of the former and the temporal frequency of the latter, involving neighbouring pixels in the process. On the other hand, the DisTrad (Disaggregation of radiometric Temperature) algorithm, establishes a link between LST and NDVI, in order to revert to the former anytime vegetation data is the only data available.

Globally, resampled MODIS and DisTrad perform better, not quite reaching the accuracy of ETM+ but all the same yielding an accurate approximation. On the other hand, STARFM struggles with the variety of land cover types, offering an acceptable performance in the desertic area, which is the most uniform of all. Categorizing the pixels according to their land cover type, it is found that vegetated areas, especially croplands, are the most difficult to interpret for the LST sources, with low performance in the early summer during the peak of the maize season. Furthermore, grouping pixels by their lighting condition (whether they are in light or in shadow) does not offer major results: data quality for shadowed pixels is quite worse than for lighted pixels, but the number of the formers is so low that the impact on the overall result is close to negligible.

Overall, MODIS and DisTrad are the best candidates to compensate for the low temporal frequency of ETM+ without losing too much accuracy. Furthermore, DisTrad application requires more input data and computing effort than re-sampling MODIS.

Land Surface Temperature (LST) is an indicator for the exchange of energy in the process of atmosphere-ground interaction. It is an important parameter indicator for global resource and environmental dynamic analysis. Sentinel-3A satellite was jointly developed by theEuropean Space Agency (ESA) and the European Meteorological Satellite Organization (EUMETSAT),and was successfully launched in February 2016. One of its main payloads is the Sea and Land Surface Temperature Radiometer (SLSTR), which has three channels (i.e. S7-S9) in the thermal infrared range with a 1000 m resolution in the nadir view mode. The central wavelengths of S8 and S9 are 10.85 μm and 12 μm, respectively. Thus, images of these two channels can satisfy the requirement of LST estimation.

The objective of this study is (i) to explore the applicability of the classical split window algorithms(SWA)for estimating LST from the SLSTR data acquired by S8 and S9 channels and (ii) to analyze the possible sources of error. Nine SWAswidely accepted by the scientific communities were selected as the candidate algorithms, including PR1984, BL-WD, VI1991, UL1994, WA2014, ULW 1994, SR2000, BL 1995, and GA2008. These SWAs were also used to develop the algorithm for the Chinese GLASS LST product.

The aforementioned SWAs were trained globally based on simulation datasets from the forward radiative transfer simulation. The SeeBor atmospheric profile database was used as the basis in the forward simulation. For each profile, 10 LST and near-surface air temperature differences were defined, i.e. from -16 K to 20 K in increments of 4 K; spectral emissivities of 48 materials were used; the view zenith angles were defined as 0 to 55°in increments of 5°. MODTRAN 5 model was employed to conduct the forward simulation. For each trained SWA, the NDVI threshold method was used to determine the land surface emissivity of each pixel. The European Mesoscale Weather Forecasting Center (ECMWF) data were used to determine the atmospheric water vapor content and near-surface air temperature of each pixel.

Results show that all the selected SWAs have accuracies better than 2 K in training. Ground measured LST at four ground sites of HiWATER with good spatial representativeness were used to validate the estimated LST from the actual SLSTR data during November 2016 to December 2017. Validation show that the accuracies of the nine SWAs are approximately 2-4 K, better than the official SLSTR LST product. The estimated LST is affected by many factors, such as the land surface emissivity, air humidity, land surface type, air temperature, and atmospheric water vapor content. The study would be beneficial for improving the SLSTR LST product.

The Lancang-Mekong river, known as the Lancang river in China, the Mekong reiver outside China. The Lancang-Mekong Basin is a trans-boundary river with an area of 795,000 km², including territorial parts of six countries: namely China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. With a total length of over 4350 km, the Lancang-Mekong River is the longest river in Southeast Asia. It originates from the glacier melting of Qinghai Tibet plateau at the elevation of 5200 m, and eventually flows into the South China Sea at Mekong Delta in Vietnam. More than 72 million people benefit from this river. Consequently, the Lancang-Mekong basin has an outstanding practical significance for the ecological and economy development of alongshore area. However, the current land use/cover in the Lancang-Mekong river basin is in a very critical situation. Large patches of primary and secondary forests have been destroyed in Laos, Myanmar and Cambodia. Crop rotation is replaced by single cropping of rubber, cashew, sugar cane, and eucalyptus etc. Social and economic transformation, urbanization and interregional cooperation brought by increasing human activities also play an important role in land use/cover change in the Lancang-Mekong river basin. The land use/cover change have influenced climate, precipitation, the energy balance, carbon budget, and hydrological cycle in the basin.

Remote sensing has long been recognized as an effective tool for broad-scale(such as global scale, regional scale and basin scale) land use /cover mapping. At present, eight land cover thematic datasets( such as USGS with 1km, UMD with 1km, BU with 1km, GLC2000 with 1km, Globcover 2005 with 300m, GlobCover 2009 with 300m, GlobCover 2010 with 250m, Globeland30 with 30m) at a global scale have been developed with resolution ranging from 30m to 1km. In recently years, remote sensing scientists are interested in land use /cover change, climate variation, and urbanization in the Lancang-Mekong river basin. Remote-sensing technology has the potential to monitor the environmental changes in basin scale. However, the Lancang-Mekong Basin is very large, and covers numerous climate zones and eco-regions, and needs seven MODIS tiles, or over 50 Landsat frames to cover the complete north–south-trending basin. Furthermore, the almost persistent cloud cover over the Lancang-Mekong Basin for large parts of the year, and optical remote sensing images are unavailabe in part of regions. The landscapes have complex spectral and textural characterization in the Lancang-Mekong river Basin. Because of these factors, there is reported about high resolution(≤10m) land use/cover products in the Lancang-Mekong river Basin in recent years. As new satellites and sensors become avaiable. the Sentinel-2A/B are optical satellites, which respectively launched in 2015 and 2017. The Sentinel-2 has multi-spectral data with 13 bands in the visible, near infrared and shortwave infared with respectively spatial resolution of 10m, 20m, and 60m. Multi-source remotely sensed images with high temporal, spatial and spectral resolution in the Lancang-Mekong river basin have obtianed by the ESA Sentinels satellites in recently years.

Lots of research has shown that the land use /cover information play a role in climate variation, ecology and environment destroy, and naural hazards. The land use/ cover is fundamental information for natural resource management, environmental change studies, urban planning to sustainable developemnt, and many other societal benefits in the Lancang-Mekong river basin. Bacause of region area large, complex topography, cloudy and rainy environment in the Lancang-Mekong river basin, so far, the high resolution land use/cover products have not presented. The relation research has became urgent. More specifically, the whole research may include the following some parts. (1) employing Sentinel Application Platform (SNAP) toolboxes to pre-process the data sets over the Lancang-Mekong river basin (2) Developing object-oriented random forest (OORF) Classifcation algorithm for mapping Land use/cover in basin scale . (3) Mapping high resolution land use/cover in the basin using the presented alogrithms in the basin.

Glaciers in the Tibetan Plateau have significantly influenced the local ecology and economy as a water resource. Southeast Tibetan Plateau is a typical region of debris-free and debris-covered glaciers in China. Automatic glacier mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Therefore, the knowledge of the changes of debris-free and debris-covered glaciers in the southeastern Tibetan Plateau is still limited. This study investigated spatial patterns and area changes at decadal scales of debris-free and debris-covered glaciers in the southeastern Tibetan Plateau by utilizing a machine-learning algorithm based on multi-temporal satellite images. Specifically, Random Forest method was applied based on Landsat and ASTER GDEM V2 data for 3 target years over 20 years (1995, 2005, and 2015). Glacier area changes were analyzed in terms of glacier characteristics (size, elevation and debris coverage) over the period of 1995 – 2015. The results demonstrated that this region has experienced a significant deglaciation of 29.86% (2842.08 km²) over a period of 20 years. The glacier size greatly influenced the change of glacier area and the shift of glacier retreat to higher elevations was notable. The melt rate of absolute area of the debris-free glaciers was faster than that of debris-covered glaciers and glaciers with varying supraglacial debris coverage responded differently. Meteorological data suggested that increasing temperature since 1995 probably represented the primary controlling factor for glacier variations in this region.

China has been affected by some of the world's most serious geological disasters and experiences high economic damage every year. Geohazards occur on remote and highly populated areas.

In the framework of the DRAGON4 32365 Project, this paper presents the main results and conclusions derived from an extensive exploitation of available remote sensing data and methods that allow the evaluation of their importance for various geohazards. Therefore, the great benefits of recent remote sensing data (wide spatial and temporal coverage) that allow a detailed reconstruction of past events and to monitor currently occurring phenomena are exploited to study various areas and various geohazard problems, including: surface deformation of the mountain slopes and glaciers; identification and monitoring of ground movements mining areas and; subsidence, landslides, ground fissure and building inclination studies. Suspicious movements detected in the different study areas were verified and validated by field investigation and measurements in the local.

In the framework of the DRAGON-4 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 industrial regions in Northeast China. The traditional heavy industrial base, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, is playing 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 geo-hazards, such as subsidence, landslides, ground breakage and building inclinations, have been occurring for decades. The continuous monitoring of the effects of the mentioned phenomena is thus of great importance for the safety of the local population. Taking advantage of the availability of dense remote sensing dataset it is possible to analyze the geo-hazards and their environmental impacts in the region; and then making forecast about their occurrence in the future and providing support for disaster prevention and damage reduction.

We adopt multi-temporal InSAR methodologies able to estimate the spatial and temporal deformation over large areas. In this study, we use time-series InSAR results from multiple stacks (from ascending and descending orbits) and different sensors to monitor gravitational deformations and subsidence phenomena in urban areas especially effecting underground paths and railways, and in mining regions.

We take advantages from both the Persistent Scatterers Interferometry (PSI) and Small Baseline Subset (SBAS) techniques, for processing SAR data stacks acquired by Sentinel-1A/B (C-Band) and COSMO-SkyMed (X-Band) exploited for several areas in the BASF region.

In comparison to the 2018 already presented mid-term project results, we investigated the same areas of Fushun and Shenyang analyzing Sentinel-1 datasets, provided by ESA, along both the tracks. The considered time interval spans from June 2015 to December 2018 for the descending orbit, and from April 2017 to September 2018 for the ascending case, respectively.

The retrieved mean ground velocity maps confirm the results from the previous exploited CSK data. Subsidence phenomena are still ongoing reaching values higher than -120 mm/yr inside the mine and -60 mm/yr at its edges. Some small areas (i.e. localized groups of pixels) show positive values (uplift) probably due to stockpile of excavation debris and/or processing waste material.

Two descending stacks of COSMO-SkyMed stripmap images and a stack of TerraSAR-X images covering Shenyang city are exploited in a small baselines subset analysis (SBAS) in our recent study. During the processing of COSMO-SkyMed images，a stack of 18 images acquired from March 13^th 2016 to April 17^th 2017 covering eastern Shenyang and a stack of 15 images acquired from March 1^st 2016 to April 21^st 2017 covering western Shenyang are processed respectively. 58 interferograms are generated out of 18 SAR images for the eastern stack, whereas 44 interferograms are generated out of 15 SAR images for the western stack. In the meantime, 68 interferograms are generated out of 20 TerraSAR-X images acquired from April 15^th 2015 to October 5^th 2016 for SBAS analysis. The topographic phase is simulated and removed from the interferograms using the TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m× 90 m) covering the study area. The SBAS approach has been proposed to overcome the limitation of decorrelation with reduced amount of SAR images by making full use of all possible interferograms with small spatial and temporal baselines. In this study, a modified SBAS approach developed in StaMPS to ensure the temporal continuity by connecting separated subsets of interferograms is implemented for data processing. The displacements acquired in line of sight direction is translated to vertical direction based on a simple assumption that no horizontal ground motion occurs for subsidence monitoring applications.

As recommended by the Guidelines of InSAR Monitoring for Geo-hazard of the Chinese InSAR community, areas presenting deformation velocities larger than 5mm/yr in LOS can be categorized as subsidence area. Taking the incidence angle into consideration, vertical deformation rate larger than 5.5 mm/yr suggests subsidence in Shenyang. Generally speaking, most parts of Shenyang are relatively stable. However, there is a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly occur in this area. Subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang.

We also processed a new CSK dataset over the Anshan city along the descending track. Moreover, we updated the processing of CSK image dataset for the western part of the city of Shenyang, thanks some new CSK acquisitions (descending track) provided by the Italian space Agency (ASI),

Our results confirm that the heavy industrial exploitation of mines and water pumping in the BASF region of Northeast China cause clear and strong ground deformation effects of high potential impact on the local infrastructures and population. The use of multiple stacks, from different sensors, of InSAR data allows monitoring such phenomena with an accuracy and temporal sampling not possible earlier.

By now, the use of EO products plays a fundamental role to monitor natural and man-induced hazards and to support Disaster Risk Management providing an important tool for local and national organizations.

Acknowledgments

This work is financially supported in part by the National Natural Science Foundation of China (Grant No. 41601378) and the Fundamental Research Funds for the Central Universities (Grant No. N150103001). The COSMO-SkyMed data is provided by ASI via the ASI-ESA Dragon4 Project ID. 32365_4. The TerraSAR-X data is provided by Airbus Defence and Space.

We present a conceptual framework that integrates data-driven modelling with remote sensing to detect and delineate landslide phenomena. The main objective of the associated MSc thesis is to test and implement the developed methodology within an Alpine study site and the Longnan region (China).

The methodological framework includes (i) an initial screening and collection of available data sets (e.g. on past landslide events, environmental data, satellite products) which can then be used to (ii) explore landslide susceptible terrain using data-driven modelling procedures. In this context, EO-based predictor variables (e.g. SRTM-derivatives, land cover information) as well as available landslide information (landslide inventory) will be included into supervised statistical/machine-learning classification techniques. The resulting spatial information on landslide prone zones allows restricting the main area of interest for the subsequent remote sensing based analysis. More specifically, optical remote sensing data (e.g. change detection based on Sentinel-2) will be tested for their potential to identify and map recent landslide phenomena. The ensuing landslide information is expected to further enhance the knowledge on the spatio-temporal occurrence of recent landslide events and to improve the previously described data-driven landslide susceptibility assessment. Depending on the progress of the previous activities, also the potential of Sentinel-1 data (e.g. SAR Interferometry) may be tested to acquire information on slope deformation. The activities associated to this MSc thesis will start in April 2019 and will be completed within December 2019.

Radar Interferometry (InSAR) can provide measurements of surface displacement from Space, with millimetric accuracy [1, 2]. These measurements are used in natural hazards analyses but also for monitoring anthroprogenic activities. In the last few years, the number of SAR satellites with shorter repeat intervals and higher resolutions make increase significantly SAR data volume. This increase as lead to challenges in terms of manual inspection [3], giving in turn rise to the search of automated ways to process the available data. The point previously described and advances in hardware lead to advances in deep learning, which has already been applied in several areas such as computer vision.

We propose a supervised Deep Learning (DL) approach for multivariate outlier detection in post-processing of multitemporal InSAR (MTI) results. We used a Convolution Neural Network (CNN) to process the data leading to one of the following labels: outliers, inliers or potentially dangerous lower coherence points. The input data were organized in such a way that for each point the model has access to the multivariate features (such as velocity, height, etc.) of the nearest points, as well as its coordinates in a local system (centered on each point).

After training and model evaluation, the accuracy, precision and recall were analyzed (the last two for each label), considering a threshold value of 0.6 applied to the model’s output. Our model achieved a 95% accuracy and a mean value of 89%, respectively in precision and recall.

Our research intends to demonstrate the usefulness of DL to detect deformation patterns in post-processing InSAR data, with the purpose of increasing point densities of Permanent Scatterers (PS) point networks, thus enhancing the reliability of InSAR post-processing data.

References

[1] Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78-89.

[2] Bakon, M., Oliveira, I., Perissin, D., Sousa, J., Papco, J. (2017). A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, NO. 6., 2791-2798.

[3] Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. R. (2018). Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data. Journal of Geophysical Research: Solid Earth, 123, 6592–6606.

Landslide is a hazard that threaten the people who lives in the mountain area, it comes active especially rainy seasons and causes a large number of casualties every year. The movement of the slope is an indicator of activity of the landslide, it is helpful to capture the precursor of the activity, the monitoring of the movement of the slope is very important. Subsidence is a "slowly vary geological hazard". Because of its lagging response and slow progress, subsidence is in mm and not easy to detect. It has the characteristics of long formation time, wide influence range, difficult prevention and control, and difficult to recover. High-accuracy displacement monitoring can help us obtain improved knowledge on the subsidence and landslides. In this work we will show the capability of up-to-date Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR), Envisat ASAR On-the-Fly Data, Archived Sentinel-1 Data in monitoring the movement of the landslide and subsidence in China, which can capture the fast and slow movement with different spatial and temporal baseline combination, the results shows that the SAR data has its advantage in monitoring the movement of the landslides and subsidence in mountain and city area.

Abstract

Dagushan Iron Mine is the deepest open-pit iron mine in Asia, with abundantiron ore resources. With continuous open-pit mining activities, the stairs extend to underground step by step, and engineering geological conditions are gradually revealed. The factors affecting slope stability are also changing gradually, e.g., exposure of surface water and groundwater. The lithological structure and composition of the slope body are also changing, as well as the effect of blasting on the orebody during mining process, along with the change of the slope safety and stability. As a huge artificial loose accumulation body, instability of the dump will lead to disasters and major engineering accidents for the mine, which not only affectingproductivity, but also causing huge economic loss. Therefore, in order to ensure the safe operation of the mine, it is necessary to conduct slope stability monitoring with non-contact strategy. This kind of non-contact monitoring doesn’t need to install measurement points on the dangerous slope, and thus no need to worry about sliding problems of the measurement points.

As an effective non-contact deformation monitoring tool, SAR interferometry has good potential in displacement monitoring of mines. With a stack of SAR images, time series InSAR is able to overcome spatial and temporal decorrelation problems, as well as the atmospheric phase artifacts, resulting in high precision deformation estimates[1][2]. Amongst various time series InSARalgorithms, small baseline subsets analysis (SBAS) is able to estimate deformation using all the high quality interferograms, which improves the utilization of SAR data and is suitable for analysis on long time series[3]. Therefore, the SBAS method is used to monitor the displacements in Dagushan open-pit iron mine. In this paper, 117 sentinel-1 images acquired from 2017 to 2019 are used, as well as the 3-arc-second DEM generated by the German TanDEM-X mission[4][5]. With height accuracy of approximately 1m, TanDEM-X DEM can be used to remove the topographic phase from the interferograms.

During data processing, a super master is first selected according to the spatial and temporal baselines. All the slave images are coregistered to the super master image during coarse coregistration and fine coregistration. Then, high quality interferograms with small spatial and temporal baselines are generated following a multi-master strategy. With the high density in time and space, as many interferograms as possible participate in displacement estimation. The short spatial baselines can reduce the influence of DEM error on deformation estimates. In order to improve the quality of interferograms, Goldstein filter is applied on all interferograms. Then, phase unwrapping based on minimum cost flow is conducted for each interferogram.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out. Based on the unwrapped interferograms, the average displacement rate and displacement time series are estimated using singular value decomposition method. The estimated displacements map in line-of sight direction show that the northern slope, western part and the northern part of the dump suffer from severe displacements. In order to assess the precision of the displacement estimates, a comparison with on-site date collected by measurement robots is carried out. There is a very good consistency between the two results. The outcome of this study can help with mine disaster prevention and mitigation, and provide technical support for ensuring safe mining activities.

Keywords: small baseline subsets analysis, displacement monitoring, open-pit mine

References

[1]Ferretti, A.; Prati, C.; Rocca, F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2202–2212.

[2] Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20.

[3] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience & Remote Sensing, 2003, 40(11):2375-2383.

[4] Torres R ,Snoeij P , Geudtner D , et al. GMES Sentinel-1 mission[J]. Remote Sensing of Environment, 2012, 120(6):9-24.

[5] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50.

Glacier motion represent a significant reference for the hazard assessment of glacier and glacial lakes. GF-3, as the first civil spaceborne synthetic aperture radar satellite in China, has important advantages in monitoring glacier motion due to its characteristics of all-weather, all-time capabilities and high spatial resolution. In this paper, based on five GF-3 images with FSⅡ imaging modes, the surface velocities of the Yiga Glacier, located in Nyenchen Tonglha Mountains, are estimated over five time periods using offset tracking technique during November 2017 to March 2018. The results were compared with the offset tracking results of sentinel-1 images which have a similar time with GF-3 image and based on the assumption that the velocity of the bedrock in the study area should be 0, the velocity residuals of the bedrock in each period are calculated, then the applicability of GF-3 image in monitoring glacier surface motion was evaluated. The results of GF-3 images show that the distribution of Yiga Glacier motion is similar in four periods, and the maximum surface velocities are all distributed in the central part of the glacier where the elevation changes dramatically. Meanwhile, the results are consistent with the results of sentinel-1 based on two images. The RMSEs of velocity residuals in the bedrock area in four periods are 1.4 cm/d, 2.0 cm/d, 1.7 cm/d and 2.3 cm/d, respectively, which validate the reliability of the deformation estimated used GF-3 images in this paper. Based on the above analysis, GF-3 SAR data can be used as one of the conventional data sources for monitoring glacier surface movement. Because of its high spatial resolution and high cost performance, GF-3 can play a unique role in monitoring the motions of glaciers.

Land subsidence is one of the most common environmentalproblems inurban areas around the world ^[1,2]. It has been hindering social stability and sustainable development for a long time.The deformation of the earth's surface and the structures upon it is usually a long-term gradual process. As the economic and cultural center of northeast China, Shenyang is developing rapidly in recent decades.With continuous above-ground and under-ground construction, Shenyang is suffering from continuous subsidence during a long time span.Therefore,continuous subsidence monitoring is essential in Shenyang.

As aspaceborne geodetic technology, synthetic aperture radar Interferometry (InSAR) is widely used in surface topography measurement and deformation monitoring.Using a stack of SAR images, Time Series InSAR is capable of overcoming decorrelation problems and monitoring land subsidence with very high accuracy. Several Time Series InSAR technologies such as Persistent Scatterer SAR Interferometry (PSI) ^[3], Small Baselines Subsets Analysis(SBAS) ^[4],Pixel Offset Tracking (POT) ^[5] and otherInSARtime series analysis algorithms have been widely used to monitor surface deformation.In this paper, three stacks of high-resolution TerraSAR-X and COSMO-SkyMed datasets are used to monitor the ground subsidence of Shenyang by means of SBAS. The COSMO-SkyMed images are acquired in descending orbit, including 15 images covering western Shenyang and 18 images covering eastern Shenyang. Both stacks are acquired during March 2016 and Apirl 2017. The 20 TerraSAR-X images are acquired in ascending orbit from August 2015 to October 2016. Besides, TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m × 90 m) covering the study area is used to simulate and remove topographic phase from the interferograms ^[6,7].

In this paper, the modified SBAS approach in StaMPS is used for time-series InSAR analysis, due to its ability to ensure temporal continuity by connecting separated subsets of interferograms with larger baselines. Theoretically, a complex multilook operation to mitigate the effects of the decorrelation noise should be independently carried out before generating interferograms^[8].In this study, the spatial resolution of COSMO-SkyMed and TerraSAR-X are similar in size, so we could skip this step.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out^[9-11].Based on an assumption that subsidence only happens in vertical direction, the estimated deformation in Line of Sight (LOS) is translated to vertical displacements.

Targeting at revealing the long-term ground subsidence, a fusion method based on nonlinear curve fitting is implemented using the overlapping time period between the TerraSAR-X and COSMO-SkyMed datasets from March 2016 to October 2016.It is revealed that the synergistic results of COSMO-SkyMed and TerraSAR-X datasets can obtain a more comprehensive understanding of the slow-moving subsidence.The subsidence results in this paper show a very good consistency with geological conditions and ground water funnel distribution in Shenyang City.Generally speaking, most parts of Shenyang are relatively stable. However, there’s a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly happen to this area. Ground subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang.

IndexTerms—Time Series InSAR, Subsidence,SBAS, Fusion

5.REFERENCES

[1]. Pradhan, B.; Abokharima, M.H.; Jebur, M.N.; Tehrany, M.S. Land subsidence susceptibility mapping atKinta Valley (Malaysia) using the evidential belief function model in GIS. Nat. Hazards 2014, 73, 1019–1042.

[2].YusupujiangA , Fumio Y , Wen L . Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan[J]. Remote Sensing, 2018, 10(8):1304-.

[3]Ferretti, APermanent scatterers in SAR interferometry.IEEE Trans Geosci Remote Sens 39(2001).

[4] Berardino, Paolo , et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing 40.11(2002):2375-2383.

[5] T., Strozzi , et al. Glacier motion estimation using SAR offset-tracking procedures.Geoscience& Remote Sensing IEEE Transactions on 40.11(2002):2384-2391.

[6] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50.

[7] The TanDEM-X 90m Digital Elevation Model. Available online: https://geoservice.dlr.de/web/dataguide/tdm90/ (accessed on 31 October 2018).

[8] Rosen, P. A., et al. Synthetic aperture radar interferometry. Proceedingsofthe IEEE. 88.3(2002):333-382.

[9] Lanari, Riccardo , et al. An Overview of the Small Baseline Subset Algorithm: a DInSAR Technique for Surface Deformation Analysis. Pure and Applied Geophysics164.4(2007):637-661.

[10] Manzo, Mariarosaria , et al. A Quantitative Assessment of DInSAR Measurements of Interseismic Deformation: The Southern San Andreas Fault Case Study.Pure& Applied Geophysics 169.8(2012):1463-1482.

[11] Pepe, A., et al. The study of the deformation time evolution in coastal areas of Shanghai: A joint CX-band SBAS-DInSARanalysis.Geoscience& Remote Sensing Symposium 2015.

[12]Jing,L, The study of Physical and Mechanical Properties of Soil and Engineering Geological In Shenyang City Center.[D]. 2015.

The application of the traditional InSAR time series technology is often limited by the little measure points on the surface of the landslides, especially in the region with dense vegetation. In order to overcome its disadvantages corresponding to the surface characteristics of landslides, the DS-InSAR time series technology was presented and employed in monitoring of Jiaju landslide status. Compared with the SBAS-InSAR technology, the presented DS-InSAR time series approach could yield much more high dense measure points on the surface of landslide. The distributed location and the motion variation of landslide were apparently shown in the final deformation results. Therefore, the DS-InSAR time series approach would be valuable and has great potential in landslide hazard monitoring.

The AMEOS (Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting) project aims at providing a tool for pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture.

The previous two years of the project were devoted to delineate the procedure enabling the satellite imagery based monitoring of crop pests and diseases. In particular, with reference to the Xylella fastidiosa threats of olive groves the approach consists in:

• 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 estimating trees by using Sentinel 2 images. This tool will be used to monitor the spread of pest.

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

In the previous year, working on Sentinel-2 images and exploiting the characteristics olive tree phenology and carotenoid indices allowed to improve, respect to the Corine Land Cover, the classification of the olive groves in the area of interest. Further, the use of a morphological approach on NDVI computed by using Sentinel-2 images of 2017 allowed to assess the olive groves density and trees number for each crop field. The quality of the results were validated by using a VHR image.

The present paper concerns the analysis of the possibility to monitor the decrease in the number of olive trees as a consequence of the spread of the xylella fastidiosa disease. The analysis has been carried out on Sentinel-2 images acquired in the second half of 2018.

The final objective of the project is the development of a satellite based system capable to assess the evolution of diseases on both permanent crops (olive groves, vineyards) and row crops (wheat, maize), in Italy and China, aiming at developing an early working tool.

Pathologically, the foliar biophysical variations are critical indicators for tracking the progressive host-pathogen interactions at different development stage. The interaction of electromagnetic radiation with plant leaves is governed by their biophysical constituents, and response to infestations. Hyperspectral- and multispectral- continuum observations could permit the acquiring of the host-pathogen processes within entire epidemic stages.

With this in mind, the launch of the hyper-spectral satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa, HyperSpectral Precursor of the Application Mission, to be launched on the 15^th of March 2019), could give a significant boost to the achievement of the project objectives.

The leaf area index (LAI) is a key parameter for describing the crop canopy structure and is of great importance for crop disease monitoring and yield estimation. Light detection and ranging (LiDAR) is an active remote sensing technology that can detect the vertical distribution of a crop canopy. To quantitatively analyze the influence of the occlusion effect, three flights of multi-route high-density LiDAR dataset were acquired using a UAV-mounted RIEGL VUX-1 laser scanner at the altitude of 15m, to evaluate the validity of LAI estimation in different layers under different planting densities. The result revealed that normalize root-mean-square error (NRMSE) for the upper, middle, and lower layers were 4.0%, 9.3%, 15.0% of 88050 plants/ha, respectively. In order to investigate the effect of different incidence angle, the accuracy of the point cloud data inversion for different air routes (different angles of incidence) was compared and found that the optimal incidence angle was −12° to −5°, and the NRMSE for the upper, middle, and lower layers were 9.3%, 8.0%, 11.5%. The voxel-based method was used to invert the LAI, and we concluded that the optimal voxel size was 0.05 m, which is approximately 2.09 times of the average point distance. The detection of different layers of different planting densities, incidence angle, and optimal voxel size can provide a guideline for UAV-LiDAR application in the crop canopy structure analysis

Yield estimation and forecasting, at the field scale, would allow farm managers to plan their agronomic operations, e.g., sowing, tillage or fertilization, on the basis of expected yield potential. At the district scale, it would be useful for public and private organization, for commercial or planning purposes. In order obtain in-season crop yield predictions, biophysical variables retrieved from remotely sensed data can be assimilated into crop growth models. Data assimilation is a group of methods allowing to combine in an optimal way different information types, dynamically integrating mathematical descriptions of processes embedded into deterministic models and physical information obtained from observations. It allows to update model simulations with observed data, e.g. from remote sensing, within a systematic estimation structure. The most advanced assimilation methods include a framework for the incorporation of model and observation errors and the quantification of prediction uncertainty.
The Ensemble Kalman Filter (EnKF) method has been shown to be particularly suitable for the assimilation of remotely sensed data into crop models and has been extensively assessed for this purpose. The efficiency of EnKF has proved to be affected by a range of factors, such as the number of observations ingested into the process. Many studies have shown that using a higher number of observations brings about an improved accuracy of estimation. Other factors such as errors and uncertainties in the remote sensing observations, the variables that are retrieved as well as crop models formulation errors and parameters uncertainties play also an important role.
The project presented in this study, carried out in the framework of the Dragon 4 Programme supported by the European Space Agency (ESA) and the Ministry of Science and Technology (MOST) of China, aims at targeting these methodological issues, affecting the operational application of data assimilation schemes to the precision farming context.
A study was carried out in Maccarese (Central Italy) for the purpose of assessing the effect of temporal and spatial resolution of multispectral satellite imagery on the accuracy of durum wheat yield data estimation at the field scale from EnKF data assimilation. Field campaigns to measure biophysical variables were carried out from January 2018 to April 2018, for different growing conditions of the crop, at dates close to Sentinel-2 and Venus satellite acquisitions. The latter is a French-Israeli mission combining a high spatial (5 m) and temporal (2 days) resolution, with spectral bands quite similar to Sentinel-2. Initially, an assessment of different hybrid methods, i.e. based on the training of non-parametric regression algorithms with PROSAIL model simulations, was made, in order to identify the most accurate technique for the retrieval of biophysical variables from satellite imagery. Subsequently to a validation study with ground data, the retrieval technique was applied to all the cloud-free images available for the wheat season, notably 25 for Sentinel-2 and 26 for Venus. Among all the biophysical variables, fractional vegetation cover (FCOVER) was estimated with a smaller error (RRMSE of 9.5%) as compared to LAI (RRMSE of 19%). The former variable was thus employed for the data assimilation using the Acquacrop crop model developed by the Food and Agriculture Organization of the United Nations (FAO), since this model uses FCOVER as the main dynamic crop state variable. Recently an open source code version, Aquacrop-OS, has been released, thus allowing the application of sequential data assimilations methods, such as the Ensemble Kalman Filter, which was previously impossible.
EnKF was thus applied to Aquacrop-OS using a) only observations from Sentinel-2, b) only observations from Venus, or c) observations from both satellites, for durum wheat fields of the Maccarese farm. The results, in terms of grain yield prediction, were compared to fields where yield map data had been collected from the combine harvester at the end of the season. In general, benefits from an increased spatial and temporal resolution for field scale yield prediction mapping could be observed, but these came at the cost of an increased computational demand, therefore suggesting that a balance would be required for an operational application in the context of precision agriculture.

The development of high-quality specialty grain and protein-based classification of different types of grain is an important measure, which accelerates the shift from agricultural production to quality improvement. Remote sensing technology had achieved the prediction of grain protein content (GPC), but there were still large deviations in the interannual expansion and regional transfer. The study was conducted in wheat growing areas of Beijing, Renqiu and Jinzhou in Hebei Province. Firstly, Spectral consistency of Landsat-8 and RapidEye respectively compared with Sentinel-2 satellites at the same ground point in the same period. Then, based on the hierarchical linear model (HLM) method, the GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Mediumrange Weather Forecasts (ECMWF). The prediction of regional GPC and its spatial expansion were validated. Results were as follows: (1) spectral information calculated from Sentinel-2 imagery were highly consistent with that from Landsat-8 (R² = 1.00) and RapidEye (R² = 0.99) imagery could be jointly used for GPC modeling. (2) Predicted GPC by using HLM method (R² = 0.55) demonstrated higher accuracy than empirical linear model (R² = 0.23) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in Xiaotangshan and Renqiu City were ideal with RMSEv of 0.91% and 0.49%, respectively. This study had great application potential for regional quality prediction and inter-annual quality prediction.

Wheat powdery mildew is caused by the fungus Blumeria graminis and is one of the most common diseases that result in significant loss of crop yield and quality in China. Accurate monitoring of wheat powdery mildew at the regional level is important for food security and environmental protection. Traditionally, wheat powdery mildew is monitored by visual inspection of individual plants, which is time-consuming and inefficient. In recent years, a new satellite-based remote sensing technology has become a more viable option for managing and controlling agricultural practices.

Wheat powdery mildew is monitored by remote sensing technologies based on changes in transpiration rate, chlorosis, leaf color, and morphology in infected plants, which in turn affects the spectral reflectance properties of wheat. The existing methods includes statistical analysis and machine learning methods. In general, wheat powdery mildew can be detected visually for only a short period of time and the field sampling is often expensive. Thus, these methods do not always meet the needs of agricultural management due to the difficulty of collecting field samples.

Limited training data is a common problem in remote sensing applications and many studies have applied transfer learning in remote sensing to increase the quality and quantity of samples. However, thus far, the use of transfer learning techniques for crop disease monitoring has received limited attention. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region.

This study was carried out in Gaocheng city, Hebei province, and the auxiliary field survey samples were acquired from western Guanzhong Plain, Shaanxi province. The samples were categorized into three groups, i.e., normal, slightly diseased and seriously diseased, according to the disease index (DI). The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wetness and greenness were extracted using Landsat-8 OLI images to indicate the growth status of wheat and the field habitat characteristics. With these VIs, TrAdaBoost, which using the support vector machine with radial basis function kernel (RBFSVM) as weak learner, was used to develop the wheat powdery mildew monitoring model. At the initialization of the algorithm, an initial weight vector was given, and the maximum number of iterations was also determined. Per iteration, some samples were taken out from the auxiliary samples and study area samples, respectively. The removed samples were used to train a RBFSVM, and the weight of a sample indicated the probability of this sample to be selected to train the RBFSVM. At the end of each iteration, the error of RBFSVM on each training sample was calculated. If an auxiliary training sample was wrongly predicted, the sample likely conflicted with the study area sample. Then, we decreased its weight to reduce its effect, where in the next round, the misclassified auxiliary training sample would affect the learning process less than the previous round. In contrast, if a study area training sample is wrongly predicted, we increased its weight to improve its effect in the next round. Through this mechanism, the auxiliary samples that were useful for improving the monitoring accuracy of wheat powdery mildew in the study area were selected.

The model was tested using a dataset with 53 study area samples and 39 auxiliary samples. The overall monitoring accuracy was 83%, and the kappa coefficient was 0.69. Moreover, TrAdaBoost was also compared with four algorithms that are commonly used to monitor wheat powdery mildew at the regional level, and TrAdaBoost performed better than other algorithms. Experimental results demonstrated that TrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

Since March the 22th 2019 the PRecursore IperSpettrale della Missione Applicativa – PRISMA is in orbit on a sun-synchronous orbit at 615km. The PRISMA mission, fully developed by the Italian Space Agency (ASI), combines two payloads: a hyperspectral and a panchromatic camera. PRISMA, together with the Chinese Gaofen-5 (GF-5), are the only hyperspectral sensor in orbit with similar characteristics in term od GSD and spectral resolution.

The PRISMA hyperspectral payload is a pushbroom scanner covering the full range (VNIR-SWIR) from 400 to 2500nm with 239 spectral bands at a spatial resolution of 30 m with a swath of 30 km, while the panchromatic camera provides 5m pixel images co-registered with hyperspectral imagery. PRISMA is a prism spectrometer and it is characterized by a variable bandwidth across the nominal spectral range, nevertheless the band width is less than 12nm (i.e. between 7.3 and 11.04nm). The PRISMA coverage is global with a 29 days (orbit repeat period), while the revisit time on a specific area of interest is of 7 days thanks to the off-nadir angle of up to ± 20.7°.

After the end of the commission phase (scheduled in the 2^nd semester 2019), it is foreseen a structured three years CAL/VAL activity, which will be performed on scattered instrumented sites in Italy. The test sites have been selected according to the peculiar thematic areas of interest for the mission (e.g., topsoil characteristics, vegetation biophysical parameter retrieval, snow and coastal waters), moreover international test site are still under definition. The Cal/Val activities includes: airborne surveys with VNIR-SWIR scanner eventually coupled with thermal LWIR multispectral data, field activities contemporary to the PRISMA acquisitions. CAL/VAL activities will be planned, whenever possible, in synergy with ESA (i.e. Fluorescence Explorer – FLEX and the candidate CHIME hyperspectral missions) and the actual ASI missions’ development (hyperspectral mission SHALOM).

The PRISMA acquisition plan is an opportunity to strengthen the ongoing collaborations between the Authors and the RADI Chinese colleagues in the framework of the active international collaborations programs (ESA Dragon-4 and CNR-CAS agreement). The opportunity to foster a synergy between the Italian PRISMA and the Chinese GF-5 missions, in order to increase the possibility to have more consistent hyperspectral time series suitable to monitor biophysical variables and agronomical processes.

This work aims to analyze the feasibility of application of multispectral imagery to detect anomalies caused by tree pests, towards an early warning monitoring system. The analysis take into account multispectral images from satellites as Sentinel-2 (10 mts. spatial resolution) and PlanetScope (3 mts. spatial resolution) imagery, and images obtained from sensors mounted on UAV (5 cms. spatial resolution), as MicaSense RedEdge sensor on board a UAV SR-SF6.

The scope of this study is the evaluation of this set of images to locate tree crowns, and: 1) distinguish between symptomatic and apparently healthy canopies, and 2) between symptomatic and affected asymptomatic trees.

The main pest of interest analyzed in this study is the Xylella fastidiosa (Xf), a vector-transmitted bacterial plant pathogen associated with serious diseases in a wide range of plants. Xf was detected on olive trees in Puglia, southern Italy, in October 2013, the first time the bacterium has been reported in the European Union. Since then it has also been reported as present in France, Spain and Germany. Controls are in place to prevent the bacterium from spreading.

In 2016, the European Food safety Authority (EFSA) concluded that research being carried out in Apulia showed that certain treatments reduce the symptoms of disease caused by Xf but do not eliminate the pathogen from infected plants (EFSA 2016). A significant difficulty that need to be taken into account for the containment of Xf, comes from its very wide host range (in September 2018 the list includes more than 500 plant species), and since infections that do not cause symptoms in some host–strain combinations, despite the infected hosts continuing to act as inoculum sources.

In (Zarco-Tejada et al. 2018) a multi-temporal trees inspection and airborne imaging data in several orchards has been carried out. The analysis found the physiological alterations caused by Xf infection at the pre-visual stage were detectable in functional plant traits assessed remotely by hyperspectral and thermal sensors. And it was confirmed the presence of Xf infection in the selected orchards by the quantitative Polymerase Chain Reaction (qPCR) assay.

The main area of interest for this study is region of Apulia, and as a reference, the current analysis took into account datasets and geo-locations of trees affected by this pest in olives orchards, identified during the field campaign carried out in (Zarco-Tejada et al. 2018). Furthermore, the study includes 3000 geo-locations of olive trees extracted from public records distributed by Apulia region, in cooperation with the Public Network of Research Laboratories (SELGE).

The framework to carry out the multi-temporal analysis with Sentinel-2 imagery was carried out in the Data Integration and Analysis System (DIAS). DIAS archive includes satellite data and ground observation data. These datasets are stored in an large volume disk array accessible via API requests.

The time lapse of the current analysis included Sentinel-2 images since 2016 until 2018, PlanetScope images for the period 2017-2018, and a UAV field campaign carried out in 2018.

As in (Zarco-Tejada et al. 2018), common multispectral vegetation indices, did not differ significantly between asymptomatic and symptomatic trees, so unable to detect non-visual symptoms of Xf infection. In the present study, despite it was carried out an identification and masking of background soil, some differences could be mainly associated to changes on soil background more than with tree anomalies, since spatial resolution of satellite imagery seems to be scarce to be used for a tree focused study but for an analysis at a parcel level, where at the same time the heterogeneity of trees status could differ.

Detailed analysis of multi-temporal vegetation indices profiles showed that remote sensing observations, can help to identify unexpected phenology patterns or anomalies that could be related to tree pests. Despite the capability detect individual trees vary according image spatial resolution, remote sensing techniques help to put into evidence a particular parcel where a further analysis need to focus.

A ground-based 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 operated in Xinglong Station. Two PICARRO G2301 instruments carry out simultaneous measurement for surface CO₂ concentration. Also, there is a MAXDOAS instrument in Xianghe, which 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. 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 Xianghe Station is now doing conventional observation of CO2 and CH4 concentration, providing a high quality dataset for validating the CO₂-measuring satellites over the world.

Key words: FTIR, PICARRO, CO₂, CH₄, Xianghe Station

As the improvement of high temporal, spatial, spectral resolution optical sensor technology, remote sensing becomes a more and more important way to understand the status and changing in both local and global scale. How to accurately calibrate the remotely sensed data becomes one of the primary problems before remotely sensed data used in different quantitative researches and applications. Consistency transfer calibration, which the measurement benchmark is obtained with ground-based and airborne standard instrument and then transferred to satellite sensors, is an efficient approach to improve the quality of satellite remotely sensed data, and it is also a key technology for operational application of the space-borne radiometric measurement benchmark. The composition of the calibration system includes: 1) the ground-based and airborne hyperspectral imager, 2) the matchup of different observation elements between different observations and 3) the standard transfer technology. Supported by the National High Technology Research and Development Program of China, the whole system has been established. And consistency transfer calibration field campaigns have also been carried out to comprehensively validate related sensors, targets and methods.

1) Totally three ground-based hyperspectral imagers and three airborne hyperspectral imagers have been developed, covering VNIR (400-1000nm), SWIR (1000-2500nm) and TIR (8-12.5μm). The spectral resolutions are 2nm, 5nm and 40nm for VNIR, SWIR and TIR ground-based imagers, respectively, and 3.5nm, 10nm and 80nm for VNIR, SWIR and TIR airborne imagers, respectively. The total field of view (FOV) of ground-based imagers is 22°, and that of the airborne imagers is up to 60°. All of the imagers have self-calibration units to guarantee the performance under actual working conditions.

2) Multi-scale data multi-element matchup methods have been developed. A spatial registration method for large resolution difference images has been proposed. The spectral matchup methods for two hyperspectral imagers and for hyperspectral imager to multispectral imager have been developed. Angular normalization models were built up for reflective solar band (RSB) and thermal emissive band (TEB), respectively. Sensitivity analysis and uncertainty analysis were performed for these methods and models.

3) Considering difference in atmospheric radiative transfer processes for different bands, consistency transfer calibration schemes were designed for RSB and TEB, respectively. Through uncertainty analysis on field non-uniformity, surface BRDF and radiative transfer calculation, the total accuracy in transfer calibration is shown to be better than 5% for RSB, and better than 1K for TEB. As to spectral calibration, the method based on atmospheric absorption lines was adopted, and brings out calibration accuracy better than 0.5nm for RSB, and better than 8nm for TEB. These indices can satisfy the specification of this sub-project.

In the last September, consistency transfer calibration experiments were carried out in the National Calibration and Validation Site for High Resolution Remote Sensors (the Baotou site). The artificial targets and natural scenes were employed as reference. More than 60 air lines were flied acquiring airborne data. Ground measured hyperspectral imager data, surface feature measurement data and atmospheric measurement data were simultaneously obtained during the flight. Overpassed high-resolution satellite data (Sentinel-2B, SV1) were used to validate the whole system. Results indicate that the total transfer calibration chain is basically feasible and reasonable. The advantages of consistent transfer radiometric calibration technology include: Firstly, in benchmark obtainment, the “ground truth” covering large area can be obtained rapidly. Therefore, the errors due to the heterogeneity of surface and temporal variance of environment can be efficiently decreased. Secondly, the airborne imager can be comprehensively calibrated by the self-calibrator and “ground truth” measured by ground-based imager so as to decrease the uncertainty. However, lots of work is still needed to improve the system, and the uncertainty traced to SI should be analyzed much more elaborately with more experimental data.

The nadiral satellite-based brightness observations were made using the Microwave Humidity and Temperature Sounder (MWHTS) instrument aboard the FY-3C polar-orbiting platform since Sept 30, 2013. Separate retrievals are demonstrated for mid-latitude conditions in extreme weather. The retrieved profile root-mean-square errors are about 0.9 K with bias error less than 1.5K. These are substantially smaller than the a priori temperature profile variations, demonstrating that 118-GHz aircraft or satellite observations can provide useful information on atmospheric vertical thermal structure. Combined with 183GHz and window channels, water vapor profiles are also retrieved accurately to be used in Sandy typhoon data assimilation model.

This paper is organized as follows. The microwave instrument is first introduced, and it is demonstrated that multiple receiver arrays can be used to multiplex a large set of channels onto a single spot on the ground. We next point out that opacity due to water vapor continuum absorption is a fundamental limitation of conventional millimeter-wave sounding and show how a multi-channel millimeter-wave approach can be used to complement the temperature-sensitive observation and overcome this limitation. We then adapt two methods to realize data assimilation based on profiles and radiance separately and in combination, and then compare with current impact in WRFDA model. Temperature, water vapor, and precipitation retrieval performance comparisons are then presented, and the impact of correlated error sources on performance is examined. Finally, we summarize and provide suggestions for further research and development of data application about polar-orbital satellite.

We present work on use a compact solar-tracking Fourier Transform Spectrometer (Bruker EM27/SUN) to precisely derive total column averaged amount of Greenhouse gases.

In order to ensure the high quality of the retrieved data, XCO₂ measured from Bruker EM27/SUN are compared to well calibrated IFS125HR in Xianghe (39.798˚N, 116.958˚E, 50m a.s.l.) as reference. We also processed the EM27/SUN data using PROFFAST and GGG2014 for further investigation the performance of the EM27/SUN. The comparison between EM27/SUN and IFS125HR shows a 0.24% bias with GGG2014+EGI, while a bias of 0.53% approached by PROFFAST. To further characteristics the differences between the two algorithms, Xgas has been measured by EM27/SUN in Beijing (IAPCAS) with coordinate weather record by WS500 weather station. The GGG2014 and PROFAST are involved in data processing, but found a bias of 0.20%, 1.23%, -1.0% for XCO₂, XCH₄ and XCO respectively. X_air calculated by the above two algorithms are approximately 1.0012、0.9831. The correlation coefficient is 0.9979 for daily median XCO₂ between the result of these two retrieval algorithms. Reasons for these differences could be attributed to the difference in pre-processing method, solar model, instrument lines shape model, gas spectroscopy.

Furthermore, field campaign collaborating EM27/SUN and aircore soundings will contribute to the greenhouse gases validation for TanSat, Sentinel-5P as well as other greenhouse gas satellites, GOSAT and OCO-2.

The surface albedo characterizes the ability of the Earth's surface to reflect solar radiation. It is an important land surface characteristic parameter that affects the radiation and energy balance of the Earth system, and determines the distribution process of radiant energy between the Earth's surface and the atmosphere. This study aims to use the Chinese FY-3C polar-orbiting meteorological satellite data to invert and verify the surface Albedo.

The inversion algorithm uses the RossThick-LiTransit semi-empirical kernel-driven BRDF (Bidirectional Reflectance Distribution Function)model, and uses the constrained least squares method to fit the model coefficients , and . The BRDF of any zenith angle and observation angle can be obtained by nuclear extrapolation . The BRDF is hemispherically integrated in the observation direction to obtain the narrow band BSA(Black-Sky Albedo) of the FY-3C band 1-4,and the double hemisphere integral is obtained in the incident and observation directions to obtain the narrow band WSA(White-Sky Albedo) of the FY-3C band 1-4.The 6S atmospheric radiation transmission model is used to simulate the surface downward radiant flux and the surface upward radiation flux covering a variety of atmospheric conditions, multiple observation angles, and various BRDF characteristics, so as to obtain the broadband albedo albedo of visible band.Using the method of multiple linear regression analysis, a linear conversion equation between narrow band albedo and broadband albedo is constructed. Using this equation, the broadband albedo of the FY-3C MERSI visible band (0.4-0.7 μm) can be obtained.

The clear sky Data of FY-3C surface albedo data, MODIS-MCD43A3, CGLS-SA and GLASS-ABD in the study area were randomly selected as the verification data, and the correlation analysis, absolute deviation analysis and root mean square error analysis were performed with these surface albedo product data. It can be seen intuitively from the scatter plot that the scatter distribution of the FY-3C surface albedo product and MODIS-MCD43A3, CGLS-SA and GLASS-ABD surface albedo product are relatively regular. The four surface albedo products have good consistency in narrowband and visible band, the correlation coefficient is about 0.88, the overall absolute deviation is 0.068, and the minimum root mean square error is 0.02, indicating FY and MODIS-MCD43A3, CGLS-SA and GLASS-ABD Have a high degree of fit.The causes of the differences in the surface albedo data verification were analyzed: (1) After geometric revision, there is a geometric error of 2~10 pixels in the MERSI reflectivity data, and the surface reflectance inversion using 16 days of clear sky data will produce a large error where the underlay surface is not uniform. (2) There is a difference between the surface reflectivity data of FY-3C after atmospheric correction and the surface reflectivity products such as MODIS-MCD43A3, CGLS-SA and GLASS-ABD, which leads to the difference of inversion.

The results showed that the surface albedo products of FY-3C and MODIS-MCD43A3, CGLS-SA and GLASS-ABD showed good correlation. The inversion algorithm of FY-3C surface albedo still needs to be studied deeply, and the improvement of inversion accuracy also depends on the key links such as data location, atmospheric revision and so on. In addition, the FY-3C surface albedo verification also needs to use the ground station point measured data for further comparative analysis.

Spaceborne SAR has been proved to be a valuable tool in measuring sea surface winds and waves in coastal waters. In this study we present evidence to show that 1) ships and ocean front have impact on SAR wind retrieval and 2) uncertainly wave retrieval exists under the hurricane conditions.

We acquired a large number of Sentinel-1 SAR images in the Yangtze River estuary, China, where the ocean environment is very complicated due to the exchanges of coastal and Changjiang Diluted Waters. The root-mean-square error between SAR wind speeds and buoy measurements reaches up to 3.81 m/s. Analysis of quasi-synchronous SAR images and sea surface temperature (SST) observations shows that the main possible causes for such a large bias are the impact of ships or changes in the atmospheric stability induced by SST change across the ocean front on the sea surface backscatter signal received by radar. A change of 1 °C in SST at low wind conditions may lead to an error of 1~2 dB in the satellite observed normalized radar backscatter cross section (NRCS). The existence of ships at sea surface even results in a falsely high NRCS value. Since the accuracy of wind speed estimation from SAR is strongly dependent on the accuracy of the NRCS measurement, great cautions should be taken when generating or using SAR wind products. Consideration of the above-mentioned effects on the NRCS may improve the accuracy of the estimated wind speeds to a certain extent.

We also investigate the performance of the wave retrieval algorithm (PFSM) when it is applied for dual-polarization C-band Sentinel-1 SAR. SAR-derived significant wave height (SWH) and mean wave period (MWP) are compared with simulation results from the WAVEWATCH-III model. The validation shows a 0.69 m root mean square error (RMSE) of SWH with a -0.01 m bias and a 0.62 s RMSE of MWP with a -0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from Sentinel-1 SAR images. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively weak.

Within the context of coastal area management, that includes promoting sustainable economy, preserving biodiversity and ensuring population safety, the continuous and effective monitoring of the shoreline is primary need. Nonetheless, a rigorous definition of the shoreline is ambiguous to some extent since it is influenced by bathymetry, tide level, etc. and, in addition, shoreline position continuously changes due to urbanization, deforestation, accretion/erosion, etc. It was shown that remote sensing tools, including optical/microwave satellite sensors and aerial UAV surveys, are valuable information sources to provide systematic observations of the shores to be integrated with ground surveys, i.e., GPS measurements.

The exploitation of spaceborne SAR measurements can improve optical-based shoreline extraction, which is affected by solar illumination and weather conditions. In addition, it was shown that polarimetric information provides extra-benefits for shoreline extraction purposes, i. e., the algorithms are more robust and accurate. Nevertheless, reliable pixel-wise land/sea separation is still a challenging task since the latter is hampered by the several SAR imaging and environmental effects that include inherent speckle noise, limited spatial resolution, bathymetry, high sea state conditions and coastal morphology.

In this framework, in this study, the applicability of patch-based filters to reduce speckle noise in polarimetric SAR imagery is investigated for shoreline extraction purposes. In fact, with respect to standard pixel-wise speckle filters, the patch-based approaches for speckle reduction exploit the measurements redundancy to look for similar local patches within the polarimetric SAR image. Then, according to a statistical-based similarity criterion, a speckle-reduced polarimetric SAR observable, i. e., coherency matrix, is obtained from which land/sea separation can be performed according to a given metric. Hence, in this study, the capability of the patch-based paradigm to be applied on polarimetric SAR images for speckle filtering is investigated and evaluated in terms of accuracy in the shoreline position. Selected showcases will be presented at the conference time to quantitatively evaluate the improvements in shoreline extraction accuracy performance.

SAR measurements have proven to be a very useful tool for tropical cyclone monitoring and forecasting applications. The sea surface wind maps derived from the SAR cross-polarized channel can provide fine-scale information about the tropical cyclone (TC) inner core. A hurricane morphology and sea surface wind vector estimation model (SHEW) based on measurements acquired by the C-band SAR onboard RADARSAT-2 has been recently proposed [1]. A limitation of this model is that it only deals with hurricanes of circular or elliptical-shaped eyewalls. In this study, a new parametric model, which uses SAR observations and allows for asymmetric description of the TC wind structure around the eyewall in storm centric coordinates, is developed. SAR observations from
 TCs in the North Atlantic and East Pacific basins are analyzed to determine the azimuthal and radial asymmetry typical in these mesoscale systems. The new asymmetric directional wind model adjusts the widely used Holland (1980) axis-symmetric model to account for the different azimuthal asymmetries of TC winds. The model will be tested against collocated NOAA hurricane hunter observations (i.e., dropsondes and the Step-Frequency Microwave Radiometer or SFMR) and its performance will be compared with other existing models, such as, the Holland [2], SHEW [1], and Olfateh [3] models. Showcases will also be presented to demonstrate the improvements related to the proposed model.

[1] Zhang, G., W. Perrie, X. Li, and J.A. Zhang. (2017), A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery, IEEE TGRS, 55(3), 1743-1751.

[2] Holland, G. J. (1980), An analytic model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108(8), 1212–1218.

[3] Olfateh, M., D. P. Callaghan, P. Nielsen, and T. E. Baldock. (2017), Tropical cyclone wind field asymmetry— Development and evaluation of a new parametric model, J. Geophys. Res. Oceans, 122, 458–469, doi:10.1002/ 2016JC012237.

Wind is a sustainable and alternative resource for producing energy and it has a good reputation of being a green form of electricity. Within this context, wind turbines are widely used at onshore and offshore sites to convert the energy of moving air into electrical power. For this reason, wind turbines are a critical infrastructure whose monitoring is an important issue for both economy and environment protection. Within this context, remote sensing can be an important tool to guarantee an effective and relatively cheaper monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The objective of this study is quantifying the added-value of polarimetric information to detect metallic targets. On this purpose, a very challenging scenario is considered that consists of mud flat area where wind turbines are present. To make the analysis fair, we selected detection algorithm that are able to work with both full- and partial-polarimetric information, i.e.; Polarimetric Notch Filter (PNF) and the change detection approach proposed in [1] and [2], respectively.

Experiments, undertaken on actual SAR data collected over the intertidal zone near Jiangsu, China, by the C-band RadarSAT-2 and Sentinel-1 missions show that the proposed methodologies, well detect the wind turbines inside mud flat areas. Furthermore, a detailed analysis shows that polarimetric information always guarantees performance better than the single–polarization counterpart.

[1] A. Marino, (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1219-1232.

[2] A. Marino; S.R. Cloude, and J. M. Lopez-Sanchez, (2013), “A New Polarimetric Change Detector in Radar Imagery”, IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2986 -3000.

The study of coastal wetlands is of paramount importance due to both anthropomorphic activities and natural phenomena, which threaten the stability of land and safety of the people.

However, the monitoring of coastal wetlands is not trivial due to the presence of different kind of habitats that include coastal plain, coastal beaches, rocky shorelines, salt marshes, mangrove, seagrass beds, mud flats and sand bars. For this reason, the study of wetlands results very challenging.

Within this context, remote sensing plays an important role for coastal wetlands monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The main goals of this study are to develop multi-polarimetric and multi-temporal methods to effectively monitor the wetland area of the WWT Caerlaverock in Scotland one of the most important wetland in the United Kingdom. The test site was selected since it is severely affected by coastal erosion that makes the monitoring a very important issue.

For this purpose, two methodologies based on the joint use of co- and cross-polarized channels [1] and on the polarimetric notch filter [2], are used to both extract the profile of the coastal area and to detect the wetlands.

Preliminary results are obtained processing a set of full polarimetric SAR (PolSAR) data collected at C-band from RadarSAT-2 sensor. The results show that PolSAR data can be effectively used to detect both coastline and wetlands.

[1] Nunziata F., Buono A., Migliaccio M., Benassai G. (2016), “Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 9, Issue: 11, Pages: 4921 - 4928

[2] Marino A. (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 6, Issue: 3, Pages: 1219 - 1232

Wind speed retrieval is a subject of paramount importance since wind estimation is extremely useful for different meteorological and oceanographic applications: problematics like coastal erosion, climate change and so on are strictly connected with wind parameter. In this context, most of the remote sensing satellite radar can provide sea surface wind field data. In particular, microwave sensors, mainly scatterometer and Synthetic Aperture Radar (SAR), are worldwide recognized as the most suitable sensors for wind field retrieval. Radar backscattered signal contains quantitative information about the condition of the sea surface roughness and, hence, can be used to infer sea surface wind data. One of the most challenging case of wind speed retrieval is represented by the tropical cyclone case. 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. Typically, in literature, scatterometer, and then SAR, data are used to implement Geophysical Model Function (GMF) to extract wind speed information. These functions link the Normalized Radar Cross Section (NRCS) with wind speed and wind direction.

While in this work, a spectral based technique is adopted: the azimuth cut-off approach.

When managing SAR microwave sensors, Doppler misregistration in azimuth occur because of the gravity wave orbital movement. This issue is the major responsible of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. This technique is used to retrieve wind speed and a few investigations have been carried out improve this approach [1]. More in detail, there is a straight link between λc values and geophysical parameters, similar to wind speed and critical wave tallness. In [1] the ACF-based λc approach has been improved to manage high wind speed routines, e.g.; extreme weather conditions. The key issues that allow to stretch out the technique to high wind regimes concern the tuning of a strategy that takes into account pixel spacing, box size and the homogeneity of the SAR image. In particular, the box size is set to be 1 km × 1 km and the median filter window is set at 90-120 m.

It is revealed in recent study that λc is related with wind speed at strong winds. In this study, we try to retrieve wind speed from Sentinel-1 SAR images in hurricanes and typhoons. The SAR-derived λc is compared with simulated azimuthal cutoff wavelength using the wave spectrum from numeric wave model in the three part of typhoon wave system. The retrieval wind speed is validated against measurements from the Soil Moisture Active Passive (SMAP) radiometer.

[1] V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transaction on Geoscience and Remote Sensing, vol. XXX, no. XXX, pp. XXXX-XXXX, 2018.

Sea oil spill monitoring is of extreme importance for researchers, ecologists, local authorities and a wider set of stakeholders since ocean pollution is a serious threat since, every day, a significant amount of oil is released into the maritime environment due to operational vessel procedures, accidental collisions, land-based discharges and all oil-related human activities. From a scientific perspective, a systematic and reliable support to sea oil pollution monitoring can be found in the exploitation of satellite-based sensors. Among them, it was shown that Synthetic Aperture Radar (SAR) plays a key role due to its almost all-weather capability to provide fine resolution (few meters) imagery with dense revisit time (few days).
The sea oil spill detection by means of SAR imagery is possible due to the damping effects oil slicks have on the wind-driven short sea waves that result, in the intensity images, in more or less homogeneous patches darker than the surrounding sea. Nevertheless, SAR-based sea oil spill detection still remains a challenging task since there exist several natural phenomena that generate the same signature in the intensity SAR imagery. They are termed as oil look-alikes and include internal waves and currents, grease ice, oceanic fronts, algal blooms, phytoplankton, etc. The latter represent false alarms that significantly reduce the performance of oil spill detection and classification algorithms.
In literature, when dealing with single-polarization SAR data, intensity-, texture- and morphologicalbased features, i.e., grey level co-occurrence matrix parameters, backscattering coefficients, shape, size and perimeter of the slick, etc. are usually adopted that often result in non-reliable results or in low detection accuracy. Those issues can be at least partly overcome by exploiting polarimetric information, even though this is paid at the expense of the area coverage thus limiting the operational application of polarimetric SAR architectures.
Hence, in this study, a new spectral method is applied on single-polarization SAR imagery for sea oil spill monitoring: the azimuth cut-off. The latter is a distortive non-linear effect occurring when the observed surface is moving during the SAR acquisition time. This is the case, for instance, of the ocean. In literature, several studies have been carried out to analyze the dependence of azimuth cutoff wavelength on sea surface parameters as wind speed, wave height, etc. In this study, preliminary results obtained on actual C-band SAR measurements collected over certified oil spills and lookalikes are shown that exhibit a different sensitivity with respect to the azimuth cut-off wavelength.

Unlike most glaciers experience mass lost in recent decades, the Karakoram and its surroundings seem rather stable. The phenomenon is claimed as ‘Karakoram anomaly’ and/or ‘Karakoram-Pamir anomaly’. Given its remote, the field observations for glacier mass balance and flow rates are hard and archive data are rare. Remote sensing technique plays an essential role in alpine glaciers observations. Previous studies yielded a positive glacier mass balance for the Karakoram and later extended to the Pamir Plateau between 2000 and ~2012, however the laser altimetry observation at almost the same period (2003 - 2009) found slight glacier mass loss but claimed that the anomaly centred at the West Kunlun.

To make a more accurate observation and to understand the ‘Karakoram anomaly’, we applied Differential SAR Interferometry (D-InSAR) technique to a set of X-band bistatic TerraSAR-SAR-X/TanDEM-X (TSX/TDX) images observed at ~2013 by respecting to SRTM DEM observed in 2000 to derive glacier mass balance. The topographic residual phase of D-InSAR is unwrapped and then transferred to height changes. By presuming density of 850 Kg/m³, the volume changes of glaciers are converted to glacier mass balance. We compare quasi-simultaneously observed C-band and X-band SRTM (both in February of 2000) to evaluate and to remove the penetration depth differences at different elevation bins. The possible seasonal variation in terms of glacier mass balance was evaluated at an adjacent site by using TSX/TDX images observed in different months in one year. The standard deviation of differential processing between SRTM and TSX/TDX is about 6.27m, which is more accurate than the previous study using SPOT DEM and SRTM. Besides, it noticed that TSX/TDX make more efficient observations at accumulating area than optical observations. The result found that both east and west part of the Karakoram presents almost zero glacier mass balances, which were −0.020 ± 0.064 m w.e. yr⁻¹ and −0.101 ± 0.058 m w.e. yr⁻¹, respectively. Most negative glacier mass balance was contributed by the southern slope of the Karakoram while the northern slope was rather stable. At the most northeastern part of the Karakoram, where are very close to the edge of Tarim basin, the glaciers presented thickening in also most every elevation levels. The glacier mass balance at the Karakoram presented a decreasing gradient from the edge of the Tarim basin to the southwest of Karakoram. The mass balance for 2000 to ~2013 is almost identical comparing to 1974 to 2000, which implies that the stable environment at the Karakoram despite the global warming trend.

The re-analysis monthly GHCN_CAMS Gridded 2m Temperature data found an increasing start from 1995 to 2000 for about 1 degree and kept stable after then. Monthly precipitation Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) suggest rather stable annual and seasonal precipitation from 1980 to 2000, followed by an increasing trend. The precipitation increased from ~260mm/yr before 2000 to ~350mm/yr by 2010. The meteorological data suggested that a warming and wetting trend after 2000 for the Karakoram, which possibly explains the ‘Karakoram anomaly’ was induced by increasing precipitation rather than a cooling trend of temperature.

Thermokarst, a process that characterizes landforms caused by thawing of ice-rich permafrost, is a key indicator of permafrost degradation. Surface dynamics of thermokarst processes on Qinghai-Tibet Plateau (QTP) of China, is still poorly quantified or understood. It is also challenging to detect and measure surface subsidence due to loss of subsurface ice over a large area. The Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique has the potential to detect local or regional thermokarst-induced surface subsidence with the advantage of full resolution and millimeter to centimeter accuracy by less affected related to the temporal or geometric decorrelations. Previous studies based on multi-baseline time series analysis have separated the seasonal and thermokarst-induced surface subsidence only using SAR images acquired during thaw seasons. To fully usage of the SAR images, we introduce frost heave processes during early freeze season and subsequent stable stage when the layer is completely frozen.

Applying our improved PSInSAR method to 17 L-band ALOS-1 PALSAR images over Eboling Mountain where 22 thermal erosion gullies are well developed, we found a mean gradual subsidence trend of 1.3 cm/year, with a maximum of 5 cm/year near the thermal erosion gullies. It is equivalent to an ice volume loss of 1.4810⁴ m³/year over the entire thermokarst landform in the study area. We also found that the ground surface nearby the thermal erosion gullies is more likely to undergo subsidence. It indicates that the thermal erosion gullies could affect the permafrost processes at its surroundings. This study promises a potential of using PSInSAR to identify thermokarst landforms, map and quantify permafrost thaw subsidence, and assess its impacts over large areas such as the QTP.

Sentinel-1 (S1) data have the potential to measure, by radar interferometry (InSAR), the present-day Earth surface deformation, whether of tectonic origin or otherwise (anthropogenic, hydrological), on the scale of specific local targets (active faults, sedimentary basins, cities) as well as on the continental scale (large lithospheric blocks bordered by mountain ranges and major fault systems). With now more than 4 years of S1 images archive, the global coverage and high-temporal resolution of these images thus allow to investigate the dynamics of slow aseismic slip on faults, a critical step to better understand physical processes involved in the generation of large earthquakes, in various tectonic contexts worldwide. They also allow a refined quantification of strain partitioning across complex fault systems, as well as of the degree of strain localization on faults, which can be confronted to different fault system evolution scenarios and lithospheric deformation mechanisms. In cases where non-tectonic deformation superimpose with tectonic deformation, time-series analysis helps extracting the specific spatio-temporal signature of each phenomena. We will illustrate these different applications with our most recent case studies, in Asia in particular, based on InSAR time-series analysis of S1 data.

With the medium-resolution (~2.0 meter in azimuth and ~13.0 meter in range for TOPS/IW mode) SAR data, it is possible to acquire large scale deformation (>1000 km) in a continuous TOPS scanning. With the temporal sampling of 6-day or times of it, Sentinel-1 SAR data were quickly accumulated since later of 2014. However, processing of the large data set is a challenge, which is useful and/or a requirement for some typical applications, such as tectonic deformation analysis or anthropogenic activity monitoring for a vast region.

We utilize high-performance computation (HPC) for this purpose, which is widely used for scientific applications. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.

We applied our two-level processing approach in multiple challenge areas, the North China Plain, the Longmenshan area and other Tibet regions for both anthropogenic activity monitoring and tectonic deformation detections. Both areas are quite tricky for normal InSAR processing, but with our HPC parallel system, we acquire consistent results compared with GPS observations. The method conducted in these tests confirmed the robustness of our approach for deformation detection with Sentinel-1 large scale InSAR data.

The North China Plain (NCP) is a vital agricultural region and is highly-populated, so the groundwater utilization is quite heavy in this region for irrigation and human beings. This leads to an overdraw of groundwater and fast subsidence over the whole area.

We utilize high-performance computation (HPC) for detection of the related deformation. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.

Our processing shows that in the center of the NCP, farming activity produces widely distributed deformation, not only localized subsidence bowls as in typical subsidence regions. Our results are also consistent with GPS observations on this scale. Tectonic units in the same region could bound the subsidence behavior, hence both kinds of activities may have some kind of interactions.

The global and systematic coverage of Sentinel-1 radar images enables to characterize, by radar interferometry, surface deformations at the scale of large active faults. This represents considerable progress in fault monitoring and opens new perspectives in seismic hazard assessment. Our study focuses on the Yushu - Ganzi - Xianshuihe active fault system (YGX), located on the eastern part of the Tibetan plateau. This left-lateral fault system accommodates the collision between the Indian and the Eurasian plates. The Ganzi segment may represent a 350 km-long seismic gap, unbroken for the past ~120 years. To measure the interseismic deformation across the YGX fault system, we perform a time series analysis of 4 years of Sentinel-1 InSAR data, acquired along ascending and descending orbits, using the New Small Baseline Subset processing chain including the latest adaptations (Doin et al., 2011, Grandin, 2015). The results are presented as mean velocity maps across the faults and compared to previous GPS studies and the long-term fault history. Simple elastic models of velocity profiles are also derived. They show that the Ganzi gap may be the site of aseismic slow slip which, depending on its spatio-temporal characteristics, could contribute to reduce seismic hazard on the fault or, conversely, facilitate the initiation of future major ruptures. The characterization of strain partitioning and strain localization across this fault system enables to precisely evaluate spatial and temporal variations of slip at various depths on the fault and constitutes a key constraint on seismic hazard assessment and lithospheric deformation mechanisms.

In the big data era, various kinds of satellite data are increasingly made easily and/or freely available in the world. Therefore, the crop type mapping with these satellite data has strongly attracted the attention from the remote sensing researchers. As a new comer, the Chinese high resolution satellite series, short name for GF, are being developed in China. GF-1 data has a 16 meters ground sampling for 4 bands, such as blue, green, red and near infrared spectra. In Europe, the Copernicus project ensures the stable Sentinel satellite series and provides multispectral and 10-meter resolution optical satellite images to the worldwide end users. These satellite images become the rich data sources for the crop type mapping with the machine learning algorithm nowadays. In support of the provincial agricultural monitoring, we have developed an approach to use GF, Sentinel 2 and other third partner satellite images to mapping crop types in irrigation area of the yellow river of Ningxia, China. Field sample photos were taken with the GPS camera in summer 2017 and 2018 respectively and thereafter the crop types for the ground truth data were interpreted with a software, named GPS Photo Data Processor. With the support of these ground truth samples, more samples for the training and validation were further visually added over a clear sky image in key crop growth stage. The Random Forest was used as the classifier for this study as many literatures have reported that the RF algorithm overperformances other algorithms in many cases, such as SVM, Maximum Likelihood. The classification results of crop type map were evaluated with the error confusion matrix, in particular, OA(overall accuracy) and F1 Score. Sentinel 2A/B images during the growing season in 2017 and 2018 were collected and processed via the ESA Sent2Agri system that UCL developed. The GF satellite images were collected from CRESDA in China. All these data were further processed and finally made spatially congruent. The performance for crop type mapping with time series of each of these data sources was analyzed and compared. The results show that the accuracies were between 84-93%. The accuracy of crop type mapping with GF data was the lower due to less bands and other limitations. The accuracy of crop type mapping with all bands of Sentinel 2A/B reached the highest due to more key bands and higher resolution. The utilization of huge volume of the high resolution satellite images, such as Sentinel 2 is challenging to the researchers.

This abstract summarises the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m NDVI time series. The Artificial Neural Network (ANN) method is used where the output is a set of Area Fraction Images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classification are used to obtain training data for the ANN. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10x10 km2. For the five dominant classes in the test sites the R2 obtained after the aggregation are 89 % (winter cereals), 71% (grasslands), 78 % (sunflower), 92 % (broad-leaved forest), and 92 % (maize).

With the unique terroir, the region in the east hillside of Helan mountain in Ningxia is well recognized one of golden regions in the world for the cultivation of wine grape and the production of high quality wine, and thereafter this region was designated the protection area of national product of geographical indication in 2002. With the strong policy support, the vineyard develops very fast on the Gobi desert in recent years and the land use has changed obviously. This objective of this study is to monitor the evolution and changes of vineyard in the region with the Landsat8 data since 2013 and provide the scientific information for the decision maker of the vineyard management. Landsat8 data were downloaded from the USGS official website and formed the time series dataset after several step fine processes. The ground truth data were collected during 2016 to 2018. Based on the ground truth data in 2016 to 2018, with the reference map of high resolution of GOOGLE EARTH, the training samples for 2013 to 2015 were further obtained. The random forest was used as the classifier to have satellite images of each year classified. The results were validated with the error matrix and further verified with the field boundary data that was drawn by another group of researchers. The evolution and changes of the vineyard was further analyzed based on the validated results of vineyard map.