Conference Agenda

To monitor air quality trends in China for the period 2005-2015 we derived SO2 columns and NOx emissions on a provincial level. To put these trends into perspective they are compared with public data on energy consumption and the environmental policies of China. We distinguish the effect of air quality regulations from economic growth by comparing them relatively to fossil fuel consumption. Pollutant levels, per unit of fossil fuel, are used to assess the effectiveness of air quality regulations. We note that the desulphurisation regulations enforced in 2005-2006 only had a significant effect in the years 2008-2009 when a much stricter control of the actual use of the installations began. For national NOx emissions a distinct decreasing trend is only visible since 2012, but the emission peak year differs from province to province. The last three years show both a reduction in SO2 and NOx emissions per fossil fuel unit, since the authorities have implemented several new environmental regulations. Despite an increasing fossil fuel consumption and a growing transport sector, the effects of air quality policy in China are clearly visible.

As a part of our research during the implementation of the DRAGON 3 project we have developed a new technique of using satellite observations to estimate the level of deterioration of the materials used in constructions and cultural monuments. This technique is mainly based on the already developed Dose Response Functions (DRFs) in which the ground-based measurements of various atmospheric pollutants (e.g. nitrogen oxides, sulphur dioxide, ozone) and several climatic parameters, such as air temperature and others, are often used as input data. The values of DRFs of specific materials provide a measure of their corrosion or soiling caused by their outdoor exposure to weather and the air pollution factors. In this work, we evaluate the performance of our proposed technique using the available deterioration experimental data from more than 10 European sites. These data were obtained during different periods since 2005 for cases of four materials (carbon steel, limestone, zinc, modern glass). The term “Remotely Sensed Data-Dose Response Functions (RSD-DRFs)” is proposed for this technique.

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci, there are three Aerosol Optical Depth (AOD) datasets of Advanced Along Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute (FMI)/University of Helsinki (UHEL), the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC) by the University of Oxford/ Rutherford Appleton Laboratory (RAL) and the Swansea algorithm (SU) by the University of Swansea. The three AOD datasets vary widely. Each has unique characteristics, so none is significantly better than the others, and each has shortcomings that limits the scope of its application. To address this, we propose a method for converging these three products to create a single dataset with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the system errors; the second part is to calculate the uncertainty and fusion of datasets using the maximum likelihood estimate (MLE) method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused dataset after mask is 148%, 13% and 181% higher than ADV, ORAC and SU respectively, and the root mean square (RMSE), mean absolute error (MAE), mean bias error (MBE) and relative mean bias (RMB) are superior to the three original datasets. Thus, the accuracy and spatial coverage of the fused AOD dataset after mask are improved compared to the original data. Finally, we discuss the selection of mask thresholds.

Satellite data from several different instruments are used to study the spatial and temporal distribution of aerosols over China since 1995. In particular ATSR-2 (1995-2003), AATSR (2002-2012), MODIS (2000-present) are used to provide the spatial distribution of the AOD, while CALIOP (2007-present) also provides information on the vertical structure of aerosols, including aerosol type information and in particular dust. The AOD data sets are validated and evaluated versus sun photometer data from AERONET and the Chinese network CARSNET. This is particularly valuable because aerosol retrieval algorithms are developed and validated over areas where many independent ground-based observations are available, such as over the eastern USA and Europe. However, over these areas the AOD levels are often relatively low as compared to China where the occurrence of very high AOD, combined with the variation in aerosol type and surface characteristics, poses particular problems as regards data selection and discrimination between high AOD and the occurrence of clouds.

The spatial distributions over China varies significantly as a result of the multitude of sources, both natural and anthropogenic, which in turn vary with the season. These include anthropogenic sources such as industry and traffic, agricultural and natural biomass, dust from two major deserts, as well as the seasonal production of precursor gases. In addition, economic development and measures to improve air quality affect the long term variation of aerosol concentrations. Meteorology and large scale circulation including the seasonally progressing monsoon have a substantial effect on the aerosol physical properties as well as on production and removal of the aerosol particles. All of these effects vary with location over China and their seasonal and year-to-year variatons.

An initial analysis of the spatial and vertical variability of the AOD will be presented together with time series showing the variation over representative areas. Satellite derived information on aerosol precursor gases NO₂, SO₂ and BVOCs will be used in the analysis.

These activities are undertaken as part of the EU-FP7 project MarcoPolo. The main objective of the MarcoPolo project is to improve air quality monitoring, modelling and forecasting over China using satellite-retrieved information on aerosols, NO_x, SO₂, and biogenic gases. This information will be used in air quality models to invert emission estimates. The results, together with known information from ground-based measurements, will then be used to construct an emission database over China. The MarcoPolo project finished in March 2017 and will in part be continued in the framework of the ESA DRAGON4 initiative.

Atmospheric pollution is a severe problem in China, it is an important to keep on our monitoring and satellite validation of trace gases, BVOC emissions, aerosols in China, especially at some representative sites. Our main activities are ground observations, validation of satellite retrievals and satellite data applications. To fulfill one of all above tasks, measurements of BVOC emissions, O₃ and solar radiation were carried out in a subtropical Pinus plantation in China during 2013-2016. BVOC emissions were measured using a relaxed eddy accumulation (REA) technique and a gradient technique. Monoterpenes were the dominant VOCs in this subtropical Pinus plantation. Isoprene and monoterpene emissions showed obvious diurnal, seasonal and inter-annual variations. In comparison with 2013, annual BVOC emissions decreased in 2015, which were associated with decreases of PAR, temperature and water vapor. O₃ concentration above the canopy level also displayed clear diurnal variation. It was found that BVOC emissions were influenced by biomass burning smoke and pine florescence. The mean emission factors determined using the MEGAN model emission algorithms and empirical model of BVOC emissions were 0.71 and 1.19 mg m^-2 h^-1 for isoprene and 1.39 and 1.65 mg m^-2 h^-1 for monoterpenes, respectively. Flux measurements of BVOCs at a subtropical bamboo plantation in China were used to evaluate the bottom-up inventory of isoprene emissions and the satellite-based MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) emission inventory for isoprene derived using inversion of satellite columns of formaldehyde. Generally, the space-based inventory provides a satisfactory agreement with the observations in summer. Further validation in this subtropical plantation will be carried out in the future. All measure and validated data will be provided for air quality models, so as to improve our abilities in the forecast of air quality.

Key words: Biogenic volatile organic compounds; emission fluxes; ground measurement; validation; satellite.

We compare 9 emission inventories of nitrogen oxides including four satellite-derived NO_x inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman Filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NO_x emissions over Mainland China, especially for trends, with an average bias of about 20% for yearly emissions. All the inventories show the typical emission reduction of 10% during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NO_x emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show: the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite derived approach is that the emissions are soon available after observation, while the strong point the bottom-up inventories is that they include detailed information of emissions for each source category.

GF-3 (GF stands for GaoFen, which means High Resolution in Chinese) is the China's first C band multi-polarization high resolution microwave remote sensing satellite. It was successfully launched on Aug. 10, 2016 in Taiyuan satellite launch center. The synthetic aperture radar (SAR) on board GF-3 works at incidence angles ranging from 20 to 50 degree with several polarization modes including single-polarization, dual-polarization and quad-polarization. GF-3 SAR is also the world’s most imaging modes SAR satellite, with 12 imaging modes consisting of some traditional ones like stripmap and scanSAR modes and some new ones like spotlight, wave and global modes. GF-3 SAR is thus a multi-functional satellite for both land and ocean observation by switching the different imaging modes.

TG-2 (TG stands for TianGong, which means Heavenly Palace in Chinese) is a Chinese space laboratory which was launched on 15 Sep. 2016 from Jiuquan Satellite Launch Centre aboard a Long March 2F rocket. The onboard Interferometric Imaging Radar Altimeter (InIRA) is a new generation radar altimeter developed by China and also the first on orbit wide swath imaging radar altimeter, which integrates interferometry, synthetic aperture, and height tracking techniques at small incidence angles and a swath of 30 km. The InIRA was switch on to acquire data during this mission on 22 September.

This paper gives some preliminary results for the quantitative remote sensing of ocean winds and waves from the GF-3 SAR and the TG-2 InIRA. Comparisons to the ECMWF ERA-Interim reanalysis data show good agreements but more valuable details.

Sentinel-1 wave mode operates in novel ‘leap frog’ acquisitionmode. A vignette is acquired every 100 km at two alternate incidence angles (23° and 36.5° respectively ), withtwo images at the same incidence 200km apart. This mode enables us not only to conduct global analysis for each incidence angle, but also to investigatethe dependence of geophysical parameters on incidence angles.

The SAR image cross-spectrum between a pair of sublooks separated in time has been widely used to help remove 180° ambiguity of detected ocean swell systems. Ocean swells move along the waves' propagation direction during this offset time of the order of SAR integration time (~0.4s for Sentinel-1), therefore, co- and cross-spectra can help to consistently estimate velocity characteristics of the randomly moving sea surface scatterers related to scales larger than SAR spatial resolution. The short radial waves longer than SAR spatial resolution but shorter than ocean swells are, to first order, driven by surface winds. Thus, a new parameter, averaged complex cross-spectra over short radial waves domain, is proposed to manifest impacts of wind speed and direction on these moving scatterers.

Large data sets have been constituted systematically by co-locating Sentinel-1A/Bwave mode acquisitions and ECMWF forecast winds. Efficiently, distinctive features are revealedfor two incidence angles as well as dual-polarizations.

Specifically, to first order,there exhibits a linear relationship between imaginary cross-spectra and radial winds. More encouragingly, it reaches the maximum at downwind (wind blowing toward to the antenna), drops to zero at crosswind (wind blowing along azimuth direction) and continuously decreases to the minimum at upwind. Its sensitivity to wind direction is potential to wind retrieval applications.

At 23° incidence, the spectral parameter does not exhibit up-to-downwind difference of spectral. At variance, a strong asymmetry with respect to radial wind at 36.5° incidence is found. This asymmetry is much more pronounced than up-to-downwind asymmetry for the Normalized Radar Cross Section (NRCS).

Taking advantage of exceptionally continuous HH acquisitions by Sentinel-1B wave mode, we also present preliminary results for HH polarization as well as dual-polarizations' comparisons. In general, HH shows similar features to VV at each incidence angle. However, both spectral magnitude and imaginary part are larger for HH than those for VV for given incidence and radial wind, which could be explained by larger tilt modulation for HH polarization.

As a signed quantity, the imaginary cross-spectra of short radial waves can be considered to be a parameter strongly correlated with the Doppler Centroid Anomaly (DCA). Indeed, both parameters are directly linked to the time evolution of the detected sea surface scatters. As such, both parameters shall closely trace the wind direction within a single SAR imagette. Yet, Sentinel-1 DCA is, today, unfortunately unreliable, with large latitudinal variations for given surface winds. Free of geometrical configuration, the imaginary cross-spectra can thus serve to replace the DCA estimate to help constrain the retrieval of wind field at moderate to high spatial resolution.
In addition, similarly to the residual DCA, a residual parameter can be derived for the residual imaginary cross-spectral parameter, i.e. the difference between the expected parameter for a given wind speed and direction and the local measurement. As anticipated, this residual spectral parameter shall complement observations of surface wind-wave-current interactions.

Conventional VV-polarized NRCS is saturated as wind speed approaching typhoon intensity. In view of this, high wind speeds are underestimated by CMOD5.N especially in the typhoon eyewall regions. Compared to VV-polarization, NRCS in VH polarization is more appropriate to retrieve high wind speeds with cross-polarized retrieval model such as C-2PO or C-2POD. However, cross-polarization wind speed retrieval models overestimate low and moderate winds. Typhoon has capacity of inducing strong dynamical and thermal impacts on the ocean. During the passage of a typhoon, surface wind stress generates intense turbulence and currents in the upper ocean. In general, CSFR data are used as a wind forcing to simulate ocean surface and subsurface response to the typhoon, while the coarse resolution of CSFR can not accurately depict fine structure characteristc of typhoon. Furthermore, there are obvious difference between typhoon intensity and eye center location from CSFR data and satellite observations.

In order to obtain reasonable wind speeds in typhoon eye and eyewall regions as well as in periphery areas, we build a cost function both involving VV and VH-polarization SAR observation. Co- and cross-polarization wind speed retrieval models, CMOD5.N and C-2POD, can yield simulated NRCS in VV and VH poalrizations. The initial first guess wind field is not dervied from numerical weather prediction model, but VH-polarized SAR image itself. According to wind steak features appearing on the typhoon SAR images, we first extracted wind directions by using local gradient method and two-diemensional interpolation technique.The retrieved wind directions are compared with HWRF simulations and WindSat measurements. Subquently, we use C-2POD model and VH-polarized NRCS to retrieve wind speeds. As a result, the initial first guess u and v components can be estimated by using retrieved wind speeds and wind directions. The optimum wind vector estimate for any given wind cell will therefore correspond to a minimum in the cost function.

We use the Regional Oceanic Modeling System (ROMS) to simulate characteristics of upper ocean response to the typhoon. The SAR-derived wind fields are used to force the ROMS. The model outputs reveal the sea surface temperature (SST) cooling and sea surface salinity (SSS) augment on the righ side of the typhoon track. The decrease of SST and increase of SSS are caused by the fact that the entrainment of cold and high salt water breaks through the thermocline and gets into the mixed layer and surface. We compare SST and SSS simulations with microwave Optimally interpolated SST products and SMAP SSS data. Moreover, we also use ROMS simulate the mixed layer depth and upwelling velocity during the passage of the typhoon. Results show the deepening of the mixed layer and increase of the upwelling velocity due to the typhoon-induced vertical mixing. We analyze NPP VIIRS-derived chlorophyll concentration before and after the passage of typhoon, and catch sight of obvious chlorophyll bloom. The increase of clorophyll concentration provides us with evidence for the cold water uplifting. The typhoon pumped cold water from the lower layer with rich nutrients which enhanced the surface biological production.

Secchi disk depth (SDD, m^-1), which can reflect the clarity and turbidity of coastal seawater, is an important parameter to describe the optical properties of water. In this work, an empirical model for SDD retrieval was established on the basis of the MODIS Rrs data and the in-situ SDD data which were collected in the Yellow Sea and the East China Sea. The SDD model was further applied to retrieve the monthly SDD during the period of 2002-2016 for the Bohai Sea, the Yellow Sea, and the East China Sea; and the variations in SDD was first analyzed with the river discharges and the eutrophication indexed by Chl-a. Over the western Yellow Sea which is characterized by "green tides" of floating Ulva spp blooms in the summer since 2007, the variations in SDD were specially analyzed to investigate the effects of "green tides" on the optical environment.

On August 10, 2016, carrying the first Chinese C-band multi-polarization SAR, Gaofen-3 (GF-3) satellite was successfully launched into a polar sun-synchronous orbit of 755 km altitude with an inclination of 98° and 26 days repeat cycle. Following several months of on-orbit commissioning phase, GF-3 SAR images have been in operation since January, 2017. As one of the primary users, the State Oceanic Administration (SOA) is operationally conducting GF-3 SAR ocean wind retrieval and plan to officially release the near real time SAR wind products soon. In this paper, we present the first results of GF-3 SAR derived winds and preliminary assessment using the buoy measurements.

In principle, the GF-3 SAR wind inversion methodology is to combine SAR observed NRCS at co-polarized channel with a priori wind information from ECMWF, taking into account that both NRCS observations and models may contain errors. In order to extract the wind speed and direction, the cost function is minimized with the help of look-up table computed from geophysical model function (GMF), making the inversion scheme more efficient.

The coastal winds were estimated from the GF-3 SAR at 1 km resolution in four imaging mode, including Standard Strip, Quad‐Polarization Strip I, Quad‐Polarization Strip II and Narrow Scan imaging mode, and the retrievals are presented. One case of the coastal katabatic wind off U.S. west coast captured by GF-3 is discussed.

The preliminary accuracy assessment of GF-3 SAR wind speed retrievals is carried out against in situ measurements from NDBC buoys. Only the buoys located inside the GF-3 SAR wind cell (1 km) were considered as co-located in space, while the time interval between observations of SAR and buoy was limited to less the 30 min. This criteria yield 38 co-locations during the period from January to April 2017, showing the RMSE of 2.59 m/s. Different performances due to GMF and Polarization Ratio (PR) are discussed. The preliminary results present that GF-3 wind retrievals are encouraging for operational implementation.

Ground remote sensing by GNSS has been a research topic for the Institute of Geodesy and Photogrammetry (IGP) for some years. In the context of the Hydrology & Cryosphere research of Dragon 4 programme we focus on the determination of the snow water equivalent and of the snow depth. Beside these experimental observations we have been carrying out measurements in order to better assess the propagation of GNSS signals in water. A further interesting, however, not yet really elaborated topic is the measurement of the soil moisture in the frame of landslide hazard monitoring.

In the case of snow water determination we devised a method where GNSS receivers buried in the snow cover are used. From differential measurements between 'snow-free' and 'snow-covered' receivers the water content can be inferred. However, to this end a special refractivity model has been developed and accounted for in the GNSS data treatment. For the determination of the snow height we are mesuring reflected signals in a geometric mode or as applied by different authors, we use the SNR data directly. The propagation through water has been investigated on a dedicated experiment, where we could show the extinction of the signal at roughly 3 cm penetration depth. The water depth can be determined by the geometric analysis of the GNSS signal. Permafrost ground might be a hazard especially if it is located on steep slopes. This is the case in the Alps where Rock Glacier are commonly encountered. The main hazard is due to warm up and to a partial de-freezing. This might be reflected in changing soil/ground moisture. Therefore, the information on this parameter could be one piece of the puzzle in natural hazard assement in alpine areas. We showed, that the temperature and the movements are highly correlated and might even allow for an inversion of the data to determine the sliding horizon.

Soil moisture, the amount of water contained in the soil, is an environmental descriptor that integrates much of the land surface hydrology. From agriculture production to flood and drought prediction, soil moisture plays an important role in human production and human life. Soil moisture monitoring based on GNSS-R technique has been proposed to measure soil moisture using GNSS signals reflected from land surface. It is economical, flexible and able to work all weather and all day. It is a good supplement to current soil moisture observation methods. GNSS signal reflection process over bare land and reflected signal characteristics are explained theoretically. Then retrieval theory is introduced briefly. It can be seen that satellite elevation and azimuth have direct relevance to key parameters involved in soil moisture monitoring, especially surface roughness and antenna gain, which adds complications to the practical application of this technique. Fortunately, BDS GEO satellites have fixed positions relative to earth surface, resulting in constant elevation and azimuth and thus a constant influnce of parameters above. Finally, soil moisture monitoring method using reflected signals of BDS GEO satellites is proposed and its signal processing flow is presented as well. A ground-based experiment was carried out to validate the retrieval method. A RHCP antenna was set up to sky to receive the direct signal while an LHCP antenna was set pointing to the field covered with wheat at initial growth stage. We collected the GPS L1 and BDS B1 signals using a multi-channel GNSS intermediate frequency signal sampler. And the true value of soil moisture was collected by oven drying method once per hour during the experiment period. Then data processing was conducted according to the signal processing flow. Experiment results show an agreement with the true values. Forther more, the results of BDS GEO satellites present a better performance in both accuracy and temporal continuity owing to the fixed position of GEO satellites. We introduce the soil moisture retrieval principle, propose a retrieval method using BDS GEO signals and present the experiment results dedicated to validate method performance. However, priori information of soil composition, surface roughness and antenna gain is needed to obtain a better performance. As result simplified and valid models needs to be further studied. Machine learning algorithms can be applied to this technique, for example.

Extensive amount of water stored in snow covers has a high impact on flood development during snow melting periods. Early assessment of these parameters in mountain environments enhance early-warning and thus prevention of major impacts. Sub-snow GNSS techniques are lately suggested to determine liquid water content, snow water equivalent or considered for avalanche rescue. GNSS antennas are submerged into soil to derive soil moisture. This technique is affordable, flexible, and provides accurate and continuous observations independent on weather conditions. However, the characteristics of GNSS observations for applications within a snow-pack or submerged into water still need to be further investigataed.

The magnitude of the main interaction processes involved for the GPS wavelength propagating through different layers of snow, ice or water is examined theoretically. Liquid water exerts the largest influence on GPS signal propagation through a snow-pack. Therefore, we focus on determining the characteristics of GNSS observables under water.

An experiment was set-up to investigate the characteristics and limitations of submerged GPS observations using a pool, a level control by communicating pipes, a geodetic and a low-cost GPS antenna, and a water level sensor. The GPS antennas were placed into the water. The water level was increased daily by a step of two millimeters up to thirty millimeters above the antenna. Based on this experiment, the signal penetration depth, satellite availability, the attenuation of signal strength and the quality of solutions are analysed. Our experimental results show an agreement with the theoretically derived attenuation parameter and signal penetration depth.

The assumption of water as the limiting parameter for GPS observations within a snow-pack can be confirmed. Higher wetness in a snow-pack leads to less transmission, higher refraction, higher attenuation and thus a decreased penetration depth as well as a reduced quality of the solutions.

In consequence, GPS applications within a snow-pack are heavily impacted by wetness which is even more pronounced during melting period.

Placing the antenna in a fresh water layer as for soil moisture retrievel, a high attenuation of signal strength leads to a signal penetration up to 3.5 centimeter.

In this poster, we present a short introduction to the principle, explain the developed algorithms and show results of experiments dedicated to the signal propagation in water.

InSAR techniques provide researchers a set of tools for topographic mapping, as well as for monitoring deformations on the Earth surface. In Dragon-1 and Dragon-2, our focus was on DEM generation and surface motion estimation with medium resolution InSAR. Since Dragon-3, SAR datasets of high spatial and temporal resolution (TerraSAR-X, COSMO-SkyMed) are available and with the availability of Sentinel-1 data, a global time-series coverage is now reality.

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

1. Topographic mapping with SAR. We developed a maximum a posteriori (MAP) estimation method for multi-baseline InSAR assisted by StereoSAR. According to Bayesian theory, the combination of StereoSAR and InSAR for topographic mapping can be viewed as update of the StereoSAR DSM with InSAR phase observations. At the same time, the StereoSAR DSM is also a constraint to InSAR phase observations, which can solve the problem of elevation ambiguity and avoid phase unwrapping problems. The Mount Song has been selected as the test area, which is one of the five sacred mountains of China. The experimental result shows that there is neither systematic error nor large data voids in MAP estimated DSM and the standard deviation of height error σ_h of MAP estimated DSM is less than 10 m with respect to the photogrammetric DEM for the whole area, while in plain areas σ_h is about 5 m.

2. Urban subsidence analysis. SAR systems can measure distances and movements with high precision. Using for example PS-InSAR, deformations can be estimated with a very high precision. The long-term surveillance of urban subsidence and the infrastructure stability in Shanghai is our major research goal since Dragon-1. With data starting from ERS-1, over ENVISAT ASAR, ALOS PALSAR, up to modern systems like TerraSAR-X, COSMO SkyMed, PALSAR-2, and Sentinel-1, we continuously monitor the subsidence over Shanghai for far over a decade now. The combination of this data and the analysis of the continuous deformation is still on-going. Remarkably, the PS-InSAR precision stays stable over time even using different sensors.

3. Coseismic displacement from Sentinel-1 TOPS data. Terrain Observation by Progressive Scans (TOPS) mode from the Sentinel-1A/B satellites provides up-to-date high-quality Synthetic Aperture Radar (SAR) images over a large coverage, making it widely applied to earthquake studies. Recent work focuses on generation of co-seismic displacement of large earthquakes from Sentinel-1 TOPS images. However, many small/deep/offshore earthquakes have relatively smooth ground displacement disturbed by strong atmospheric influence. The coherence images spanning these earthquakes maybe not desirable e.g. due to the complex topography. Therefore, methods to derive such smooth co-seismic displacements from time-series is needed. We developed a new Sentinel-1 TOPS images analysis strategy with applications to earthquakes occurred recently in China.

The reconstruction of topographic information and their changes in the context of our dynamic Earth is one of the main applications of photogrammetry and remote sensing. With advancements in sensor technology and data processing, three-dimensional information can be retrieved in unprecedented precision. With the release of the TanDEM DEM, the DLR released the most precise world-wide DEM, an invaluable data set for numerous applications. The global availability and the high precision are proof of the extra-ordinary capability of Synthetic Aperture Radar (SAR) interferometry (InSAR) for 3D measurements.

With the SAR geodesy concept of the DLR, the radargrammetric measurements are having a comeback. Thanks to the high orbit-precision of TerraSAR-X and TanDEM-X, and by using collateral data to correct the atmospheric delay, absolute 3D positioning precision within a few centimeters is nowadays possible. However, there are several limitations for reaching the maximum precision in SAR geodesy and interferometric SAR. Therefore, a deeper understanding of the underlying error sources is necessary to fully use the potential of the aforementioned methods.

With the rapid developments in photogrammetric computer vision the multi-view photo-consistency measures for dense and accurate 3D reconstructed evolved and based on developments like semi-global matching for multi-view-stereo, photogrammetry reaches relative height precisions in the centimeter domain, comparable to or even outplaying LiDAR.

In this context, the Dragon-4 project “Topographic Mapping - Validation (32278-1)” is working on the three- and four-dimensional high-precision measurement using SAR and photogrammetry. Currently, we are working on three main objectives:

1. Validating the final version of the TanDEM DEM in test sites in China and Germany
2. Demonstrating the high absolute precision of SAR geodetic measurements with tests conducted in Wuhan
3. Showing the achievable precision of dense image matching in photogrammetry

With the recent progress in the three-dimensional measurement precision of photogrammetry and SAR, four-dimensional time-series measurements for surface motion estimation can become possible offering alternatives to differential interferometry based methods. This is especially beneficial, because these methods can be used as supplement to differential interferometry and derived methods, because they are especially applicable for fast surface motions that are otherwise especially difficult to be measured.

In this paper, we provide a better understanding of temporal decorrelation of tropic forest from TropiScat in various timescales. TropiScat is a ground-based campaign operated at the Paracou field station, French Guiana since October 2011, which allows gathering the tomogram of the forest in all polarizations at P band with a temporal sampling of 15 minutes . To analyze the temporal decorrelation we evaluate the intensity and coherence of the signal array of 15 transmitting and receiving antenna pairs. The vertical distribution of temporal coherence is obtained by comparing two tomograms acquired at different times. To further understand the temporal decorrelation, we evaluate the coherence between tomogram obtained from antenna pairs of different times and that of the same time. The procedure to produce the tomogram and different tomogram processors will be discussed in the final paper .

Terrain Observation by Progressive Scans (TOPS) mode from the Sentinel-1A/B satellites provides up-to-date high-quality Synthetic Aperture Radar (SAR) images over a large coverage, making it widely applied to earthquake studies. Recent work focuses on driving coseismic displacement of large earthquakes from a pair of TOPS images. However, many small/deep/offshore earthquakes have relatively smooth ground displacement disturbed by strong atmosphere influence. The coherence images spanning these earthquakes maybe not desirable either duo to the complex topography. Therefore, methods to derive such smooth coseismic displacement from time-series analysis is needed. Here we present a novel Sentinel-1 TOPS images analysis strategy with applications to a few earthquakes occurred recently in China.

First is the 5 February 2016Mw 6.4 MeiNong earthquake that occurred in Taiwan. For this case, we take a modified spectral diversity method for coregistration of TOPS images to get a smooth interferogram in which the obvious phase jumps are well corrected. Then we take time-series analysis using TOPS images acquired before and after the earthquake to improve the derived coseismic displacement. The results are validated with GPS data near the epicenter area.

Another case includes a series of M<7 earthquakes occurred on the Qinghai-Tibet plateau, To study these earthquake, we collect time-series Sentinel-1 TOPS images acquired before and after earthquakes. We improve the time-series TOPS data processing chain to estimate the coseismic displacement on detected persistent scatters. Our results show that the temporally uncorrelated atmospheric signal can be largely reduced and the subtle coseismic displacement signal can be derived more precisely than single interferogram.

Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool for large-area topographic mapping due to its capability of all-time all-weather imaging and high sensitivity to terrain relief. However, there is an inherent contradiction between geometric decorrelation and sensitivity of height measurement for topographic mapping with a single InSAR pair. A normal baseline of proper length is required to keep a balance between the two issues. A promising solution to this problem is the so-called multi-baseline InSAR analysis. The basic principle of multi-baseline InSAR is to derive an optimal height estimate by joint analysis of multiple phase measurements from several interferograms with different normal baselines. Compared with single-baseline InSAR, the major benefit of using multi-baseline observations is the possibility of exploiting redundant topographic phase observations with different height of ambiguities to improve the accuracy of phase unwrapping, or even avoid phase unwrapping.

In this study, after the analysis and discussion of the probability distribution of interferometric phase, we propose a maximum likelihood (ML) estimation method assisted by SRTM DEM data for multi-baseline InSAR topographic mapping. Based on our method, a spaceborne data multibaseline InSAR processing flow is established. In order to well adapt it to the repeat-pass interferometric pairs, the processing flow integrates the atmospheric effect correction method to improve the reliability of multi baseline estimation. The simulation experiments were designed to test the effectiveness of the maximum likelihood height estimation method and atmospheric effect correction method. The proposed multibaseline InSAR processing flow was applied in ALOS/PALSAR dataset covering Mount Tai area, China. The accuracy of resultant DEMs at spatial resolution of 20 m is evaluated by using a standard Chinese national DEM of scale 1:10,000 as reference. The results show that multi-baseline InSAR can improve DEM accuracy compared with single-baseline case. The quality of multi-baseline InSAR DEM can meet the American DTED-2 standard.

Hysteresis refers to the asymmetric path between to alternative states. Specifically, we apply the concept to analyse differences between degradation and regeneration processes, a phenomena pointed out in the dynamics of several ecosystems [1]. The topic is quite relevant for the ongoing debate about the irreversibility of desertification, challenged by different studies that support re-greening in degraded areas [2].

It is possible to deduce the existence of hysteresis through the speed of the aforementioned opposed processes. For that, a statistically sound analysis based on 2dRUE results [3] has been implemented. Previously, 2dRUE was applied to assess land condition in China Drylands using time series NPP data computed from Envisat Meris images [4]. The method incorporates a stepwise regression to signify the effects of time and aridity on vegetation. This allows distinguishing if biomass changes are explained by the impact of wet and dry years, or by land degradation itself. In this way, 2dRUE supplies standard partial regression coefficients, an explicit and untainted measure of changes in land condition.

We have selected the Mann-Whitney U test to compare two variables: Positive and negative time regression coefficients, i.e. regeneration and degradation do not related with aridity fluctuations. The null hypothesis is that both processes happen at the same rate. Data is divided into 3 categories, each one with a different number of classes, Land-use (20); 2dRUE Assessment (8); FAO Aridity (3). Exploratory results show that in half the cases there are significant differences between the speed of degradation and regeneration. In most of them, the pace of regeneration is higher than degradation. Forthcoming works require a detailed insight within those significant categories in aim to interpret the scope of our preliminary results.

Land degradation is a process by which the land productive capacity declines or even is completely lost under the influence of natural forces and human activities. The scope of land degradation has become global in the last decades, which compromises sustainable land management and threatens the safety of food production, especially in the poverty-stricken areas of developing countries. Desertification is one kind of land degradation and mainly occupied in arid, semi-arid and semi-humid areas. China is one of the most seriously affected countries by desertification. By the end of 2014, the desertified land area of China was 2.61×10⁶km². Post-hoc mitigation approaches are expensive and often ineffective. Therefore early warning systems based on Earth Observation make the most accepted scientific basis for controlling land degradation.

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, In+-ner Mongolia Autonomous Region, China, where the land degradation is prevailing in the first decade of the 21^st 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.

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots because of the stronger nonlinear spectral mixture effects. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, nonlinear spectral mixing effects for Nitraria shrubs and Haloxylon were validated to be different, additional research is necessary to validate their performance from the canopy to the landscape scale.

The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, greenhouse gases are challenging. The validation of the measurements is highly important in the development of satellite remote sensing systems.

The Chinese team has developed XCO₂ retrieval algorithm for TanSat Level 2 data processing and has updated it after the successful launch of TanSat in December 22, 2016. The FMI team has carried out validation studies of GOSAT and OCO-2 satellite retrieval results and they plan to contribute to the validation of TanSat observations. To support the satellite validation using FTS observations, AirCore CO₂ profile observations at Sodankylä will be used. Both FMI and IAP teams will work together to verify and investigate the precision of the retrieval results.

It is well established that the increase in atmospheric concentrations of CO₂ and CH₄ due to anthropogenic activities is a major driver for climate change. However, our understanding of the role of natural and anthropogenic contributions to the carbon cycle within a dynamic Earth system is still insufficient which leaves predictions of our future climate uncertain.

We have now access to dedicated satellite observations of atmospheric CO₂ and CH₄ concentrations which provide us with densely sampled data over regions poorly sampled by surface networks. This allows us to critically test and evaluate models of the carbon exchange for key regions such as China.

Currently, three CO₂ satellite sensors are in orbit, JAXA’s GOSAT, NASA’s OCO-2 and the Chinese TanSat mission, which gives us an unique opportunity to intercompare observations from multiple space-based CO₂ datasets over China and to use them jointly to assess model calculations.

Due to the anthropogenic greenhouse gas emissions our climate is changing. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The high northern latitudes are especially sensitive to climate change. The quantification and monitoring of the carbon cycle processes are crucial for the understanding of climate system feedbacks. Recently launched satellite instruments mesuring greenhouse gases provide important information related to the carbon cycle processes that can jointly be analysed with ground based observations and compared/combined with modeling. In this presentation we discuss recent recearch activities that have taken place at the the Finnish Meteorological Instiute related to satellite observation of greenhouse gases. Moreover, we present our plans to participate in the validation of Chinese TanSat satellite’s carbon dioxide observations using ground based instruments at Sodankylä as well as our plans on using the TanSat data for studying spatial and teporal variability of carbon dioxide and emisison regions.

Atmospheric Carbon dioxide (CO₂)is one of the major anthropogenic greenhouse gas that remains significant uncertainties in global carbon cycle and climate change studies. Hyper spectral near infrared and shortwave infrared (NIR/SWIR) measurement from space could provide global column-average CO₂ dry-air mixing ratio (XCO₂) in advanced accuracy and precisions to reduce the uncertain of climate prediction. After the Greenhouse gas monitoing satellite including GOSAT and OCO–2 from USA-NASA and Japan-JAXA, China’s carbon dioxide observation satellite (TanSat) has been successful launched in 2016. TanSat is a carbon dioxide observation satellite funded and supported by the Ministry of Science and Technology of the People’s Republic of China and the Chinese Academy of Sciences. The TanSat retreival algorithm has been developed to approeach the XCO₂ in a highly accuracy and precision reqiurment. The TanSat algortihm has been applied on GOSAT (ATANGO) and OCO-2 measumrent and well optimaized before it applied in TanSat operational data processing. The retrieval algorithm and its perfomance over China has been studied. The ATANGO data product has been used in carbon flux inversion in China.

Anthropogenic CO₂ emissions from fossil fuel combustion have large impacts on climate. In order to monitor the increasing CO₂ concentrations in the atmosphere, accurate spaceborne observations—as available from the Orbiting Carbon Observatory-2 (OCO-2)—are needed. This work provides the first direct observation of anthropogenic CO₂ from OCO-2 over the main pollution regions: eastern USA, central Europe, and East Asia. This is achieved by deseasonalizing and detrending OCO-2 CO₂ observations to derive CO₂ anomalies. Several small isolated emission areas (such as large cities) are detectable from the anomaly maps. The spatial distribution of the CO₂ anomaly matches the features observed in the maps of the Ozone Monitoring Instrument NO₂ tropospheric columns, used as an indicator of atmospheric pollution. The results of a cluster analysis confirm the spatial correlation between CO₂ and NO₂ data over areas with different amounts of pollution. We found positive correlation between CO₂ anomalies and emission inventories. The results demonstrate the power of spaceborne data for monitoring anthropogenic CO₂ emissions.

China represents a significant contribution to global observed variations of atmospheric carbon dioxide (CO₂) due to its large landmass, and its high fossil fuel emissions associated with unprecedented economic growth. We report CO₂ fluxes in 2014 and 2015 inferred, using an ensemble Kalman Filter [Feng et al, 2009], from data collected from five new regional background ground-based sites over China (together with available NOAA sites), and fluxes inferred from OCO-2 (version 7) and GOSAT (UoL v7) XCO₂ data. We find that the resulting posterior CO₂ concentrations are generally consistent with independent validation data downwind of the Chinese mainland. To better understand the response of the Chinese biosphere to the 2015 El Niño we compare the magnitude and distribution of CO₂ fluxes inferred from different data sets. We find that the net CO₂ emissions over China inferred from GOSAT and OCO-2 XCO₂ retrievals are higher than those from the in-situ data. Our results highlight the importance of space-based observations for top-down flux inversions. Chinese TanSat project, together with existing and planned missions, will significantly improve flux estimates over China.

CO₂ is a long-lived trace gas which acts as the most important greenhouse to the global climate. For its short-wavelength transparent and long-wavelength absorbed characteristic, the increase of CO₂ caused by human activities induces global warming and a series of climate changes since the industrial revolution. Although it is almost well-mixed in the atmosphere, CO₂ mixing ratio varies with time, decreases with altitude, and spaces between sources and sinks, the temporary emissions such as fires and burning occurring near the surface on regional scale changing CO₂ mixing ratio sharply and damped by atmospheric transport and diffusion in a short time. It is necessary to infer the spatial distribution of carbon sources and sinks distribution with repeated CO₂ measurements on a large scale, and this requires very demanding measurement accuracy to the relative small variation of CO₂ in the atmosphere. A stringent precision of space-borne CO₂ data, for example 1 ppm or better, is required to address the largest number of carbon cycle science questions. A high measurement sensitivity and global covered observation is expected by space-borne IPDA (Integrated Path Differential Absorption) lidar which has been designed as the next generation measurement. By selecting the absorption features of the lidar operating wavelength appropriately, IPDA lidar could obtain the dry air total column CO₂ mixing ratio named XCO₂ by compared two echo pulse signals reflected from the Earth with a high weight to the boundary layer. In this paper an assessment is made to describe the various error sources limiting the accuracy and precision of the measurement. An overview is presented of the relative contribution of each error source including the inadequate knowledge of the atmosphere pressure and temperature, surface reflectivity, reflected surface elevation, and errors from lidar system such as shot noise, dark noise and spectral purity. A global simulation is used to investigate the sources of errors associated with the configurations of lidar system and the environment parameters. The simulation is carried out over global scale with atmosphere pressure and temperature from NCEP, surface elevation model and surface reflectivity from MODIS, and the CO₂ distribution is from OCO-2 dataset. The results identifies that surface albedo plays an important role in the process of satellite remote sensing. It may be inferred that errors impacted by reflectance of the Earth vary with seasons. This would need an improved acknowledge on seasonal variations of surface reflectivity and provide a guidance for improving the accuracy of space-borne IPDA detection.

Arctic region is experiencing faster warming than that in the middle and low latitude region, which triggers extensive change of physical and biological Arctic systems. Sea ice, as one of the most important indicator of climate change in Arctic, is showing an accelerating declination trend. The loss of sea ice in the Arctic is not only a simple outcome of climate change, but it also has strong feedback to regional and global climate. In addition to the climate change issue, Arctic is being paid more attentions than ever before, as increasingly accessible for extensive shipping (e.g., the recently opened Northern passages in summer) and oil and gas exploration. Satellite observation have been exploited for Arctic sea ice monitoring for more than a few decades, as they have advantages of large coverage and flexible acquisitions. In contrast to optical imaging, microwave sensors are not impeded by cloud coverage or lack of daylight. Among the microwave sensors, Synthetic Aperture Radar (SAR) has unique advantages of high resolution, as well as large coverage.

For Arctic sea ice monitoring, in the first step, we focus on deriving static and dynamic information, e.g., classification of ice types, ice-water discrimination, ice concentration and drift of sea ice. We will present our recently collaboration on deriving such information based on C- and X-band SAR, including TerraSAR-X, Radarsat-2 and the Chinese recent launched GaoFen-3 (GF-3). Different algorithms and approaches have been developed for these spaceborne SAR data derive sea ice information. In particular, the SAR data acquired in multiple polarizaiton are exploited. In addition to derive sea ice information from SAR data, we will also present some preliminary studies on interaction of swell and ice in the marginal ice zone (MIZ).

A fully automatic SAR oil spill monitoring system briefly consists of three parts, 1) dark target segmentation on SAR image 2) dark target feature extraction 3) dark target classification. In order to build a fully automatic system with high detection and high classification performance, the first step requires all oil spill targets to be extracted in the first step, the second step requires the category distinction of dark targets in the feature space to be as high as possible to reduce the classification complexity and the third step requires the classifier to deal with imbalance problems.

The classification complexity can be simply expressed as the size of the overlapping regions of the distribution of the two types in the feature space. The larger the overlap, the more difficult it is to train a high performance classifier. The negative effect of imbalance is equivalent to a multiplicative amplification factor attached to the complexity. The imbalance problem would disappear if complexity is reduced zero. However, the complexity is impossible to be zero, therefore the technical effort to build a high performance automatic oil spill monitoring system is to try to reduce classification complexity and to deal with imbalance problem.

In order to ensure that all oil spills are extracted, the existing segmentation algorithm for extracting dark targets from SAR images will lead to a large number of look-alikes appear, which contain not only dark targets caused by common atmospheric or oceanic phenomena, but also lots of unexplained ones. That is the source of the imbalance problem. In the segmentation stage, an adaptive threshold segmentation algorithm based on multi-scale background normalization is adopted at first to ensure that all oil spills are extracted, and then a post-processing filter chain is used to reduce the imbalance of the targets from 1:100 to n1:5 below.

77 features collected from different researchers are used to improve the category distinction of the types in the feature space, that is, to reduce the complexity of the classification problem. The more the features, the more weights the network needs to adjust, therefore the more training samples are required. However, all 77 features are still used in our study since there are sufficient samples available and computation amount is not so high. Some wrong category labels of samples due to human error may increase classification complexity, especially in the case of a large number of samples. By using an iterative sample category designation method combined with manual classification and machine classification, the number of error labels is significantly reduced and thus the classification complexity is reduced either.

Double-hidden-layer neural network has strong capability to fit complex decision surface, but it also easy to fall in over-fitting and suffer high false discovery rate under the circumstance of imbalance. The Adaboost integrating multiple double-hidden-layer neural networks plays a role in smoothing the decision surface, thus weakening the over-fitting effect and enhancing the generalization performance of the classifier. Moreover, Adaboost trends to reduce the false alarms in imbalance condition since its component classifiers pay more attention on the samples misclassified by the preceding ones. Therefore the Adaboost based on neuronal network has capability to deal with the imbalance problem.

By applying the techniques mentioned above, a fully automatic SAR oil spill monitoring system with detection rate of 81%, false discovery rate of 17%, false alarm rate of 5% and correct recognition rate of 92% is obtained for the sample set consisting of 23768 dark targets extracted from 337 scences of Envisat ASAR and Cosmo-SkyMed images.

In 2008, an extensive Ulva Prolifera bloom occurred in the Yellow Sea (YS) just before the Olympic Sailing Regatta and draw much attention of the world. Then Ulva bloom annually occurred in the YS each summer and attracted the interest of the ocean remote sensing and marine biologist community. Wide swath and short re-visit time optical satellite data were first utilized to monitor the Ulva bloom and estimate their coverage area using methods such as NDVI and FAI. However, the estimated Ulva bloom coverage area were very different in the published literature mainly due to adopted different thresholds T₀ and T₁. T₀ was used to differentiate the algae-containing pixel with seawater pixel while T₁ was used to differentiate algae full-covered pixel with partial-covered pixel. In this study, those two thresholds were further studied.

Threshold T₀ was derived as follows: (1) First, generate the FAI and FAI gradient image for each optical satellite imagery; (2) classify all pixels in the satellite image as seawater pixel and non-seawater pixels based on the FAI gradient image;(3) replace non-seawater FAI values with median FAI of the neighboring seawater pixels to construct the seawater FAI background image; (4) generate seawater-removed FAI image by subtracting the seawater FAI background image from the FAI image, where pixels greater then zero will be algae pixels.

In the recent study, the author developed the Ulva bloom biomass estimation model and found that (1) FAI threshold T₁ was significantly underestimated in the former study (2) FAI linearly increased with the increase of algae coverage but increased slowly after algae completely covered, thus the turn-point in the FAI-biomass curve was the FAI threshold T₁. In this study, the lookup table of threshold T₁ for specific satellite sensor were simulated in different sun/sensor viewing geometry and atmospheric conditions based on the filed measured algae spectrum.

The high-resolution satellite data were used to validate the results in this study. (1) The Ulva bloom coverage estimated from MODIS data and concurrent high-spatial resolution Chinese GF-1 WFV data (16m) were compared with the relative difference 10%. (2) The threshold T₁ was validated with high-spatial Worldview-2 satellite data.

This method can be implemented to OLCI/Sentinel-3 data and the subsequent estimation Ulva bloom coverage from OLCI data will be conducted in this summer.

Synthetic aperture radar (SAR) imagery provides an effective source of data for observing, measuring and quantifying oceanographic phenomena. The ability of SAR sensors in retrieving data in almost all weather conditions, independently of sunlight illumination, is an extremely useful aspect, important for oceanographic applications. The quality of SAR imagery is dependent on the mode of acquisition and raw data processing. SAR data acquisition techniques introduce a significant backscattering trend in the range direction of the received signal. In Wide Swath and Extra Wide Swath modes, results in a progressive reduction of brightness over images from near to far range, introducing errors on the detection and classification of dark features to oil spills and lookalikes. The present research, aims to examine normalization techniques previously applied to ENVISAT SAR Wide Swath data. We investigate possible methods for limiting the issue of Normalized Radar Cross-Section (NRCS or σ°) variation, due to the incidence angle variations in Sentinel-1 Wide and Extra Wide Swath and data. NRCS depends on the relative azimuth angle between the radar look direction and wind direction. At low incidence angles over a certain wind speed and direction NRCS values are different from these at high incidence angles. In order to eliminate errors during image processing and analysis for oceanographic feature extraction, a normalization is required to limit the NRCS variation over the various incidence angles is suggested. Based on previous studies, the most widely used incidence angle correction technique is the square cosine correction. However, the square cosine correction is valid for surfaces with Lambertian reflectance properties and is not expected to perform in a satisfactory way over the sea. Here we apply two sensor independent functions aiming to improve the dark object detection in SAR imagery over ocean: a theoretical backscattering shape function which is derived from a minimum wind speed and an empirical range fit of NRCS against incidence angle θ. The former method exploits only the modelled NRCS values, while the latter only the image content. The aim of this paper is to apply a simple but consistent scheme for reducing the dynamic range of SAR images by removing the mean incidence angle dependence. The approach targets to normalize the Wide and Extra Wide Swath SAR image to a fixed reference angle. From the approach a single global threshold is applied to detect dark objects in SAR imagery.

ABSTRACT

The pollution of the marine environment due to deliberate discharge of untreated ballast waters is not only an operational procedure done by ships but also by offshore platforms. Although, the amount of oil released into the ocean by the offshore platforms using this procedure on one-time basis is derisory, if compared with the massive spill event in case of platform accidents like the Montara and Deepwater Horizon, the long term damages on the flora and fauna are severe.

As the North Sea is characterized by fairly shallow water bathymetry, it has been extensively exploited in the past years for oil extraction and production. By now, it hosts a significant number of offshore installations and therefore the probability of minor leaks is quite high. A large number of Synthetic Aperture Radar (SAR) images are being acquired and processed operationally over North Sea platform installations for potential oil pollution using the TerraSAR-X satellite [1]. The ScanSAR and WideScanSAR mode in VV polarization imagery have been preferred due to large coverage and higher oil-water contrast. Among the dataset collected, a constant leakage from the platforms belonging to the Forties oil field has been observed.

Thanks the low TerraSAR-X orbit altitude and relatively high latitude location of the Forties oil field, leaks from the same platforms have been observed with temporal interval of less than 13 h. While most previous studies on tracking oil spills assume that the observed slicks by consecutively acquired SAR images are the same and spatial displaced by the drift effect [2], a completely different situation is outlined by the analysis in [3]. By model simulation it is shown that leaks were not start-stop but continuous with only part of the old oil being drifted.

REFERENCES

[1] S. Singha, D. Velotto, and S. Lehner, “Near real time monitoring of platform sourced pollution using TerraSAR-X over the North Sea,” Mar. Pollut. Bull., vol. 86, no. 1–2, pp. 379–390, Sep. 2014.

[2] Y. Cheng et al., “Monitoring of Oil Spill Trajectories With COSMO-SkyMed X-Band SAR Images and Model Simulation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 7, pp. 2895–2901, Jul. 2014.

[3] X. M. Li, T. Jia, and D. Velotto, “Spatial and Temporal Variations of Oil Spills in the North Sea Observed by the Satellite Constellation of TerraSAR-X and TanDEM-X,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 11, pp. 4941–4947, Nov. 2016.

Glaciers on the Tibetan Plateau are the main reservoirs of water in the region and much attention is being given to monitor the response of these glaciers to climate variability. Climate forcing varies across the Plateau with the westerlies from inner Eurasia and monsoons from India and East Asia. The surface properties of glaciers characterize the interaction between the atmospheric boundary layer and glaciers. Traditionally, the surface properties of glaciers are observed in situ, but on the Plateau the spatial and temporal variability of such properties cannot be captured by in – situ experiments only. Our main goal is to characterize the surface energy balance of glaciers using a suite of measurements by space – borne observing systems.

Building upon the work done in the previous Dragon investigations, during this 1^st Dragon 4 year we focused on the following aspects:

• Temporal and spatial variability in the albedo of glaciers and their close surroundings
• Monitoring glacier surface flow velocity over an extended period of time
• Estimating and mapping the aerodynamic roughness of glaciers
• Discriminating ice cover types using observations of spectral reflectance and albedo at high spatial resolution
• Relating the albedo of glaciers to depth and duration of snow cover
• Parameterizing glacier albedo in the WRF model

Time series of data products on albedo and net radiation were analyzed to assess the variability in space and time of albedo in glacial areas and to document the corresponding impact on net radiation. Snowfall leads to sudden and large increase in albedo, which may be as high as 0.8 with deep, fresh now. This leads to a change in net radiation from about 800 Wm^-2 to 200 Wm^-2, i.e. an extremely large reduction in radiative load, slowing down snow-melting. The same analysis documented a large spatial variability on both minimum, i.e. glacier, and maximum albedo. Such variability is due to a combination of surficial deposit (ice cover) and terrain, which affects irradiance.

The response of glaciers to climate variability was evaluated in different ways, including the retrieval of the glacier surface velocity using image correlation of paired high spatial resolution images (Landsat TM and OLI). This study focused on the Nyainqêntanglha Range and the period from 1993 to 2015. We analyzed wintertime images at intervals of about one year. The analysis of these time series of ice surface displacement, revealed that the observed signals were a combination of a linear trend and a multi-annual component with variable amplitude from place to place.

Surface features, namely roughness, slope and elevation were retrieved with a combination of ICESat/GLAS and ASTER GDEM data to estimate and map the aerodynamic roughness of glaciers. This property modulates fluxes of latent heat (evaporation, sublimation) and sensible heat. The response of the laser waveform to morphology was studied in detail, showing that roughness and slope of the surface can contribute several meters to even several tens of meters to the pulse shape.

The spatial variability of spectral reflectance and albedo within a glacier was evaluated using Landsat TM and GuoFeng multi –spectral images. Areas covered by debris and lakes or ponds linked with a glacier were clearly delineated, particularly using the Guofeng very high spatial resolution images. Differences in spectral reflectance and albedo were rather large and the consequences in terms of spatial variability, within a glacier, of radiative forcing are being evaluated.

The work on time series of glacier albedo revealed large and rapid variations in the radiative forcing on glaciers. These variations are not reproduced by the land surface schemes currently implemented in advanced atmospheric models such as WRF. We have combined WRF snow depth with MODIS albedo to parameterize the dependence of albedo on snow depth and snow age. This parameterization has been evaluated against in – situ measurements at the Plateau permanent observatories.

The parameterization constructed in this way has been implemented in WRF and the sensitivity of the WRF land surface energy balance has been evaluated, showing rather large impacts on both radiative and convective fluxes.

One great challenge of cryosphere and hydrosphere science in high elevation regions is the scarcity and sparseness of data on the multiple variables and processes relevant to the understanding of the water cycle.

Quantitative information on water losses is important to understand the global terrestrial water cycle and land – atmosphere interactions. The global evapotrasnspiration in 2008-2013 with a spatial resolution of 1 km was determined using ETMonitor as the sum of the evapotranspiration components, i.e. plant transpiration, soil evaporation, open water evaporation, rainfall interception, snow and ice sublimation. All these variables were retrieved using the ETMonitor model driven by multiple satellite data products. The ASCAT (Advanced Scatterometer) soil moisture data product was applied as a key input to scale ET between 0 and ET_max. To allow the estimation of ET at high spatial resolution, the 0.1° resolution ASCAT data product was downscaled to 1km spatial resolution globally using bilinear resampling method. We have developed a different, bio-phyisical, downscaling procedure applicable for regional studies and based on high resolution surface temperature and vegetation index data products. The estimated water losses agreed well with the in situ tower based observations at a number of FLUXNET sites, with high correlation, low bias, and low root mean square error. The retrieved ET captures the expected global patterns and the details of spatial and temporal patterns were consistent with the current available global evapotranspiration products such as data from GLEAM (Global Land Evaporation Amsterdam Model) and the GLDAS (Global Land Data Assimilation System) Noah product. The ETMonitor data product is superior due to the high spatial and temporal resolutions. We have also experimented with the ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product to replace the ASCAT one. A first evaluation was carried out by retrieving ET in China during 2001-2015 and the results used to contribute to the National Remote Sensing Monitoring for Sustanable Devepoment Report in China (2016).

To improve the accuracy of ETMonitor in cold and high elevation regions, the algorithm to estimate snow and ice sublimation was improved, by adapting the Penman – Monteith combination equation. This method was evaluated against eddy – covariance measurements of latent heat flux at high elevation sites in the Heihe river Basin N – W China: on average the RMSE was 8.75 W m^-2. We have also evaluated the bulk aerodynamic (BA) method against the same measurements and the BA performance was slightly worse. The main driver of the P – M equation is net radiation, which is very variable at high elevation due to the variability of albedo, which enhances the scope of using satellite observations to estimate and monitor sublimation. A case – study on the upper reach of the Heihe River Basin has been carried out using MODIS data products on surface albedo and temperature.

Work contiuned on improving other algorithms and data products. An algorithm to retrieve the total precipitable water was developed, based on the ratio of brightness temperature changes ΔTb_18.7/ ΔTb_23.8 , using atmospheric profiles obtained from the globally distributed radiosonde observations and applying a microwave radiative transfer model. This algorithm can retrieve total precipitable water under both clear and cloudy sky condition over land, and can be easily transferred to MWRI on board the FY-3 satellites. To retrieve the surface freeze and thaw condition, an innovative freeze/thaw index based on microwave observations at 18.7 and 36.5 GHz was defined and assumed to be linearly correlated with the radiometric land surface temperature retrieved with thermal infrared observations. It was found that this linear relationship is quite reliable for most areas, and can provide high-resolution information on near surface soil freeze/thaw state. The validation of the high-resolution freeze/thaw state against soil temperature measured at active layer monitoring sites along the Qinghai-Tibet Highway illustrated a moderate accuracy over a decade scale.The daily snow cover fraction at 500m resolution was retrieved in the Tibet Plateau in 2013, and missing values due to the cloud cover were filled to obtain a cloud-free dataset.

Mun river basin is largest basin in Mekong river region, where both flood and drought frequently occur. Water management by hydrological model is difficult due to lack of accurate and spatial/temporal continuous products to force and localize the model. The new Precipitation and soil microwave products gradually plays important role for hydrological modelling. The study introduced the precipitation product at 0.1 degree spatial resolution (GPM mission) for SM simulation by Variable Infiltration Capacity (VIC) model. Then we assessed accuracy of 10cm soil moisture simulation by comparison with SMAP soil moisture product at a spatial resolution of 9km. The results show that:(1) GPM precipitation can be compared with gauge-based monthly precipitation in Mun river;(2) GPM products can be used to simulate spatial pattern of soil moisture in dry season; the simulated soil moisture has a high accuracy in the upperstream of basin but poor in the downstream due to spatial pattern of irrigation application in Mun river basin. It suggests that based on GPM and SMAP products, we can make model calculation and cross validation by remote sensing technology. Therefore microwave remote sensing products are expected to be introduced for water management in remote or data-lacking regions.

Soil hydrologic parameterization, as well known control the energy and water fluxes of hydrologic basin surface playing a crucial role in hydrological model simulation for operative application in the field of water engineering. Despite their importance their definition for large areas is always a source of uncertainty due to difficulty of representative ground measurements , their spatial variability also strongly affected by land use change and agricultural practices.

In the framework of Dragon 4 Project “Forcing, calibration, validation and data assimilation in basin scale hydrological models using satellite data products”, the paper presents a procedure for soil hydrologic parameterization based on the assimilation of satellite LST data into a thermodynamic distributed water balance model (FEST-EWB). The 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 from MOST, ESA and NASA agencies. This approach will be compared with traditional ones based the pixel wise use of pedo transfer function, calibrated for available soil maps.

The case study is the upper part of the Heihe River basin where a consistent historical data set will allow to test this approach.

A simple two-source evapotranspiration (ET) model was applied to the Yingke and Daman irrigation districts of the Zhangye Oasis, which is located in the middle reaches of the Heihe River, China. The ET model was composed of two parts, including an evaporation (E) sub-model and a transpiration (T) sub-model. A separated parameter estimation scheme was conducted using Bayesian inference. First, an empirical multiplier was estimated for an E sub-model using observations that were collected after crop harvests. The empirical multiplier was then assigned to the most-likely value in the simple two-source ET model. Second, a global sensitivity analysis was performed to identify the key parameters that were responsible for most of the variability in the λET results within the T sub-model. To avoid equifinality or over-parameterization, Bayesian inference was applied to estimate the key parameters that induced the most variability in the first set. A second set of Bayesian inference was then performed by fixing the most-likely values of these parameters, and the other parameters were defined one-by-one as Bayesian parameters. These parameters were estimated for seven sites. The coefficient of determination for the modeled λET and the observed values exceeded 0.9. Next, a cluster analysis was conducted using the canopy height, leaf area index and soil moisture content to classify the fields with the highest similarities and then to distribute the same parameter values to similar fields. Finally, λET was estimated using the most-likely values of the parameters at the regional scale. The root-mean-square error of the remotely sensed estimates was less than 20 Wm^-2, the mean absolute percent error did not exceed 4%, and the correlation coefficient was greater than 0.97. The validation was conducted for both the modeled λET at the point scale and for the remotely sensed λET at the satellite pixel scale. The results demonstrate that using cluster analysis, the most-likely values of the parameters can be effectively applied to estimate remotely sensed λET.

The effect of climate change on snow is complicated at regional scale. The high spatio-temporal resolution snow related variables simulated by weather research and forecast model including snowfall, snow water equivalent and physical snow depth and the high spatial resolution fractional snow cover data extracted from MODIS/Terra are adopted to evaluate the effect of climate change on snow over the Heihe River Basin (HRB) in last 15 years using Empirical Orthogonal Function (EOF) analysis and Mann-Kendall / Theil-Sen trend analysis. The results indicate 1) Due to the air temperature increasing, the fractional snow cover, snow water equivalent, physical snow depth over the whole HRB region decrease in last 15 years, especially at the height over 4500 m, however, the snowfall increases at mid-altitude ranges over the upstream of HRB. 2) Over the upstream of HRB, the total snow flux increased, however, the number of snowfall days decreased in last 15 years, so the occurrence of extreme snow events over the upstream of the HRB might increase. 3) The air temperate over the downstream increased at the most of the HRB, however, the snowfall over this region decreased in last 15 years, so the weak ecological system over the downstream may be exacerbated in future.

As the global sea level rises, there is increased concern about the growing urbanization of the low-lying deltaic coastal regions, and the related coastal hazards. Furthermore, the local relative sea-level rise can be significantly affected by vertical ground motions, either due to natural processes (e.g., global isostatic adjustment, tectonics, sediment consolidation and compaction, upstream sediment load reduction) and to human activities (e.g., groundwater extraction, land reclamation, building construction and consolidation). Deformation phenomena can be in the same order of magnitude (or greater) than climate-induced sea level rise. However, coastal ground motions are in practice often poorly known, and in many cases, little information is available about the patterns and time evolution of ground motion. It is, therefore, worth to monitor coastal delta regions through advanced Earth Observation (EO) systems that are capable detecting the ongoing surface deformation phenomena, recovering their spatial extent on the ground and following their temporal variability. This is beneficial for the subsequent interpretation of natural/anthropogenic processes causing surface motion.

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

More specifically, the aim is to retrieve long-term displacement time-series from EO data, specifically satellite Synthetic Aperture Radar (SAR), of the investigated areas through advanced differential interferometric synthetic aperture (DInSAR) techniques, as well as to complement DInSAR results with information derived from GPS/leveling campaigns with the aim to perform sound and extended geophysical analyses, and coastal erosion or accretion rates, which will be obtained by jointly exploiting archives of SAR data and the new generation of optical remote sensing systems.

In order to evaluate the combined risk of sea level rise, storm surges, and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas is mandatory. Added-value EO data products, such as the updated DEMs of coastal areas subject to sea-level rise, the time-series of terrain displacement, mean displacement velocity maps, and time-series of SAR backscattering maps, will be obtained by exploiting archives of SAR data with different levels of spatial resolution spanning a long time interval of about 20 years since the beginning of 2000s to 2020.

During the first year of this present D4 project, some preliminary activities have been conducted for processing Sentinel-1 data on both Delta regions by using different DInSAR tools in such a way to perform combined Small Baseline Subset (SBAS) [1] and Permanent Scatterers (PS) [2] analyses. Sentinel 1 time-series span about last three years. However, in order to have the historical perspective of the deformations occurring in the two areas, a few experiments have been carried out to obtain deformation maps covering the time lapse between 2002 to present days using (when available) ENVISAT and Cosmo-SkyMed SAR acquisitions. The latter are worth to complement the analyses performed over the last four years over the reclaimed lands of the Shanghai city [3].

The availability of updated DEMs of the areas subject to sea-level rise (and/or flooding) is also of fundamental importance. It is worth emphasizing that nowadays, global coverage catalogues of DEMs imaging most of the Earth’s surface are available (such as the ones recovered through the Shuttle Radar Topography Mission in 2001) and also new SAR missions have recently been specifically deployed (such as the TanDEM-X mission [4]) for the generation of updated profiles of the terrain. During the first months of activities, a DEM of the coastal area of the Shanghai megacity has been generated by applying the method [5]-[6] based on the use of bistatic SAR data acquired by the TerraSAR-X and TanDEM-X sensors. The analysis of the generated DEM, as well as its use for the application of flooding models in the Shanghai area, is still in progress.

The preliminarily results of the activities performed during the first year of this D4 project will be analyzed and properly integrated with the results of other investigations, for instance by performing SAR Tomography [] experimentations over Delta river cities.

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

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

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

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

[5] Kubanek J., Westerhaus M., Schenk A., Aisyah N., Brotopuspito K., Heck B. (2015a) Volumetric change quantification of the 2010 Merapi eruption using TanDEM-X InSAR. Remote Sensing of Environment, 164,16-25.

[6] Kubanek J., Richardson J., Charbonnier S., Connor L. (2015b) Lava flow mapping and volume calculations of the 2012-13 Tolbachik, Kamchatka fissure eruption using bistatic TanDEM-X InSAR. Bulletin of Volcanology, 77,1-13.

Mean sea-level (MSL) is rising worldwide, and correlated changes in the ocean tides are also occurring; the combination of both may increase or diminish total sea-levels (TSL) in a complex manner. Higher ambient water levels can increase the flood risk to coastal zones, more so under storm surge episodes. However, even without a storm event, increasing MSL coupled with increasing tides may lead to more nuisance flooding, or flooding events produced from the coupling of high tides and rising MSL. Hong Kong is particularly sensitive to changes in water levels, as it has a large population with much of its infrastructure near the shore. Furthermore, land subsidence and past reclamation projects may have induced changes in resonant and frictional properties of the tides, which may yield non-uniform spatial changes in water levels throughout Hong Kong. In this work, the historical full-spectrum tidal variability at a number of coastal tide gauges in Hong Kong is determined, and the past behavior of the relations between major tides and shallow-water overtides is examined to understand the changes in friction and resonance and the implication for future water level spectra in the region. Additionally, the locations surrounding each tide gauge is examined via remote sensing images from the Sentinel series of earth observing satellites, and past and present images are contrasted to determine changes to coastal morphology, and compared to the frictional determinations of the ground based gauges and to the history of land subsidence and land reclamation in the region.

In recent years, quantitative measurements of significant wave height (SWH) by the use synthetic aperture radar (SAR) and radar cross section (RCS) methods has been proven effective in different approaches without prior knowledge of wind information. An update of an existing simple semi-empirical algorithm, aided by parameterized filtering of RCS, is presented in this study to address the issue of how the RCS values depend on empirical algorithms in different systems, using the wavelength value of the dominant wave peak and the relationship function of RCS to incidence angle derived from the SAR image. To determine the wavelength of dominant wave peaks, we implement a contrast limited adaptive histogram equalization (CLAHE) method, based on a parameterized image texture analysis. An adaptive filtering method ensures a statistically robust determination of the filtering parameter, resulting in a higher contrast SAR image that allows more efficient dominant wave peak identification, as clearer wave patterns can be revealed by higher image contrast level. We also propose a preliminary empirical update for the backscatter cross-section to incidence angle function for vertical polarization in a 5.405 GHz SAR system. Standard meteorological buoy data from National Buoy Data Center (NDBC) is used in development of the empirical model through validation. This research employs Level-1 GRD Sentinel-1A SAR images from 2015 to early 2017 that look at Hawaii and the central part of the west coast of the United States of America, selected to represent deep to shallow water depth and river influenced estuaries. However, extreme sea states are not considered due to the limitation of the image repository and buoy data availability. Beside the two analysis methods described above, additional detailed analyses are conducted on the sea state relation to the velocity bunching mechanism, based on SWH estimation result.

Among those diseases threatening human health and well-being, many epidemic and infectious diseases are closely related to natural environment due to the presence, breeding and evolution of their pathogens or reservoir hosts, especially vector-borne diseases (e.g. schistosomiasis, malaria and dengue, etc.) which rely heavily on their vectors. Therefore, monitoring the diseases' vector is an important way to prevent and control the vector-borne diseases.Because of complex spatial distribution and dispersion of typical diseases and their vectors, it is difficult to acquire relevant environmental factor data by traditional in-situ measurements. Remote sensing technology provides the capability of obtaining temporal-spatial variations of ground environmental factors. However, remote sensing experts may not exactly know what environmental factors are required to identify the incubators of vector-borne diseases. On the other hand, effective RS data processing and parameters retrieval techniques are also challenges for hygiene experts who are lack of experience of remote sensing applications. Taking into account of different type of massive data are involved, computing scientists with substantial intelligent data analysis expertise is crucial to successfully incorporate advance intelligent data analysis, such as data mining, pattern analysis. Consequently, any single of these disciplines is insufficient, it is essential to bring together scientists from computing science and remote sensing along with domain experts to foster a substantial collaboration.This proposed project aims to apply advanced remote sensing and computing technologies into monitoring and early warning of vector-borne diseases, e.g. schistosomiasis, malaria and dengue. First is to reveal environmental factors which have significant influences on the breeding of epidemic disease and its vectors. Then the project will make full use of the advantage of European and Chinese earth observation resources and the partners capability to develop parameter inversion, feature extraction and pattern analysis methods that will be used to characterise environmental features and habitants that are mostly suitable for the growth and dispersion of vector-borne disease and dynamic monitoring. Furthermore, temporal-spatial models of the distribution of vector-borne diseases will be developed by data mining techniques. Finally, the driving mechanism and data assimilation methods of land surface process model will be explored in order to implement identification and early warning of vector-borne disease transmission areas. All the institution of the project can provide sufficient funding to run the whole project successfully. The outcomes of the project will help to decrease the scope and extent of vector-borne diseases, and improve prevention & control capabilities to vector-borne diseases. Additionally, the research results can be used to assess environmental characteristics around the sits of major infrastructure and facilities, and provide the suggestion on site selection and implementation of infrastructures. The synthetic feature extraction techniques developed for multi-source multi-level remote sensing data can also be applied to other service fields, sustainably making contribution to knowledge within the communities.

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

The atmospheric particles have a remarkable impact on the global environment and climate change. The long-range transport of dust is an important part of the global biogeochemical cycles. It is significant and urgent to investigate dust on its optical properties, long-range transport, aging, and deposition. 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 will carry the first wind lidar in space and launch is currently scheduled for the end of 2017. EarthCARE will carry cloud profiling radar, HSRL (High Spectral Resolution Lidar) and multispectral imager and is scheduled for launch in 2018. The research goals of TROPOS are investigations to aerosol type characterizations and the impact of aerosols on clouds and their properties. For this purpose, 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 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). 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. 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. In order 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 (IPA) 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-IPA conducted several flights in the Mediterranean area which aimed at aerosol (incl. Saharan dust) detection using the ALADIN Airborne Demonstrator (A2D).

The first project objective is 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. Based on the ground-based PollyXT, WACAL, CDL and HSRL, ADM-Aeolus and EarthCARE satellites, combining back trajectory model from NOAA, it is available to determine the dust source region, the main transport route and the main deposition areas. The second 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.

The spaceborne wind lidar ALADIN shall provide vertical wind profiles
within the ESA Earth Explorer Atmospheric Dynamics Mission (ADM) Aeolus. After
launch end of 2017 such profiles will be available on a global scale for the
first time to improve the quality of numerical weather prediction (NWP). The ALADIN
Airborne Demonstrator (A2D) was developed at DLR (German Aerospace Center)
focusing on a high degree of commonality in terms of laser source and Doppler
lidar receiver design. With these features operating at the same ultraviolet
wavelength of 355 nm as the satellite, it is the key instrument for the
planned calibration and validation activities. Since 2005, A2D has been
deployed in a series of ground and airborne campaigns to support the Aeolus
mission by validating retrieval algorithms, the instrument concept and
operation procedures. In September and October 2016, the international airborne
campaign NAWDEX (North Atlantic Waveguide and Downstream Impact Experiment)
based in Iceland was the framework to significantly extend the wind data-set
from A2D and the coherent 2-µm wind lidar, both on-board the DLR Falcon
research aircraft. While the Falcon mainly focused on jet-stream intersections
and calibration-validation procedures, other aircraft and ground stations
delivered a comprehensive suite of additional measurements to complement the
meteorological picture. For the first time flights with this payload of the
Falcon were performed in coordination with the German HALO deploying an aerosol
lidar, cloud radar and dropsondes as well as the French Falcon with on-board
cloud radar and UV aerosol Doppler lidar. Subsequent to the NAWDEX campaign additional
flights were conducted in the Mediterranean area which aimed at aerosol (incl. Saharan
dust) detection using the A2D. An overview of the field experiments will be
presented together with first results from the ongoing data analysis.

The spaceborne Aerosol and Carbon dioxide Detection Lidar (ACDL) will measure the global column concentrations of carbon dioxide (CO₂) and aerosols profiles. The column concentrations of carbon dioxide are measured by integrated path differential absorption (IPDA) lidar technique. The aerosols and clouds profiles are obtained by high resolution spectrum lidar (HRSL) technique. Both techniques are combined in the ACDL lidar payload. The dedicated atmosphere and environment monitoring satellite will carry the ACDL lidar and is scheduled to launch in about 2020. The spaceborne engineering prototype of the lidar with 1m SiC telescope was developed in 2016. And the single frequency and three wavelength laser with high pulse energy was developed and the environmental vibration testing was carried out. A ground-based double-pulse 1572 nm IPDA lidar is developed for carbon dioxide concentrations measurements. The lidar measured carbon dioxide concentrations continuously by receiving the scattered echo signals from a building about 1300 m away. On the same time the other two instruments of TDLAS (Tunable Diode Laser Absorption Spectroscopy) and in-situ carbon dioxide analyzer measured the carbon dioxide concentrations. The carbon dioxide concentrations bias between IPDA lidar and TDLAS measurement was validated to be less than 2 ppm. An airborne lidar prototype with high altitude platform is being developed to verify the retrieval algorithms of spaceborne ACDL lidar. The lidar will also be implemented to validate the Chinese passive CO₂ measurement satellite TanSat in future.

China successfully launched the geostationary orbiting optical satellite GF-4 on December 29^th, 2015. The satellite can image the specific location of interest many times during the daytime with 50m (400m) spatial resolution in the visible (infrared) bands. Based on the GF-4 data, the following researches were performed. (1) The signal-to-noise ratio and georeference accuracy of GF-4 images was evaluated. (2) The vicarious radiometric calibration of GF-4 was performed with the concurrent GOCI data, and the atmospheric correction of GF-4 was further carried out. (3) The diurnal variability of the drifting velocity of the floating macro-algal bloom (green tide) in the Yellow Sea was analyzed with the GF-4 images.

Every satellite has its own revisited period, so it is impossible to monitor a whole process of green tide bloom using only one kind of data source acquired from one satellite. The requirement of monitoring green macroalgae collaboratively by multi satellites instruments has been put on the table. In order to make green macroalgae monitoring by MODIS (250-m resolution) and Landsat TM/ETM+ (30-m resolution) images possible, the detection results in the same region and the same time from the two data sources are compared, and the relationship between them is analyzed. It is found that the detection result from MODIS image is over 5 times of the one from Landsat TM/ETM+ image. According to this statistic relationship, a transformation-detection method of green macroalgae from 250m resolution MODIS image to 30m resolution results is presented. The experimental results show that the proposed approach can provide an effective method to monitor green macroalgae collaboratively by MODIS and Landsat TM/ETM+ images.

On February 16th, 2016, ESA successfully launched the Sentinel-3A satellite. Among the four Earth-observing instruments, the OLCI (Ocean and Land Colour Instrument) is designed to continue the ENVISAT MERIS capability of ocean color monitoring.

Based on the OLCI L1 data, the following researches were performed. (1) The TOA reflectance, signal-to-noise ratio and georeference accuracy of OLCI images were evaluated and compared with those of MODIS and VIIRS. (2) The floating macro-algal bloom (green tide) in the Yellow Sea was detected using the OLCI images.

In contrast to the glacier mass losses observed at other locations around the world, some glaciers in the High Mountains of Asia appear to have gained mass in recent decades, which was called as ‘Karakoram anomaly’ or ‘Karakoram-Pamir anomaly’. Recent study to laser altimetry data found the centre of the anomaly might locates at the West Kunlun instead of the Karakoram. We performed differential interferometry to 14 pairs of bistatic TerraSAR-X and TanDEM-X observed the West Kunlun and its surroundings obtained at ~2013 by referring to SRTM observed in 2000. After removing seasonal effect and penetration depth differences, it found during 2000 and 2013, glacier mass balance rate at the West Kunlun was 0.128 ± 0.055 m w.e.a^-1 and most of its surrounding area also experienced a mass gain that varied from 0.043 to 0.363 m w.e.a^-1, with a decreasing gradient from northeast to southwest. At southwest of this study region, glacier presented significant mass lost at -0.286 ± 0.067 m w.e.a^-1. For the West Kunlun region, northern slope gained mass quicker than southern slope, eastern and western part gained mass quicker than central part. Comparing to previous studies applied ICESat satellite laser altimetry data, similar results of glacier height changing was obtained at their footprints. Our results suggested a decreasing gradient of glacier mass balance from the Upper Tarim edge to the Karakoram, which is similar to previous ICESat derived gradient rather than topographic difference results derived with SPOT/HRS and SRTM. Glacier surging was common at the West Kunlun. Surging and quiescent glaciers identified by glacier height changing pattern was almost the same to previous study derived with feature tracking. They cover almost one third of the total glacierized region. For the West Kunlun, glacier height changes in different elevation bins for non-surging glaciers present significant and homogeneous height increasing above 5450 m, while below 5400 glaciers shows significant thinning, which indicate the warming and moisturizing trend in the centre of the anomaly area.

Glacier is one of the most important climate change indicators in both regional and global scale. High Mountain Asia has the largest extend of glacier outside the polar region. As the Asian Water Tower, glacier in this region also has distinct ecological significance due to its vast water source. Its movement has close correlation with the risk of glacier lake outburst in highland. As a consequence, glacier dynamics in High Mountain Asia including mass balance, surface velocity and outline detection have been research hotspot. In recent years, synthetic aperture radar (SAR) observation has been regarded as an effective tool for glacier surface motion monitoring with wide range and high resolution. Therefore, it is essential to employ SAR observation to evaluate the velocity of glaciers in Tibet, China, which may be the basis for glacier dynamics and even climate change conditions in High Mountain Asia region.

SAR echo records both amplitude and phase information. From one aspect, the phase-based traditional differential SAR interferometry (D-InSAR) can achieve a millimeter accuracy in deformation detection theoretically while its application to glacier dynamics is generally limited by decorrelation. From another aspect, the intensity-based pixel offset tracking (POT), taking advantages of the intensity from backscatter signals, can implement large displacement in both range and azimuth direction whereas redundancy and error estimation in matching decorrelated patches arises in the calculation procedure. Hence an integrated method combining these two complements is applied to sub-region in High Mountain Asia in exploring glacier dynamics which can improve utilization of SAR observation.

In this study, acquisitions from Sentinel-1A and Sentinel-1B constitutes the data stack with a 6-day temporal baseline. Qualitative and quantitative evaluations of the glacier velocity are made for understanding the surface motion and glacier dynamics. Compared with other space-borne SAR acquisitions, the C-band Sentinel-1 data have smaller temporal decorrelation effects with shorter revisit time, which might be attributed to the good interferometry analysis.

The synthetic aperture radar (SAR) interferometry (In-SAR) technique has already shown its importance in landslide mapping and monitoring applications. However, the usefulness of traditional differential In-SAR applications is limited by disturbing factors such as temporal decorrelation and atmospheric disturbances. And the Persistent Scatters Interferometry (PSI) technique need plenty of scenes, at least 25 scenes. Small Baseline Subsets (SBAS) is recently developed In-SAR approach. And it has a wide range of application prospects in the surface deformation monitoring because it could attain tiny deformation information of the surface and obtain long time series, slow deformation field of the surface. In this paper, we choose the Minjiang river basin as a study area, using SBAS approach obtained the surface deformation, and the deformation points which are unreasonable is removed according to certain conditions. The results show that the surface deformation points are mainly distributed on the right bank of the river, which is related to the low sensitivity of the ENVISAT data whose orbit is descending direction. Analyzed spatial relationship the potential landslide areas extracted using the spatial analysis method in ArcGIS software and the historical landslides. Meanwhile, the potential landslide areas are mainly distributed along the river. The main reason for this rule is the influence of the river erosion on the slope foot so that landslides distributed along the River; What’s more, among 30 historical landslides, 16 points lie in the deformation area, and 11 points lie in the potential landslide area, only 3 points are outside of the deformation area. Which shows that the landslide deformation points can better reflect the spatial distribution of historical landslides. Therefore, in the absence of historical landslide inventories, potential landslides detection by using deformation points provides effective assistance for regional disaster prevention and investigation.

Key words: landslide, SBAS technology, landslide deformation point, Minjiang River Basin.

The surface deformation caused by mining is seriously threatening the safety of Mining area. It is necessary to continuously monitor the surface deformation of mining areas, in order to ensure the safety of production, as well as supporting early warning and risk control. Limited by the side-looking geometry of SAR sensors, only deformation along line-of-sight can be retrieved with time series InSAR technology from a single stack of SAR images. Since the terrain varies greatly in mining areas, it is very difficult to monitor the overall 3D deformation of mining pits and dump sites with a single stack of SAR images. Therefore, fusion of PS point clouds from multi-stack SAR images is necessary.

In this paper, fusion of PS point clouds retrieved by time series InSAR from multi-stack SAR images would be carried out. For the first step, the best PS points are select from the geocoded PS points with a threshold on the variance of estimated heights within a certain window. Then, binary images are generated with the selected PS points. Coarse offsets of the same object between binary images from different stacks are estimated and compensated. After that, point correspondences are created with the compensated PS points, followed by the least squares adjustment to calculate precise offsets between corresponding points. In the final step, offset compensation would be carried out, leading to precisely fused point clouds. the offset between PS points This fusion approach includes several key steps, such as selection of excellent PS points, finding the best PS point matching model between different stacks with iteration, least-squares adjustment without any reference data, PS point cloud re-locating with least squares adjustment, etc.

With this fusion method, three dimensional deformation of an open pit mine in Anshan will be estimated from multi-stack SAR images and presented in the fullpaper. 3D deformation of open pit mines is beneficial to monitoring the mining process. The relationship between mining activity and surface damage can also be analyzed with the assistance of surface deformation pattern, subsidence rate and other possible factors in the mining area.

Key words: Time Series InSAR; Surface Deformation; Multi-stack Fusion; Open Pit Mine

Accurate and recursive maps of crop and weeds at the field-scale could be of great interest with major economic and environmental impacts, including competition with native plant species, choking irrigation infrastructure, reducing agricultural yields and affecting the health of livestock.

The development of new generation multispectral satellite sensors and hyperspectral UAVs sensors has resulted in significant interest in their use for crop and weeds classification applications in view of their high spatial/spectral resolution. Small UAVs are more suited to site-specific weed management applications, as they can collect data at high spatial resolutions, which is essential for the classification of small or localized weed outbreaks. The extraction of features (spatial and/or spectral) that discriminate between the weeds of interest and background objects (i.e. different soils and crops) is crucial to map and monitor weeds in agricultural fields. Weed classification is an essential requirement for site-specific weed management in the context of precision agriculture, allowing a considerable reduction of herbicide spraying, with favorable environmental consequences. However, the spectral discrimination between crop and weeds from multi and hyperspectral remote sensing has yet to solve several issues, which is mainly due to the small spectral differences between crops of different species with also the requirement of a very high spatial resolution. Soil background effects are another problem that complicates weed detection in post-emergence row crops. Currently, no established methodology has been widely accepted, but different authors have employed successfully multivariate and machine learning algorithms such as PLSR-DA (Partial Least Squares Discriminant Analysis) and Support Vector Machine with a Radial Basis Function Kernel (Gaussian SVM) to discriminate and classify weeds from crops (Hermann et al., 2013; Hadoux et al., 2014) in using imaging spectroscopy field-based studies.

In the WP1 of the Topic 1, the main research activities are focused on: (a) setting up a spectral library of the most common crops and weeds in the investigated agricultural fields; (b) testing the capability of images acquired from both Sentinel 2 and UAVs imagery using different classification tools for crop mapping and weed detection; (c) mapping crops and weed patches in operational situations of site-specific crop management.

Improved classification methods are applied to Sentinel-2 and UAVs imagery for crop and weed detection: Minimum Distance (MD), Support Vector Machine (SVM), Neural Network (KNN), Random Forest (RF) and Spectral Feature Fitting (SFF).

Performance of the applied crop and weeds classification methods are compared on Sentinel-2 and UAVs’ data sets related to agricultural fields in Central Italy. Classification performances that can be obtained depends not only on the scenario and, thus, data set, but also on the specific sensor and platform as well as on the classification methods employed.

Moreover, with global revisit times of five days from next months onwards, Sentinel-2 based classifications can probably be further improved by using (a) temporal information in addition to the spectral signatures and (b) textural as well as canopy height information from Sentinel-1 radar images.

References

Herrmann, I., Shapira, U., Kinast, S., Karnieli, A., Bonfil, D.J. (2013). Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precision Agriculture, 14, 637-659.

Hadoux X., Gorretta N., Roger J.-M., Bendoula R., Rabatel G. (2014). Comparison of the efficacy of spectral pre-treatments for wheat and weed discrimination in outdoor conditions. Computers and Electronics in Agriculture, 108 , 242-249.

As field measurement of massive germplasm for complex traits in breeding is challenging, there's a strong demand for real-time, fast and nondestructive phenotyping to accelerate the breeding efficiency. Unmanned aerial platforms‑based remote sensing can be used to rapidly and cost‑effectively phenotype large numbers of plots and field trials. Strategies for high-throughput field-based phenotyping in breeding was investigated by National Engineering Research Center for Information Technology in Agriculture (coordinated by NERCITA) in recent years, where proximal remote sensing is the deployment of sensors using aerial platforms. Strategies include the following: 1) Selection and specification of UAV platforms for application of phenotyping in breeding; 2) Rapid processing of multi-source remote sensing data for high-throughput phenotyping; 3) Analysis of phenotypic information for soybean, maize and wheat in breeding (over 10,000 plots) by proximal remote sensing under different growth periods; 4) Determining the optimal growth stage, indexes and algorithm model for crop yield prediction; 5) Validation of the phenotypic information resolution and yield prediction using agricultural UAV for breeding plots to ascertain its stability and accuracy; 6) Genome-wide association study of morphological indicators and genotype of maize and the identification of candidate gene.

Many studies have demonstrated the advantage of the exploitation of multi-temporal and multi-sensor remote sensing data for the improvement of crop classification methods. However the methods based on optical and SAR data for monitoring arable crops, such as cereals and forage crops (e.g. wheat, barley, oats, triticale, ryegrass...), both at the local and regional level, are still far from being effective and operational. This is because usually small differences in the canopy reflectance and SAR backscatter occurs among these crops, hampering a clear and robust discrimination. There is however, an increasing requirement, e.g. in the context of the European agricultural policy of "coupled subsidy" (payment related to real crop cultivation on parcel), to identify individual crops. For example in the "crop diversification" measure, imposed by the “Greening Policy”, it is necessary to separate barley and wheat on the same farm, which is rather difficult when using only a type of data. The objective of WP1 of the topic 1 of the Dragon4 project ID. 32275 "Combined Exploitation Of Sino EU Earth Observation Data for Supporting The Monitoring and Management of Agricultural Resources", is the development of classification algorithms based on multitemporal optical and radar data allowing the incorporation of cropping systems dynamics information. In this context, a study was carried out in the Maccarese farmland test site (Central Italy), in which a quite extensive dataset, including both optical (RapidEye, Landsat8 and Zy-Yuan 3) and radar (Cosmo SkyMed and Sentinel-1) data, was collected during the 2015 crop growth season. These data were used to test a multi-temporal phenology-based classification algorithm, based on pixel-level decision tree (DT) incorporating information on temporal crop dynamics. The results were validated using the ground data available within a farm management geographic information system (GIS).

The same dataset was also employed for field-based wheat yield estimation, which is the objective of WP5 of topic 1 of the mentioned Dragon4 project. For this purpose, the remote sensing data were converted into biophysical variables (LAI and above ground biomass), using empirical relationships with ground data obtained during the 2015 crop season, in field campaigns carried close to the satellite acquisitions (on wheat, barley, alfalfa, broad bean and maize). For optical data, an algorithm based on the training of artificial neural networks with PROSPECT+SAIL model simulations was employed to retrieve biophysical canopy variables. These variables were then assimilated into the SAFY model (Duchemin et al., 2008, Environ Model Softw. 23, 876-892), using the Ensemble Kalman Filter (EnKF) method, to estimate grain yield. The models were re-calibrated on the basis of a preliminary sensitivity analysis study. Two versions of the SAFY model were compared, i.e. the original SAFY and a modified version (SAFYE) which includes a description of the soil water balance and water stress factors (Veloso, 2014, PhD Thesis, University of Toulouse, France). The results were validated using yield map data collected using a yield monitor system available on the farm's combine harvester machine. A very small difference emerged between the results of the two model versions, suggesting that the original SAFY version, which includes less parameters and input variables than SAFYE, is a suitable and practical choice for farm-based grain yield estimation, although water stress is inferred indirectly, from the processes regulating leaf growth.

Yellow rust is one of the most important fungal diseases of wheat and have caused serious yield loss of winter wheat in China. Studies focused on detecting and monitoring of rust infestation highly rely on field investigation and visual judgement with an advanced pathogen attack. Automatic methods based on remotely sensed techniques and spectral absorption features for an early detection of rust disease are rarely discussed. On the other side, in Italy, the Province of Lecce, located in the region (Apulia) where the 35% of the Italian olive oil is produced has been significantly affected by the Xylella fastidiosa disease which caused a rapid decline in olive plantations, the so-called olive quick decline syndrome (OQDS, in Italian: complesso del disseccamento rapido dell'olivo). By the beginning of 2015 it had infected up to a million trees in the Lecce Province. Then this paper has two main objectives:

- determine the most sensitive spectral vegetation indices (SVIs) for characterizing specific pathological lesions of winter wheat leaves infected with rust disease, and then to propose a hyperspectral analysis procedure for the early detection and identification of rust diseases before specific symptoms became visible. Based on in situ hyperspectral data measured in experiments of 2002 and 2003 with a total of 122 samples, 14 pro-existing SVIs that are related to foliar physiological and photochemistry variations were assessed at different pathogen stages. And then, in order to extract the subtle spectral features between the healthy and diseased winter wheat leaves, an enhanced feature space (EFS) were developed by the non-linear combination and transformation of the identified SVIs. Based on this feature space, early differentiation of healthy leaves and leaves infected with yellow rust could be achieved by a Support Vector Machine (SVM) classifier. Finally, for validation and extension, this approach was also successfully implemented on MODIS data for mapping the rust occurrence conditions in the regional level in the major winter wheat planting area.

- presenting the preliminary results of the analysis of a time series of Landsat images of the Province of Lecce (Italy) spanning a period of seven years on the way to analyze the possibility of using available satellite images (e.g. L8 and Sentinel-2) datasets to assess the evolution of diseases on permanent crops (olive groves, vineyards), for example, the spread of phytosanitary threats as the Xylella fastidiosa (olive groves) or fungal trunk diseases (vineyards) in Italy.

Keyword: Yellow rust; Spectral vegetation indices; Early detection; Support vector machine

Nitrogen (N) is an important indicator of the plant nutritional status and then affects the end of wheat production. Rapid real-time monitoring of wheat N status is crucial for precision N management during wheat growth. Traditional inversion methods by remote sensing are mostly based on its relationship with vegetation index or spectral band. Some models are not suitable as the change of time and location. N-PROSAIL model (Yang et al., 2016) was developed by replacing the absorption coefficient of chlorophyll in the original PROSAIL model (with an equivalent N absorption coefficient, and it gave us a physical method to estimate canopy nitrogen density (CND, g m^-2). Therefore, the objective of this study was to estimate CND at different growth stages by inverting the N-PROSAIL model.

Field experiments were carried out in 2012-2013, 2013-2014 and 2015-2016 at the National Precision Agriculture Experimental Base of Xiaotangshan town, Changping district, Beijing, PR China. Data acquisition included canopy hyperspectral reflectance, leaf area index (LAI, m² m^-2), and leaf nitrogen density (LND, µg cm^-2), which were measured at four wheat growth stages, jointing (Z.S. 31), heading (Z.S. 47), anthesis (Z.S. 65), and filling (Z.S. 75). First, parameters of N, Cm, Cw, LAD, hspot, psoil, and solar zenith angle in N-PROSAIL model (Jacquemoud et al., 2009) were calibrated at different growth stages. Second, LAI and LND were inversed by Shuffled Complex Evolution (SCE-UA) algorithm and the estimation accuracy was tried to improve by using the calibrated parameters set at various growth stages. Finally, CND was calculated by LAI multiplied LND.

The inversion results with and without considering calibrating parameters at different growth stages were compared (Fig. 1). Without considering calibrating parameters at different growth stages (unified parameters at all growth stages, Li et al., 2015), The R² and RMSE values for LAI and LND were 0.67 and 0.74, and 0.30 and 58.49 μg cm^-2, respectively. Relationship between the simulated and measured CND showed well (R² = 0.66, RMSE = 2.45 g m^-2). After considering calibrating parameter at different growth stages, the estimation accuracy of LAI was improved (R² =0.75, RMSE = 0.73), and LND estimation showed a significant improvement (R² = 0.59, RMSE = 17.43 μg cm^-2). In the end, the CND estimation performed better than CND estimation with unified parameter set at all growth stages, with R² and RMSE values of 0.75 and 1.32 g m^-2, respectively. These results confirm the potential of using N-PROSAIL model for CND retrieval in winter wheat at different growth stages and under variables climatic conditions.

Wheat powdery mildew is one of the serious crop diseases which affects the food safety of China. Integrating multi-source information (Earth Observation-EO, meteorological, etc.) to support decision making in the sustainable management of wheat powdery mildew in agriculture is demanded. In this study, the Landsat8 remote sensing image of May 22nd, 2014 was used to extract the land surface temperature (LST) and many vegetation indices including normalized difference vegetation index (NDVI), enhanced vegetation index(EVI), triangular vegetation index (TVI), wetness index and renormalized difference vegetation index (RDVI). Site daily meteorological data including temperature, humidity, rainfall, sunshine hour from April to May was used to get the parameters delineating the environment condition such as average temperature from April 22nd to May 22nd, average humidity from April 22nd to May 22^nd, total sunshine hour from April 22nd to May 22nd and number of rainy days with more than 0.1 mm rainfall from April 22nd to May 22nd. Corresponding space meteorological features were got by combining remote sensing image and site daily meteorological data. In the field work, 148 field sites were collected in study area and the degree of wheat powdery mildew in these sites was recorded. An independent t-test analysis was used to test the difference between disease and healthy sites based on calibration data. Those vegetation indices and environmental factors that failed to show a statistical significant(p<0.001) were eliminated. Five variables including RDVI, temperature, humidity, average temperature from April 22nd to May 22^nd and average humidity from April 22nd to May 22^nd were identified as optimal explanatory variables for developing the powdery mildew forecasting model. The artificial neural network was trained using feedforward neural network learning algorithm in combination with simulated annealing technique to learn the relationship between selected factors and powdery mildew. The powdery mildew forecasting model based on artificial neural network (ANN) was established to predict powdery mildew occurrence of wheat in Gaocheng, Jinzhou and Zhaoxian County, Shijiazhuang City, Hebei Province. The results obtained from the ANN model were compared with prediction model developed using support vector machine(SVM) technique. The accuracy of models respectively based on validation samples were obtained to evaluate the difference of performance of the models. The result showed that the overall accuracy of the ANN model was 86.49%, which is higher than SVM model(78.38%). The result reveals that ANN model could be used to forecast the occurrence of wheat powdery mildew and compared with the traditional forecasting model based on support vector machine, ANN model has a better performance.

Crop yield is one of the most concerned complex traits in crop research, which is linked to CO₂ fixation through the photosynthetic process and partitioning of photoassimilates to the harvested part of the plant. The traditional methods for measuring crop yield are the use of manual sampling or establishing the relationship between agronomic factors or climatic factors and crop yield using statistical analysis methods. Many observations and samplings in field experiments are required to determine the parameters of the yield prediction model, which is time-consuming, low efficiency, and incomplete spatial coverage. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become important means for fast and non-destructive access to complex traits and have the advantages of flexible and convenient operation, on-demand access to data and high spatial resolution, which provide a new way for studying phenomics and genomics. As improving the accuracy and adaptability of the yield estimation model is a prerequisite for the application of UAV remote sensingthe objective of the research is to build crop yield prediction models that combine crop physiology and remote sensing parameters to improve the accuracy of yield prediction by UAV-RSP.

Field experiment tested 98 breeding materials of soybean was conducted by NERCITA in 2015. Unmanned aerial platform equipped with digital camera, multi spectral camera and hyper-spectrometer was used for field-based high-throughput phenotyping of soybean in breeding plots. Ten vegetation indices (VIs) including NDVI, RVI, GNDVI, PVI, OSAVI, EVI, DVI and NDVI₇₀₅ and plant height, combining algorithms including partial least-squares regression (PLS), multiple linear regression (MLR) and multiple stepwise regression (MSR) have been adopted for predicting yield of soybean in breeding plots. The results showed that MLR performed best, with R², NRMSE and d values of 0.83, 6.61 and 0.95, respectively, while MSR had the lowest accuracy, with R², NRMSE and d values of 0.72, 8.59 and 0.91, respectively.

The UAV-based field phenotyping platform can be as a preliminary method for screening cultivar in soybean breeding. The UAV platform equipped with multi-sensors was able to identify the differences of yields among the cultivars of soybean. Combing proximal sensing data and crop physiological traits can improve the accuracy of yield prediction in soybean breeding. The UAV-based proximal sensing platform provided novel insights in accelerating the breeding efficiency.

Leaf area index(LAI) and above ground biomass (AGB) are considered as two most important physiological parameters for crops, its correct estimation has been the focus in the crops growth monitoring and yield prediction. With the development of remote sensing technology and its application in agriculture, LAI and ABG estimation have been one of the main research areas of agricultural remote sensing. In recent years, unmanned aerial vehicle (UAV) technology has emerged as a new platform for remote sensing sensors, can provide remote sensing image in higher temporal resolution and spatial resolution. This study provides insight into the LAI and ABG estimation using an ASD Field Spec 3 spectrometer on the ground and applied to mapping using an UHD 185 hyperspectral sensor on board an UAV. The UAV was an 8-propellered UAV platform with 6 kg take-off weight, 50 meters flying height and 8m/s speed. The canopy spectral of winter wheat was measured by two methods, a DJI-S1000 UAV equipped with UHD 185 hyperspectral sensor fly 50m high, and a ASD Field Spec 3 spectrometer on the ground. By using 550nm, 680nm and 800nm spectral from ASD Field Spec 3 spectrometer, a linear model for LAI and ABG estimation was established. Results indicate that the R² was 0.78 and 0.60, RMSE was 0.60m²/m² and 1.46 t/ha, MAE was 0.48 m²/m² and 1.19 t/ha, respectively. After the hyperspectral image fusion and mosaic processing, hyperspectral image of whole study area was obtained. We applied the models for LAI and ABG estimation on the hyperspectral image of whole study area, and completed the winter wheat LAI and ABG monitoring, with results of LAI and ABG as follows: R² was 0.76 and 0.44, RMSE was 0.64 m²/m² and 1.48 t/ha MAE was 0.54 m²/m² and 1.15 t/ha. The results suggest the UAV UHD 185 hyperspectral system and the models have high application potential.

The Microwave Radiometer (MWR) flown on Envisat, ERS-1 and ERS-2 provides a nearly uninterrupted time series of microwave observations over a period almost 21 years between 1991 and 2012. This dataset is complementary to other microwave datasets. From 2011, the Atmospheric Correction Microwave Radiometer (ACMR) on HY-2 provides similar measurement.

Firstly, we report on our efforts towards a fully inter-calibrated and validated physical retrieval of TCWV and LWP for MWR. We will address issues related to satellite inter-calibration, homogeneity of the derived time series of brightness temperatures, observation-simulation biases, and provided first results of fully physical retrievals.

Secondly, an outlook on ongoing and planned activities within Dragon-4 will be given and the importance of the results in light of the upcoming Sentinel-3 mission and the HY-2 series will be discussed.

Calibration and validation (Cal&Val) is one of the most important means for assuring satellite payload performance and data quality. It could guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The challenges in the Cal&Val include the lack of consistent remote sensing (RS) product assessment standards, the uncertainties introduced by atmospheric effect, as well as the gaps in non-synchronous measurements between satellite observation and in situ measurements. In this project, great achievement has been done in carrying out cooperative research on the high-frequency optical calibration with the aid of the Radiometric Calibration Network (RadCalNet) activities and permanent targets over Baotou site in China.

(1) Development of automated radiometric calibration system and its operations. On benefit of RadCalNet and the cooperation of the Cal&Val experts from European and Chinese intuitions in the DRAGON programme, the automated surface spectral reflectance measurement system has being developed and installed over four permanent targets with different reflectance in Baotou site, which can automatically and traceably acquire the target characteristics and atmospheric parameters. In consideration of the land surface variety and the adjacent effect, the TOA radiance is simulated with the input of a background reflectance driving the MODTRAN, and the Level 1 BOA reflectance product is generated according to the input files defined by RadCalNet data center.

(2) Automated radiometric calibration demonstration for Chinese and European satellites. The data portal for standard product service has been established under the leadership of ESA, and now is in operation. Since the late last year, ESA have organized test users to calibrate their satellites using the RadCalNet standard products over four demonstrated sites. Among them, the predicted TOA radiance over Baotou site is compared with the observations of Sentinel-2a, ZY-3, GF-1, etc. This is not only important to validate the calibration capability of Baotou site, but also would greatly benefit improving the calibration accuracy of optical sensors, and assuring the data consistency from different sensors.

(3) The uncertainty analysis on the standard radiometric calibration product. The calibration uncertainty over Baotou site include the following aspects: 1) Traceable accuracy and measurement repeatability of the automated measurement system, traced to the primary standard of NPL through the transfer calibrator; 2) Non-uniformity and BRDF effect of the ground standard targets; 3) Measurement errors of typical atmospheric parameters, which are given by AERONET since the sun-photometer Cimel CE-318 in Baotou site has been included in AERONET; 4）Adjacent effect, caused by the point spread function (PSF) of the imaging system and the multiple scattering among surface and atmosphere; 5) Uncertainty aroused by radiative transfer calculation of MODTRAN, which is simulated based on Monte-Carlo theory.

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

Key words: MAX-DOAS, FTIR, NO₂, CO₂, Xianghe Station.

Global monitoring of precipitation is important because of its significant human consequences. However, the multiplicity of hydrometeor types and their small- and large-scale spatial inhomogeneities make accurate measurements difficult. For example, rain gauge measurements are significantly impaired by wind, poor global coverage, and the non-uniformity of rain. Both ground-based radars and passive microwave satellite sensors sense precipitation aloft and are generally unable to discern how much of that precipitation evaporates before impact.

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

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

The Environmental trace gases Monitoring Instrument (EMI) onboard Chinese high-resolution remote sensing satellite GF-5 is an imaging differential optical absorption spectrometer, expected to be launched in September of 2017. The EMI instrument will measure earthshine radiances with the wavelength range from 240 to 710 nm at moderate spectral resolution (0.3-0.5nm) at nadir. The EMI is tasked with quantitatively measuring global distribution of tropospheric and stratospheric trace gases such as NO2, O3, and SO2. The prelaunch calibration phase is essential to acquire necessary knowledge on the properties and performance of the EMI instrument as well as support data processing and retrieval. This work focus on nitrogen dioxide retrieval from on-ground measurements of gas cell and atmospheric scattering light, and an evaluation of the performance of the EMI instrument from the retrieval.

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

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

To reach the above-mentioned goals, single-polarization and polarimetric models will be analyzed and/or developed to generate added-value products that consist of risk/vulnerability maps, targets at sea maps and pollutants maps, cyclone/typhoon monitoring. Typhoon monitoring will be addressed using a multi-sensor approach based on the exploitation of SAR, scatterometer and radiometer measurements. Hence, models to deal with extreme wind conditions will be analyzed/developed.

The monitoring and forecasting of tropical and extra-tropical storm tracks and intensities are strategic for the protection of coastal infrastructures and residents. The Synthetic Aperture Radar (SAR) can potentially be very useful for such purposes. In fact, its high spatial resolution and its capability to observe the sea surface in all weather conditions and at all times (regardless of the day or night-time) makes it one of the best candidate instrument for these goals.

Several empirical forward models or geophysical model functions (GMFs), which relate the normalized radar backscatter cross section to the sea surface wind vector, have been developed and successfully used for a wide variety of scatterometer and SAR systems at different frequencies and polarizations. The GMFs have high accuracy under no-rain and low-to-moderate conditions, although for C-band radar systems good-quality winds are also derived under rainy conditions. For high wind speed measurements, the accuracy rapidly decreases due to saturation of the co-polarized backscattered intensity and the reduction of friction between the sea surface and the wind. An improved high-wind GMF for C-band and SAR spatial scales (most GMF developments are based on scatterometer data) as well as new high-wind GMFs for other frequencies (e.g., X-band) are required for the successful wind retrievals under tropical and extratropical cyclone conditions.

In this study, the radar backscatter sensitivity to high winds (as given by the different GMFs) is revisited for both C-band and X-band systems, as well as for co-polarized and cross-polarized beams, using the NOAA P-3 flight winter campaign data from January-February 2017. During this campaign, the NOAA P-3 plane, equipped with several sea-surface wind sensing systems, i.e., the Step Frequency Microwave Radiometer (SFMR), the Imaging Wind and Rain Profiler (IWRAP), and dropsondes, underflew both Sentinel-1 (S-1) and Cosmo-SkyMed (CSK) satellite passes under storm conditions in the North Atlantic region. These extra-tropical storms are characterized by vast areas of nearly uniform very high-wind conditions, up to 30-35 m/s. The sensitivity analysis will therefore provide a comprehensive view of the main sensitivities of the radar backscatter (e.g., co-polarized, cross-polarized, polarization difference, polarization ratio) to relatively high winds, for a variety of incidence angles.

Moreover, two wind retrieval approaches commonly used by the SAR community, i.e., the azimuth cut-off method and the combined radar backscatter and doppler centroid scheme, will be used to derive the sea surface wind field for the mentioned SAR scenes and validated against the mentioned NOAA wind data sources as well as collocated scatterometer and Soil Moisture Active Passive (SMAP) derived winds.

The amount of offshore platform is increasing significantly due to the improvements in drilling technology [1]. In particular, the advances due to deep water drilling technology allows installation to be mobile (to not have a stable location) and to be in regions far from coastal water.

Offshore platforms pose a risk to environment with the threat of oil and gas spillage, especially due to their exposition to extreme weather conditions. Besides being a risk to the environment, since their location is not mapped on maps, they are also obstacles for yachts, low flying airplanes and merchant ships in low visibility conditions.

Offshore platforms are generally large metallic constructions, which should make them easily detected and mapped by using satellite Synthetic Aperture Radar (SAR) medium resolution imagery [2]. However, we recently obtained analysed measurements [3] showing that some of the platforms in some acquisition geometries may be invisible in single-polarization backscattering images, leading to miss-detection. On the other hand the detection is still feasible if the dual polarimetric information is used.

In this work we exploiting a time series of dual-polarization TerraSAR-X data acquisitions over a cluster of offshore platform in the Gulf of Mexico. Among others, factors affecting the backscattering include polarization, resolution and incidence angle. Finally in this paper we also address how incoherent and coherent polarimetric observables can be exploited to detect platforms when the single polarimetric acquisition may fail detection.

REFERENCES

[1] “International Energy Agency.” [Online]. Available: http://www.iea.org/.

[2] S. Casadio, O. Arino, and A. Minchella, “Use of ATSR and SAR measurements for the monitoring and characterisation of night-time gas flaring from off-shore platforms: The North Sea test case, “ Remote Sens. Environ., vol. 123, pp. 175–186, Aug. 2012.

[3] A. Marino, D. Velotto and F. Nunziate, “Offshore metallic platforms observation using dual-polarimetric TS-X/TD-X satellite imagery: a case study in the Gulf of Mexico,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., under review, 2016.

The C-band synthetic aperture radar on-board ESA’s Sentinel satellites have the capability to provide high resolution wind vector information over the ocean surface. These wind vector data derived from SAR observations are available to the data assimilation system with real-time information of high accuracy. In this study, several comparison experiments are designed to investigate the impact of Sentinel-1 SAR winds data in the three-dimensional variational assimilation system for the Weather Research and Forecast model (WRF 3DVAR). The powerful Typhoon Lionrock is selected for this case study. Typhoon Lionrock struck and caused significant flooding and casualties in Japan and Russia in late August 2016. Its route changed several times and there are also several rapid intensification processes during its lifetime, which made its track very difficult to predict. Totally, 10 SAR images from 3 overpasses from August 27 to 29, 2016 are used in this study. The preliminary results demonstrate that SAR wind data can complement the scarce observations over the sea surface and improve the prediction of wind and pressure fields of Typhoon Lionrock. More detailed experimental results and analyses will be provided in the Dragon-4 symposium.

Coastal regions are rapidly growing with increase in urbanization and population density. This results in increasing threats for terrestrial habitats and marine ecosystems. Hence, ever growing attention must be paid to the pollution of the coastal waters fed from urban watersheds and human-related activities discharges. Pollutants include pesticided, fertilizers, hydrocarbons, trace of heavy metals, organic compounds, pathogens and other anthropogenic debris. They alter the physical and biogeochemical state of coastal waters affecting marine life negatively.

Stormwater and wastewater runoff are the main sources of pollution in coastal areas that result from untreated runoff and pollutants coming from urban watersheds entering the coastal waters after rainstorms, through river discharges or during publicly owned treatment works. They impact coastal water quality severely through an increase in bacterial contamination due to the significant load of particles and dissolved compounds discharged during and immediately after storm events.

The environmental observation of stormwater runoff in near-shore waters is commonly undertaken through sparse sampling onboard coastal research ships. Nonetheless, the information those field measuremets provide is sporadic, expensive and coarse. The space/time gap left by in-situ monitoring can be filled in by satellite measurements.

Satellite synthetic aperture radar (SAR)-based and ocean color imagery from Aqua MODIS have been used to analyze the different compounds of runoff plumes, surfactants and sediment discharge. Nevertheless, optical observations are limited by rainy events, cloud cover and solar illumination. In particular, polarimetric SAR (polSAR) represents the most suitable tool for monitoring the pollution of coastal waters due to the all-day and almost all-weather detailed information it provides on the scattering mechanisms of the observed scene routinely, with wide area coverage, dense revisit time and fine spatial resolution. The capability of polSARs to detect marine oil spills, to identify natural oil seepages and to roughly characterize the damping properties of surfactants was widely proved.

In this study, the sensitivity of polarimetric parameters derived from fully-polarimetric (FP) SAR measurements on the presence of surface pollutants in coastal waters is investigated. Preliminary results are obtained processing a set of FP SAR data collected at C-band from Radarsat-2 over the coastal area of Piana del Sele (Salerno, Italy). The test site was selected since it is one of the most industrialized areas of Southern Italy that is severely affected by coastal water pollution. Preliminary results show that polSAR data can be effectively used to retrive detailed information on the coastal water quality that may be useful for the sustainable development and managament of coastal areas, including marine ecosystems protection, aquacultures and fisheries safety, water resources management.

REFERENCES:

1) Holt, B, Trinh, R. and Gierach, M.M., 2017,

“Stormwater runoff plumes in the Southern California Bight: a comparison study with SAR and MODIS imagery”,

in print on Marine Pollution Bullettin.

2) DiGiacomo, P. M., Washburn, L., Holt, B. and Jones, B. H., 2004,

“Coastal pollution hazards in southern California observed by SAR imagery: stormwater plumes, wastewater plumes, and natural hydrocarbon seeps”,

Marine Pollution Bulletin, vol. 49, pp. 1013-1024.

3) Migliaccio, M., Nunziata, F., Kurata, N., Vella, K., Matt, S., Soloviev, A. and Perrie, W., 2013, “Marine bacteria monitoring via polarimetric SAR”,

ESA PolInSAR Proceedings, ESA-ESRIN, Frascati, Italy.

4) Svejkovsky, J. and Jones, B., 2001,

“Satellite imagery detects coastal stormwater and sewage runoff”,

Eos Transactions, vol. 82, no. 50, pp. 621, 624, 625, 630.

5) Migliaccio, M., Nunziata, F. and Buono, A., 2015,

“SAR polarimetry for sea oil slick observation”,

International Journal of Remote Sensing, vol. 36, no. 12, pp. 3243-3273.

This study proposes a multi-sensor approach to promote an effective coastal area monitoring strategy over areas that include critical infrastructures, e.g. on-shore and off-shore oil/gas extraction platforms and groundwater reservoirs*. The monitoring strategy includes both land-side and sea-side observations using remotely sensed measurements.

With respect to the land-side, multi-temporal differential Interferometric Synthetic Aperture Radar (DInSAR) and Global Navigation Satellite System (GNSS) techniques are exploited to monitor subsidence phenomena along on-shore hydrocarbon and groundwater reservoirs, where surface deformations can be correlated to the extraction of resources from the subsoil.

With respect to the sea-side, effective SAR techniques are exploited to take benefit of multi-polarization SARs to observe oil/gas rigs/platforms and to observe oil discharges close to the oil extraction sites.

The proposed approach aims at testing and improving the standards of security for the exploitation of underground resources, as well as providing ad-hoc procedures to monitor interested areas.

*The present work is supported and funded both by Italian Economic Development Ministry (MISE) under the MISE-DGRME research project (ID 0752.010) and the DRAGON-4 Cooperation Proposal “SARCO - SAR-based Coast Observation” (ID 32235).

ABSTRACT

Many countries are focusing on maritime surveillance due to the interests in maritime shipping and the environment. The fact that more than eighty percent of goods traded worldwide are transported by sea is an indicator of the large amount of maritime traffic [1]. Not all the vessels uses the Automatic Identification System (AIS) [2], especially some smaller ships, and therefore they need to be monitored with independent systems. Therefore, using SAR data to detect ships has become more and more valuable.

Because of the complex structure of ships, their scattering mechanisms, which mainly consist of multiple reflections, are different from those of ocean surface [3]. These differences can be easily detected by using some polarimetric features. Compared with single-polarization backscattering images, dual- or quad-polarization images can provide more information about the scattering mechanisms composing the ships. This will therefore increase the discrimination between ships and ocean also in the case of ships where the overall amplitude is comparable to the one of the sea.

In this paper, we start from the Geometric Perturbation Polarimetric Notch Filter (GP-PNF) and extend the detector feature vector with many more polarimetric features [4-5]. Then we perform a Principal Component Analysis (PCA) to reduce the size of the vector and use this in the represent the full dataset in a new basis and use this basis to perform the PNF.

In our work, we choose some dual-polarization Sentinel-1 data as the experimental datasets. Experimental results show the effectiveness of the new method, especially in extreme weather conditions.

REFERENCES

[1]Gao, “Preliminary observations on efforts to target security inspection of cargo containers,”

USA Office Report, Dec. 2003.

[2]P. A. Lessing, L. J. Bernard, and C. B. J. Tetreault et al., “Use of the Automatic Identification

System (AIS) on autonomous buoys for maritime domain awareness applications,” Oceans, pp.18-

21, Feb. 2007.

[3]D. Velotto, M. Scoccorsi, and S. Lehner, “Azimuth ambiguities removal for ship detection using full polarimetric X-band SAR Data,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 76-88, Jan. 2014.

[4]A. Marino, S. R. Cloude, and I. Woodhouse, “Detecting depolarized targets using a new

geometrical perturbation filter,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3787-3799,

Oct. 2012.

[5]A. Marino, “A Notch Filter for Ship Detection With Polarimetric SAR Data,” IEEE J. Sel.

Topics Appl. Earth Observ., vol. 6, no. 3, pp. 1219-1232, 2013.

Satellite Earth Observation is of paramount importance for a wide range of applications. In fact, the synoptic view of the Earth offered by remotely sensed space-borne measurements allows gathering useful information on climate, environment, etc. Among those applications, the observation of oceans from space represents one of the key topics since oceans cover more than 70% of the Earth’s surface and they are responsible for life. In addition, monitoring the oceans makes us able to better understand natural and human-related processes as marine pollution, climate change, sea level rise, etc.

In this context the synthetic aperture radar (SAR), being a microwave active sensor, routinely provides, information on the Earth’s surface during day and nighttime and in almost any weather condition, with fine resolution and dense revisit time if virtual constellation are exploited.

In this study, European Space Agency (ESA) Sentinel-1 scanSAR Extra Wide Swath (EW) Ground Range Multilook Detected Medium Resolution (GRDM) dual-polarimetric (DP) VH-VV SAR data measurements are considered to extract information, at C-band, on hurricanes. The underpinning idea is to retrieve wind speed both with co-polar and with cross-polar channels. We used a Geophysical Model Function (GMF), the C-band Cross-Polarization Ocean (C-2PO) which is developed for cross-polarized channel and it is independent of incidence angle and wind direction, for the VH channel [1]. With respect to the VV channel, the azimuth cut-off method is studied to calculate a coefficient of linearity, that allows linking directly the azimuth cut-off with wind speed. The link between azimuth cut-off and wind speed is here investigated under extreme wind conditions.

The water-area variations of large lakes are affected by both long-term climate change and short-term localized human activities. In recent years, there has been increasing human activities in large inland lakes worldwide [1]. Within this context, remote sensing plays an important role for lake monitoring. Optical images have the great advantage of being simple to interpret and 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, Synthetic Aperture Radar (SAR) can be very useful for lake monitoring purposes, because of its fine spatial resolution. Nevertheless, the monitoring using single-polarization SAR data is not simple due to both speckle noise, that makes image interpretability a challenge.

The main goals of this study are to develop multi polarimetric and multi-temporal methods to effectively monitor the change of surface water area of the dam of Monte Cotugno, Basilicata (Italy), the largest embankment dam in Europe. The test site was selected since it is one of the main civil strategic infrastructures of Southern Italy that is severely affected by seepage and leakage.

For this purpose, a method based on the joint use of co- and cross-polarized channels, is used in order to extract the profile of the artificial lake [2].

Preliminary results are obtained processing a set of dual polarimetric (DP) SAR data collected at C-band from Sentinel-1. The results show that Polarimetric SAR data can be effectively used to detect the changes of the water-level in the artificial lake.

[1] Ding, X., Li, X. (2011), “Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images”, International Journal of Applied Earth Observation and Geoinformation, Volume: 13, Issue: 6, Pages: 894-901

[2] 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

Rockglaciers are the best visual expression of mountain permafrost and are widespread in the Tien Shan. These ice-debris landforms can, in contrast to permafrost itself, be mapped and monitored directly using remotely sensed data. Previous studies showed that changes in rockglacier flow can be related to climate conditions. However, no consistent rock glacier inventory of the whole Tien Shan exists and information about rockglacier flow is rare. Most previous studies concentrated in a few valleys in the Ile Range of Northern Tien Shan (Kazakhstan).

We have systematically mapped active rock glaciers of Northern Tien Shan located in Kazakhstan, Kyrgyzstan and Xinjiang, China’s north-western-most province, based on differential SAR interferograms and the best available optical imagery from Google Earth or other sources. Different SAR interferograms from various sources, including ERS-1/2, ALOS-1/2 and Sentinel-1, were used to identify and manually map surface deformations at elevations where rockglaciers can occur. The optical imagery were subsequently applied to distinguish rockglaciers from other deformations, e.g. due to subsidence, landsliding or solifluction. The rockglaciers were finally classified according to their state of activity (surface velocity), origin of the debris and topographic parameters (e.g. aspect, slope).

We identified so far more than 700 objects with an extent of about 250 km² within an area of 4000 km². Most of the rockglaciers are moraine-derived and have a northern exposition. The altitude distribution varies significantly depending on the location. Work is ongoing to extend the study region, refine the inventory, classify the rock glaciers according to their activity, and investigate the changes in velocity and surface elevation of selected rockglaciers over time.

Global warming caused significant changes in mountain glaciers. Observations showed on average clear glacier mass loss. However, recent studies revealed also regions with balanced mass budgets especially in parts of High Mountain Asia (HMA). These heterogeneous changes significantly influence the hydrology, i.e. regionally they alter the river run-off and cause the rise of endorheic lakes on the Tibetan Plateau and globally they affect the sea-level. Glaciers are a major contributor of sea-level rise and affect population that rely on fresh water from glaciers. Rock glaciers have so far only rarely been investigated in HMA but may also of hydrological importance.

The purpose of this sub-project within the more general cryosphere project is to generate an up to date glacier and a rock glacier inventory for selected benchmark regions located in different climatic settings in HMA. Glacier mapping will be based both on optical and radar imagery distributed by the Chinese and European Space programs and combine information about surface flow (as derived in the subproject 2), surface reflectance and backscatter. The generated outlines will be compared to existing ones of previous periods to detect changes in glacier area. As area changes can only indirectly related to climate and hydrology, we will also investigate glacier mass changes using digital elevation models from different time periods (DEM differencing) and altimetry data. Both data sources are complimentary in regard to their spatiotemporal coverage. We will apply ICESat and Cryosat-2 altimetry data to investigate the trends over the whole of HMA and apply DEM differencing in the benchmark regions using existing DEMs (e.g. SRTM, ASTER or TanDEM-X DEMs) or DEMs derived from stereo data. Field measurements and high resolution data will be employed to validate and calibrate the remote-sensing derived results.

The outcome of this project will be improved methodologies for glacier mapping and glacier change assessments and a better knowledge about rock glacier occurrence, the spatial and temporal variability of glacier area and mass changes in HMA, its influence on hydrology and its control by local and climatic forcing. This will be realised thanks to the large archive of satellite data available via Dragon, data available from other sources and thanks to the coordinated effort of the several institutions partnering in the project. The link to hydrology, local and climate forcing will be investigated within this subproject via data assimilation into mass balance models, interaction with the two other sub-projects under the umbrella of cryosphere, and via interaction with the hydrology consortium.

Glacier fluctuations are regarded one of the most significant indicators of climate change. The expansion and contraction of glaciers can be observed by outlining glacier boundaries or measuring snow lines with optical Earth observation satellites. New satellites, such as Sentinel-2A/B, provide high spatial resolution images and short revisit times that can be used to make ample measurements to accurately determine glacial variability. Likewise, ever increasing volumes of satellite data make automated boundary and snow line detection a desirable solution for researchers. Two regions of interest for boundary and snow line detection are the Himalaya and Tibetan Plateau. They are home to the world's highest mountains and some of the world's largest non-polar glaciers. These regions also provided valuable water resources to over a billion people in nearby countries, and therefore are not just ecologically, but also economically important. Clouds, however, present a challenge to obtaining useful image data. Mountainous regions are often surrounded or covered by clouds. Clouds can be a menacing phenomenon in remote sensing because they greatly attenuate and reflect short wavelengths used by optical Earth observation satellites. Currently, many techniques exist to automatically detect clouds and classify them. However, they are not perfect. Many techniques have encountered difficulties in cases where snowy and icy landscapes share similar properties with clouds. The Himalaya topographic relief also adds to the challenge. Steep slopes and topographic shadows have a profound effect on surface reflectances and can lead to misclassifications. To address these issues, this study presents an assessment of multiple cloud detection techniques. For initial analysis, 19 Sentinel-2A images were acquired at various times between 12/07/15 and 31/12/16. The images are centered on the Bara Shigri Glacier in Himachal Pradesh, India, a large 10 km long glacier which drains into the Chenab River (an Indus River tributary). The images vary from cloudy to clear (cloud-free), but also have variations in snow cover and cloud shadows. The set was reduced to 6 images that were selected for further classification analysis, as partly shown in Figure 1. Figure 1 shows, starting from the top-left, a natural color image for reference, a manually created cloud mask for validation, and well-known spectral analysis methods: Fmask and maximum likelihood classification. Goal of the study is to first evaluate the performance of existing methods in the automatic identification of pixels contaminated by clouds, and secondly, if necessary, design an improved method, for example, by incorporating the high temporal revisit time of the Sentinel-2 imagery. In doing so, this research seeks to better understand cloud cover over mountainous regions and distinguish them from snow cover and glaciers. It should be noted that cloud detection is a valuable pre-processing step that, when successful, will increase data availability to glacier researchers. Thus, the final goal is to incorporate the cloud-free pixel identification in automated workflows for snow cover studies.

Catastrophic event - fast moving glacier destroying pastures and killing livestock near the glacier tongue in Kongur Mountains, Xinjiang Uygur Autonomous Region, was reported on May 14^th, 2015. In this letter, Sentinel-1 SAR and ALOS/PALSAR data feature-tracking was employed to obtain the glacier surface velocities. Time series of the estimated glacier surface velocities suggested that the left tributary had been a fast moving flow for eight years at least. Analysis of the obtained glacier surface velocities Variation made the surge occurrence confirmed, and from Mar 24th, 2015 to May 11th, 2015, the left tributary has pushed the trunk gradually detected by the analysis of glacier flow vector. Surface velocities of three stages, prior to the surging, during surging and post surging were mapped and analyzed. This research suggested that monitoring glacier surface velocities could be regarded as an effective way for glacier catastrophic warning.

The mass balance history (1980–2010) of a monsoon-dominated glacier in the southeast Tibetan Plateau is reconstructed using an energy balance model and later interpreted with regard to macroscale atmospheric variables. The results show that this glacier is characterized by significant interannual mass fluctuations over the past three decades, with a remarkably high mass loss during the recent period of 2003–2010. Analysis of the relationships between glacier mass balance and climatic variables shows that interannual temperature variability in the monsoonal season (June–September) is a primary driver of its mass balance fluctuations, but monsoonal precipitation tends to play an accentuated role for driving the observed glacier mass changes due to their covariation (concurrence of warm/dry and cold/wet climates) in the monsoon-influenced southeast Tibetan Plateau. Analysis of the atmospheric circulation pattern reveals that the predominance of anticyclonic/cyclonic circulations prevailing in the southeastern/northern Tibetan Plateau during 2003–2010 contributes to increased air temperature and decreased precipitation in the southeast Tibetan Plateau. Regionally contrasting atmospheric circulations explain the distinct mass changes between in the monsoon-influenced southeast Tibetan Plateau and in the north Tibetan Plateau/Tien Shan Mountains during 2003–2010. The macroscale climate change seems to be linked with the Europe-Asia teleconnection.

The Tibetan Plateau (TP), the highest and largest plateau in the world, with complex and competing cryospheric-hydrologic-geodynamic processes, is particularly sensitive to anthropogenic warming. The quantitative water mass budget in the TP is poorly known. Here we examine annual changes in lake area, level, and volume during 1970s −2015. We find that a complex pattern of lake volume change during 1970s−2015: a slight decrease of –2.78 Gt yr^-1 during 1970s−1995, followed by a rapid increase of 12.53 Gt yr^-1 during 1996−2010, and then a recent deceleration (1.46 Gt yr^-1) during 2011−2015. We then estimated the recent water mass budget for the Inner TP, 2003−2009, including changes in terrestrial water storage (TWS), lake volume, glacier mass, snow water equivalent (SWE), soil moisture, and permafrost. The dominant components of water mass budget, namely changes in lake volume (7.72 ± 0.63 Gt yr^-1) and groundwater storage (5.01 ± 1.59 Gt yr^-1), increased at similar rates. We find that increased net precipitation contributes the majority of water supply (74%) for the lake volume increase, followed by glacier mass loss (13%), and ground ice melt due to permafrost degradation (12%). Other term such as SWE (1%) make a relatively small contribution. These results suggest that the hydrologic cycle in the TP has intensified remarkably during recent decades.

Multitemporal interferometric synthetic aperture radar (InSAR) observations have been widely used to characterize spatial and temporal variations of ground deformation of tectonic, climatic or anthropic origin. An accuracy in the order of a millimeter per year on velocity measurements can be achieved in areas where a dense archive of InSAR data exists, and when methodological developments are applied to enhance coherence and improve the signal to noise ratio. With systematic acquisitions and a revisit time of 24 days over Tibet, the new Sentinel-1 radar data offer new perspectives to retrieve small deformation signal and better separate various sources of deformation. We review here our most recent studies over the tibetan plateau, emphasing improvments in InSAR processing of both the Envisat archive and newly acquired Sentinel-1 data.

We characterize the ground motion induced by the response of the permafrost active layer to climatic forcing, over a 60000m2 area in the northwestern part of the tibetan plateau, during 8 years spanned by the Envisat archive. This phenomena is limited to Cenozoic sedimentary basins and is spatially variable in both its seasonal amplitude (2.5–12 mm) and multiannual trend (−2 to 3 mm/yr). A degree-day integrated model adjusted to the data indicates that subsidence occurs when the surface temperature exceeds zero (May to October) over areas where seasonal movements are large (>8 mm). The period of subsidence is delayed by 1–2 months over areas where smaller seasonal movements are observed, suggesting an unsaturated soil where water occurs in the deeper part of the active layer. We use the same Envisat archive to characterize the interseismic strain pattern across the Altyn Tagh fault system. The previous permafrost study helps better unwrapping interferograms and referencing velocity maps on adjacent tracks, which leads to a better quantification of the tectonic signal. Here we also test various strategies to correct tropospheric delays, improving as well the differentiation between secular tectonic deformation and seasonal signals. A linear feature of strain localization (1-2 mm/yr of velocity change in the line of sight -LOS-) is identified north of the Altyn Tagh strike-slip Fault (ATF) within the Tarim basin, parallel to the ATF. We suggest that the ATF is connected at depth with a transpressive ramp emerging north of it in the basin, both structures accommodating by slip partitioning an oblique (N52°E) convergence rate of 14.2 mm/yr. South of the ATF, we also observe strain localization along a continuous feature, possibly the Jinsha suture, corresponding to 3 mm/yr of velocity change in the LOS. Finally, taking advantage of our experience in InSAR processing of long-tracks, small deformation and difficult terrain conditions areas, we adapt our chain to the processing of Sentinel-1 (S1) data, focusing on the eastern-southeastern border of the tibetan plateau, from the Haiyuan fault to the north to the Xian Shui He fault to the south.

It is challenging to effectively extract the near-field deformation of large earthquakes using InSAR approach, especially from the short-wavelength radar sensors, such as the C-, X- band systems. The issue leads to incomplete deformation field of large earthquakes in the vital regions, which are critical for earthquake studies. Some of the well-known decorrelation effects could lead to signal loss and prevent successful phase unwrapping, such as the geometric (or spatial), temporal, and Doppler decorrelation, etc. The decorrelation effects may reduce the received energy of satellite radar sensors from the back-scattering of ground targets. Hence, the incoherence (or partial of it) of radar signals will greatly reduce the signal-to-noise ratio of InSAR phase and leads to information loss in the deformation field or serious phase jumping issues in the final products. In the near-field of large earthquakes, high phase gradient may be irresolvable by traditional unwrapping methods. In extreme situations, the complete incoherence of radar returns could occur when the phase difference of neighbor pixels exceedingπradians. Except for this physical limitation of the radar systems, it is possible to extract useful information from InSAR data. Moreover, severe DEM errors (depending on baselines and topography) will also greatly influence the phase unwrapping process and introduce heavy phase jumping errors, leading to unreliability of InSAR observations.

To overcome the limitations described above, we developed a simple strategy for phase unwrapping. Due to strong phase gradient following large earthquakes, we first multi-look the InSAR phase to a lower resolution and implement conventional Goldstein filtering and unwrapping. After the step, we use a 2-D spatial filter to estimate the first order deformation signals and remove this component from the unwrapped phase. We do the same procedure in an iterative way until it is impossible to unwrap the residual phase. The final residual phase plus the filtered-out phase using Goldstein filter are deemed as the total residuals for the wrapped phase. We then resample the total residuals to the original spatial resolution of InSAR phase and removed it from the observations. Then the conventional unwrapping procedures will be able to used for extracting InSAR phase covering even the near-field of earthquake deformation area. The total residuals could also include useful information excluded from earthquake deformation, such as localized deformation of landslides, small areas of uplift or subsidence, besides the DEM error phase.

We had successfully applied the method to some large earthquakes, such as the 2015 Illapel, Chile earthquake, the 2016 Ecuador earthquake, the 2016 Central Itlay earthquake sequence, and the 2016 New Zeland earthquake, etc. So far, we only consider the Sentinel-1 data here, due to its small temporal decorrelation effects, but it would be much easier to work with L-band data, such as ALOS-1/2. Also, in some mountainous regions, it is also valuable to use the method to overcome the phase unwrapping issues for interseismic or postseismic InSAR phase retrieval.

In eastern Tibet, the left-lateral, ~1400 km-long, Xianshuihe fault system (XFS) is oneof the most tectonically active intra-continental fault system in China. More than 20 M>6.5 earthquakes broke this fault since 1700, including the recent 2010 Mw6.9 Yushu earthquake. We focus here on the northwestern segment of the XFS, the Ganzi fault, east of the Yushu rupture, identified as a seismic gap capable of producing Mw7.6 earthquakes. The Quaternary slip-rate of this Ganzi segment is estimated to 6-8 mm/yr, from offsets measurements and cosmogenic dating of moraine crests and alluvial fan edges (Chevalier et al., 2017). To characterize the present-day behavior of this segment, we process the first two-years of Sentinel-1 InSAR data to produce an average velocity map across the Ganzi fault, using both descending and ascending data. We adapt our InSAR processing chain (NSBAS) to the specificities of Sentinel-1 data and produce a simple elastic model of the InSAR-derived velocity field. We discuss our first results in comparison with the existing GPS data and the long-term tectonic setting.

The satellite data application in agricultural has a long history. The new
developments of satellite series in China and Europe are bringing new opportunities
to advance the agricultural monitoring with abundant satellite data. The Sentinel
and GF are both quite similar high resolution satellite series onboard European
and Chinese satellites, respectively. The Proba-V and FY3-MERSI both have quite
similar channels and their own advantages in the medium to low resolution
satellite. This project is going to focus on the crop mapping, crop condition
monitoring and crop drought monitoring with both satellite data. The Ningxia
Hui autonomous region, one of small size provinces in China, was selected as
the study area for the orchard and crop mapping study with GF and Sentinel
optical satellite data. The field survey was conducted in June, 2016 and will
be organized again in June this year. The GF satellite data were collected as
much as possible for the coverage of Ningxia in the growing season. The processing
method of GF data is now developing in order to automatically ingest large
volume data in the next step. The preliminary results will be hopefully presented
at the symposium. Another two major agricultural production areas in China, North
China Plain and Northeast China Plain were also selected for the crop
monitoring and crop mapping with both medium to low resolution satellite data. The
field surveys were conducted in summer 2016 and spring in 2017. The further
field surveys will be organized in summer this year. The relevant Proba-V satellite
data have been downloaded and a processing code was developed to extract the Proba-V
data for the area of interesting. The FY-MERSI process chain has been developed
in the past. The classification method is still in developing. Hopefully the
methods and the preliminary results may be reported at the symposium.

Abstract: The orchard is developing very fast in Ningxia Hui autonomous region in Northwest China with the development of wine brewing Companies in recent year. Agrometeorological monitoring for the growth of grapes and other fruit trees is one of key process to identify the high quality fruit in the region but there is lack of the baseline map of orchard in Ningxia. This study aims at developing an orchard map in Ningxia in support of the agrometeorological service. The study collected high resolution GF satellite as much as possible which resolutions are around 8 t0 16 meters for multispectral bands. The decision tree was used to classify the images. All classified images were merged and finally an orchard map was formed. The validation was conducted with the field data.