esa-most china dragon cooperation

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The Third Pole Environment plays a significant role in global atmospheric circulation and is highly sensitive to climate change and its impact on Asia’s largest rivers which provide water to 1.5 billion people across ten countries. A fundamental understanding of intensive exchanges of water and energy fluxes between the Asian monsoon, the plateau land surface and the plateau atmosphere at various temporal and spatial scales especially at the climate scale is crucial to understand the role of TPE on global climate and the impact of climate change on TPE.   The CLIMATE-TPE project aims to advance the understanding of the interactions between the Asian monsoon, the plateau surface (including its permafrost and lakes) and the Tibetan plateau atmosphere in terms of water and energy budgets in order to assess and understand the causes of changes in cryosphere and hydrosphere, in relation to changes of plateau atmosphere in the Asian monsoon system, and to predict the possible changes in water resources in the Third Pole Environment. A core innovation of the project is to verify or falsify recent hypotheses (e.g. links between plateau heating and monsoon circulation, snow cover and monsoon strength, soil moisture and timing of monsoon) and projections of the changes of glaciers and permafrost in relation to surface and tropospheric heatings on the Tibetan plateau as precursors of monsoon pattern changes and glaciers retreat, and their impacts on water resources in South East Asia.   We use earth observation, in-situ measurements and modelling to advance process understanding relevant to monsoon scale predictions, and improve and develop coupled regional scale hydroclimatic models to explain different physical links and scenarios that cannot be observed directly. Three work-packages (WP) are defined to address three specific objectives. 1) advancement of the understanding of microwave scattering and emission under complex terrains with permafrost and freeze – thawing conditions. The focus is to reduce uncertainties in current microwave satellite observations over complex terrain and improve retrieval accuracies of soil moisture and freeze-thaw states by deploying in-situ observations, laboratory experiment and numerical modelling. 2) Advancement of physical understanding and quantification of changes of water and energy budgets in the TPE. The focus here is to integrate current understandings in the mechanism of changes in water and energy budget in TPE using satellite data products and numerical modelling. Objective 3) Advancement of quantifying changes in surface characteristics and monsoon interactions. All variables related to water and energy budgets in TPE will be subject to systematic analysis to ensure their consistence in terms of climate data records. The variables will include albedo, vegetation coverage, soil thermal and hydraulic properties, LST, soil moisture, lake levels and land use changes among others.   In this contribution we focus on WP1: Observation and modelling of microwave scattering and emission under complex terrains and including permafrost and freeze and thawing; and WP2: Advancement of physical understanding and quantification of changes of water and energy budgets in the TPE.   Since 2006 the Tibetan plateau observatory for soil moisture and soil temperature (Tibet-Obs, Su et al., 2011, HESS) has been in operation and has provided valuable dataset for land-atmosphere process studies. The networks and collected data have been used for calibration and validation of satellite soil moisture retrieval algorithms and data products as well as for improving numerical model parameterizations (Su et al., 2013, JGR; Zheng et al., 2015a, b, JHM; 2017a, JHM, b, JGR) and for understanding passive and active microwave signals (Dente et al., 2015, RSE; Wang et al., 2016, JAG; Lv et al., 2014, RSE). Most recently an in-situ microwave radiometer (ELBARA III from ESA) has been operating at the Maqu site of the Tibet-Obs, as such coherent process observation, process modeling and radiative transfer modeling can be conducted (Zheng et al., 2017, TGRS) to examine land-atmosphere interactions. We report here recent results of these experiments in combined radiative transfer and heat-water transfer processes and in understanding satellite observation signals and data products – these are related to a new insight of the penetration depth and its quantification for soil moisture products (Lv et al., 2018, RS; Lv et al., 2019, TGRS), benefit of synergistic use of active and passive microwave observations for soil moisture retrieval (Wang et al., 2018, RSE; Wang et al., 2019, JAG) and its use in closing water and energy budgets in TP, as well as inference of subsurface parameters from radiometric observations.   A reflection is made on modeling land-atmosphere radiative and heat-water transfer processes as a key component of Earth System Model (Zhao et al., 2018, ESSD; Yu et al., 2018, JGR). A specific investigation has also been conducted, as part of the ESM, on the turbulent flux and energy budget over a high-altitude lake on the Tibetan Plateau (Wang et al., 2015, 2017, JGR; Wang et al., 2018, TAC).   As part of capacity building, three PhD (will) have graduated at the University of Twente (Q. Wang, S. Lv and B. Wang) in 2018-2019 and five new students have contributed to different aspects of the project (J. Du, Y. Yu, P. Zhang, M. Li, S. Mwangi).

In the past one year, based on in-situ measurements, reanalysis data, satellite remote sensing and numerical modeling, several main achievements have been acquired to promote the understanding of water and energy cycles over the Tibetan Plateau (TP). (1) Based on geostationary and polar orbiting satellite data, the surface energy balance system (SEBS) was used to derive hourly land surface heat fluxes at a spatial resolution of 10 km. Six stations scattered through the TP and equipped for flux tower measurements were used to perform cross-validation. The results showed good agreement between derived fluxes and in situ measurements through 3738 validation samples. The RMSEs for net radiation flux, sensible heat flux, latent heat flux and soil heat flux were 76.63 Wm-2, 60.29 Wm-2, 71.03 Wm-2 and 37.5 Wm-2, respectively. The derived results were also found to be superior to GLDAS flux products. (2) Based on field albedo measurements, moderate resolution imaging spectrometer (MODIS) albedo products and numerical simulation, we evaluated the ice albedo parameterization schemes in existing lake models and investigate the characteristics of the ice surface albedo in six typical TP lakes, as well as the influence of ice albedo error in the FLake model. Compared with observations, several ice albedo schemes all clearly overestimate the lake ice albedo by 0.26 to 0.66, while the average bias of MODIS albedo products is only 0.07. The MODIS-observed albedo of a snow-covered lake varies with the snow proportion, and the lake surface albedo in a snow-free state is approximately 0.15 during the frozen period. The simulated lake surface temperature is sensitive to variations in lake ice albedo especially in the spring and winter. (3) The climatological characteristics of water vapor and its interannual variability over the TP were investigated by using the ERA-interim monthly mean reanalysis datasets from 1979 to 2014. The analyses show that the TP is a water vapor convergence area, where the convergence was enhanced from 1979 to 2014. The TP is a moisture sink at a climatological mean, with an annual average net water vapor flux of . Detailed features in the water vapor flux and transport changes across the TP’s four boundaries were explored by simulating backward trajectories with a Lagrangian trajectory model (hybrid single-particle Lagrangian integrated trajectory model, HYSPLIT). In the study period, the water vapor contribution rate of midlatitude westerlies to the eastern and southern boundaries increased. However, the South Asian monsoon’s water vapor contribution decreased. (4) Six numerical experiments using the Weather Research and Forecasting (WRF) model were conducted to simulate a snow event over the TP in March 2017. The best performance was achieved when applying the CLM land surface physics. A potentially important factor is the advanced parameterization of albedo in CLM. The WRF model has a certain advantage to identify the snow event, but poor performance on accurate snow depth simulation; Sensitivity of total solid precipitation over the entire event to the land surface physics was larger than to the initial and boundary conditions. (5) Training of young scientists in the area of climate and environment. Six PhD students have been sent to European partner for joint training. Three of them have got PhD degree in University of Twente under the supervision of European PI (Prof. Zhongbo Su) and Chinese PI (Prof. Yaoming Ma). Several European students from our partner also come to China regularly for joint field visiting and academic exchange. 

It is well known that the distribution of cloud and precipitation is affected by atmospheric parameters such as water vapor and updraft motion, as well as by topography. Due to the high altitude of Qinghai-Xizang Plateau (QXP) impacting on cloud and precipitation associated with latent heat release, lots of little known characteristics of them has been revealed continuously based on the measurements of Precipitation Radar (PR) onboard the Tropical Rainfall Measurement Mission (TRMM) satellite. In this study, the characteristics of the vertical structure of precipitation on QXP were studied and compared with the surrounding areas based on the 15 years' measurements of the PR. The results show that, firstly, there is no obvious brightness band in the vertical structure of precipitation over QXP, but it occurs in vertical structure measured by ground based precipitation radar that has the vertical resolution of 33 meters. The thickness of the brightness band is so thin that it is speculated that the PR with vertical resolution of 250 meters is not enough to distinguish the brightness band of precipitation over QXP. Secondly, according to the characteristics of sounding temperature and humidity profile, the precipitation in QXP can be divided into three types: deep strong convection, deep weak convection and shallow convection The Statistical calculations show that the precipitation over QXP is mainly in the form of week deep convection, which occupies 67. 8% followed by the form of shallow precipitation with 26. 4% and the strong deep convection with 5. 8%. Thirdly, the precipitation frequency peaks of deep strong convection and deep weak convection occurred at 16:00 (local time, the same below), and the precipitation intensity peaks for both types at 18:00 and 13:00, respectively. There is second peak at 0:00 for deep strong convection. The peak of precipitation frequency and intensity for shallow precipitation appeared at 20:00, which showed the characteristics of night rain. Finally, the shape of the average profile of deep strong convective precipitation over the QXT is similar to that of deep convective precipitation on the mainland of the Mid-East China, but different from that on the ocean surface. Key Words: Precipitation, TRMM PR, Vertical Structure, Diurnal Variation

Surface soil moisture (SSM) plays a significant role in the water and energy fluxes at the land-atmosphere interface and its spatiotemporal dynamics is of crucial importance for e.g. water resources and agricultural management. In recent decades, with the development of remote sensing technologies, regional/global soil moisture estimation has been proceeding rapidly. The validation of soil moisture products from remote sensing is normally conducted by in-situ measurements directly or by interpolated soil moisture. However, the problem of mismatching scales between satellite imagery and ground-based observations typically imposes a large range of uncertainty in assessing the accuracy of remote sensing based soil moisture products. Interpolation schemes to upscale soil moisture from point measurements are a suitable alternative option of validating remote sensing soil moisture retrievals. This study aims to evaluate four upscaling methods (Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Universal Kriging (UK) and Spline function (S)) individually on analysing a specific data source over a typical study area (Maqu in the north-eastern Qinghai-Tibet Plateau, HiWATER in the Heihe river basin and REMEDHUS in the semi-arid parts of the Duero Basin of Spain). The strengths and weaknesses of four upscaling methods are explained in detail by cross validation to enumerate all possibilities, thereby maximally reducing the generation of error. In summary, OK and UK show two best upscaling results at similar level, while IDW is inferior to them. S is the least suitable method for soil moisture upscaling, showing weak performance. As expected, a dense soil moisture network with a high number of stations is an important factor for a reliable upscaling of soil moisture. It is found that at least 30 stations (5.5 km × 5.5 km square area) over HiWATER are required to accurately compute geostatistical algorithms (UK, OK or IDW), among which OK performs best in upscaling soil moisture. In cases with less than 30 stations, it’s not recommended to apply geostatistical algorithms to upscale soil moisture, especially for Maqu and REMEDHUS, two research areas which are widely used for soil moisture validation.

With combined remote sensing observations in the microwave- and optical- region of the electromagnetic spectrum we intend to study the dynamics of an alpine meadow floral ecosystem. The microwave observations are made with a broadband (1 – 10 GHz) full polarimetric ground-based scatterometer. From these observations we want to retrieve the soil moisture content, above-ground biomass, and Leaf Area Index. Installed next to the scatterometer is a dual field-of-view (upwelling and downwelling) high resolution (up to 0.1 nm) spectrometer system covering the 400 – 900 nm  range. From the spectrometer observations we want to retrieve chlorophyll fluorescence vegetation water content, and pigment factions. Both observations are done over multiple month periods with one measurement every hour. During the conference we will present a poster on the experimental approach, the technical specifications, and measured observations of the scatterometer. The scatterometer setup was designed to be simple consisting of commercial off-the -shelf components:  a Vector Network Analyser and two dual polarization broadband antennas, one for transmit, the other for receive. Also, the experimental approach for the long-term observations entails the two antennas fixed in one position, so that an (automated) rotational stage was not necessary. Due to the broadband approach the antennas used have broad Gain patterns, especially for the lower frequencies. This property, together with the antenna’s close proximity to the ground (5 m above the surface) requires that for the derivation of the backscattering coefficient (s0) the ground surface mapping of G2/R4 must be evaluated to deduce the scatterometer’s footprint and associated angle of incidence interval. To reduce the fading-induced uncertainty of s0  we employ frequency averaging techniques. In order to select proper bandwidths over which to average we use predictions of the frequency- and angle dependent behaviour of s0 calculated with the Advanced Integral Equation model (I2Em). We will present an analysis on the spatial variability of s0 at our measurement site. Data for this analysis were obtained by measuring the s0 over different antenna elevation- and azimuth angles. Additionally, we show the temporal behaviour of the measured s0 during some of the key seasonal moments: frozen soil with senescent vegetation, thawing period, spring period (low vegetation) and summer period (maximum vegetation biomass).

L-band passive 1 microwave remote sensing is one of the most effective methods to map the global soil moisture distribution, yet, at which soil depth satellites are measuring is still inconclusive. Recently, with the Lv’s multilayer soil effective temperature scheme, such depth information can be revealed in the framework of the zeroth-order incoherent model when soil temperature varies linearly with soil optical depth. In this paper, we examine the relationships between soil temperature microwave sensing depth, penetration depth, and soil effective temperature, considering the nonlinear case. The soil temperature sensing depth often also named penetration depth is redefined as the depth where soil temperature equals the soil effective temperature. A method is developed to estimate soil temperature sensing depth from one pair of soil temperature and moisture measurement at an arbitrary depth, the soil surface temperature, and the deep soil temperature which is assumed to be constant in time. The method can be used to estimate the soil effective temperature and soil temperature sensing depth.

With combined remote sensing observations in the microwave- and optical- region of the electromagnetic spectrum we intend to study the dynamics of an alpine meadow floral ecosystem. The microwave observations are made with a broadband (1 – 10 GHz) full polarimetric ground-based scatterometer. From these observations we want to retrieve the soil moisture content, above-ground biomass, and Leaf Area Index. Installed next to the scatterometer is a dual field-of-view (upwelling and downwelling) high resolution (up to 0.1 nm) spectrometer system covering the 400 – 900 nm  range. From the spectrometer observations we want to retrieve chlorophyll fluorescence vegetation water content, and pigment factions. Both observations are done over multiple month periods with one measurement every hour. During the conference we will present a poster on the experimental approach, the technical specifications, and measured observations of the scatterometer. The scatterometer setup was designed to be simple consisting of commercial off-the -shelf components:  a Vector Network Analyser and two dual polarization broadband antennas, one for transmit, the other for receive. Also, the experimental approach for the long-term observations entails the two antennas fixed in one position, so that an (automated) rotational stage was not necessary. Due to the broadband approach the antennas used have broad Gain patterns, especially for the lower frequencies. This property, together with the antenna’s close proximity to the ground (5 m above the surface) requires that for the derivation of the backscattering coefficient (s0) the ground surface mapping of G2/R4 must be evaluated to deduce the scatterometer’s footprint and associated angle of incidence interval. To reduce the fading-induced uncertainty of s0  we employ frequency averaging techniques. In order to select proper bandwidths over which to average we use predictions of the frequency- and angle dependent behaviour of s0 calculated with the Advanced Integral Equation model (I2Em). We will present an analysis on the spatial variability of s0 at our measurement site. Data for this analysis were obtained by measuring the s0 over different antenna elevation- and azimuth angles. Additionally, we show the temporal behaviour of the measured s0 during some of the key seasonal moments: frozen soil with senescent vegetation, thawing period, spring period (low vegetation) and summer period (maximum vegetation biomass).

Altimeter and radiometer are import means for marine dynamic phenomenon monitoring by remote sensing. In this paper we will present a comprehensive comparison of marine remote sensing products e.g. wave height, mesoscale eddy, sea ice, and sea surface salinity. The performance and accuracy of remote sensing production are validated. For oceanic dynamic production, sea surface height and significant wave height of Sentinel-3A/3B SRAL are compared at their self-crossovers and mutual-crossovers, and biases, trends and precisions of these data are analyzed. Furthermore, mesoscale eddy detection applications based on unified Sentinel-3A/3B SRAL data are analyzed by comparing with the mesoscale eddy detection results of the Jason-2/Jason-3 series satellites. For sea surface salinity, a new method is introduced to estimate the representativeness error and apply it to the triple collocation data set of Argo, SMAP and SMOS for the year of 2015-2017. The spatial-temporal scales of all three data sets are studied and the representativeness error as well as the random errors is obtained. For sea ice, ice freeboard extracted by CryoSAT-2 and Sentinel-3 are compared at different waveform retracking methods, snow models and retracking thresholds. Accuracy of two kinds of ice freeboard productions is also validated by Operation IceBridge data.

With an increasing number of SAR systems in space that are operated at different frequenciesand provide different imaging modes, there is a need to re-evaluate strategies for operational seaice mapping and for scientific process studies such as interactions between atmosphere, sea ice,and ocean. In our presentation we will focus on aspects of using images acquired at differentradar frequencies, different polarizations, and spatial resolutions. Examples will be shown thatdemonstrate how the anaylsis of sea ice conditions can be improved for a given problem suchas thin ice detection and classification or identification of ice deformation structures. For this purposewe used L-, C-, and X-band SAR images from different satellites and from airborne campaigns acquired over different ice regimes. We address the additional gain of complementary data sourcessuch as optical and thermal images and model simulations of ice growth.

Sea ice is a fundamental component of the Earth climate system since it influences directly the albedo of our planet and regulates the heat exchange between the atmosphere and the ocean. The launch of the EC/ESA's CryoSat-2 and Sentinel-3 missions offer the opportunity to observe the near real-time sea ice thickness information. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, a comprehensive comparison between freeboard measurements of the CryoSat-2 and Sentinel-3 is tested. We first examine the relationship between snow depth and ice and radar freeboard by comparing in situ snow depth measurements from OIB missions. And then move to the intercomparison of freeboards from CryoSat-2 and Sentinel-3 to investigate the penetration of the radar signal into the snow layer. We also compare total ice freeboard calculated from CryoSat-2 and Sentinel-3 with OIB data. Further we also investigate whether we can improve the accuracy of the freeboard retrieval from CryoSat-2 and Sentinel-3 by changing the threshold used in the retracking process, and examine the merits of adjusting the retracking threshold in a quest for an accurate sea-ice freeboard estimation.

The main detection modes of the existing sea-ice microwave remote sensors are normal incidence (0°, represented by the altimeter) and medium-angle incidence (20°-60°, represented by the scatterometer and SAR). With the development of the remote sensing technology, the low-incidence-angle microwave detectors have been put into use increasingly, which are represented by the Precipitation Radar (PR, Global Precipitation Measurement program) and the Surface Wave Investigation and Monitoring (SWIM, CFOSAT Satellite). Both of the Dual-frequency Precipitation Radar (DPR) and the SWIM can observe the Pole sea ice and have the potential of the sea-ice detection. SWIM is now in the in-orbit test phase whose data has not been released yet. Therefore, the microwave scattering characteristics of the sea ice at the low-incidence angles will be studied, and the sea-ice detection capability of the DPR will been assessed based on the CryoSat-2 and DPR scatterometer data of the Antarctic Weddell Sea from 2014 to 2019 in this paper. Firstly, the Cryosat-2 and DPR backscattering coefficients are matched for the resolution and the temporal-spatial information, the type information of the objects (including three types of the sea ice, sea water and leads) provided by the Cryosat-2 L2I products are also added in. It is ensured that each point in the study area contains both the backscattering coefficients of the two remote sensors and the type information; Secondly, intra- and inter-annual analysis of the sea-ice backscattering coefficients derived from the Cryosat-2 and DPR are carried out. For example, during the period of the sea-ice freezing, developing and melting from January to December of each year, the microwave scattering characteristics of the sea-ice at the low-incidence angles are studied based on the DPR data, which are compared with the Cryosat-2 normal incidence mode. Thirdly, intra- and inter-annual analysis of the different types for the backscattering coefficients of the Cryosat-2 and DPR are carried out. For example, during the freezing period of the sea ice, the DPR backscattering characteristics of the sea ice, sea water and leads are compared, which are compared with those of the Cryosat-2. Finally, the sea-ice detection capability of the DPR at low-incidence angles is evaluated. In the next step, we will add the sea-ice thickness information for research and analysis.

The Sentinel-3A and Sentinel-3B satellites, which were equipped with SAR Rader Altimeter (SRAL), were launched by ESA on February 26, 2016 and April 25, 2018, respectively. The simultaneous observations of Sentinel-3A/3B satellite altimeters will increase the spatial and temporal coverage of altimeter global observations and improve their data application. In this study, observation data (Sea Surface Height, Significant Wave Height and backscattering coefficient) and corrections data (wet troposphere correction, ionosphere correction and sea state bias correction) of Sentinel-3A/3B SRAL are compared at their self-crossovers and mutual-crossovers, and biases, trends and precisions of these data are analyzed. Data unification method of Sentinel-3A/3B SRAL data is given based on their comparisons. Furthermore, mesoscale eddy detection applications based on unified Sentinel-3A/3B SRAL data are analyzed by comparing with the mesoscale eddy detection results of the Jason-2/Jason-3 series satellites, and the joint application capabilities of Sentinel-3A/3B SRAL data are summarized.

Sea surface salinity (SSS) is one of the key parameters for us to understand the oceans better. Since the L-band radiometers SMOS, Aquarius and SMAP have observed the SSS from space for years, the scientific community have devoted enormous efforts to the validation of the retrieved SSS data, based on the “double match” procedure between the in-situ and remote sensed measurements. However, the direct comparison based on the double match procedure has its limitations. Firstly, it assumes the in-situ data is error free and only give the relative accuracy of remote-sensed SSS data. To resolve this problem, the triple match method, which uses three independent SSS data sources to develop a triple collocation data set, can estimate the random error of all three data sets. Secondly, the in-situ data present the “point” measurements and its spatial-temporal scale is clearly smaller than the space borne SSS data which is the spatial average within the antenna footprint with the typical value of 100 km. Consequently, the in-situ data contain the true small-scale SSS variation information which cannot be resolved by radiometer retrieved SSS data. The effect of this small-scale SSS is neglected in the direct comparison method and it is regarded as a part of remote sensed SSS error which leads to an overestimation of retrieved SSS error. In the triple match method, researchers introduce the representativeness error to describe the effect of this small-scale SSS variations in high resolution SSS data. However, the estimation of representativeness error remains challenging. In this study, we introduce a new method to estimate the representativeness error and apply it to the triple collocation data set of Argo, SMAP and SMOS for the year of 2015 ~2017. The spatial-temporal scales of all three data sets are studied and the representativeness error as well as the random errors is obtained. It is founded that the ascending order of spatial-temporal scale of all three data sources is Argo, SMAP and SMOS. The representativeness error (SSS variations observed by Argo and SMAP but not by SMOS) is 0.093 psu2. The random error of Argo in-situ measurements is better than 0.21 psu which is superior to the other data sources. At the spatial-temporal scale of SMOS, the random error of SMOS (0.41 psu) is better than SMAP (0.45 psu). But at the spatial-temporal scale of SMAP, SMAP has the lower random error (0.32 psu) than SMOS (0.51 psu).

he amplitude of internal solitary waves is one of the key techniques for  parameters inversion on remote sensing images. The relationship is established, which aims to match optical remote sensing characteristic parameters with internal solitary wave factors. In this paper, based on the mechanism of optical remote sensing imaging, combined with the dimension analysis method and similarity principle of hydrodynamics, an optical remote sensing detection system for internal solitary waves is constructed in the laboratory, as shown in Figure 1. The optical remote sensing imaging characteristics of internal solitary waves under the condition of two-layer fluid are studied. Internal solitary waves are generated in a three-dimensional straight flume by gravity collapse method. Optical platform and observation recording equipment are set up to complete the continuous synchronous observation of optical remote sensing images and internal solitary wave factor images in the field of view. The optical remote sensing response of internal solitary waves is detected in the laboratory. The convergence and divergence of surface are modulated by the propagation of internal solitary waves in the pycnocline. In addition to bright-dark pattern distance, the grayscale change is also a significant phenomenon in the experiment. Figure 2 shows the synchronous response of optical remote sensing of internal solitary waves extracted by time series method. Therefore, the relationship between gray difference of optical remote sensing images and amplitude of internal solitary waves is explored in the laboratory. The amplitude of the incident internal solitary wave is setting by the height of gravity collapse, the water stratification is setting by the thickness ratio of upper and lower fluid. The experimental results show that the gray difference caused by internal solitary waves on optical remote sensing images increases with the increase of amplitude. The linear fitting for the scatters is shown in Figure 3. The relationship between gray difference and amplitude of all collapse heights under the same stratification is Δgray=0.57A+0.80. With the increase of the proportion of upper water depth to total water depth, the gray difference caused by internal solitary waves with the same amplitude decreases, as shown in Figure 4. The quantitative expression is kΔgray-A=4.00exp(-8.13h1/h). The change of grayscale coincides with the fact that the upper layer of the real ocean is thicker in winter and internal solitary waves with small amplitude are difficult to be observed.

Since the large-scale bloom in 2008, green tide, as a marine natural disaster, happens every year along the coast of Qingdao. It brings huge economic losses to society every year. So, obtaining the real time dynamic information about green tide distribution becomes very urgent. Generally, researches on green tide are mainly focused on the coverage area. For Operational Application of Disaster Emergency Response,the influence rangeof the green tide is what people isconcerned about. The influence rangeof the green tide can not only give information about the gaps between small green tide patches but also show the trend of development of greed tide. Our research is mainly about the influence range of the green tide. Wedesigned an algorithm for extracting the green tide distribution boundaries automatically.Principle of the algorithmis based on mathematical morphology dilation/erosion operation. Several issues such as the division of green tide regions, the extraction of basic distributions, the abnormity of distribution contour, and the elimination of islands, are solved in the paper. Since green tide mainly bursts along the Qingdao Coast and there is no established system so far, a green tide monitoring system is built. The system is based on IDL/GIS secondary development technology in the integrated environment of RS and GIS. It has the abilities of RS monitoring and information extraction. Optical remote sensing and microwave remote sensing are employed in this system. Special processing flow and information extraction algorithm are designed according to the different characteristics of these data. Without using this system, a complete data process from beginning to endingneeds 2 hours, but it can be finished in 10-15 minutes now in our system. The system runs smoothly and successfully in the State Oceanic Administration for three years till now.

Mesoscale eddies are rotating coherent structures of ocean currents, which generally refer to ocean signals with spatial scales from tens to hundreds of kilometers and time scales from days to months. Eddies can be found nearly everywhere in the world ocean, and dominate the ocean’s kinetic energy. Over the recent decades, with the advancements in remote sensing satellites and the abundance of in-situ observations data, people find that mesoscale eddies can transport water, heat, salt, and energy as they propagate in the ocean. By combining satellite altimetry and Argo profiling float data, the analysis of eddy three-dimensional structure becomes an important part of studying the oceanic eddy. The South China Sea (SCS) is the largest marginal sea in the tropics and has a maximum depth of over 5000 m. Mesoscale eddies are an important phenomenon in the SCS, that change dynamic conditions within the ocean and play an important role in the transport of heat, salt and other chemical substances. SCS climate is part of the East Asia monsoon system. In winter, the SCS is dominated by the strong northeasterly monsoon, whereas in summer the winds reverse direction to southwesterly. The alternating monsoons in winter and summer lead to the transformation of the upper circulation and formation of several seasonal eddies in the SCS. The higher eddy kinetic energy (EKE) centers in the SCS are observed to the east of Vietnam and to the west of Taiwan, areas that are also characterized as having high mesoscale eddy activity. In this paper, we investigated mean properties and the spatiotemporal variability of eddies in the SCS, identified using the winding angle method and 25 years of satellite altimetry data. A series of statistical analysis methods were used to study the statistical characteristics of the mesoscale eddies in the region, e.g., eddy number and lifetime, geographical distribution of eddies, evolution of eddy properties, and seasonal variation of eddy activities. Then, based on Argo profile data and climatology data, the eddy synthesis method is used to construct the three-dimensional temperature and salt structure of the eddy in SCS. The growth and decay of long-lived eddies and characteristics and mechanism of temporal variation in eddy activity are incompletely documented. 

During the last decade the radar altimetry has entered in its golden age as demonstrated by the different number of missions (Jason-2/-3, CryoSat-2, Saral/Altika, Sentinel-3) currently operating and the forthcoming ones (Sentinel-6). The relatively new operational synthetic aperture radar (SAR) mode in CryoSat-2 and Sentinel-3 missions, opens a new paradigm in the capabilities that can offer an altimetric radar mission. In this line, a scientific proposal within the DRAGON-4 tries to exploit the lessons learned from classical 2-D SAR focusing to evaluate the imaging-like capability of delay-Doppler altimetric radar mounted on Sentinel-3 over coastal areas. In this way, the altimetric product gets closer to the conventional SAR imaging data, but in the altimeter case a “strip-like” image is obtained compared to the classical 2D SAR image. Conventional delay-Doppler processor (DDP) coherently integrates a series of pulses (around some miliseconds) to provide specific Doppler beams focused to a specific location, which after being correctly aligned provide several looks that can be incoherently averaged, increasing the performance in terms of geophysical retrieval and leading to along-track resolutions of ~300 m. The fully focused DDP moves one step ahead and performs a coherent processing over the entire aperture defined by the along-track antenna beamwidth (around some seconds) to get an even higher along-track resolution (~0.5 m) and with higher number of looks for the same resolution as the conventional DDP. The main objective of the scientific proposal within the DRAGON-4 is to evaluate the potential capabilities offered by the fully focused DDP (FF-DDP), specifically when exploiting state-of-the-art Sentinel-3 operational synthetic aperture radar (SAR) mode. This will confer the SAR altimetric product a very high resolution (in the order of 0.6 m) of great interest for Coastal Altimetry (being able to get closer to the coastline), providing much higher number of looks that can be averaged to improve the altimetric performance as anticipated by Raney in [RD-1]. The core of this presentation is to show the operation of the FF-DDP and its evaluation using simulated data. The current implementation of the processor, based on a backprojection algorithm, will be revisited. The potential capability of the FF-DDP in terms of resolution will be verified using point target simulations. This exercise will be complemented by processing open ocean scenario simulations with a comparison on the retrieved waveforms against conventional DDP. ESA Sentinel-6 simulated data with its two operation modes (RAW and RMC) will be exploited as testbed; these data sets might be complemented with simulations from the new altimetric mission proposal PICE (polar ice). References: [RD- 1] Raney, R. K., 1998. The delay/Doppler radar altimeter. IEEE Transactions on Geoscience and Remote Sensing, 36, 1578–1588. doi:10.1109/36.718861. [RD- 2] Egido, A., & Smith, W. H. F., 2017. Fully Focused SAR Altimetry: Theory and Applications, in IEEE Transactions on Geoscience and Remote Sensing, 55, 1, 392-406, Jan. 2017. doi: 10.1109/TGRS.2016.2607122 [RD- 3] E. Makhoul, M. Roca, R. Escolà, A. Garcia-Mondejar, G. Moyano, P. Garcia, M. Fornari, M. Kuschnerus, R. Cullen. S6 P4 GPP: Fully Focused Delay-Doppler Processing applied on RAW and RMC data- preliminary results, in Ocean Surface Topography Science Team Meeting, 27-28 September 2018.

All over the world, and more precisely in China, freshwater is an increasingly scarce resource suffering from rapidly changing environments due to human activities Study location Poyang and Dongting Lake are China’s first and second largest freshwater lake in the middle reaches of Yangtze River catchment. Its lakes and associated wetlands deliver important ecosystem functions such as freshwater supply, water purification, flood and climate regulation, and biodiversity’s sanctuaries. The development of comprehensive water resource management and nature conservation strategies requires detailed mapping and monitoring of inland waters. The generation of such information requires either large human resources for conventional ground surveying or expensive data. In addition to costly methods, the monitoring of large wetlands such as the Poyang and Dongting lakes study site with a water surface of up to 3.500 km² is difficult due to its inaccessibility during annual flood period. Remote sensing offers a mature and comprehensive tool to solve this task with large area coverage at very low costs. Until today, in addition to satellite data with lower temporal resolution such as Envisat ASAR, for SAR sensors, or HJ1A and Landsat for the optical High resolution satellite, as well as daily middle resolution MODIS satellite data and the MERIS sensors onboard of Envisat, were frequently selected source for capturing lake dynamics. Satellite data from the new European Sentinel and Sentinel2 fleet has been available since 2014 and provides high-resolution information. In this paper we present the application of Sentinel-1and Sentinel 2 time series data for spatio-temporal high-resolution wetland and lakes mapping. New is the level of detail that can be achieved with Sentinel data. Potential and limitations are analyzed. Water surface obtained from the Sentinel were also compared with  water bodies database such the GRW of Pekel as well as  the ones generated by the UCLA lakes group.

Wetlands have two distinctive features, which are spatiotemporal dynamics and spatial heterogeneity. The dynamic characteristic of the water makes its cover fraction show seasonal changes. At the same time, the spectrum of wetland vegetation has a high similarity with agricultural land vegetation, causing accurately distinguish wetlands difficultly by remote sensing technology. The amount of cloud in the mid-latitude region is relatively abundant, and a single sensor cannot provide the time density required for accurate identification of wetlands. Multi-sensor technology has been applied well in monitoring urban landscapes and invasive species mapping. Compared with previous remote sensing data, Sentinel-1/2 and GF-1 data have an advantage of high temporal and spatial resolution, but their application in wetland classification is worthy of the further exploration. The landscape types of wetland ecosystems not only have large heterogeneity in space, but also have strong dynamic characteristics with changes in water bodies. This brings many challenges to accurately map and monitor wetlands. The normalized difference vegetation index (NDVI) is effective in monitoring multi-temporal vegetation phenological changes. There are many studies using time series remote sensing data which has a significant advantage of high observed density to monitor highly dynamic systems. Dongting Lake is located in the northern part of Hubei Province, the middle reaches of the Yangtze River Basin. It is the second largest freshwater lake in China. As one of the two remaining natural rivers in the middle and lower reaches of the Yangtze River, Dongting Lake plays an extremely important role in regulating runoff and protecting biodiversity. In this paper, the Dongting Lake wetlands, which contains three important international wetlands is considered as study area. The data source is the Sentinel-1/2 and GF-1 time series data acquired in 2017, the Sentinel-2 and GF-1 data construct the NDVI time series, and the Sentinel-1 data constructs the backscatter coefficient time series. we fuse the Sentinel-2 and GF-1 data to improve temporal and spatial resolution. In order to avoid the errors caused by a single classifier, the study area is classified using support vector machine and random forest classifier. Results showed the following: (1) The accuracy of the three types of data using the RF classifier is not much different from that of the SVM classifier. Prove that in the aspect of time series wetland classification, the impact of the data source is more significant than which classifier to choose. (2) The overall classification accuracy of Sentinel-2, GF-1 and Sentinel-1 time series are 93.93%, 84.30% and 48.04%, the accuracy of seasonal marshes that symbolizes the dynamic characteristics of wetlands is 92.23%, 79.43% and 48.32%. The classification method based on optical remote sensing time series data can fill the needs of wetland dynamic mapping, while the wetland mapping making use of SAR time series data is not available. (3) The classification accuracy of Sentinel-2+GF-1 fusion time series is 94.09%, and the commission rate of each class is significantly lower than that of the three sensors. The time series mapping based on multi-source data fusion has a  wonderful robustness in the aspect of wetland classification.

In inland and coastal waters, organic carbon undergoes seasonal and spatial variations in relation to river run-off and biological processes. Particulate organic carbon, while being a smaller fraction of the total organic carbon with respect to dissolved organic carbon, plays an important role in the local and regional carbon dynamics. In fact, it is the variation of POC that can be used to examine carbon sequestration and well as carbon sink. Particulate matter strong influences the optical, chemical and biological conditions of most inland and coastal aquatic ecosystems. There are numerous challenges to identifying particulate carbon, and most importantly specifying particulate carbon classes. These latter are dominated largely by productive particulate carbon from phytoplankton and detrital particulate organic carbon. These two pools play very different roles in the aquatic carbon dynamics, with high temporal and spatial variability within a single waterbody in relation to local sources and seasonal changes. However, many studies examine particulate carbon dynamics with a single algorithm, which leads to poor estimation where multiple numerous sources and sinks are present, typical for inland and coastal water environments.  The BioGeoLakes team has been working on an absorption-based approach was used to determine surface particulate organic carbon based on the specification of local POC absorption characteristics of dominant POC sources; phytoplankton or detritus based. This specification was made using a new particulate organic matter index, which was tested across a range of modelled and real lake/coastal conditions. Based on remote sensing reflectance in four wavebands, the model provided a good separation of organic particulate types and a good estimate of organic particulate concentrations in shallow lakes in the Yangtze River valley and estuary. These study lakes include Taihu, Chaohu and Poyang while work in the coming year will include a large number of smaller lakes in the Valley using the OLCI/Sentinel-3 satellite data. The approach shows a good potential to quantify particulate carbon dynamics in ecosystems where multiple organic carbon sources are present

As the second largest freshwater lake of Yunnan Province,Erhai Lake is an important drinking water source in Dali. In recent decades. With the rapid development of economy, the impact of human activities on Erhai is becoming increasingly prominent. Water quality deterioration and eutrophication led to the occurrence of cyanobacterial blooms and affected the normal ecological function of lakes. Based on multi-source remote sensing data, this paper studies the spatiotemporal distribution of cyanobacteria in Erhai lake from 1999 to 2016 and analyzes the effects  of both meteorological and human activites on the cyanobacteria. By combining the results of random forest classification with the results of DMSP(Defense Meteorological Satellite Program-the Operational Linescan System) nightlight remote sensing images, the method decision tree was used to extract the new building land in Erhai Basin. Remote sensing monitoring of cyanobacteria in Erhai sea, extracted by Landsat TM, ETM + and Sentinel 2A, shows that from 1999 to 2016, there was an increasing trend in the scale and duration of water blooms. Based on the analysis of the correlation between the scale, duration, frequency and first occurrence time of water and the new building rate, it is found that since 2003, the algal bloom frequency of Erhai has been positively correlated with the growth rate of the building, especially in the double porch and Taoyuan Town. For short-term effect factor researches, we obtained the data of cyanobacterial blooms in Erhai Lake in 2013 and analyzed the effects of temperature, sunshine, precipitation, air pressure and wind, we summarized the meteorological characteristics when blooms breaking-out, that is the blooms often occurred when the sun shines again after rain, the strong sunshine and higher daily temperature variation were the main factor of blooms, low wind speed and lower air pressure were in favour of the formation of blooms.

Biodiversity stakes within Yangtze watershed are very important at national level but also international ones. These very rich ecosystems, being key wintering areas for many waterfowl of SE Asia, are suffering from rapidly changing environments due to human activities. It’s crucial to understand what the key factors and their effects are. As data within large spatial and temporal scales are difficult to get, remote sensing and spatial analysis technology turns out to be a useful tool to access information. Coupling environmental information obtained from remote sensing and biological and ecological data could help understand what factors are driving observed phenomena. Studies are on progress within the Yangtze River basin to understand the link between the lakes water surfaces and the date of arrival and departure of some bird species during migration. Being the eldest of the Copernicus program, the Sentinel -1 mission has been imaging the Earth since April 2014 using a C-band Synthetic Aperture Radar. The high temporal and spatial resolutions (12 days at the equator, 10x10m pixel spacing in full resolution GRD) allow to create dense and accurate time series. Standing water has a distinguishable signature using radar wavelengths, which are very rarely disturbed by cloud coverage. This makes the use of radar imagery and thus Sentinel-1 more than relevant for the study of water body dynamics. However data quality comes with the price of high data volume. As these data flows are exponentially increasing, the use of “cloud computing” becomes more and more appealing. It allows to process information without downloading teraoctets of data. Google EarthEngine is an interface proposing such solution. The extensive catalog of available data allows advanced processing over large amount of data without being limited by local computational capacities. The general aim is to use the backscatter GRD Sentinel-1 images to extract water surfaces of lakes of interests in the Yangtze River basin for years 2015, 2016, 2017 and 2018, and then put them in relation with ecological data. The first intermediate objective was to export monthly aggregated water surfaces for above years and for the months of January, February, July, November and December. A total of 513 images, distributed among 227 dates were processed. The water class was created using a simple k-mean algorithm and corrected with a DEM, considering standing water presence possible only on slopes inferior to 5°. In a second time, each Sentinel-1 GRD image is processed independently and the water extents are extracted. This captures faster dynamics, but classifications are more difficult to conduct due to noisier signal compared to a multi date composition. The final time series is composed of 340 images in 114 dates, from October to December for the years 2016, 2017 and 2018. The use of a k-mean algorithm makes the pre-processing step almost inexistent and keeps the processing time within minutes; the most time consuming step is the download of the final binary rasters. These time series of water extraction, put in relation with bird species presence, will allow to study if water levels in studied lakes have an impact on the arrival and departure dates of the flocks. Radar signal is by itself a very powerful tool for water classification and the Sentinel-1 mission makes access to such products very easy. Even though confusion exist between water and some smooth surfaces such as roads, airports or desert areas during classification, the amount of data available is enormous and the use of cloud infrastructures like Google EarthEngine makes computing significantly faster and easier, allowing to study large areas for a long period of time. This allows to generate the water dynamics for more than 80 lakes of the Yangtze watershed, from the very large Poyang Lake to smaller ones such as Wuchang, Shengjin or Baitu Lakes. This study provides the first analysis of the water dynamics of these keys lakes in terms of biodiversity with such a time frequency.

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed: (i) The phase decorrelation caused by heavy vegetation can be weakened by use of SAR imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced InSAR time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry (RSSI) techniques; (ii) Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The Generic Atmospheric Correction Online Service (GACOS) developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis; (iii) The geometric distortions such as shadows and layovers can be pre-analyzed using an external DEM for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM; and (iv) Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. Ground-based radar, Lidar and GNSS) to develop an automated landslide detection and monitoring system. It is hoped that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.

We used SBAS (Small BAseline Subset) method to have obtained the cumulative surface deformation at some high coherent points for different time (each 12 days interval from September 10, 2017 to June 1, 2018.) after the 6th nuclear explosion of Democratic People’s Republic of Korea (DPRK) in the 17 km*22 km range of the center of this explosion event. These measured points are aggregated into 14 sets according to their spatial neighbourhood. The cumulative deformation of each set is computed by weighted averaging the deformation of each points inside the set according to their coherence. The relationship of the cumulative deformation of different sets with time processes is fitted by using Weibull model. Furtherly, the spatial analysis of the maximum vertical deformation has been carried out, it has been determined that the driving force of the sink was the gravity of the upper rock and itself which became soften and crisp under the action of high temperature and high pressure released from the nuclear explosion. The influencing factors are modeled and analyzed. The results show that by using SBAS-InSAR, the deformation process in the thermal radiation aftereffect stage of the 6th nuclear test could be effectively observed. Surface uplift still existed near the epicenter during about 10 days after the explosion, and then began to sink. The sinking rate and total sinking amount are different in different places. Meanwhile, the phenomenon of subsidence slowing down or even uplifting caused by freeze-thaw of water in underground rock in winter has been observed. After May 24, 2018, deformation began to rise due to the government of DPRK bombed the entrance of the nuclear facilities. The results of modeling analysis are as follows: 1) The InSAR data acquired in short revisit period can be used to observe the deformation process after the DPRK‘s 6th nuclear test and the freeze-thaw deformation process of rock crevice water in winter and spring. 2) In the thermal radiation aftereffect stage of the DPRK’s sixth nuclear expolsion, the surrounding rock has been softened under high temperature and high pressure, then the surrounding metamorphic rock was compressed under the action of own gravity and began to sink. This time-varying process can be model with Weibull function. 3) Considering the factors such as the layer thickness of metamorphic rock and the distance from epicenter, modeling the spatial distribution relationship of maximum cumulative vertical deformation. The following results has been drawn: the maximum vertical impact distance of the explosion from epicenter is about 2000 meters, and the deformation coefficient of the metamorphic rock is about 7*10-5, the statistical fitting degree is about 0.8, and the confidence closes to 1.

On 21 September 1999, the Mw 7.6 Chi-Chi earthquake, one of the largest inland earthquakes in Taiwan happened and struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. Revisiting this earthquake with a range of earth observations will allow better understanding of regional fault properties. ERS images from the descending track 232 and covering the period from 21 January 1999 to 28 October 1999 were interferometrically processed using the ESA open-source software SNAP to investigate the co-seismic deformation. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experienced the main deformation in this event, is densely vegetated resulting in low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes in the footwall which is equivalent to a surface displacement of up to approximately 30 cm. In order to obtain observations of the hanging-wall, Cosi-Corr software was used to correlate pre and post SPOT optical images. In addition to these two datasets, GNSS and leveling data were also used. PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, was used to determine the fault geometry and the slip distribution. Firstly, the relative weights of the four datasets were determined using the generalized Akaike’s Bayesian Information Criterion (gABIC). Secondly, the Particle Swarm Optimization (PSO) was utilised in the geodetic modelling to determine an optimal uniform model with 4 fault segments. Thirdly, a joint inversion of InSAR and geodetic data (SPOT, GNSS and leveling) was realised to estimate the slip distribution. These datasets enabled us to get information about the hanging-wall of the fault and to improve the modelling.

Tropospheric phase delays (TPDs) are a dominating error source in InSAR measurements.  External atmospheric observations from, e.g., GNSS (Global Navigation Satellite Systems) have been used to correct the effects of TPDs on InSAR measurements.  The spatial and temporal resolutions of such external data are however often not enough to accurately estimate the TPDs.  We propose a multi-temporal InSAR data processing model that jointly estimates TPDs, ground deformation, and residual topographic errors. The spatial variability of the relationship between TPDs and topographic height is considered by using localized estimation windows formed according to height gradients. We demonstrate the performance of the proposed method by using both simulated and real datasets from ALOS/PALSAR and Sentinel-1 images.

Over the last few years, several techniques have been developed for monitoring dam displacements and water surface levels. The use of Global Navigation Satellite System (GNSS) allows us to determine the displacements of a dam, located in southern Italy, along the orthogonal direction, while remote sensing techniques are used to retrieve the reservoir levels. The latter have been evaluated by using different strategies involving the use of a consistent dataset of optical and Synthetic Aperture Radar (SAR) images with different spatial and radiometric resolution. Initially, a preliminary comparison between the water’s edge and the existing contour lines and the use of unsupervised classification have been tested. Subsequently, two other Object-Based Image Analysis (OBIA) were performed on the dataset, one based on the use of four similarity indices, the other based on the evaluation of the distance between the water’s edge and the contour lines. The dam displacements were retrieved using the static positioning involving a GNSS receiver on the top of the dam and a Continuously Operating Reference Station (CORS), approximately 30 km away. Measured displacements over the dam and the surrounding area have employed Interferometric SAR (InSAR) techniques which have been evaluated, using different Multi-Baseline Construction methods applied to Sentinel-1A TOPS-SAR dataset to test the accuracy of the techniques over extra-urban areas. Preliminary results show that the behaviour of the dam, in terms of displacements, is related to reservoir levels but also to meteorological effects.

3D SAR Tomography (TomoSAR) [1-4] and 4D SAR Differential Tomography (Diff-TomoSAR) [8-14] exploit multi-baseline SAR data stacks to create an important new innovation for SAR Interferometry, to sense complex scenes with multiple scatterers mapped into the same SAR range cell. In addition to 3-D shape reconstruction and resolving deformation in complex urban/infrastructure areas [2,4], and recent cryospheric ice investigations [5], emerging tomographic remote sensing applications include forest scenarios [3,6,7], e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, often these scenes are characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to enhance characterisations of temporal decorrelation, orbital, tropospheric and ionospheric phase distortion. We report here on 3D imaging (especially of vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China. A new TanDEM-X 12m DEM is first used to assist co - registration of all the data stacks. Then, orbit baseline estimation is introduced. Atmospheric correction is assessed using a weather model with inputs derived from ERA-I and GACOS which are compared alongside ionospheric correction methods to remove ionospheric delay. The Compressive sensing (CS) TomoSAR method with the TanDEM-X 12m DEM is described in order to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract tomograms (imaging), their 3D positions and motion parameters (deformation). Examples will be demonstrated of 3D TomoSAR imaging results over Dujiangyan Dam, Sichuan, China as well as sample datasets from the ESA BioSAR 2008 L band data in Sweden (forest) and ALOS L band data in San Francisco Bay (urban building and bridge). This work is partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL. [1] A. Reigber, A. Moreira, “First Demonstration of Airborne SAR Tomography using Multibaseline L-band Data,” IEEE TGARS, 38(5), pp.2142-2152, 2000. [2] G. Fornaro, F. Serafino, F. Soldovieri, “Three Dimensional Focusing With Multipass SAR Data,” IEEE TGARS, 41(3), pp. 507-517, 2003. [3] M. Nannini, R. Scheiber, R. Horn, “Imaging of Targets Beneath Foliage with SAR Tomography,” EUSAR’2008. [4] F. Lombardini, F. Cai, D. Pasculli, “Spaceborne 3-D SAR Tomography for Analyzing Garbled Urban Scenarios: Single-look Superresolution Advances and Experiments," IEEE JSTARS, 6(2), pp.960-968, 2013. [5] L. Ferro-Famil, C. Leconte, F. Boutet, X. Phan, M. Gay, Y. Durand, “PoSAR: A VHR Tomographic GB-SAR System Application to Snow Cover 3-D Imaging at X and Ku Bands,” EuRAD’12. [6] F. Lombardini, F. Cai, “3D Tomographic and Differential Tomographic Response to Partially Coherent Scenes,” IGARSS’08. [7] M. Pardini, K. Papathanassiou, “Robust Estimation of the Vertical Structure of Forest with Coherence Tomography,” ESA PolInSAR ’11 Workshop. [8] F. Lombardini, F. Cai, “Evolutions of Diff-Tomo for Sensing Subcanopy Deformations and Height-varying Temporal Coherence,” ESA Fringe’11 Workshop. [9] F. Lombardini, “Differential Tomography: A New Framework for SAR Interferometry”, IEEE TGARS, 43(1), pp.37-44, 2005. [10] Xiang, Zhu Xiao, and Richard Bamler. "Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm." In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp. 17-20. IEEE, 2010. [11] F. Lombardini, M. Pardini, “Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data,” IEEE TGARS, 50(4), pp.1117-1129, 2012. [12] F. Lombardini, F. Viviani, F. Cai, F. Dini, “Forest Temporal Decorrelation: 3D Analyses and Processing in the Diff-Tomo Framework,” IGARSS’13. [13] Tebaldini, S., & Rocca, F. (2012). Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 232-246. [14] Huang, Y., Ferro-Famil, L., & Reigber, A. (2012). Under-foliage object imaging using SAR tomography and polarimetric spectral estimators. IEEE transactions on geoscience and remote sensing, 50(6), 2213-2225.

On October 11, 2018, a landslide occurred at the junction of Baiyu County, Ganzi Prefecture, Sichuan Province, and Boro Township, Jiangda County, Changdu County, Tibet, resulting in the breakdown of the Jinsha River and the formation of a barrier lake. On November 3, 2018, the secondary landslide occurred at the original landslide site of Baige Village, Boro Township,which endangers the lives and property of people in Baiyu County, Batang County, Derong County and other downstream areas of Ganzi Prefecture, and poses a threat to many hydropower stations. After the landslide, we collected and processed the Planet optical satellite images (with resolution of 3 m) from 29 August 2018 to 5 December 2018, and constructed the time series of landslide spatial distribution. The change of Baige landslide at this stage is analyzed. According to the Planet satellite images, there are obvious sliding signs in this area for a long time. The phenomenon of rock strata falling off and baring has appeared in the mountain area. The deformation before and after the landslide is measured to obtain the total deformation of the landslide. Meanwhile, we collected sentinel-1 and ALOS-2 satellite data and used D-InSAR technology to analyze landslide deformation information. For the incoherent region caused by rapid deformation, the Pixel Offset Tracking (POT) technique is introduced to analyze the deformation information of the deformation body edge. The results obtained from the two kinds of satellite data can better reflect the continuous deformation of the slope at an earlier stage. In the early stage of occurrence, the deformation characteristics of the whole slope of the landslide body are very obvious, and the deformation magnitude and range have increased significantly. Comparing with the high-resolution optical satellite landslide, it is found that the cumulative deformation is consistent with the deformation measured by optical remote sensing. After the secondary landslide, we also collected COSMO-SkyMed satellite data and monitored the landslide continuously. The results of deformation monitoring show that the trend of deformation in the landslide area decreases from November 8 to 23. The results of deformation monitoring can monitor the development and movement of landslide, and provide information support for landslide emergency response. To avoid new landslides occurring in the upper and lower reaches of Baige landslide in Jinsha River Basin, Sentinel-1A data was used to survey and monitor the potential hazard points. 5 km buffer is used to extract the coherent point targets along the Jinsha River, and high-resolution optical image validation is used to interpret and verify the hidden point areas with obvious sliding signs. In the follow-up, in view of these key hidden dangerous areas, we will continue to use time-series SAR images to carry out key monitoring work, so as to achieve real-time dynamic monitoring of the latest deformation information of landslide hidden dangerous points. We thank Beijing Global Nebula Remote Sensing Technology Co., Ltd. and Beijing Vastitude Technology co.,ltd for providing ALOS PALSAR, COSMO-SkyMed and Planet optical data. ESA is acknowledged for providing Sentinel-1 data.

It is well known that ground moving target indication (GMTI) using SAR image is based on the differences of SAR data characteristics between moving target and stationary background cluster. In an along-track interferogram, the phase of background cluster should be 0 while that of moving target not be. Therefore, GMTI could be performed by utilizing along track inteferometry (ATI) technology. However, for the real interferometric SAR images, there are many factors affecting ATI phase, which make the phase of most stationary background objects interfered rather than zero, resulting in interferometric phase confusion between moving and stationary targets. That makes it difficult to effectively indicate the moving targets from cluster by using ATI phase information alone. In the past decades, it has become one of the research trends to comprehensively utilize the phase and amplitude of ATI for GMTI. Combining of constant false alarm rate (CFAR) and ATI is considered to be a promising method to improve the detection rate, referred to as ATI-CFAR method. Gao at al.(2015) proposed an ATI-CFAR method to furtherly improve the detection accuracy. Unlike a general ATI-CFAR method, it adds two steps to the processing flow: the coarse detection for purified background cluster before estimating parameters of cluster distribution model; and the filtering for interferometric amplitude and phase after ATI-CFAR detection. This method has been validated in their research of airborne SAR GMTI. Applying it for TerraSAR-X GMTI, however, the ATI phase threshold is easy to be overestimated, which leads to a large number of missed detection and even unable to detect moving targets. In this paper, Gao’s method has been improved in two aspects, which are mainly presented in: 1) introducing a priori knowledge about vehicle velocities into the estimation of interferometric phase threshold, so as to improve the detection rate of moving targets; 2) using the graphic analysis method for the proportion of strong scattering pixels in full-aperture image to make the estimation of the interferometric amplitude threshold more intuitive. The main goal of this paper is to test the ability of detecting moving vehicle using ATI-CFAR and TerraSAR-X data. Based on the above improved ATI-CFAR method, a GMTI experiment is carried out on a section of Beijing's North Fifth Ring Road. TerraSAR-X data with DRA mode, including 1 full-aperture and 2 sub-aperture SAR images, was acquired on November 30, 2015. In-situ information related to the moving vehicles on target road was obtained through two ways: one is, information including the number, type and speed of vehicles, acquired by the ground video-recording of the testing area synchronized with TerraSAR-X satellite flying over; and the other is, the average speed of vehicles on the testing road, collected via navigation service by Baidu Company of China. The detection area in the SAR image, which is located in the Olympic Park in the south of the target road, that was determined by the offsetting in the azimuth direction based on the real vehicle velocities. By comparing the two kinds of speeds derived from ATI phase and offsetting in the azimuth direction, the 14 among 16 moving targets detected are considered to be reasonable vehicles, and their average speed is accordingly comparable with in-situ vehicle velocities both from video-recording and Baidu. Then the detection rate is up to 70%, and the correctness of detection is about 88%. The experimental results show that the improved ATI-CFAR method can effectively detect moving vehicles in the TerraSAR-X images. The authors would like to thank German Aerospace Center (DLR) for providing the TerraSAR-X DRA data（ATI_TRAF6781）.

Abstract：It is an effective way to reduce casualties by obtaining earthquake-induced building damage information accurately and rapidly. However, traditional methods mainly depend on in-depth field investigation to obtain seismic disaster information, which have some shortcomings, such as time-consuming, heavy workload and poor timeliness. Comparing to traditional methods, Synthetic Aperture Radar (SAR) remote sensing overcomes the above shortages, playing an important role in disaster assessment by means of its all-day and all-weather capability. European Space Agency (ESA) provides Sentinel-1A SAR data which are widely used to derive global disaster information. The 2016 Italy earthquake, in which a large number of buildings collapsed and 299 people died was taken as study case of this paper. Three Sentinel-1A VV and VH dual-polarization images are obtained. Two of them are pre-event and one is post-event. The method to detect building damage has three steps as follows. Firstly, intensity and coherence are derived from data preprocessing and are calculated into normalized difference respectively. In order to fully use polarization features, combine VV and VH to obtain mean of normalized intensity difference and of normalized coherence difference. Secondly, this paper selects some samples of damaged and intact buildings randomly, acquiring corresponding mean of normalized intensity and coherence and built a new discriminant function. It can classify all collapsed and intact buildings of the study area by setting a threshold value. Finally, validation data are derived from visual interpretation of high resolution optical images and are used to evaluate the accuracy of the method.   The result reveals that the method can evaluate damaged and intact buildings accurately and accuracy of the method is up to 81%. However, the result displays two anomalies because a lot of cars and tents for rescuing are together, which are taken as damaged buildings. The method can satisfy the timeliness of post-earthquake disaster assessment and accurately evaluate the spatial distribution of damaged and intact buildings, which has great potential in guiding rapid rescue. Key words: Building damage assessment; Dual polarization; Sentinel-1A; SAR; Discriminant function  

Since the launch of Sentinel-1 (S1) satellite in 2014, the public freely available Sentinel-1 SAR data has been widely used in ground deformation mapping. The Interferometric Wide-swath (IW) mode, as the main operation mode of Sentinel-1 mission, utilizes the Terrain Observation by Progressive Scan (TOPS) technique to achieve wide-swath coverage and short revisit interval at the cost of lower azimuth resolution and smaller Doppler bandwidth. This leads to lower accuracy of along-track measurements using conventional Multi-Aperture Interferometry (MAI) or Offset Tracking techniques, which limits the capability of Sentinel-1 TOPS data in three-dimensional (3D) monitoring of land subsidence. However, the large squint angle diversity of ~1° between consecutive bursts of TOPS mode provides an opportunity of using modified MAI / Spectral Diversity (SD) techniques in burst overlap regions to retrieve along-track displacements with much higher accuracy. This method, referred to as Burst Overlap Interferometry (BOI), has been applied to measure large-scale earthquakes with metre-level displacement rate, but has not yet been assessed in time series analysis of slow deformation. This study aims to evaluate the use of Burst Overlap Interferometry with Sentinel-1 time series TOPS images for along-track measurements of millimetre-level land subsidence induced by land reclamation and recent subway construction in the city of Shenzhen, China. The feasibility of reconstructing the along-track displacement field in the non-overlap regions between consecutive bursts by interpolation methods will be investigated in the case of small-scale and slow surface motion. This work has been supported by the National Key Research and Development Program of China (Project ID. 2017YFB0504200) and National Natural Science Foundation of China (Project ID. 41801360). This research is linked to the ESA-MOST DRAGON-4 Project #32244: Earth observations for geohazard monitoring and risk assessment.

Landslide monitoring activities are of paramount importance for landslides hazard and risk assessment. Ground-based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of the highly dynamic target with sub-millimetric accuracy. GBIR has already been successfully used to identify and classify landslides, that can be considered complementary or alternative to space-borne SAR interferometry for terrain monitoring. The Xuyong landslide occurred at 16:20 (Beijing time, UTC+8) on the 9th of the December 2018 in Xichuan, China. In this paper, terrestrial radar interferometry used to monitor the Xuyong landslide, GAMMA Portable Radar Interferometer (GPRI), was developed by Gamma Remote Sensing. In this monitoring campaign, the GPRI-II monitoring was carried out five hours and 43 SLC (single-look complex) images were acquired from 2018-12-12 11:30 to 2018-12-12 16:30 (Beijing time, UTC+8). We use a continuous mode and apply the direct integration method to integrate the 42 interferograms formed by processing each SLC images with the subsequent one. The time-series analysis involves the following steps: 1) Select a reference point located in a stable area. A set of points can be chosen instead of a single point. 2)Calculate 42 interferograms phases relative to the reference point. If a set of reference points are chosen, the last term of the equation is the mean phase computed over the reference points. 3)Integrate phases over time. The result shows that the displacement at the top of the landslide was very obvious. The maximum measured displacement of the landslide was up to 28mm/d towards the radar during this observation period. The GPIR can observe and recognize the deformation zone in a short time and play an important role in investigating and evaluating landslide stability. Keywords: Landslide monitoring; GPIR; Time series analysis; Investigate and evaluate

Abstract: Since July 29, 2018, several earthquakes with magnitude 5.5 or above have occurred on Lombok Island, Indonesia, including two earthquakes with magnitude 6.9 on August 5 and 19, and several hundred aftershocks caused by strong earthquake sequences. The continuous occurrence of strong earthquake sequences caused hundreds of deaths, nearly 10,000 people were injured, and thousands of buildings were damaged. As Lombok is a tourist area, the earthquake on July 29 did not cause much damage. The island's tourism plan was still in operation, causing a large number of casualties after the August earthquake. After the occurrence of strong earthquake sequence, many landslides occurred in Rinjani volcano of Lombok Island, and the landform of the whole island changed. Indonesia is located in the collision area of the Pacific plate, the Indian Ocean plate and the Eurasian plate, sandwiched between the circum-Pacific seismic belt and the Eurasian seismic belt. The crustal activity is intense and the seismicity is frequent in recent years. The Indian Ocean earthquake near Sumatra at the end of 2004 triggered a massive tsunami that killed more than 200,000 people, and the number of deaths in Indonesia alone reached 170,000. Indonesia is located in the Pacific Volcanic Seismic Zone, and the Indian Ocean near the eastern coast of Indonesia is the junction of three major plate tectonic zones. The three plates are Sunda plate in the east, India plate in the northwest and Australia plate in the southwest. Fractures occur at the concentration of the Indian and Burmese plates. The earthquake in Indonesia occurred further south because the northeastern end of the Australian plate fell below the Sunda plate and, as a result, fell to the lower part of Central Java Province, forming the so-called submerged zone. The downward sliding of the lower plate in the submerged zone usually triggers earthquakes. Experts pointed out that the earthquake in Indonesia was caused by the compression of the two plates in common motion, and the compression of the smaller fault lines behind the submergence lines of the two plates, resulting in the lateral rupture of the plates, which triggered the earthquake. In this paper, 20 SENTINEL-1A wide-band SAR data are processed by differential interferometry of synthetic aperture radar (D-InSAR), and the co-seismic deformation field of each earthquake in the swarm is obtained. At the same time, Stacking time series analysis and processing were carried out to obtain the results of time series deformation of Lombok Island from the first earthquake in July 2018 to October 2018. The results show that the earthquake swarm caused obvious crustal deformation of Lombok Island in Indonesia, and there are volcanoes in Lombok Island area where the earthquake occurred. The occurrence of strong earthquake swarms destroyed the stability of Rinjani volcano, and landslides occurred continuously. Deformation around the mountain is obvious, with the island falling by 5 to 15 cm, while the surface uplift near the epicenter in the north is about 30 cm. The surrounding areas of Lombok and Rinjani are very unstable. Due to the special location, it is necessary to conduct long-term sequence observations on Lombok and its surrounding islands in order to prevent disasters and reduce disasters. Keywords: Lombok Island Strong Earthquake Swarm; Rinjani volcano; InSAR; Time Series Analysis

 On October 11 and November 3, 2018, two large-scale landslides occurred in Baige Village, Polo Township, Jiangda County, Changdu City, Tibet. The high-speed sliding body rushed into the Jinshajiang River and formed a barrier dam. The barrier lake formed by two sliding failures poses a serious threat to the upstream and downstream areas, which has attracted wide attention. In order to evaluate the hazards of landslides, several questions must be considered: when and where will landslides occur again? How big will the landslide be? How fast and how far do they move? What areas will landslides affect or destroy? How often do landslides occur in a particular area? The answers to these questions require accurate mapping of landslide deformation and prediction of the occurrence of landslides, as well as information on how to avoid or mitigate the impact of landslides. This paper will focus on monitoring the stability of Baige landslide with ground-based radar and provide technical support for subsequent landslide risk assessment. In this paper, the ground-based radar system will be used to obtain the deformation rate and stability of the slope after the landslide occurs. The results show that there is a very large landslide signal on the landslide surface, and the maximum deformation is more than 200 mm/day. At present, the slope is in a relatively stable state, but attention should be paid to the slope stability in rainy season.

As a unpredictable natural disaster, earthquake is common problem in the world. The scenario is a panoramic description of emergencies combined with historical cases and risk simulations. Scenario is different from risk assessment, it is not focus on the local losses, but the risk systems. Through the comprehensive analysis of complex disasters, the corresponding strategy system is formulated. Because the seismic scenario covers almost all complex systems, such as the natural environment, artificial environment, and social environment. Therefore, the large amount of parameter extraction efficiency necessary for seismic scenario simulation is an important factor that restricts the development of the field. Because remote sensing data has the characteristics of short revisit period, wide field of view, and high data accuracy, this research combines remote sensing and geographic information system (GIS) to simulate an earthquake disaster scenario in Beijing. Firstly, a batch of high-quality remote sensing data of Landsat, which were taken in 1977,1983, 1988, 1993, 1998, 2003, 2008, 2013 and 2017, were selected for change detection, and the age of the buildings in the study area were extracted. Secondly, the historical images of GF2 and GeogleEarth were used to extract building height parameters based on the architectural shadow method, and then the relationship between the age, height and structure of the regional building was established by the survey sample to assess the distribution of building structures in the study area. Thirdly, the construction parameters of the study area were input into the seismic damage factor model to simulate the building damage, and combined with the distribution data of economy, population, lifeline system and key targets by GIS to cross-analyze the seismic impact. In summary, the combination of remote sensing technology and GIS greatly reduces the extraction efficiency of impact factors for complex disaster systems in large regions, and enables spatial analysis and process simulation of seismic impacts. It can providing clear targets for regional earthquake preparation. Keywords: Seismic Scenario; Disaster system; Geographic information system (GIS)

On 21 September 1999, the Mw 7.6 Chi-Chi earthquake, one of the largest inland earthquakes in Taiwan happened and struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. Revisiting this earthquake with a range of earth observations will allow better understanding of regional fault properties. ERS images from the descending track 232 and covering the period from 21 January 1999 to 28 October 1999 were interferometrically processed using the ESA open-source software SNAP to investigate the co-seismic deformation. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experienced the main deformation in this event, is densely vegetated resulting in low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes in the footwall which is equivalent to a surface displacement of up to approximately 30 cm. In order to obtain observations of the hanging-wall, Cosi-Corr software was used to correlate pre and post SPOT optical images. In addition to these two datasets, GNSS and leveling data were also used. PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, was used to determine the fault geometry and the slip distribution. Firstly, the relative weights of the four datasets were determined using the generalized Akaike’s Bayesian Information Criterion (gABIC). Secondly, the Particle Swarm Optimization (PSO) was utilised in the geodetic modelling to determine an optimal uniform model with 4 fault segments. Thirdly, a joint inversion of InSAR and geodetic data (SPOT, GNSS and leveling) was realised to estimate the slip distribution. These datasets enabled us to get information about the hanging-wall of the fault and to improve the modelling.

One of the objectives of Dragon 4 Project 31470_2 is the investigation of upscaling and adaptation models and algorithms for 3D multi-functional and scales forestry inventory by using airborne data and products as basis. The main achievements acquired during the last years could be summarized as follows: (1)   Improvement of forest carbon flux simulation by incorporating remotely sensed model with process-based model. The improved simulation of forest carbon fluxes was conducted by incorporating a remote-sensing-based MODIS MOD_17 GPP (MOD_17) model with a process-based model (Biome-BGC) using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17) model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis. Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the pre-selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC) and tree ring measurements than the original model did. To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF) was used to assimilate Global LAnd Surface Satellite (GLASS) LAI products into the calibrated Biome-BGC model. Finally, the calibrated and data-assimilated model was applied to simulate the large scale and long-term forest carbon fluxes. (2)   Estimation of forest structure parameters by using multi-source remotely sensed data As important forest parameters, the leaf area index (LAI), canopy closure (CC), forest height (h) and forest above-ground biomass (AGB) are indispensable for ecological process models and carbon cycle models. Therefore, the accurate estimations of regional or global scale forest parameters are of great significance for a deep understanding of inherent laws of environmental change. With the diversification of remote sensing technology, the single-source remote sensing data has been unable to meet the application demand of the region and high precision. Recently, a large amount of effort has been devoted to the joint utilization of multi-source remote sensing data for the estimation of regional forest parameters. However, the regional application, topographic influence, and mixed pixel decomposition have become the three major scientific problems in the joint retrieval of the multi-source remote sensing data. In response to these three problems, this study has proposed methods for the prediction of the mountain forest height, the canopy closure, and the effective leaf area index (LAIe). Furthermore, the forest AGB model was constructed based on vegetation indices, topographic indices and these structure parameters with physical significance. The research includes the following three main aspects: 1) Predicting forest height using the GOST model and multisource remote sensing data for sloping terrains. 2) Predicting canopy closure and effective leaf area index using the Li-Strahler geometric-optical model and multisource remote sensing data. 3) Multi-parameter synergic retrieval of forest AGB. (3)   Monitoring forest change by integrating active and passive remote sensing data Accurate and rapid acquisition of forest land change information is critical for the study of ecological environment changes and forest management planning. At this aspect, multi-temporal  Sentinel-1 Synthetic Aperture Radar (SAR) and Chinese Gaofen-1/2 (GF-1/2) optical images were applied to detect the forest changes. For SAR images, the difference image was extracted by using the improved log-ratio method. The Bayesian theory based minimum threshold error adaptive threshold selection method (Kilter and Illingworth, K&I) was used to segment the threshold and extract the change areas. For GF images, the difference image was extracted by using the multivariate change detection (MAD) algorithm, and the maximum inter-class variance method (OTSU) was used to segment the threshold and extract the change areas. Finally, incorporating the change detection results of above two tests was conducted to determine the local forest land changes. The validation based the field survey showed that the incorporation of active and passive remote sensing techniques can efficiently and timely detect the forest land changes with high spatio-temporal resolution, due to high temporal resolution (12 days) of Sentinel-1 and high spatial resolutions (GF-1:2m, GF-2:1m) of GF-1/2 data. (4)   Modeling forest above-ground biomass dynamics using multi-source data and incorporated models Forest dominates the terrestrial carbon cycle and forest above-ground biomass (AGB) has been the critical index for carbon sequestration capacity. However, any individual method, such as ground-measurement-based method, remote-sensing-based method, and ecological model-based model, cannot efficiently describe the changing processes and driven mechanisms of forest AGB dynamics. Based on multi-mode remote sensing, time-space dynamic knowledge of forest ecological process, and continuous multi-disciplinary ground observation data, this project is planning to model spatial-temporal continuous, physical quantity-synergy forest AGB dynamics. Firstly, a highly accurate regional forest AGB product obtained by applying multi-mode remote sensing and scaling connection is used as the AGB basis. Then, the uncertainties of simulation of forest growth processes are alleviated by use of model-model and model-data fusion strategies. Finally, modeling of forest AGB dynamics is accomplished by combining forest AGB basis with succeeding dynamic forest growth processes, which taking the effects of tree mortality, forest disturbance into account. The methodology of spatio-temporal synergetic modeling of Forest AGB dynamic information proposed by this project, can explore the eco-physiological mechanisms of spatio-temporal pattern of forest AGB dynamics and the driven forces of natural and anthropogenic disturbances. Moreover, this methodology can extend the spatial and temporal dimensions of forest AGB dynamics and in order to precisely improve forest quality and promote the national ecological civilization.  Keywords: Forest above-ground biomass, carbon cycle, model coupling, data assimilation, spatio-temporal synergy  

Synthetic Aperture Radar Tomography (TomoSAR) is a microwave imaging technology to focus the illuminated scatterers in the 3D space, by jointly processing multiple acquisitions from parallel trajectories. TomoSAR has been applied with success to the 3D analysis of forested environments. In principle, TomSAR can be easily understood by considering that the availability of multiple flight lines allows the formation of a 2D synthetic aperture, which permits to focus the signal not only in the range-azimuth plane, as in conventional 2D SAR imaging, but also in elevation. Although the concept is straightforward, the application of TomSAR using spaceborne sensors is hindered by the fact that different baselines are usually acquired at time lags on the order of days, limiting the analysis to temporally stable targets (like urban scenarios). A possible way out of this blocking circumstance is the employment of single pass interferometers, as in the case of Tandem-X (currently operating) and possible future systems. Such systems achieve the 3D imaging capabilities by collecting a number of simultaneous interferometric pairs acquired by two satellites. The observed complex coherence corresponds to a particular vertical wavenumber of the imaged scene, depending on the interferometric baseline, i.e., the across-track distance between the two satellites. By collecting multiple pairs with varying interferometric baseline it is then possible to get multiple vertical wavenumbers, which allows the reconstruction of the vertical distribution of the backscattered power of the imaged scene through spectral estimation techniques. Such tomographic data acquired using spaceborne sensors are characterized by some specific features that may limit the performance of classical 3D focusing techniques. Such data are generally gathered into stacks of a limited number of images, having a coarse spatial resolution, and specific correlation properties. As a result, the available 2nd order statistical information is largely incomplete and lacks of the redundancy used by classical spectral analysis techniques to enforce a sufficient output signal quality. The achievable vertical resolution is, in general, extremely coarse, due to a limited spatial resolution of the individual SAR images and to a low-pass effect of the spectral interpolation techniques used to reconstruct the missing information. This contribution summarizes some solutions to these intrinsic limitations. The coarseness of the naturally available vertical resolution, obtained using classical Fourier focusing, is partially compensated with super-resolution techniques based on the processing of a reconstructed covariance matrix. An improved reconstruction of a positive semi-definite covariance matrix is achieved using an original multi-resolution technique which ensures a good conditioning of the estimated information all-along the evaluation process. The validity and usefulness of this approach in the polarimetric mode is assessed using simulated spaceborne data sets obtained from airborne ESA campaigns

Accurate and large-scale access to forest height information is of great significance for the fine management of forests, carbon cycle modeling and scientific research on climate change. Interferometric synthetic aperture radar (InSAR) data without or with very low temporal de-correlation is sensitive to the vertical structure of vegetation and is one of the most potential remote sensing technologies to map forest height in large area. It has been demonstrated by few studies that TanDEM-X InSAR data can be used to map forest height by applying RVoG model or InSAR water-cloud model if the terrain was not so steep, otherwise, descending and ascending InSAR data should be used together for generating one wall-to wall map of an interested region. However, we still need much more detailed investigations on this topic for area of steep terrain in order to apply them to practical mapping activities. So we established two test sites in the Northeast forest region of China: Chaozha forest farm in Genhe district and Wangyedian forest farm in Chifeng district. Chaozha test site is relatively flat, while Wangyedian test site is of steep terrain. Firstly, the performance of forest height inversion using the difference method (DIFF method, in short, taking the forest height as the difference between the DSM from Tandem-X and the DEM from LiDAR) and the SINC model based method (SINC method, in short, where SINC model is one simplified Random Volume over Ground model) was analyzed. Secondly, the effects of signal-to-noise ratio (SNR) de-correlation, spatial baseline, ground-to-volume scattering ratio and extinction coefficient on the estimation of SINC model were studied; Finally, the influence of terrain on the SINC model was investigated, and a threshold determination method was proposed based on Monte Carlo simulation and RVoG model, so as to provide a basis for masking the region severely affected by terrain and for doing multi-track data fusion further. We established a technical process for estimating forest height from space-borne InSAR data without temporal de-correlation under steep terrain conditions, which will provide very useful technique supporting for forest resources monitoring and forestry management activities.

Forest stand height is one of the most important parameters in forest inventory and has a close relationship with other parameters (such as DBH, biomass). Nowadays airborne laser scanning is considered the most accurate remote sensing method for forest height extraction. However, these airborne surveys are relatively expensive and there is a desire to identify more affordable options for collecting or updating this information. In this paper, ZY-3 satellite stereo images are used to derive a digital surface model, which together with a high-resolution digital terrain model (DEM) from airborne laser scanning (ALS) to estimate forest stand height. Forest stand height derived from LIDAR is used as the reference for validation. The results show that ZY-3 stereo satellite images are suitable to extract forest stand height with reliable accuracy when a high-resolution DEM is available. 

I. INTRODUCTION   This paper proposes a decomposition technique that accounts the influence of coherent and incoherent double-bounce scattering mechanisms. In order to assess our physical understanding of the interaction between an emitted radar wave and a forested area, a man-made miniaturized RVoG-like scene is imaged. Consisting of a volume lying above a ground, this scene highlights the presence of ground/volume double-bounce and ground/trunk double-bounce.   II. Validation on in-situ data   An electromagnetic wave encounters four potential scattering mechanisms in a forest. The single-bounce on ground, double-bounce ground/trunk, double-bounce ground/volume and volume scattering.   The equivalent distance of a wave encountering a double-bounce is the distance of a single-bounce originating from the ground. Meaning that the double-bounce mechanism is considered as a ground response due to the fact that classical imagery algorithms will represent it on the ground. Taking all potential double-bounces along the volume, it follows that a projection of volume contributions will be located on the ground beneath the volume.   Bare soil contributions ks are therefore estimated by subtracting double-bounce contributions kst+ksv from total ground response, i.e. ks = kg − (kst+ksv).   PolTomSAR (Polarimetric SAR Tomography) acquisitions over a man-made miniaturized RVoG-like scene show that ground/trunk double bounce is coherent and that ground/volume double-bounce is incoherent. Existing decomposition techniques such as SKP (Sum of Kronecker Products) or HySKP (Hybrid SKP) introduced respectively in [1] and [2] are therefore able to separate the coherent double-bounce from the ground but the incoherent double-bounce remains.   The proposed approach to separate this incoherent double-bounce is to estimate volume contributions and subtract a portion of these contributions from the ground by using the Freeman decomposition for volume estimation [3].           REFERENCES   [1] S. Tebaldini, "Algebraic Synthesis of Forest Scenarios From Multibaseline PolInSAR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 12, pp. 4132-4142, Dec. 2009. doi: 10.1109/TGRS.2009.2023785   [2] M. Pardini and K. Papathanassiou, "On the Estimation of Ground and Volume Polarimetric Covariances in Forest Scenarios With SAR Tomography," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1860-1864, Oct. 2017. doi: 10.1109/LGRS.2017.2738672   [3] A. Freeman and S. L. Durden, "A three-component scattering model for polarimetric SAR data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 3, pp. 963-973, May 1998. doi: 10.1109/36.673687

See attached list of posters

It is presented the recent progresses of ocean surface winds and typhoons remote sensing including (1) GF-3 SAR ocean wind retrieval: the first view and preliminary assessment; (2) Preliminary analysis of Chinese GF-3 SAR quad-polarization measurements to extract winds in each polarization; (3) Assessments of ocean wind retrieval schemes and geophysical model functions used for Chinese GF-3 SAR data at each polarization; (4) Combined co- and cross-polarized SAR measurements under extreme wind conditions; (5) Sea Surface Wind Speed Retrieval and Validation of the Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory; (6) Top cloud motion field of Typhoon Megi–2016 revealed by GF-4 Images.

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

A new space-borne dataset of global ocean swell, called Environment Monitoring Services (CMEMS) “fireworks” Level-3 product, derived from Sentinel-1A/B Level-2 ocean swell spectra is presented and its performances are assessed. The Level-2 swells inverted from synthetic aperture radars (SAR) images are retro-propagated along the great circle and refocused at their remote origins (coinciding strong storms), producing a higher level product to describe the swell temporal and spatial evolution from origin until land across the oceans. The Level-3 “fireworks” are now operationally produced by Copernicus CMEMS. Here, we assess their performances using sentinel-1A/B wave mode data for the period from July 2016 to Nov. 2018, based on the  “virtual” buoy concept. Reference data are  in situ directional wave measurements from two different buoy networks:National Data Buoy Centre (NDBC) and Coastal Data Information Program (CDIP). Comparison results show a good agreement between Sentinel-1 Level-3 swells and buoy measurements, with root mean square error of 48 cm, 45.66 m and 21.21° for swell height, peak wavelength and direction, respectively. Influence of buoy network on the validation results are also examined, revealing better wave directional measurement accuracy for Waverider buoys used in CDIP than in NDBC network.

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

Swells from strong storms can spread over very long distances. Ocean currents alter this propagation, with the possible formation of constructive or destructive interference. This effect, still neglected in current models of atmospheric, oceanic, and even wave prediction, is often traced in current measurements, altimetry or even scatterometry / radiometry at medium and high resolution. Large-scale currents are indirectly measured by satellite. Since the small-scale currents are generally unknown, we propose to consider them as random in wave dynamics simulations. Specifically, the statistical spatial structure of these currents is inferred from large-scale currents through self-similar assumptions. The temporal correlations of the small-scale currents is neglected due to the short-time wave-current interaction. The dispersion ratio is modified and becomes stochastic. From there, we can derive and simulate the random dynamics of wave group the rays. Analytic and semi-analytic solutions have also been derived for simple – though realistic – cases. Our results not only improve wave simulation capabilities, but also bring new insights about the large wave’s developments at small scales and the wave-current effects on satellite measurements. At longer term, those type of random dynamics will bring new data assimilation procedures for joint wave-current estimations from space.

Nearly ¾ of the world’s mega cities are by the sea and almost 80% of the global population live within 100km from the coast. These numbers make very clear that coastal regions worldwide are not only socially important but also economically critical with e.g. harbours, fish farms and exploitation sites for natural resources (oil/gas rigs or offshore wind farms). Moreover the seaside is important for recreational activities and as a natural habitat for local marine life. With respect to the extreme importance of the coastline, constant monitoring of this region is compulsory. Just like the diversity of perspectives in the coastal areas, the diversity of maritime information is complex. Many parameters and layers of information are needed to obtain a comprehensive picture for a given application. Driven by the self-evident advantages of Earth observation methods to monitor large areas while keeping costs at a reasonable level, numerous methods have been developed and improved by the Chinese and German Dragon partners to extract maritime information from satellite-based sensors and will be outlined in the presentation. While most of the information is only available separately, the combination of different information layers is needed to generate a holistic maritime situation awareness. For this an integrated platform is needed to simultaneously visualize and generate a synopsis of different types of information, selected according to the respective application. We present a prototype of a web-based near-real-time information platform to combine information such as sea state, wind information, AIS messages, SAR-based ship detections and sea ice information to obtain a thorough maritime situation awareness. The system can be expanded for oil spill detection and other relevant information and can thus serve as a powerful decision support system for national or international authorities e.g. in catastrophy or disaster management. The platform can help uncover exceptional conditions or behavior and represents an important constituent in sustainable coastal management.

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

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

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

The HY-2B satellite is the second dynamic environment satellite in China. It was successfully launched on October 25th, 2018 with a sun-synchronous orbit at an altitude of ~970km. Repeat cycles of 14 days are planned for the first two years with oceanographic purpose and 168 days geodetic cycles will follow for the third year of the mission. The satellite is equipped with a Ku/C bands altimeter and the orbit is determined thanks to SLR, GPS. The HY-2B satellite altimeter provides sea surface height, significant wave height, sea surface wind speed, and polar ice sheet elevation. First of all, the description of instruments and the instrument parameters will be put forward briefly in this research. And then, the current status of the HY-2B products will be described in detail, including the measurement accuracy. Comparing with Jason-2 and Jason-3 satellite radar altimeters and on-site buoys, the objects of comparison include significant wave height, sea surface wind speed, and sea level anomaly and so on. It is found that the precision of HY-2B satellite radar altimeter secondary products reaches the same kind of satellite radar altimeter products in the world, and some products are better than Jason-2 and Jason-3 standard products.

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

High-resolution satellite earth observation data are available in large archives, as data collection increases the ability to inspect and investigate each scene becomes impossible due to the scale and quantity of observations. Computer assisted classification, segmentation and description of satellite data over aquatic bodies can provide invaluable information for focusing analysis to experts and the general public on everyday use of water resources.   Convolutional Neural Networks are capable of classifying and segmenting objects across thousands of images in a fraction of the time a human operator would require inspecting these images visually. These Deep Learning networks have previously been applied to classifying both land usage and land cover, they have been shown to be accurate using multi- or hyper-spectral data such as those collected by the Sentinel-2 MultiSpectral Instrument.   In this work, a training dataset consisting of coastal and in-land waters has been assembled from Sentinel-2 imagery covering multiple sites across North America, South Africa and China and extensively labelled to be compatible with Deep Learning methods. Convolutional Neural Networks developed and trained for natural image classification and segmentation have been extended and retrained through transfer learning to detect and segment the extents of Algal Blooms and River Plumes in the imagery.    Current Convolutional Neural Network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines.

Spaceborne SAR has been proved to be a valuable tool in measuring sea surface winds and waves in coastal waters. In this study we present evidence to show that 1) ships and ocean front have impact on SAR wind retrieval and 2) uncertainly wave retrieval exists under the hurricane conditions. We acquired a large number of Sentinel-1 SAR images in the Yangtze River estuary, China, where the ocean environment is very complicated due to the exchanges of coastal and Changjiang Diluted Waters. The root-mean-square error between SAR wind speeds and buoy measurements reaches up to 3.81 m/s. Analysis of quasi-synchronous SAR images and sea surface temperature (SST) observations shows that the main possible causes for such a large bias are the impact of ships or changes in the atmospheric stability induced by SST change across the ocean front on the sea surface backscatter signal received by radar. A change of 1 °C in SST at low wind conditions may lead to an error of 1~2 dB in the satellite observed normalized radar backscatter cross section (NRCS). The existence of ships at sea surface even results in a falsely high NRCS value. Since the accuracy of wind speed estimation from SAR is strongly dependent on the accuracy of the NRCS measurement, great cautions should be taken when generating or using SAR wind products. Consideration of the above-mentioned effects on the NRCS may improve the accuracy of the estimated wind speeds to a certain extent.   We also investigate the performance of the wave retrieval algorithm (PFSM) when it is applied for dual-polarization C-band Sentinel-1 SAR. SAR-derived significant wave height (SWH) and mean wave period (MWP) are compared with simulation results from the WAVEWATCH-III model. The validation shows a 0.69 m root mean square error (RMSE) of SWH with a -0.01 m bias and a 0.62 s RMSE of MWP with a -0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from Sentinel-1 SAR images. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively weak.

Within the context of coastal area management, that includes promoting sustainable economy, preserving biodiversity and ensuring population safety, the continuous and effective monitoring of the shoreline is primary need. Nonetheless, a rigorous definition of the shoreline is ambiguous to some extent since it is influenced by bathymetry, tide level, etc. and, in addition, shoreline position continuously changes due to urbanization, deforestation, accretion/erosion, etc. It was shown that remote sensing tools, including optical/microwave satellite sensors and aerial UAV surveys, are valuable information sources to provide systematic observations of the shores to be integrated with ground surveys, i.e., GPS measurements. The exploitation of spaceborne SAR measurements can improve optical-based shoreline extraction, which is affected by solar illumination and weather conditions. In addition, it was shown that polarimetric information provides extra-benefits for shoreline extraction purposes, i. e., the algorithms are more robust and accurate. Nevertheless, reliable pixel-wise land/sea separation is still a challenging task since the latter is hampered by the several SAR imaging and environmental effects that include inherent speckle noise, limited spatial resolution, bathymetry, high sea state conditions and coastal morphology.  In this framework, in this study, the applicability of patch-based filters to reduce speckle noise in polarimetric SAR imagery is investigated for shoreline extraction purposes. In fact, with respect to standard pixel-wise speckle filters, the patch-based approaches for speckle reduction exploit the measurements redundancy to look for similar local patches within the polarimetric SAR image. Then, according to a statistical-based similarity criterion, a speckle-reduced polarimetric SAR observable, i. e., coherency matrix, is obtained from which land/sea separation can be performed according to a given metric. Hence, in this study, the capability of the patch-based paradigm to be applied on polarimetric SAR images for speckle filtering is investigated and evaluated in terms of accuracy in the shoreline position. Selected showcases will be presented at the conference time to quantitatively evaluate the improvements in shoreline extraction accuracy performance.

SAR measurements have proven to be a very useful tool for tropical cyclone monitoring and forecasting applications. The sea surface wind maps derived from the SAR cross-polarized channel can provide fine-scale information about the tropical cyclone (TC) inner core. A hurricane morphology and sea surface wind vector estimation model (SHEW) based on measurements acquired by the C-band SAR onboard RADARSAT-2 has been recently proposed [1]. A limitation of this model is that it only deals with hurricanes of circular or elliptical-shaped eyewalls. In this study, a new parametric model, which uses SAR observations and allows for asymmetric description of the TC wind structure around the eyewall in storm centric coordinates, is developed. SAR observations from
 TCs in the North Atlantic and East Pacific basins are analyzed to determine the azimuthal and radial asymmetry typical in these mesoscale systems. The new asymmetric directional wind model adjusts the widely used Holland (1980) axis-symmetric model to account for the different azimuthal asymmetries of TC winds. The model will be tested against collocated NOAA hurricane hunter observations (i.e., dropsondes and the Step-Frequency Microwave Radiometer or SFMR) and its performance will be compared with other existing models, such as, the Holland [2], SHEW [1], and Olfateh [3] models. Showcases will also be presented to demonstrate the improvements related to the proposed model.  [1] Zhang, G., W. Perrie, X. Li, and J.A. Zhang. (2017), A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery, IEEE TGRS, 55(3), 1743-1751. [2] Holland, G. J. (1980), An analytic model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108(8), 1212–1218. [3] Olfateh, M., D. P. Callaghan, P. Nielsen, and T. E. Baldock. (2017), Tropical cyclone wind field asymmetry— Development and evaluation of a new parametric model, J. Geophys. Res. Oceans, 122, 458–469, doi:10.1002/ 2016JC012237.

Wind is a sustainable and alternative resource for producing energy and it has a good reputation of being a green form of electricity. Within this context, wind turbines are widely used at onshore and offshore sites to convert the energy of moving air into electrical power. For this reason, wind turbines are a critical infrastructure whose monitoring is an important issue for both economy and environment protection. Within this context, remote sensing can be an important tool to guarantee an effective and relatively cheaper monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution. The objective of this study is quantifying the added-value of polarimetric information to detect metallic targets. On this purpose, a very challenging scenario is considered that consists of mud flat area where wind turbines are present. To make the analysis fair, we selected detection algorithm that are able to work with both full- and partial-polarimetric information, i.e.; Polarimetric Notch Filter (PNF) and the change detection approach proposed in [1] and [2], respectively. Experiments, undertaken on actual SAR data collected over the intertidal zone near Jiangsu, China, by the C-band RadarSAT-2 and Sentinel-1 missions show that the proposed methodologies, well detect the wind turbines inside mud flat areas. Furthermore, a detailed analysis shows that polarimetric information always guarantees performance better than the single–polarization counterpart. [1] A. Marino, (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1219-1232. [2] A. Marino; S.R. Cloude, and J. M. Lopez-Sanchez, (2013), “A New Polarimetric Change Detector in Radar Imagery”, IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2986 -3000.  

The study of coastal wetlands is of paramount importance due to both anthropomorphic activities and natural phenomena, which threaten the stability of land and safety of the people. However, the monitoring of coastal wetlands is not trivial due to the presence of different kind of habitats that include coastal plain, coastal beaches, rocky shorelines, salt marshes, mangrove, seagrass beds, mud flats and sand bars. For this reason, the study of wetlands results very challenging.   Within this context, remote sensing plays an important role for coastal wetlands monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.   The main goals of this study are to develop multi-polarimetric and multi-temporal methods to effectively monitor the wetland area of the WWT Caerlaverock in Scotland one of the most important wetland in the United Kingdom. The test site was selected since it is severely affected by coastal erosion that makes the monitoring a very important issue.   For this purpose, two methodologies based on the joint use of co- and cross-polarized channels [1] and on the polarimetric notch filter [2], are used to both extract the profile of the coastal area and to detect the wetlands. Preliminary results are obtained processing a set of full polarimetric SAR (PolSAR) data collected at C-band from RadarSAT-2 sensor. The results show that PolSAR data can be effectively used to detect both coastline and wetlands.   [1] Nunziata F., Buono A., Migliaccio M., Benassai G. (2016), “Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 9, Issue: 11, Pages: 4921 - 4928   [2] Marino A. (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 6, Issue: 3, Pages: 1219 - 1232

Wind speed retrieval is a subject of paramount importance since wind estimation is extremely useful for different meteorological and oceanographic applications: problematics like coastal erosion, climate change and so on are strictly connected with wind parameter. In this context, most of the remote sensing satellite radar can provide sea surface wind field data. In particular, microwave sensors, mainly scatterometer and Synthetic Aperture Radar (SAR), are worldwide recognized as the most suitable sensors for wind field retrieval. Radar backscattered signal contains quantitative information about the condition of the sea surface roughness and, hence, can be used to infer sea surface wind data. One of the most challenging case of wind speed retrieval is represented by the tropical cyclone case. Although, tropical cyclones are among the most dangerous and destructive natural disasters, current models are still not able to give an accurate forecast of their intensity and track. Typically, in literature, scatterometer, and then SAR, data are used to implement Geophysical Model Function (GMF) to extract wind speed information. These functions link the Normalized Radar Cross Section (NRCS) with wind speed and wind direction. While in this work, a spectral based technique is adopted: the azimuth cut-off approach. When managing SAR microwave sensors, Doppler misregistration in azimuth occur because of the gravity wave orbital movement. This issue is the major responsible of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. This technique is used to retrieve wind speed and a few investigations have been carried out improve this approach [1]. More in detail, there is a straight link between λc values and geophysical parameters, similar to wind speed and critical wave tallness. In [1] the ACF-based λc approach has been improved to manage high wind speed routines, e.g.; extreme weather conditions. The key issues that allow to stretch out the technique to high wind regimes concern the tuning of a strategy that takes into account pixel spacing, box size and the homogeneity of the SAR image. In particular, the box size is set to be 1 km × 1 km and the median filter window is set at 90-120 m. It is revealed in recent study that λc is related with wind speed at strong winds. In this study, we try to retrieve wind speed from Sentinel-1 SAR images in hurricanes and typhoons. The SAR-derived λc is compared with simulated azimuthal cutoff wavelength using the wave spectrum from numeric wave model in the three part of typhoon wave system. The retrieval wind speed is validated against measurements from the Soil Moisture Active Passive (SMAP) radiometer. [1] V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transaction on Geoscience and Remote Sensing, vol. XXX, no. XXX, pp. XXXX-XXXX, 2018.

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

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

Soil moisture plays an important role in water cycle study. Modern remote sensing technique has demonstrated that L-band is very sensitive to soil moisture variation. With the design and implementation of the Global Navigation Satellite System (GNSS) which working on L-band as well, remote sensing using navigation signal of opportunity gained wide interests. With two decades’ development, two technique based on signal reflection have been proposed including GNSS-R (GNSS-Reflectometry) and GNSS-IR (GNSS-Interferometric Reflectometry). More recently, some researchers tried to utilize the penetrating signal to measure soil moisture (Franziska Koch et al., 2016) and snow water equivalent (Franziska Koch et al., 2014 and Ladina Steiner et al,. 2018).  For the soil moisture measurement, the investigation of the penetrating signal leads to better understanding of the sensing depth of the reflected signal, which is related to estimating the Root-Zone soil moisture and Field Capability.   We are going to study how different soil moisture affect the signal attenuation in the soil and the penetration depth of the signal under different soil moisture condition. We hope to predict the reflection caused by the soil based on the above analysis. Finally, we want to analyze the sensing depth of the GNSS signal which is defined as the maximum depth from where the signal reflected off can be received under certain receiving sensitivity. A long term experiment is carried out along with this study. Two identical antennas are used with one placed in the air and the other is placed at the bottom of a big plastic bag filled with soil of different thickness. At the same time, three FDR soil moisture probes are evenly buried at vertical direction with one probe always stay at the bottom of the bag. The increment of soil thickness is about 2 cm with its initial depth being 2 cm. Different navigation system will be investigated such as GALILEO and BEIDOU. Particularly, the BEIDOU System contains different kinds of satellite orbits including GEO, IGSO, and MEO. The GEO satellite can give quasi-static measurements, while the IGSO and MEO can give dynamic measurements.

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

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

The effect of monsoon on the mass balance of glaciers in High Mountain Asia, especially of those that are partly or fully covered by debris, has not yet been fully understood. Due to its insulating effect, debris strongly alters energy fluxes reaching the ice, and thus affects the rates and timing of melt. Monsoon conditions, dominated by persistent clouds, lower temperature ranges, high atmospheric water content, lower incoming shortwave radiation and higher receipts of incoming longwave radiation, can result in very distinct surface fluxes and mass balance of glaciers. The energy balance further changes under the presence of water within the debris, which controls conductive and latent heat fluxes, while another flux is added to the balance by rainfall. These effects have rarely been quantified, and to date only for single glaciers. In this study, we investigate how monsoon events influence the summer surface energy balance of debris-covered glaciers along the climatic gradient of High Mountain Asia, where monsoon dominates in the Eastern regions and progressively looses influence when moving westwards towards the Karakoram, where westerlies influence is predominant. We use for this energy balance models (EB) developed to simulate melt of ice under debris, or debris energy balance (DEB) models, and Automatic Weather Stations (AWS) data. Most DEB models have been developed and tested for glaciers in temperate and arid climates, where the influence of water within the debris plays a less important role and many neglect or treat only simplistically the water content of the debris. We thus also evaluate the transferability of DEB models to monsoonal environments, and test distinct schemes to account for water content in the debris. We validate results against in-situ measurements,  and describe how these events influence the summer surface energy balance of debris-covered glaciers. This work is fundamental to the development and optimization of more simplified approaches, such as the Debris-Enhanced Temperature Index (DETI) model, to distributed glacier melt modelling and as a result, to catchment-scale glacio-hydrological modelling.

Heihe River Basin (HRB), regarded as the second largest endorheic river basin in China, originates from the alpine region, flows through the Hexi Corridor, and ends at desert hinterland. The unique and various climatic and landscape types make the basin an ideal testbed for multi-disciplinary research, including hydrology, climatology, geography, ecology and so on. Over the past decades, extensive research was conducted over the basin, and fruitful new findings were obtained consequently. Based on these scientific bases, we have carried out two large-scale remote sensing experiments [1, 2] and Integrated research on the eco-hydrological process of the Heihe River Basin [3]. The main scientific contribution of the Heihe remote sensing experiments and integrated research can be summarized into: 1) a comprehensive watershed observing system was established [4] and a multi-scale dataset for understanding watershed ecohydrological processes was obtained [5], 2) a comprehensive modeling platform was designed and implemented for integrated hydrological simulation [6], and 3) a multivariate land data assimilation system was established [7]. These key progresses have been well documented and reported. Nevertheless, several new points have been observed in recent years, including: 1) developing an integrated watershed system model and closing hydrological cycle at watershed scale, 2) improving data assimilation algorithm and data assimilation system, and 3) developing key water cycle elements estimating algorithms and products. Here we focus on summarizing these recent progresses. New modeling strategy and model platform for hydrological simulation were proposed. We proposed a new modeling framework to incorporate emerging knowledge into integrated models through data exchange interfaces to comprehensively understand complex watershed systems and to support integrated river basin management [6, 8]. The model is expected to represent the coevolution of the water-land-air-plant-human nexus in a watershed and provide capability of decision-making support and to provide an overarching framework for linking natural and social sciences. Based on the framework of the watershed system model, we analyzed the hydrological cycle in the Heihe River Basin [9]. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012. An updated data assimilation scheme was proposed and parallelized assimilation system was implemented. A soil moisture assimilation scheme that jointly assimilated the brightness temperature of Advanced Microwave Scanning Radiometer-Earth Observing System and Land Surface Temperature products of Moderate Resolution Imaging Spectroradiometer [10] was proposed recently, which could correct model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter. In addition, we developed a physically based hydrological data assimilation system using the gridded and parallelized Soil and Water Assessment Tool distributed hydrological model [11]. The system integrated remotely sensed and ground-based observational data with the Parallel Data Assimilation Framework. The system could accurately characterize watershed hydrological states and fluxes. As to the application of data assimilation to hydrological flux, significant progress has been obtained as well. For instance. Pan et al. [12] assimilated the two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM and Fengyun-2D: FY-2D) into the weather research and forecasting model under framework of the 4D-Var data assimilation method in Heihe River Basin. The improved precipitation forecasting has been observed. Key retrieval algorithms for hydrological elements have been witnessed progress. For instance, Li et al. [13] estimated continuous daily evapotranspiration at a 90-m spatial resolution using the Surface Energy Balance System (SEBS) by fusing high-temporal resolution Moderate Resolution Imaging Spectroradiometer and high spatial-resolution Advanced Space-borne Thermal Emission Reflectance Radiometer images. Ma et al. [14] proposed a probabilistic inversion algorithm for soil moisture estimation based on Bayes’ theorem and the Markov Chain Monte Carlo technique. They not only obtained highly accurate soil moisture estimation, but also quantified the uncertainties in the inversion algorithm. Overall, ecohydrological research over HRB in terms of the hydrological observation, modeling and data assimilation has been witnessed huge progress. The Chinese Academy of Sciences is performing the Second Tibetan Plateau Scientific Expedition (STEP) Program. The Qilian mountain and other endorheic river basins are the key expedition regions. The scientific findings and practical experiences of HRB should and could provide very useful prior knowledge for the program. Simultaneously, the observing system design scheme, modeling idea and data assimilation systems can be extensively examined, extended and widely applied in a more generic scope.

The main objective of this project is to improve the estimate of water balance under natural and human pressure on the Heihe basin in China and in some Italian river basin by using MOST, ESA and NASA multi-source satellite data coupled with distributed hydrological models.  In this year presentation, following the scheduled activities, results will be presented for the Chinese Heihe basin and for the Italian Capitanata irrigation district. For both case studies, the FEST-EWB hydrological model will be used in a synergic way with satellite data. In particular, its algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET) that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to the land surface temperature (LST) as retrieved from operational remote sensing data (SENTINEL3, MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale. Vegetation information (LAI, NDVI, fractional cover) and albedo are obtained from satellite data (SENTINEL2, MODIS, LANDSAT) and used as input parameters to the hydrological model.  For the Heihe river basin, FEST-EWB model is run at spatial resolution of 0.05° and temporal resolution of 1 hour. Results are provided in terms of hourly evapotranspiration, soil moisture and land surface temperature maps for the 2012. Evapotranspiration estimates are then compared at local scale with two eddy covariance data, showing good agreement, and at basin scale with the estimates from the Chinese ETMonitor, and also global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM, reporting a general agreement but with irregularities, due to the different models hypotheses and algorithms.  For the Capitanatairrigation district, the model is run at 30 m of spatial resolution using the SENTINEL2 and LANDSAT images for vegetation input data and LANDSAT data for land surface temperature hydrological model calibration and data assimilation. Good estimates are obtained at basin scale in terms of RET, but also at local scale in terms of evapotranspiration and soil moisture against eddy covariance stations. The district is an intensive cultivation area, mainly devoted to wheat, tomatoes and fresh vegetables cultivation. Hence, distributed irrigation quantity maps are also estimated from the combined use of satellite data and hydrological modelling.

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

Observation and modelling of the coupled energy and water balance is the key to understand hydrospheric and cryospheric processes at high elevation. In the Qinghai – Tibet Plateau (QTP) in – situ observations of liquid and solid precipitation are very sparse and studies on the mass balance of glaciers and the water balance of catchments are hampered by this gap. We are exploring the potential of using model forecasts of precipitation at high spatial resolution to replace or complement in-situ observations. A first experiment on applying WRF to model an extensive snowfall event on the entire QTP was completed and the results are very encouraging. In this study in – situ observations of air temperature, snow depth and snow water equivalent were used to evaluate model performance and particularly alternate model configurations. Our experiments did show that the WRF configurations with advanced land surface physics schemes captured better the spatial distribution of the snow event, but overestimated the intensity and extent of SD and SWE. Next, we focused on areas at lower elevation to carry out experiments with a coupled energy and water balance model of a catchment using again model output on precipitation. A second set of experiments with WRF targeted the evaluation of model precipitation and other at-surface fields, e.g. air temperature and wind speed, for individual glaciers. This approach can potentially overcome a major challenge in energy and mass balance of glaciers, i.e. the lack of spatially distributed forcing data at high spatial resolution. The energy and mass balance of glaciers was also analysed using a suite of earth observation data. The trend in glacier thickness at very high spatial resolution was determined for several glaciers using multi – temporal DEM-s generated with ZY – 3 stereo-image data. This study determined changes in glacier surface elevation separately for the accumulation and ablation zone. For the same glaciers, accurate surface velocity fields were retrieved by staking L-TM, L8-OLI and S2-MSI images.

Quantitative information on water losses is important to understanding the global terrestrial water cycle and land – atmosphere interactions. However, land surface water loss (evapotranspiraiton, ET) estiamted by land surface models usually neglects the sub-grid heterogeneity of landatmosphere parameters, and it will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. To evaluatethe  aggregation bias, a numerical analysis was conducted to compare the aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the Probability Distribution Function (PDF), which complies with energy conservation. Four types of PDF were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces.Overall, the numerical experiments indicate that impacts on tile ET related to LAI are smaller than the ones related to SSM. Different meteorological conditions combined with the nonlinear dependence of ET on SSM/LAI may lead to large changes in the aggregation bias, even from underestimates to overestimates or conversely. In climate conditions with larger atmospheric water demand, enhancing evaporation, underestimation is more likely, and vice versa. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET in semi-arid areas. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0). The relation between aggregation bias and meteorology found in this study has the potential to identify or even as a starting point to correct the possible serious underestimations and overestimations in applications. Meanwhile, to achieve a better water loss products, the ETMonitor algorithm was further improved following the former study of last year, to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature - vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d-1, which showed better accuracy than the former ET products, especially for the forest sites. The improved algorithm was further applied based on ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product, and ET products in the northeastern Thailand from 2001 to 2015 was achieved and analyzed.

This project aims at developing new algorithms and finding new synergies between different remote sensing products in order to better monitor water resources in the Red River basin and in the Luan River basin, namely by combining water level, soil moisture (SM) and runoff products. Over the Red River, water balance and water management can prove quite challenging, for a number of different reasons: it has a complex topography, with a high drop of 2574 m, and since it is a transboundary river, there is a lack of information on reservoir management. The Luan River is characterized by steep hills and deep ravines at its upper reaches, leading to it overflowing during the rainy season. On the contrary, the water flow is much reduced in winter, with the river being icebound for some months. All these features amount to difficulties in getting the information on time for flood or drought early warning systems. Nevertheless, by using remote sensing data, the aim is to tackle these difficulties and obtain a better estimation of water resources, leading to a better management. In this respect, SM products can be a powerful asset. Currently the spatial resolution of satellite SM products is quite coarse, ranging from 36 km for SMAP (Soil Moisture Active Passive) to 40 km for SMOS (Soil Moisture and Ocean Salinity). However, in some cases, higher resolutions are required. In this respect, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) is an algorithm that downscales the SMOS and/or SMAP SM data by using MODIS (Moderate Resolution Imaging Spectroradiometer) or Sentinel-3 land surface temperature (LST) and vegetation cover data, along with a self-calibrated evaporation model. The algorithm estimates the SM variability at a 1km resolution within a low resolution pixel by relying on the self-calibrated evaporation model. More specifically, it derives a term, called soil evaporative efficiency (SEE), defined as the ratio of actual to potential evaporation, from LST and vegetation cover data. By taking into account the instantaneous spatial link between SEE and SM, it then distributes the high resolution SM around the low resolution observed mean values. Previous results obtained over the Red River basin and derived from SMOS needed further investigation due to Radio Frequency Interference (RFI) detected over the area. Since then, work has been undergone to filter the RFI from the SMOS-derived 1 km SM products. For this study, 1 km SM products have been produced over the entire Red River basin and the Luan River basin for the 2015-2018 time period, derived from both SMOS and SMAP. Preliminary results show to be promising, with an improvement with respect to the SMOS-derived products, thanks to the RFI filtering. SMAP-derived SM products have also shown promising results over the area. By combining these enhanced 1 km soil moisture products over the Red River basin and the Luan River with water level products, the hydrological model estimations can be further improved (Li et al. 2019).  

Land surface temperature (LST) plays an important role in the processes controlling energy and water exchanges at the surface-atmosphere boundary. It has been widely used in studies such as hydrology, ecology, meteorology, and climatology. Satellite remote sensing makes it possible to retrieve LST at relatively dense and regular spatial sampling intervals over large areas. Over the past three decades, satellite thermal infrared (TIR) remote sensing has become one of the most important approaches to estimate LST. However, a major shortcomingof satellite TIR remote sensing for LST estimation is its extremely low tolerance to clouds. Clouds not only reduce the spatial coverage of the TIR LST but also decreases the actual temporal resolution. The evidence has been reported for current satellite TIR LST products over different areas in previous studies. Therefore, the performance of current satellite TIR LST product is greatly limited in many applications, especially for those requiring LST with both high temporal resolution and dense spatial coverage. In contrast, passive microwave (MW) remote sensing is insensitive to clouds: thus, it is an important independent source for LST estimation complementing the available TIR LST. Merging TIR and MW observations is able to overcome shortcomings of single-source remote sensing to derive such a LST, in which how to efficiently improve the spatial resolution of MW LST to the same level as the TIR LST is a crucial link. However, in current merging methods, models adopted for downscaling MW LST fails to quantify the effect of temporal variation of LST. Thus, the accuracy and the image quality of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, this study proposes a practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: the annual temperature cycle component (ATC), diurnal temperature cycle component (ΔDTC) as prescribed by solar geometry and weather temperature component (WTC) driven by weather change. For each component, a dedicated algorithm was applied to improve its spatial resolution or optimize its accuracy according to its thermal properties. The merged LST can be obtained by combining the improved components together,  The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over northeast China: the resulting merged LST have a standard deviation of error (STD) of 1.29-2.71 K compared to the 1-km MODIS LST (MYD11A1) and successfully fill missing pixels due to clouds. Validated against in-situLST from three ground sites with diverse land cover types, the merged LST have a root mean square of error (RMSE) of 1.20-2.75 K, which is comparable to MODIS LST; besides, no obvious differences in the accuracy of the merged LST were found between daytime and nighttime, or under clear sky and unclear sky conditions. The generated all-weather LST was also compared with the 1-km AATSR LST from the European GlobTemperature project. Good agreement between these two products was also found: the mean bias error (MBE) was from -0.04 to 0.14 K and the STD was from1.02 to 1.61 K. Compared to a 1-km all-weather LST from a previous method, the merged LST derived from this study performs better in both accuracy and image quality, indicating the proposed method has an improved capability to generate 1-km all-weather LST data. The method was further applied to generate the daily all-weather LST during 2003-2017 for the Tibetan Plateau and its surrounding areas. This dataset is now being utilized in the modelling of water cycle over the Tibetan Plateau.

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

Mountain glaciers can directly reflect local climate change and play a crucial role in the terrestrial water cycle and food security of local people. Nyainqentanglha Mountains (NM) have about 9600 km2 glaciers, which account for 18.47% of the Tibetan Plateau (TP) and are the major water resources of rivers, lakes and human activities as well. The field observation is difficult to implement because of the high altitude and risk, therefore, many different experimental remote sensing techniques have been applied to estimate the glacier mass balance by several authors. Although the spaceborne optical photogrammetry is one of the promising ways to capture the glacier mass balance, the High Resolution TLA stereo images have been used less frequently. And the glacier mass balance patterns in the EM need to be further explored.   In this study, we used Zi Yuan-3 (ZY-3) Three-Line-Array (TLA) stereo images to extract the glacier mass balance in two study sites during 2000–2017. One is located in the western of the NM (WNM), a moderately complex terrain. The other one lies in the eastern end of the NM in the southeastern TP (ENM), where the topography is more complex than in the WNM. The glaciers in the WNM and ENM are of a subcontinental and maritime type, which provides an opportunity to compare and analyze the glacier mass balances of different glacier types during a decade.   The results showed that the glaciers in the WNM and ENM experienced mass loss in the 2000-2017, and the glacier thinning rates in the ablation regions were apparently larger than in the accumulation regions. In the WNM, the mean glacier elevation change and mass balance were -0.31 m a-1 and -0.26±0.18 m w.e. a-1, while the glaciers in the ENM obviously melted faster than the WNM, and these two values became -0.92 m a-1 and -0.78±0.12 m w.e. a-1, respectively. In the WNM and ENM, the glacier mass balances in the ablation zones were -0.57±0.18 m w.e. a-1 and -1.02±0.12 m w.e. a-1, while both values in the accumulation zones were 0.16±0.02 m w.e. a-1 and -0.08±0.12 m w.e. a-1. 

Multi-source remote sensing data, from visible to thermal infrared are used for forcing, calibration, validation and data assimilation of/into basin scale hydrological models. FEST-EWB model is run for the whole Heihe River basin at spatial resolution of 0.05° and temporal resolution of 1 hour. Results are provided in terms of hourly evapotranspiration, soil moisture and land surface temperature maps for the 2012. FEST-EWB model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET) that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to LST as retrieved from operational remote sensing data (MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale. Evapotranspiration estimates are then compared at local scale with two eddy covariance data showing an overall agreement between the estimated and measured data, as certified by numerous statistical indexes. Then, at basin scale the modelled Evapotranspiration has been compared with a number of global products: the Chinese ETMonitor, and with global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM. At basin scale, the agreement between the model and the ET data is consistent but presents some irregularities, as a consequence of each ET product’s own foundational hypotheses and algorithms.

The objective of this study is the evaluation of the potential of two Land Surface Temperature downscaling algorithms with respect to high resolution LST from LANDSAT 7 ETM+ and resampled MODIS LST (MOD11A1). Four different LST sources have been compared over the Heihe river basin, an endorheic basin in China, characterized by a wide variety of ecosystems, from desert oases to irrigated croplands and wooded mountain ridges. The first two LST sources are measurements provided by the ETM+ instruments (aboard satellite Landsat-7) at 30m spatial resolution every 16 days, and MODIS (aboard Terra at the lower resolution of 1000m. The other two sources are products of downscaling algorithms. The STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm merges ETM+ and MODIS images to obtain LST data with the spatial resolution of the former and the temporal frequency of the latter, involving neighbouring pixels in the process. On the other hand, the DisTrad (Disaggregation of radiometric Temperature) algorithm, establishes a link between LST and NDVI, in order to revert to the former anytime vegetation data is the only data available. Globally, resampled MODIS and DisTrad perform better, not quite reaching the accuracy of ETM+ but all the same yielding an accurate approximation. On the other hand, STARFM struggles with the variety of land cover types, offering an acceptable performance in the desertic area, which is the most uniform of all. Categorizing the pixels according to their land cover type, it is found that vegetated areas, especially croplands, are the most difficult to interpret for the LST sources, with low performance in the early summer during the peak of the maize season. Furthermore, grouping pixels by their lighting condition (whether they are in light or in shadow) does not offer major results: data quality for shadowed pixels is quite worse than for lighted pixels, but the number of the formers is so low that the impact on the overall result is close to negligible. Overall, MODIS and DisTrad are the best candidates to compensate for the low temporal frequency of ETM+ without losing too much accuracy. Furthermore, DisTrad application requires more input data and computing effort than re-sampling MODIS.  

Land Surface Temperature (LST) is an indicator for the exchange of energy in the process of atmosphere-ground interaction. It is an important parameter indicator for global resource and environmental dynamic analysis. Sentinel-3A satellite was jointly developed by theEuropean Space Agency (ESA) and the European Meteorological Satellite Organization (EUMETSAT),and was successfully launched in February 2016. One of its main payloads is the Sea and Land Surface Temperature Radiometer (SLSTR), which has three channels (i.e. S7-S9) in the thermal infrared range with a 1000 m resolution in the nadir view mode. The central wavelengths of S8 and S9 are 10.85 μm and 12 μm, respectively. Thus, images of these two channels can satisfy the requirement of LST estimation. The objective of this study is (i) to explore the applicability of the classical split window algorithms(SWA)for estimating LST from the SLSTR data acquired by S8 and S9 channels and (ii) to analyze the possible sources of error. Nine SWAswidely accepted by the scientific communities were selected as the candidate algorithms, including PR1984, BL-WD, VI1991, UL1994, WA2014, ULW 1994, SR2000, BL 1995, and GA2008. These SWAs were also used to develop the algorithm for the Chinese GLASS LST product. The aforementioned SWAs were trained globally based on simulation datasets from the forward radiative transfer simulation. The SeeBor atmospheric profile database was used as the basis in the forward simulation. For each profile, 10 LST and near-surface air temperature differences were defined, i.e. from -16 K to 20 K in increments of 4 K; spectral emissivities of 48 materials were used; the view zenith angles were defined as 0 to 55°in increments of 5°. MODTRAN 5 model was employed to conduct the forward simulation. For each trained SWA, the NDVI threshold method was used to determine the land surface emissivity of each pixel. The European Mesoscale Weather Forecasting Center (ECMWF) data were used to determine the atmospheric water vapor content and near-surface air temperature of each pixel. Results show that all the selected SWAs have accuracies better than 2 K in training. Ground measured LST at four ground sites of HiWATER with good spatial representativeness were used to validate the estimated LST from the actual SLSTR data during November 2016 to December 2017. Validation show that the accuracies of the nine SWAs are approximately 2-4 K, better than the official SLSTR LST product. The estimated LST is affected by many factors, such as the land surface emissivity, air humidity, land surface type, air temperature, and atmospheric water vapor content. The study would be beneficial for improving the SLSTR LST product.

The Lancang-Mekong river, known as the Lancang river in China, the Mekong reiver outside China. The Lancang-Mekong Basin is a trans-boundary river with an area of 795,000 km2, including territorial parts of six countries: namely China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. With a total length of over 4350 km, the Lancang-Mekong River is the longest river in Southeast Asia. It originates from the glacier melting of Qinghai Tibet plateau at the elevation of 5200 m, and eventually flows into the South China Sea at Mekong Delta in Vietnam. More than 72 million people benefit from this river. Consequently, the Lancang-Mekong basin has an outstanding practical significance for the ecological and economy development of alongshore area. However, the current land use/cover in the Lancang-Mekong river basin is in a very critical situation. Large patches of primary and secondary forests have been destroyed in Laos, Myanmar and Cambodia. Crop rotation is replaced by single cropping of rubber, cashew, sugar cane, and eucalyptus etc. Social and economic transformation, urbanization and interregional cooperation brought by increasing human activities also play an important role in land use/cover change in the Lancang-Mekong river basin. The land use/cover change have influenced climate, precipitation, the energy balance, carbon budget, and hydrological cycle in the basin. Remote sensing has long been recognized as an effective tool for broad-scale(such as global scale, regional scale and basin scale) land use /cover mapping. At present, eight land cover thematic datasets( such as USGS with 1km, UMD with 1km, BU with 1km, GLC2000 with 1km, Globcover 2005 with 300m, GlobCover 2009 with 300m, GlobCover 2010 with 250m, Globeland30 with 30m) at a global scale have been developed with resolution ranging from 30m to 1km. In recently years, remote sensing scientists are interested in land use /cover change, climate variation, and urbanization in the Lancang-Mekong river basin. Remote-sensing technology has the potential to monitor the environmental changes  in basin scale. However, the Lancang-Mekong Basin is very large, and covers numerous climate zones and eco-regions, and needs seven MODIS tiles, or over 50 Landsat frames to cover the complete north–south-trending basin. Furthermore, the almost persistent cloud cover over the Lancang-Mekong Basin for large parts of the year, and optical remote sensing images are unavailabe in part of regions. The landscapes have complex spectral and textural characterization in the Lancang-Mekong river Basin. Because of these factors, there is reported about high resolution(≤10m) land use/cover products in the Lancang-Mekong river Basin in recent years. As new satellites and sensors become avaiable. the Sentinel-2A/B are optical satellites, which respectively launched in 2015 and 2017. The Sentinel-2 has multi-spectral data with 13 bands in the visible, near infrared and shortwave infared with respectively spatial resolution of 10m, 20m, and 60m. Multi-source remotely sensed images with high temporal, spatial and spectral resolution in the Lancang-Mekong river basin have obtianed by the ESA Sentinels satellites in recently years. Lots of research has shown that the land use /cover information play a role in climate variation, ecology and environment destroy, and naural hazards. The land use/ cover is fundamental information for natural resource management, environmental change studies, urban planning to sustainable developemnt, and many other societal benefits in the Lancang-Mekong river basin. Bacause of region area large, complex topography, cloudy and rainy environment in the Lancang-Mekong river basin, so far, the high resolution land use/cover products have not presented. The relation research has became urgent. More specifically, the whole research may include the following some parts. (1) employing Sentinel Application Platform (SNAP) toolboxes to pre-process the data sets over the Lancang-Mekong river basin (2) Developing object-oriented random forest (OORF) Classifcation algorithm for mapping Land use/cover in basin scale . (3) Mapping high resolution land use/cover in the basin using the presented alogrithms in the basin.  

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

Unlike most glaciers experience mass lost in recent decades, the Karakoram and its surroundings seem rather stable. The phenomenon is claimed as ‘Karakoram anomaly’ and/or ‘Karakoram-Pamir anomaly’. Given its remote, the field observations for glacier mass balance and flow rates are hard and archive data are rare. Remote sensing technique plays an essential role in alpine glaciers observations. Previous studies yielded a positive glacier mass balance for the Karakoram and later extended to the Pamir Plateau between 2000 and ~2012, however the laser altimetry observation at almost the same period (2003 - 2009) found slight glacier mass loss but claimed that the anomaly centred at the West Kunlun.   To make a more accurate observation and to understand the ‘Karakoram anomaly’, we applied Differential SAR Interferometry (D-InSAR) technique to a set of X-band bistatic TerraSAR-SAR-X/TanDEM-X (TSX/TDX) images observed at ~2013 by respecting to SRTM DEM observed in 2000 to derive glacier mass balance. The topographic residual phase of D-InSAR is unwrapped and then transferred to height changes. By presuming density of 850 Kg/m3, the volume changes of glaciers are converted to glacier mass balance. We compare quasi-simultaneously observed C-band and X-band SRTM (both in February of 2000) to evaluate and to remove the penetration depth differences at different elevation bins. The possible seasonal variation in terms of glacier mass balance was evaluated at an adjacent site by using TSX/TDX images observed in different months in one year. The standard deviation of differential processing between SRTM and TSX/TDX is about 6.27m, which is more accurate than the previous study using SPOT DEM and SRTM. Besides, it noticed that TSX/TDX make more efficient observations at accumulating area than optical observations. The result found that both east and west part of the Karakoram presents almost zero glacier mass balances, which were −0.020 ± 0.064 m w.e. yr−1 and −0.101 ± 0.058 m w.e. yr−1, respectively. Most negative glacier mass balance was contributed by the southern slope of the Karakoram while the northern slope was rather stable. At the most northeastern part of the Karakoram, where are very close to the edge of Tarim basin, the glaciers presented thickening in also most every elevation levels. The glacier mass balance at the Karakoram presented a decreasing gradient from the edge of the Tarim basin to the southwest of Karakoram. The mass balance for 2000 to ~2013 is almost identical comparing to 1974 to 2000, which implies that the stable environment at the Karakoram despite the global warming trend.   The re-analysis monthly GHCN_CAMS Gridded 2m Temperature data found an increasing start from 1995 to 2000 for about 1 degree and kept stable after then. Monthly precipitation Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) suggest rather stable annual and seasonal precipitation from 1980 to 2000, followed by an increasing trend. The precipitation increased from ~260mm/yr before 2000 to ~350mm/yr by 2010. The meteorological data suggested that a warming and wetting trend after 2000 for the Karakoram, which possibly explains the ‘Karakoram anomaly’ was induced by increasing precipitation rather than a cooling trend of temperature.

Thermokarst, a process that characterizes landforms caused by thawing of ice-rich permafrost, is a key indicator of permafrost degradation. Surface dynamics of thermokarst processes on Qinghai-Tibet Plateau (QTP) of China, is still poorly quantified or understood. It is also challenging to detect and measure surface subsidence due to loss of subsurface ice over a large area. The Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique has the potential to detect local or regional thermokarst-induced surface subsidence with the advantage of full resolution and millimeter to centimeter accuracy by less affected related to the temporal or geometric decorrelations. Previous studies based on multi-baseline time series analysis have separated the seasonal and thermokarst-induced surface subsidence only using SAR images acquired during thaw seasons. To fully usage of the SAR images, we introduce frost heave processes during early freeze season and subsequent stable stage when the layer is completely frozen. Applying our improved PSInSAR method to 17 L-band ALOS-1 PALSAR images over Eboling Mountain where 22 thermal erosion gullies are well developed, we found a mean gradual subsidence trend of 1.3 cm/year, with a maximum of 5 cm/year near the thermal erosion gullies. It is equivalent to an ice volume loss of 1.48104 m3/year over the entire thermokarst landform in the study area. We also found that the ground surface nearby the thermal erosion gullies is more likely to undergo subsidence. It indicates that the thermal erosion gullies could affect the permafrost processes at its surroundings. This study promises a potential of using PSInSAR to identify thermokarst landforms, map and quantify permafrost thaw subsidence, and assess its impacts over large areas such as the QTP.

Modern SAR technology offers various approaches for processing stacks of interferometric SAR data. For surface motion estimations in urban areas, normally short wavelength data, like X- or C-band, is preferred. For applications in urban areas and infrastructure monitoring. Long wavelength data offers a certain amount of penetration capability and they are less sensitive to temporal decorrelation. PS-InSAR is a widely used method for surface motion estimation from interferometric SAR data stacks. It is used in commercial applications and also in many projects in the Dragon program, starting from Dragon-1 until today. We consider it a stable technique, proven to successfully and reliably offer surface motion estimations in numerous projects. We used PS-InSAR in Shanghai and Wuhan for estimating urban subsidence and infrastructure stability. With the availability of Sentinel-1 and the large global SAR archive, it is nowadays possible to process PS-InSAR and estimate subsidence in regions of interest all over the world, opening this field up to the public even further. SAR tomography with long wavelength SAR data, preferably with P-band data, allows foliage penetration and the true 3D reconstruction of the SAR signal under the foliage. This can be used for various applications, e.g. for the estimation of above ground forest biomass. SAR tomography here allows to measure the biomass, instead of estimating it based on tree canopy heights, on a global level. ESA will use this with the upcoming BIOMASS mission. There are still several problems to be solved though. On problem is the temporal decorrelation. Although P-band is less sensitive to temporal decorrelation, it is still not immune to it. Especially changes in rainfall and canopy water content / water content layers, can cause problems in the 3D reconstruction. With one of the main areas of interest along the tropical rainforest, rain and changes in the rainfall patterns are to be expected though. Minimizing the amount of data necessary for a tomographic inversion is therefore important to allow a good biomass estimation with few acquisitions. In Dragon-4 we are working closely together towards these goals, continuing our research on PS-InSAR and related techniques, but also extending towards the 3D reconstruction using SAR tomography with long wavelength SAR data.

In recent years, advanced techniques such as polarimetric SAR interferometry (Pol-InSAR) and SAR tomography (TomoSAR) have been widely used to retrieve forest parameters by means of SAR measurements. Pol-InSAR was developed based on the Random Volume over Ground (RVoG) model, which assumes a penetrable volume layer consisting of randomly oriented particles over an underlying rough surface. On this basis, Cloude and Papathanassiou proposed a parametric inversion scheme to retrieve forest height, which has been successfully applied for a variety of forest sites at different frequency bands. SAR tomography is instead an imaging technique based on the collection of multiple flight lines. It allows focusing the received signal not only in the range/azimuth plane, as in conventional 2-D SAR imaging, but also in elevation, hence providing 3-D resolution capabilities. The retrieval of canopy height using SAR tomography has been considered since the early experiments. Indeed, wave scattering from forested areas is bound to occur between the terrain and the top of the canopy. Hence, canopy height can be retrieved, at least in principle, by tracing the upper envelope in tomographic sections. In this paper, we aim at presenting an experimental assessment of vegetation height retrieval in tropical forests based on P-band SAR acquisitions. Two approaches are considered: i) parametric height estimation under the assumption of the Random Volume over Ground (RVoG) model, and ii) thresholding the vertical backscattering profiles that are focused by SAR tomography. The data-set under analysis is from the ESA AfriSAR campaign that was flown over Gabon in 2016. Results show that both of the two approaches are able to retrieve forest height to within an accuracy of about 3 m or better over the interval of forest height between 30 m to 50 m at a resolution of 25 m × 25 m

Several upcoming SAR satellite constellations, are going to be operated in bi-static mode, like TanDEM-L or TwinSAR-L, or may have a bi-static companion, like Sentinel-CS. Until now, bi-static data is mainly used for DSM generation, as in the TanDEM mission. In the future, the goal is to use such data also for surface motion estimation. However, current multi-baseline D-InSAR approaches are not well suited for processing this data and need to be adjusted. The main advantage of a bi-static operation is the minimization of the temporal decorrelation and the atmospheric influence. But, a temporal difference close to zero between the acquisitions also means that ground deformations cannot be measured. Motion related phase components will only appear with a significant time difference between the acquisitions. By acquiring several image pairs over the same area, bi-static missions can deliver such repeat-pass acquisitions with a required temporal baseline, but these interferograms will again suffer from temporal decorrelation and atmospheric effects like the standard acquisitions. In terms of PSInSAR or related processing methods, that is to say that we would expect an improved estimation of PS point height, but not necessary a better estimation of the deformation phase components, as the most severe problems still occur. Even more, standard processing chains for PSInSAR will not work well, or at all, with such stacks. In our experiments, we used pursuit mono-static data from the TanDEM-X science phase. The along track baseline is extended to 10 seconds between the satellites, allowing both satellites to transmit and receive data undisturbed from each other. The data is therefore not bi-static and generally suitable for standard InSAR and PSInSAR processing. However, the very small temporal baseline of 10 seconds compared to the 11 days repeat-pass baseline can cause numerical problems in the estimation of the deformation phase. To avoid this, we separated the estimation of the topographic phase from the estimation of the deformation phase component and use different image pairs in both cases. We estimate the topographic phase only from the 10s pairs. Based on the estimated heights from this first step, we process the deformation phase using the repeat-pass images. Having two images per time can reduce the noise, however we found no significant difference in the performance from this. In areas with high skyscrapers, like our testing area in Guangzhou, China, the deformation estimation can vastly benefit from the much better height estimation of this approach. However, unfortunately, the amount of data available is currently very limited, so that we can only present preliminary results for deformation estimation, showing only slight improvements in this regard.

In synthetic aperture radar (SAR) remote sensing, Differential SAR Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography, producing advanced 4D (3D+Time) SAR imaging capabilities extensively applied to urban deformation monitoring. More recently, it has been shown that through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating (forest) scatterers, the important decorrelation-robust forest Tomography functionality is obtained. To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an advanced adaptive, just semi-parametric, generalized-Capon Diff-Tomo method conceived and developed at University of Pisa (UniPi) for extraction of height and dynamical information of natural distributed (volumetric) scatterers. In addition to robust Tomography, particular reference is to separation of decorrelation mechanisms in forest layers. Simulated and P-band results are shown. A review of other advanced Diff-Tomo tools developed at UniPi for information extraction in decorrelating forest scenarios is also presented. Ack.: the Authors thanks Dr. Francesco Cai, formerly at UniPi and now with Leonardo Company, for his support in the SW development.   Reigber, A., Moreira, A.: ‘First demonstration of airborne SAR tomography using multibaseline L-band data,’ IEEE Trans. Geosci. Remote Sens., 2000, 38, (5), pp. 2142-2152 Pardini, M., Papathanassiou, K.: ‘On the estimation of ground and volume polarimetric covariances in forest scenarios with SAR tomography,’ IEEE Geosci. Remote Sens. Lett., 2017, 14, (10), pp. 1860-1864 Huang, Y., Ferro-Famil, L., Reigber, A.: ‘Under-foliage object imaging using SAR tomography and polarimetric spectral estimators,’ IEEE Trans. Geosci. Remote Sens., 2012, 50, (6), pp. 2213-2225  Azcueta, M., Tebaldini, S.: ‘Non-cooperative bistatic SAR clock drift compensation for tomographic acquisitions,’ Remote Sensing, 2017, 9, (11), pp. 1-11 Lombardini, F.: ‘Differential tomography: a new framework for SAR interferometry,’ IEEE Trans. Geosci. Remote Sens., 2005, 43, (1), pp. 37-44 Lombardini, F., Cai, F.: ‘Temporal decorrelation-robust SAR tomography,’ IEEE Trans. Geosci. Remote Sens., 2014, 52,(9), pp.5412-5421  

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

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

The world s population density in flood-prone coastal zones and megacities is expected to grow up to 25% by 2050. Global sea-levels have risen during the 20th century, and they will rise by up to ~60 cm by 2100. Non-climate-related anthropogenic processes (such as ground subsidence due to groundwater extraction, ground settlements due to large scale land-reclamation, and fast and non-linear subsidence phenomena of artificial sea wall), as well as frequently encountered natural hazards (such as storms and storm-surge) will exacerbate the risk to coastal zones and megacities and amplify local vulnerability. Making the situation worse is the combination of sea-level rise resulting from climate change, local sinking of land resulting from anthropogenic and natural hazards. The coastal vulnerability of Yangtze River Delta (YRD) and Pearl River Delta (PRD) is currently being amplified by the compounding effects of the time-dependent ground subsidence, the accelerated rate of sea level rise, and natural hazards. The provided examples of delta regions affected by the combination of sea-level rise, significant modifications over time, and natural hazards make clear the need of extended analyses for the understanding of the mechanisms at the base of the surface modifications of coastal areas, estimating of future regional sea level change, and evaluating the potential submerged land area [1]-[3]. In this project, the use of well-established remote sensing technologies, based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the advanced Differential Interferometric Synthetic Aperture (DInSAR) techniques [4]-[5], GPS/leveling campaigns aiming to perform sound and extended geophysical analyses, satellite altimeter data and tide gauge data, and the Coupled Model Inter-comparison Project Phase 5 (CMIP5) climate model projections are being employed for these purposes. The results obtained in this project represent an asset for the planning of present and future scientific activities devoted to the monitoring of such fragile environments. These analyses are essential to assess the factors that will continue to amplify the vulnerability of the low-elevation coastal zones. The main goals of the project are to provide a full characterization of the scene modifications over time and causes of the coastal delta region environments, to provide estimates of future regional sea level change, to derive coastal submerged area and wave field, and to provide suggestions for implementing coastal protection measures to adapt and mitigate the multi-factors induced coastal vulnerability. The main achievements obtained during the years of the project will be summarized and discussed at the forthcoming D-4 conference, highlighting the scientific relevance and the expected added value of the project, itself.   References [1] Yang S. L., Belkin I. M., Belkina A. I., Zhao Q. Y., Zhu J., Ding P. X. (2003) Delta response to decline in sediment supply from theYangtze River: evidence of the recent four decadesand expectations for the next half-century, Estuarine, Coastal and Shelf Sciences, 57, 689-699. [2] Wang W., Liu H., Li Y., Su J. (2014) Development and managment of land reclamation in China, Ocean & Coastal Management, 102, 415-425. [3] Zuo J, et al. 2013. Prediction of China’s submerged coastal areas by sea level rise due to climate change. Journal of Ocean University of China, 12(3): 327–334. [4] Berardino P., Fornaro G.,Lanari R.,Sansosti E.(2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,IEEETransaction on Geoscience and Remote Sensing, 40, 11, 2375-2383. [5] Zhao Q., Pepe A., Gao W., LuZ., Bonano M., He M.L., Wang J., Tang X. (2015) A DInSAR Investigation of the ground settlement time evolution of ocean-reclaimed lands in Shanghai, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1763-1781

Shanghai is located at the midpoint of the north–south coastline of China. In order to solve the problem of land scarcity, several reclamation and siltation promotion projects have been implemented since 1995. Due to land reclamation, ground settlement as an inherent problem has arisen in the new lands area, which is responsible for serious damage to infrastructures. Spaceborne Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an investigating technique capable of extracting line of sight (LOS) cumulative ground settlement measurements with millimeter or even sub-millimeter accuracy. However, the original deformation time-series is produced by single dataset with the using of MT-InSAR technique. Recent years, massive and different types of SAR data are available with the continuous launching of Synthetic Aperture Radar (SAR) satellites. Lei Yu and other scholars have combined 3 platforms’ deformation time-series to retrieve long-term displacement time-series in the ocean-reclaimed areas of Shanghai. In this study, five deformation time-series as well as deformation rates are derived by 5 independent SAR datasets respectively with the using of Small BAseline Subset (SBAS) algorithm. Then, we not only combined 3 platforms’ deformation time-series, we also combined 4 platforms’ deformation time-series. And the combinations were compared by us. Specifically, five independent SAR datasets are used for this study. The first dataset consists of 35 images, collected by ENVISAT/ASAR(ENV) sensor operated at C band (Ascending, VV polarization)  from February 2007 to September 2010. The second dataset consists of 11 images, collected by TerraSAR-X sensor operated at X band (TSX1, Ascending, HH polarization) from December 2009 to December 2010. The third dataset is also collected by TerraSAR-X (TSX2, Descending, VV polarization) from April 2013 to July 2015, consists of 11 images. The fourth dataset consists of 61 images, collected by COSMO-SkyMed(CSK) sensor operated at X band (Descending, HH polarization) from December 2013 to March 2016. The last dataset consist of 33 images, collected by Sentinel-1A(S1A) sensor operated at C band (Ascending, VV polarization) from February 2015 to April 2017. At the beginning, interferometric process is implemented in each dataset separately, and 91, 36, 66, 155, 368 better interference image pairs are sequentially selected. After removing the elevation phase by using ASTER elevation data (30m*30m), it is unwrapped by the Delaunay Minimum Cost Flow(MCF) method. Then, the time coherence coefficient is set to be greater than 0.65 for ENV and CSK, and the other three datasets are set to be greater than 0.55. After that, the deformation time-series and deformation rates of 5 time periods are obtained. Since TSX1 and TSX2 do not have the overlapping area and they share common areas with other three SAR datasets respectively, we combined deformation time-series of time-overlapped datasets by using Singular Value Decomposition (SVD) method and combined non-time-overlapped datasets by using time-dependent geotechnical models. Three joint strategies, ENV+CSK+S1A, ENV+TSX1+CSK+S1A and ENV+TSX2+CSK+S1A, are implemented respectively. By analyzing the feature points, we found that the annual deformation rate difference between the three joint methods is less than 1mm/y in the area with small settlement. In the areas with obvious subsidence, such as the fourth and fifth runway of Pudong Airport, the annual deformation rate of the three combination fluctuate by ±2.5 mm/y. In terms of deformation time-series, all three combinations have consistent settlement trends.

Over the last decades, the use of Differential Synthetic Aperture Radar Interferometry (DInSAR) [1] technology has gained an increasing attention for its capability to investigate large-scale Earth’s surface deformation phenomena. The DInSAR technique allows the timely monitoring of displacement phenomena with dense grids of measurement points. The availability of measurements over dense spatial grids represents the typifying factor of the DInSAR technology with respect to other conventional approaches (e.g. GPS and levelling measurement campaigns), thus making nowadays DInSAR largely adopted both in scientific and operational frameworks. However, in regions where the density of coherent points is large, the use of dense grid of measurement points leads DInSAR being not very efficient from the computational point of view. Many solutions have been proposed to overcome such a problem. A role of particular importance is covered by the multiresolution/multi-grid [2] algorithms that not only improve computational efficiency but also allow performing a more comprehensive analysis of the deformation phenomena that characterize Earth’ssurface, which exhibit different characteristics at multiple scales of resolution [3].Recently, DInSAR techniques have largely been used to analyze subsidence phenomena in coastal delta region like Yangtze and the Pearl River Deltas where man-made lands, reclaimed from the sea, are used to build airports, harbors, and industrial areas. In such a context, advanced DInSAR approaches, such as the Persistent Scatterer Interferometry (PSI) [4] and the Small BAseline Subset (SBAS) technique [5], [6], [7] , allow to produce spatially dense velocity maps as well as long-term displacement time-series (with millimeter accuracy even sub-millimeter) corresponding to coherent targets location. In this study, we develop and discuss the potential of an adaptive quadtree-based decomposition method [8] applied to DInSAR data, which allows one to produce DInSAR deformation products at different scales of resolutions. The latter are adaptively chosen within the imaged scene to better analyze the on-going deformation signals. Specifically, the multi-grid algorithm exploits a multiresolution scheme for the phase unwrapping of sequences of DInSAR interferograms, and shares some similarities with [9]. The selection of the used multi-grids is based on the analysis of the statistical properties of a sequence of interferometric phase that allow to recognize major deformation areas where phase unwrapping operations can be performed more efficiently with a computational improvement and without losing significant information. The algorithm preserves details of deformation as much as possible, and achieves efficient data reduction. The area of interest analyzed is the Pearl River Delta (PRD) region, in particular the island of Hong Kong, which is characterized by subsidence phenomena. Pearl River Delta (PRD) is located on the southern coast of mainland China. It is the third largest delta in China and adjacent to the South China Sea from the north. In the past 50 years, reclaimed lands were merged into just over 100 enclosures protected by flood defenses. However, the coastal area has always been under threat from natural hazards, including river flood, waterlog, typhoon, and tidal flood. These hazards will no doubt be intensified by the predicted sea level rise. The analysis relies on a set of 60 SAR data acquired by the Sentinel 1A/B radar sensor from December 2017 to January 2019. Starting from these data, we generated a stack of interferograms on which we have tested the new adaptive quadtree decomposition method. The goal of this present investigation is to prove that, at least in correspondence to the highly coherent targets on the ground, the deformation signals can be detected at different scales of resolutions using local, adaptive multilook factors (e.g., 2 x 10, 20 x 4, 40 x 8 and 80 x 16). The proposed method can be integrated with adaptive multi-looking noise filtering techniques [10], [11] to improve accuracy of estimated deformation. The preliminarily results will be presented and discussed at the next Dragon-IV meeting. [1] D. Massonnet and K. L. Feigl, "Radar Interferometry and its application to changes in the earth's surface," Rev. Geophys., vol. 36, pp. 441-500, 1998. [2] M. D. Pritt, "Phase Unwrapping by Means of Multigrid Techniques for Interferometric SAR," IEEE Transaction on Geoscience and Remote Sensing, vol. 34, no. 3, pp. 728-738, 1996. [3] T. Kobayashi, Y. Morishita, H. Yarai and S. Fujiwara, "InSAR-derived Crustal Deformation and Reverse Fault Motion of the 2017 Iran-Iraq Earthquake in the Northwestern Part of the Zagros Orogenic Belt," Geospatial Information Authoriti of Japan, vol. 66, 2018. [4] A. Ferretti, C. Prati and F. Rocca, "Permanent scatterers in SAR interferometry," IEEE Trans.Geoscience, vol. 39(1), pp. 8-20, 2001. [5] Q. Zhao , A. Pepe, W. Gao, Z. Lu, M. Bonano, M. He, J. Wang and X. Tang , "A DInSAR investigation of the ground settlement time evolution of ocean-reclaimed lands in Shangai," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 1763-1781, 2015. [6] R. Lanari, O. Mora , M. Manuta , J. J. Mallorqui, P. Berardino and E. Sansosti , "A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms," IEEE Transaction on Geoscience and Remote Sensing, no. 7, pp. 1377-1386, 2004. [7] F. Falabella, A. Pepe, Q. Zhao, M. Guanyu, C. Serio and R. Lanari, "A hybrid multi-scale InSAR approach to study the 2014-2018 Surface Deformation of the Shanghai Coastal Region through Sequences of Time-Gapped Cosmo-SkyMed SAR acquisitions," Proceedings of Dragon 4 Programme Symposium, 19-22 June 2018, Xi'an, P.R. China. [8] R. B. Lohman and M. Simons, "Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling," Geochem. Geophy. Geosy., vol. 6, no. 1, pp. Q01007-1-Q01007-12, 2005. [9] C. Wang, X. Ding, Q. Li and M. Jiang , "Equation - Based InSAR Data Quadtree Downsampling for Earthquake Slip Distribution Inversion," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 12, pp. 2060-2064, 2014. [10] P. Mastro and A. Pepe, "Adaptive Spatial Multi-looking of Differential SAR Interferograms Sequences using Circular Statistic," VDE, pp. 1-6, 2018. [11] A. Ferretti et al., "A new algorithm for processing interferometric datastacks: SqueeSAR," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, p. 3460–3470, 2011.

Shanghai is situated at the mouth of Yangtze River, on the coast of the East China Sea. It is one of the 47 megacities in the world with a population of more than 10 million and is also the financial and science and technology center of China (Dsikowitzky et al. 2016; Kuang et al. 2014). Due to the explosive expanding of the population and economic, Shanghai has started large-scale exploitation of groundwater and infrastructure since the last century which leads to significant land subsidence. According to the records, the cumulative subsidence in the downtown area of Shanghai has exceeded two meters since have leveling data by the 1920s and land subsidence has become one of the most serious urban risks (Zhang et al. 2002). Although groundwater exploitation has been effectively controlled in recent years, the city is still suffering serious risks in view of global sea level rise and the continuous subsidence caused by large-scale infrastructure construction and land reclamation. During the last century, the subsidence monitoring in Shanghai was mainly by leveling or GPS which was based on single-point survey and will produce large amount of cost. Fortunately, the development of InSAR technology in recent decades has made large-scale, high-frequency, low-cost urban deformation observation possible (Burgmann et al. 2000; Massonnet and Feigl 1998). Especially, MT-InSAR technology, can achieve the ground deformation monitoring with accuracy of millimeter-level, which can meet the needs of high accuracy urban deformation monitoring practice (Lanari et al. 2007; Solari et al. 2016). Disaster risk assessments is an effective means to qualitatively describe the degree of disaster impact. There have been a large number of related studies in the fields of geology, urban flood risk, and drought, such as the geological risk assessment by fuzzy cluster-analysis methods(FCM) and the flood risk grading based on risk matrix (Efendiyev et al. 2016; Klein et al. 2013). Although MT-InSAR method have been effectively used for studying the ground subsidence in Shanghai in recent decades, these studies are mainly concentrate on the quantitative study of the surface deformation, which are lack of further grading the hazard risk with auxiliary data, such as economic losses, infrastructure vulnerability and land use/land cover data. In this work, in order to make a relatively detailed assessment, Shanghai is divided into a regular grid matrix according to the size of 100m*100m. And we quantitative scoring each grid with the comprehensive risk assessment matrix, which was consists of deformation time series obtained by Small Baseline Subset (SBAS) algorithm and Sentinel-1A datasets acquired from 2016 to 2018, the land use/land cover data obtained by Landsat-8 images, and the major infrastructure data includes main buildings, flood control levees, and road networks of Shanghai. Subsequently, all of the grids are divided into three levels, including low risk, medium risk and high risk, with support vector machine. Finally, the land subsidence risk rating map of Shanghai was acquired, which will provide a useful reference for the urban risks assessment and the comprehensive management of relevant departments. [1]Burgmann, R., Rosen, P.A., & Fielding, E.J. (2000). Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation. Annual Review of Earth and Planetary Sciences, 28, 169-209 [2]Dsikowitzky, L., Ferse, S., Schwarzbauer, J., Vogt, T.S., & Irianto, H.E. (2016). Impacts of megacities on tropical coastal ecosystems The case of Jakarta, Indonesia. Marine Pollution Bulletin, 110, 621-623 [3]Efendiyev, G.M., Mammadov, P.Z., Piriverdiyev, I.A., & Mammadov, V.N. (2016). Clustering of Geological Objects Using FCM-algorithm and Evaluation of the Rate of Lost Circulation. Procedia Computer Science, 102, 159-162 [4]Klein, J., Jarva, J., Frank-Kamenetsky, D., & Bogatyrev, I. (2013). Integrated geological risk mapping: a qualitative methodology applied in St. Petersburg, Russia. Environmental Earth Sciences, 70, 1629-1645 [5]Kuang, W., Chi, W., Lu, D., & Dou, Y. (2014). A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. Landscape and Urban Planning, 132, 121-135 [6]Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M., & Pepe, A. (2007). An overview of the small BAseline subset algorithm: A DInSAR technique for surface deformation analysis. Pure and Applied Geophysics, 164, 637-661 [7]Massonnet, D., & Feigl, K.L. (1998). Radar interferometry and its application to changes in the earth's surface. Reviews of Geophysics, 36, 441-500 [8]Solari, L., Ciampalini, A., Raspini, F., Bianchini, S., & Moretti, S. (2016). PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization. Remote Sensing, 8 [9]Zhang, W., Duan, Z., Zeng, Z., & Kang, Y. (2002). Feature of Shanghai Land Subsidence and Its Damage to Social-economic System. Journal of Tongji University, 30, 1129-1133,1151

China has been affected by some of the world's most serious geological disasters and experiences high economic damage every year. Geohazards occur on remote and highly populated areas. In the framework of the DRAGON4 32365 Project, this paper presents the main results and conclusions derived from an extensive exploitation of available remote sensing data and methods that allow the evaluation of their importance for various geohazards. Therefore, the great benefits of recent remote sensing data (wide spatial and temporal coverage) that allow a detailed reconstruction of past events and to monitor currently occurring phenomena are exploited to study various areas and various geohazard problems, including: surface deformation of the mountain slopes and glaciers; identification and monitoring of ground movements mining areas and; subsidence, landslides, ground fissure and building inclination studies. Suspicious movements detected in the different study areas were verified and validated by field investigation and measurements in the local.

In the framework of the DRAGON-4 Project, the National Institute of Geophysics and Volcanology of Rome (INGV, Italy) and the Northeastern University of Shenyang (China) collaborate to study the surface movement over industrial regions in Northeast China. The traditional heavy industrial base, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, is playing an important role in the economic development of the region, although severe consequences on the local environment are taking place due to the continuous mining activities. Various geo-hazards, such as subsidence, landslides, ground breakage and building inclinations, have been occurring for decades. The continuous monitoring of the effects of the mentioned phenomena is thus of great importance for the safety of the local population. Taking advantage of the availability of dense remote sensing dataset it is possible to analyze the geo-hazards and their environmental impacts in the region; and then making forecast about their occurrence in the future and providing support for disaster prevention and damage reduction. We adopt multi-temporal InSAR methodologies able to estimate the spatial and temporal deformation over large areas. In this study, we use time-series InSAR results from multiple stacks (from ascending and descending orbits) and different sensors to monitor gravitational deformations and subsidence phenomena in urban areas especially effecting underground paths and railways, and in mining regions. We take advantages from both the Persistent Scatterers Interferometry (PSI) and Small Baseline Subset (SBAS) techniques, for processing SAR data stacks acquired by Sentinel-1A/B (C-Band) and COSMO-SkyMed (X-Band) exploited for several areas in the BASF region. In comparison to the 2018 already presented mid-term project results, we investigated the same areas of Fushun and Shenyang analyzing Sentinel-1 datasets, provided by ESA, along both the tracks. The considered time interval spans from June 2015 to December 2018 for the descending orbit, and from April 2017 to September 2018 for the ascending case, respectively. The retrieved mean ground velocity maps confirm the results from the previous exploited CSK data. Subsidence phenomena are still ongoing reaching values higher than -120 mm/yr inside the mine and -60 mm/yr at its edges. Some small areas (i.e. localized groups of pixels) show positive values (uplift) probably due to stockpile of excavation debris and/or processing waste material. Two descending stacks of COSMO-SkyMed stripmap images and a stack of TerraSAR-X images covering Shenyang city are exploited in a small baselines subset analysis (SBAS) in our recent study. During the processing of COSMO-SkyMed images，a stack of 18 images acquired from March 13th 2016 to April 17th 2017 covering eastern Shenyang and a stack of 15 images acquired from March 1st 2016 to April 21st 2017 covering western Shenyang are processed respectively. 58 interferograms are generated out of 18 SAR images for the eastern stack, whereas 44 interferograms are generated out of 15 SAR images for the western stack. In the meantime, 68 interferograms are generated out of 20 TerraSAR-X images acquired from April 15th 2015 to October 5th 2016 for SBAS analysis. The topographic phase is simulated and removed from the interferograms using the TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m× 90 m) covering the study area.  The SBAS approach has been proposed to overcome the limitation of decorrelation with reduced amount of SAR images by making full use of all possible interferograms with small spatial and temporal baselines. In this study, a modified SBAS approach developed in StaMPS to ensure the temporal continuity by connecting separated subsets of interferograms is implemented for data processing. The displacements acquired in line of sight direction is translated to vertical direction based on a simple assumption that no horizontal ground motion occurs for subsidence monitoring applications.     As recommended by the Guidelines of InSAR Monitoring for Geo-hazard of the Chinese InSAR community, areas presenting deformation velocities larger than 5mm/yr in LOS can be categorized as subsidence area. Taking the incidence angle into consideration, vertical deformation rate larger than 5.5 mm/yr suggests subsidence in Shenyang. Generally speaking, most parts of Shenyang are relatively stable. However, there is a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly occur in this area. Subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang. We also processed a new CSK dataset over the Anshan city along the descending track. Moreover, we updated the processing of CSK image dataset for the western part of the city of Shenyang, thanks  some new CSK acquisitions (descending track) provided by the Italian space Agency (ASI), Our results confirm that the heavy industrial exploitation of mines and water pumping in the BASF region of Northeast China cause clear and strong ground deformation effects of high potential impact on the local infrastructures and population. The use of multiple stacks, from different sensors, of InSAR data allows monitoring such phenomena with an accuracy and temporal sampling not possible earlier. By now, the use of EO products plays a fundamental role to monitor natural and man-induced hazards and to support Disaster Risk Management providing an important tool for local and national organizations. Acknowledgments This work is financially supported in part by the National Natural Science Foundation of China (Grant No. 41601378) and the Fundamental Research Funds for the Central Universities (Grant No. N150103001). The COSMO-SkyMed data is provided by ASI via the ASI-ESA Dragon4 Project ID. 32365_4. The TerraSAR-X data is provided by Airbus Defence and Space.

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

Radar Interferometry (InSAR) can provide measurements of surface displacement from Space, with millimetric accuracy [1, 2]. These measurements are used in natural hazards analyses but also for monitoring anthroprogenic activities. In the last few years, the number of SAR satellites with shorter repeat intervals and higher resolutions make increase significantly SAR data volume. This increase as lead to challenges in terms of manual inspection [3], giving in turn rise to the search of automated ways to process the available data. The point previously described and advances in hardware lead to advances in deep learning, which has already been applied in several areas such as computer vision. We propose a supervised Deep Learning (DL) approach for multivariate outlier detection in post-processing of multitemporal InSAR (MTI) results. We used a Convolution Neural Network (CNN) to process the data leading to one of the following labels: outliers, inliers or potentially dangerous lower coherence points. The input data were organized in such a way that for each point the model has access to the multivariate features (such as velocity, height, etc.) of the nearest points, as well as its coordinates in a local system (centered on each point). After training and model evaluation, the accuracy, precision and recall were analyzed (the last two for each label), considering a threshold value of 0.6 applied to the model’s output. Our model achieved a 95% accuracy and a mean value of 89%, respectively in precision and recall. Our research intends to demonstrate the usefulness of DL to detect deformation patterns in post-processing InSAR data, with the purpose of increasing point densities of Permanent Scatterers (PS) point networks, thus enhancing the reliability of InSAR post-processing data.   References [1] Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78-89. [2] Bakon, M., Oliveira, I., Perissin, D., Sousa, J., Papco, J. (2017). A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, NO. 6., 2791-2798. [3] Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. R. (2018). Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data. Journal of Geophysical Research: Solid Earth, 123, 6592–6606.

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

Abstract Dagushan Iron Mine is the deepest open-pit iron mine in Asia, with abundantiron ore resources. With continuous open-pit mining activities, the stairs extend to underground step by step, and engineering geological conditions are gradually revealed. The factors affecting slope stability are also changing gradually, e.g., exposure of surface water and groundwater. The lithological structure and composition of the slope body are also changing, as well as the effect of blasting on the orebody during mining process, along with the change of the slope safety and stability. As a huge artificial loose accumulation body, instability of the dump will lead to disasters and major engineering accidents for the mine, which not only affectingproductivity, but also causing huge economic loss. Therefore, in order to ensure the safe operation of the mine, it is necessary to conduct slope stability monitoring with non-contact strategy. This kind of non-contact monitoring doesn’t need to install measurement points on the dangerous slope, and thus no need to worry about sliding problems of the measurement points. As an effective non-contact deformation monitoring tool, SAR interferometry has good potential in displacement monitoring of mines. With a stack of SAR images, time series InSAR is able to overcome spatial and temporal decorrelation problems, as well as the atmospheric phase artifacts, resulting in high precision deformation estimates[1][2]. Amongst various time series InSARalgorithms, small baseline subsets analysis (SBAS) is able to estimate deformation using all the high quality interferograms, which improves the utilization of SAR data and is suitable for analysis on long time series[3]. Therefore, the SBAS method is used to monitor the displacements in Dagushan open-pit iron mine. In this paper, 117 sentinel-1 images acquired from 2017 to 2019 are used, as well as the 3-arc-second DEM generated by the German TanDEM-X mission[4][5]. With height accuracy of approximately 1m, TanDEM-X DEM can be used to remove the topographic phase from the interferograms. During data processing, a super master is first selected according to the spatial and temporal baselines. All the slave images are coregistered to the super master image during coarse coregistration and fine coregistration. Then, high quality interferograms with small spatial and temporal baselines are generated following a multi-master strategy. With the high density in time and space, as many interferograms as possible participate in displacement estimation. The short spatial baselines can reduce the influence of DEM error on deformation estimates. In order to improve the quality of interferograms, Goldstein filter is applied on all interferograms. Then, phase unwrapping based on minimum cost flow is conducted for each interferogram.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out. Based on the unwrapped interferograms, the average displacement rate and displacement time series are estimated using singular value decomposition method. The estimated displacements map in line-of sight direction show that the northern slope, western part and the northern part of the dump suffer from severe displacements. In order to assess the precision of the displacement estimates, a comparison with on-site date collected by measurement robots is carried out. There is a very good consistency between the two results.  The outcome of this study can help with mine disaster prevention and mitigation, and provide technical support for ensuring safe mining activities.   Keywords: small baseline subsets analysis, displacement monitoring, open-pit mine   References [1]Ferretti, A.; Prati, C.; Rocca, F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2202–2212. [2]    Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20. [3] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation   monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions        on Geoscience & Remote Sensing, 2003, 40(11):2375-2383. [4]  Torres R ,Snoeij P , Geudtner D , et al. GMES Sentinel-1 mission[J]. Remote Sensing of         Environment, 2012, 120(6):9-24. [5] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50.

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

Land subsidence is one of the most common environmentalproblems inurban areas around the world [1,2]. It has been hindering social stability and sustainable development for a long time.The deformation of the earth's surface and the structures upon it is usually a long-term gradual process. As the economic and cultural center of northeast China, Shenyang is developing rapidly in recent decades.With continuous above-ground and under-ground construction, Shenyang is suffering from continuous subsidence during a long time span.Therefore,continuous subsidence monitoring is essential in Shenyang. As aspaceborne geodetic technology, synthetic aperture radar Interferometry (InSAR) is widely used in surface topography measurement and deformation monitoring.Using a stack of SAR images, Time Series InSAR is capable of overcoming decorrelation problems and monitoring land subsidence with very high accuracy. Several Time Series InSAR technologies such as Persistent Scatterer SAR Interferometry (PSI) [3], Small Baselines Subsets Analysis(SBAS) [4],Pixel Offset Tracking (POT) [5] and otherInSARtime series analysis algorithms have been widely used to monitor surface deformation.In this paper, three stacks of high-resolution TerraSAR-X and COSMO-SkyMed datasets are used to monitor the ground subsidence of Shenyang by means of SBAS. The COSMO-SkyMed images are acquired in descending orbit, including 15 images covering western Shenyang and 18 images covering eastern Shenyang. Both stacks are acquired during March 2016 and Apirl 2017. The 20 TerraSAR-X images are acquired in ascending orbit from August 2015 to October 2016. Besides, TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m × 90 m) covering the study area is used to simulate and remove topographic phase from the interferograms [6,7]. In this paper, the modified SBAS approach in StaMPS is used for time-series InSAR analysis, due to its ability to ensure temporal continuity by connecting separated subsets of interferograms with larger baselines. Theoretically, a complex multilook operation to mitigate the effects of the decorrelation noise should be independently carried out before generating interferograms[8].In this study, the spatial resolution of COSMO-SkyMed and TerraSAR-X are similar in size, so we could skip this step.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out[9-11].Based on an assumption that subsidence only happens in vertical direction, the estimated deformation in Line of Sight (LOS) is translated to vertical displacements. Targeting at revealing the long-term ground subsidence, a fusion method based on nonlinear curve fitting is implemented using the overlapping time period between the TerraSAR-X and COSMO-SkyMed datasets from March 2016 to October 2016.It is revealed that the synergistic results of COSMO-SkyMed and TerraSAR-X datasets can obtain a more comprehensive understanding of the slow-moving subsidence.The subsidence results in this paper show a very good consistency with geological conditions and ground water funnel distribution in Shenyang City.Generally speaking, most parts of Shenyang are relatively stable. However, there’s a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly happen to this area. Ground subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang.       IndexTerms—Time Series InSAR, Subsidence,SBAS, Fusion     5.REFERENCES [1]. Pradhan, B.; Abokharima, M.H.; Jebur, M.N.; Tehrany, M.S. Land subsidence susceptibility mapping atKinta Valley (Malaysia) using the evidential belief function model in GIS. Nat. Hazards 2014, 73, 1019–1042. [2].YusupujiangA , Fumio Y , Wen L . Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan[J]. Remote Sensing, 2018, 10(8):1304-. [3]Ferretti, APermanent scatterers in SAR interferometry.IEEE Trans Geosci Remote Sens 39(2001). [4] Berardino, Paolo , et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing 40.11(2002):2375-2383. [5] T., Strozzi , et al. Glacier motion estimation using SAR offset-tracking procedures.Geoscience& Remote Sensing IEEE Transactions on 40.11(2002):2384-2391. [6] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50. [7] The TanDEM-X 90m Digital Elevation Model. Available online: https://geoservice.dlr.de/web/dataguide/tdm90/ (accessed on 31 October 2018). [8] Rosen, P. A., et al. Synthetic aperture radar interferometry. Proceedingsofthe IEEE. 88.3(2002):333-382. [9] Lanari, Riccardo , et al. An Overview of the Small Baseline Subset Algorithm: a DInSAR Technique for Surface Deformation  Analysis. Pure and Applied Geophysics164.4(2007):637-661. [10] Manzo, Mariarosaria , et al. A Quantitative Assessment of DInSAR Measurements of Interseismic Deformation: The Southern San Andreas Fault Case Study.Pure& Applied Geophysics 169.8(2012):1463-1482. [11] Pepe, A., et al. The study of the deformation time evolution in coastal areas of Shanghai: A joint CX-band SBAS-DInSARanalysis.Geoscience& Remote Sensing Symposium 2015. [12]Jing,L, The study of Physical and Mechanical Properties of Soil and Engineering Geological In Shenyang City Center.[D]. 2015.

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

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

With the medium-resolution (~2.0 meter in azimuth and ~13.0 meter in range for TOPS/IW mode) SAR data, it is possible to acquire large scale deformation (>1000 km) in a continuous TOPS scanning. With the temporal sampling of 6-day or times of it, Sentinel-1 SAR data were quickly accumulated since later of 2014. However, processing of the large data set is a challenge, which is useful and/or a requirement for some typical applications, such as tectonic deformation analysis or anthropogenic activity monitoring for a vast region.    We utilize high-performance computation (HPC) for this purpose, which is widely used for scientific applications. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.   We applied our two-level processing approach in multiple challenge areas, the North China Plain, the Longmenshan area and other Tibet regions for both anthropogenic activity monitoring and tectonic deformation detections. Both areas are quite tricky for normal InSAR processing, but with our HPC parallel system, we acquire consistent results compared with GPS observations. The method conducted in these tests confirmed the robustness of our approach for deformation detection with Sentinel-1 large scale InSAR data. 

The North China Plain (NCP) is a vital agricultural region and is highly-populated, so the groundwater utilization is quite heavy in this region for irrigation and human beings. This leads to an overdraw of groundwater and fast subsidence over the whole area. We utilize high-performance computation (HPC) for detection of the related deformation. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.  Our processing shows that in the center of the NCP, farming activity produces widely distributed deformation, not only localized subsidence bowls as in typical subsidence regions. Our results are also consistent with GPS observations on this scale. Tectonic units in the same region could bound the subsidence behavior, hence both kinds of activities may have some kind of interactions.  

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

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

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

The objective of Dragon 4 Project 32396_2 aims at detecting land degradation in dry lands at a regional scale. The main achievements acquired during the last years could be summarized as follows: (1)    Assessment and monitoring of land degradation in dry lands of China: T With the development of remote sensing technology, long time series remote sensing data have been available for land degradation assessment and monitoring, and the vegetation indicators, such as the NDVI, NPP, Vegetation coverage and biomass were commonly used. However, time series vegetation index will fluctuate severely due to the impact of climate change, especially the fluctuation of annual precipitation, thereby the land production capacity could not be determined accurately. Therefore, to solve the problem, Xilin Gol League, Inner Mongolia Autonomous Region, China, where the land degradation is prevailing in the first decade of the 21st century was selected as the study area. Based on the annual NPP dataset estimated by 10-Day composite NDVI from Envisat-Meris data at 1.2km resolution during 2003 to 2013 and the same period meteorological raster dataset, a new Moisture-responded Net Primary Productivity (MNPP) method, for identifying areas of land degradation based on the change of annual NPP and MNPP over time and Moisture Index (MI) was developed. It was expected that provide technical support and scientific reference data for land degradation assessment and monitoring in study area, even in the whole drylands in China. (2)    Estimating Soil Organic Carbon Density in the Otindag Sandy Land, Inner Mongolia, China. Accurate quantitative estimates of soil organic carbon density (SOCD) can effectively represent regional carbon cycle processes and regulation mechanisms, and can serve as reference data when making land management decisions. Limited research, however, has been carried out in arid or desert zones covered with sparse vegetation, despite the fact that these cover wide areas of the earth and play a significant role in global carbon cycles. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) in the Inner Mongolia Autonomous Region of China was selected as the study area. The study introduces a useful technique for making high spatial coverage SOCD estimates for drylands by utilizing GF-1 WFV optical satellite images and a time series of MODIS satellite remote sensing datasets, and using these to optimize parameters for simulation models in conjunction with other technical procedures that are described. We expect this research to provide useful technical support and scientific reference data for land management and for land degradation/desertification assessments, for the study area monitored, as well as across the whole dryland area of China. (3)    Estimating Above Ground Biomass in the Otindag Sandy, Inner Mongolia, China by Using Sentinel-2 data. Above ground biomass (AGB) is an important measure of terrestrial ecosystem productivity, and it is used in quantifying the role of vegetation in the carbon cycle, the potential for energy production, and the carbon stock estimation for climate change modelling. Dryland AGB, also recommended as the indicator of land productivity by UNCCD in desertification assessment and monitoring, need to be quantitative assessed and evaluated. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) was set as the study area and a useful method for sparse vegetation aboveground biomass inversion in dryland was promoted. Firstly, the Sentinel-2 remote sensing data coved the whole area in growing season (May to September) during 2015 to 2018 and synchronous field survey data was collected and processed. Then, the estimation model was constructed by linear regression model, power function model, exponential model and machine learning model by taking band information, texture information and different vegetation index into consideration. In addition, total 2/3 field survey sampled AGB data were used for modelling, and the remaining 1/3 measured AGB data was set as the testing to evaluate the AGB estimation models. Finally, the AGB distribution of the OSLAIS was mapped and analysed based on the optimal model. This research is expected to provide technical support and scientific reference data for vegetation assessment and monitoring in the study area, and even across the entire dryland of northern China.

Land condition results from land use in a managed territory. However, the reverse holds too: only a subset of land uses can be applied given a state of land condition. Such reciprocal feedbacks can be of utmost importance for assessing the land management options of a territory. This was the main hypothesis of this study. To test it, we explored associations and dependencies between land condition states and land cover classes in the China drylands. More precisely, the study area was the Potential Extent of Desertification in China (PEDC), determined after applying the FAO-UNEP aridity index to an archive of climatic surfaces. The study period was 2002-2012. Land condition states resulted from the application of the 2dRUE method to an archived time-series of Net Primary Productivity (NPP), derived from MERIS satellite data by the CASA algorithm. Such states describe ecological maturity in terms of aboveground vegetation biomass and turnover, and lend well to an ordinal scaling. Land cover classes resulted from the aggregation of thirty-eight classes of level II built for China for the year 2010 following the Land Cover Classification System of the FAO. Land uses were excluded from this preliminary run. The spatial resolution for all the analyses was of 4 km. We performed two statistical tests on the described data set, stratified by aridity zones. First, associations between land condition states and land cover classes were determined by chi-square tests, using the Monte Carlo method. Wherever significant associations were found between these variables, we interpreted the standardized residuals to determine the significance and sign of individual combinations of the corresponding contingency table. The second test was a non-parametric ANOVA with unequal samples, using the Kruskal-Wallis and Median test. We also determined homogeneous groups of land cover classes (in terms of land condition) through non-comprehensive search of land condition differences in pairwise combinations of them. The associations between land condition and land cover resulted significant for all the dryland aridity zones. In general, areas of low vegetation cover such as desert or bare soil were positively associated with more degraded states, whilst higher vegetation cover was positively associated with states of higher maturity and complexity. As for the second test, it was significant too and two homogeneous groups of land cover could be formed for all the aridity levels. The results, as reported in this abstract, are somewhat limited because of the exclusion of proper land uses. The land cover classes used here were only five (Deserts and bare soils, Grasslands, Shrubs, Open forests and Forests) and these are likely to be controlled in balanced terms by physical gradients and human intervention. Still, 2dRUE detects land condition states after a climate correction, and both mature and reference states have been found in real deserts of North Africa, for example. This suggests that significant associations in this study between class Deserts and land degradation involves human management to some extent. In other words, this class might be a mixture of proper zonal deserts and desertified areas that were possibly with a denser vegetation cover in earlier times. The approach will be repeated using a full classification of land uses. Meanwhile, we can preliminarily conclude that it produces interpretable results that will help determining interconversion pathways between land cover classes, which in turn supports the paradigm that land degradation can be defined as loss of management options.

Approximately half of the world’s population is at the risk of at least one vector-borne parasitic disease. The survival of intermediate hosts of vector-borne parasitic diseases is governed by various environmental factors, and remote sensing can be used to characterize and monitor environmental factors related to intermediate host breeding and reproduction, and become a powerful means to monitor the vector-borne parasitic diseases. Schistosomiasis is a parasitic disease that menaces human health. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Landsat 8 and Sentinel 2 had been used to obtain the environmental factors (vegetation, soil, temperature, terrain et al.), which are related to the living, multiplying and transmission of intermediate host. Then this study used T-S Fuzzy RS model to establish a new suitable index membership function due to the different RS data, and a long time series dynamic monitoring of snail distribution in Dongting Lake were achieved. A comparative analysis had been performed to validate the predicted results against the field survey data. The results demonstrate the accuracy of the developed model in predicting distribution of snails.

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

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

The AMEOS (Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting) project aims at providing a tool for pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture. The previous two years of the project were devoted to delineate the procedure enabling the satellite imagery based monitoring of crop pests and diseases. In particular, with reference to the Xylella fastidiosa threats of olive groves the approach consists in: •     Improving the classification of the agricultural areas devoted to olive trees, starting from what has been made available from the Corine Land Cover initiative; •     Developing an approach suitable to be automated for estimating trees by using Sentinel 2 images. This tool will be used to monitor the spread of pest. •     Developing an approach suitable to be automated for counting trees by using very high spatial resolution images in areas at high risk of infection.   In the previous year, working on Sentinel-2 images and exploiting the characteristics olive tree phenology and carotenoid indices allowed to improve, respect to the Corine Land Cover, the classification of the olive groves in the area of interest. Further, the use of a morphological approach on NDVI computed by using Sentinel-2 images of 2017 allowed to assess the olive groves density and trees number for each crop field. The quality of the results were validated by using a VHR image. The present paper concerns the analysis of the possibility to monitor the decrease in the number of olive trees as a consequence of the spread of the xylella fastidiosa disease. The analysis has been carried out on Sentinel-2 images acquired in the second half of 2018. The final objective of the project is the development of a satellite based system capable to assess the evolution of diseases on both permanent crops (olive groves, vineyards) and row crops (wheat, maize), in Italy and China, aiming at developing an early working tool. Pathologically, the foliar biophysical variations are critical indicators for tracking the progressive host-pathogen interactions at different development stage. The interaction of electromagnetic radiation with plant leaves is governed by their biophysical constituents, and response to infestations. Hyperspectral- and multispectral- continuum observations could permit the acquiring of the host-pathogen processes within entire epidemic stages. With this in mind, the launch of the hyper-spectral satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa, HyperSpectral Precursor of the Application Mission, to be launched on the 15th of March 2019), could give a significant boost to the achievement of the project objectives.

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

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

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

Wheat powdery mildew is caused by the fungus Blumeria graminis and is one of the most common diseases that result in significant loss of crop yield and quality in China. Accurate monitoring of wheat powdery mildew at the regional level is important for food security and environmental protection. Traditionally, wheat powdery mildew is monitored by visual inspection of individual plants, which is time-consuming and inefficient. In recent years, a new satellite-based remote sensing technology has become a more viable option for managing and controlling agricultural practices. Wheat powdery mildew is monitored by remote sensing technologies based on changes in transpiration rate, chlorosis, leaf color, and morphology in infected plants, which in turn affects the spectral reflectance properties of wheat. The existing methods includes statistical analysis and machine learning methods. In general, wheat powdery mildew can be detected visually for only a short period of time and the field sampling is often expensive. Thus, these methods do not always meet the needs of agricultural management due to the difficulty of collecting field samples. Limited training data is a common problem in remote sensing applications and many studies have applied transfer learning in remote sensing to increase the quality and quantity of samples. However, thus far, the use of transfer learning techniques for crop disease monitoring has received limited attention. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. This study was carried out in Gaocheng city, Hebei province, and the auxiliary field survey samples were acquired from western Guanzhong Plain, Shaanxi province. The samples were categorized into three groups, i.e., normal, slightly diseased and seriously diseased, according to the disease index (DI). The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wetness and greenness were extracted using Landsat-8 OLI images to indicate the growth status of wheat and the field habitat characteristics. With these VIs, TrAdaBoost, which using the support vector machine with radial basis function kernel (RBFSVM) as weak learner, was used to develop the wheat powdery mildew monitoring model. At the initialization of the algorithm, an initial weight vector was given, and the maximum number of iterations was also determined. Per iteration, some samples were taken out from the auxiliary samples and study area samples, respectively. The removed samples were used to train a RBFSVM, and the weight of a sample indicated the probability of this sample to be selected to train the RBFSVM. At the end of each iteration, the error of RBFSVM on each training sample was calculated. If an auxiliary training sample was wrongly predicted, the sample likely conflicted with the study area sample. Then, we decreased its weight to reduce its effect, where in the next round, the misclassified auxiliary training sample would affect the learning process less than the previous round. In contrast, if a study area training sample is wrongly predicted, we increased its weight to improve its effect in the next round. Through this mechanism, the auxiliary samples that were useful for improving the monitoring accuracy of wheat powdery mildew in the study area were selected. The model was tested using a dataset with 53 study area samples and 39 auxiliary samples. The overall monitoring accuracy was 83%, and the kappa coefficient was 0.69. Moreover, TrAdaBoost was also compared with four algorithms that are commonly used to monitor wheat powdery mildew at the regional level, and TrAdaBoost performed better than other algorithms. Experimental results demonstrated that TrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

Since March the 22th 2019 the PRecursore IperSpettrale della Missione Applicativa – PRISMA is in orbit on a sun-synchronous orbit at 615km. The PRISMA mission, fully developed by the Italian Space Agency (ASI), combines two payloads: a hyperspectral and a panchromatic camera. PRISMA, together with the Chinese Gaofen-5 (GF-5), are the only hyperspectral sensor in orbit with similar characteristics in term od GSD and spectral resolution. The PRISMA hyperspectral payload is a pushbroom scanner covering the full range (VNIR-SWIR) from 400 to 2500nm with 239 spectral bands at a spatial resolution of 30 m with a swath of 30 km, while the panchromatic camera provides 5m pixel images co-registered with hyperspectral imagery. PRISMA is a prism spectrometer and it is characterized by a variable bandwidth across the nominal spectral range, nevertheless the band width is less than 12nm (i.e. between 7.3 and 11.04nm). The PRISMA coverage is global with a 29 days (orbit repeat period), while the revisit time on a specific area of interest is of 7 days thanks to the off-nadir angle of up to ± 20.7°. After the end of the commission phase (scheduled in the 2nd semester 2019), it is foreseen a structured three years CAL/VAL activity, which will be performed on scattered instrumented sites in Italy. The test sites have been selected according to the peculiar thematic areas of interest for the mission (e.g., topsoil characteristics, vegetation biophysical parameter retrieval, snow and coastal waters), moreover international test site are still under definition. The Cal/Val activities includes: airborne surveys with VNIR-SWIR scanner eventually coupled with thermal LWIR multispectral data, field activities contemporary to the PRISMA acquisitions. CAL/VAL activities will be planned, whenever possible, in synergy with ESA (i.e. Fluorescence Explorer – FLEX and the candidate CHIME hyperspectral missions) and the actual ASI missions’ development (hyperspectral mission SHALOM). The PRISMA acquisition plan is an opportunity to strengthen the ongoing collaborations between the Authors and the RADI Chinese colleagues in the framework of the active international collaborations programs (ESA Dragon-4 and CNR-CAS agreement). The opportunity to foster a synergy between the Italian PRISMA and the Chinese GF-5 missions, in order to increase the possibility to have more consistent hyperspectral time series suitable to monitor biophysical variables and agronomical processes.

This work aims to analyze the feasibility of application of multispectral imagery to detect anomalies caused by tree pests, towards an early warning monitoring system. The analysis take into account multispectral images from satellites as Sentinel-2 (10 mts. spatial resolution) and PlanetScope (3 mts. spatial resolution) imagery, and images obtained from sensors mounted on UAV (5 cms. spatial resolution), as MicaSense RedEdge sensor on board a UAV SR-SF6. The scope of this study is the evaluation of this set of images to locate tree crowns, and: 1) distinguish between symptomatic and apparently healthy canopies, and 2) between symptomatic and affected asymptomatic trees. The main pest of interest analyzed in this study is the Xylella fastidiosa (Xf), a vector-transmitted bacterial plant pathogen associated with serious diseases in a wide range of plants. Xf was detected on olive trees in Puglia, southern Italy, in October 2013, the first time the bacterium has been reported in the European Union. Since then it has also been reported as present in France, Spain and Germany. Controls are in place to prevent the bacterium from spreading. In 2016, the European Food safety Authority (EFSA) concluded that research being carried out in Apulia showed that certain treatments reduce the symptoms of disease caused by Xf but do not eliminate the pathogen from infected plants (EFSA 2016).  A significant difficulty that need to be taken into account for the containment of Xf, comes from its very wide host range (in September 2018 the list includes more than 500 plant species), and since infections that do not cause symptoms in some host–strain combinations, despite the infected hosts continuing to act as inoculum sources. In (Zarco-Tejada et al. 2018) a multi-temporal trees inspection and airborne imaging data in several orchards has been carried out. The analysis found the physiological alterations caused by Xf infection at the pre-visual stage were detectable in functional plant traits assessed remotely by hyperspectral and thermal sensors. And it was confirmed the presence of Xf infection in the selected orchards by the quantitative Polymerase Chain Reaction (qPCR) assay. The main area of interest for this study is region of Apulia, and as a reference, the current analysis took into account datasets and geo-locations of trees affected by this pest in olives orchards, identified during the field campaign carried out in (Zarco-Tejada et al. 2018). Furthermore, the study includes 3000 geo-locations of olive trees extracted from public records distributed by Apulia region, in cooperation with the Public Network of Research Laboratories (SELGE). The framework to carry out the multi-temporal analysis with Sentinel-2 imagery was carried out in the Data Integration and Analysis System (DIAS). DIAS archive includes satellite data and ground observation data. These datasets are stored in an large volume disk array accessible via API requests. The time lapse of the current analysis included Sentinel-2 images since 2016 until 2018, PlanetScope images for the period 2017-2018, and a UAV field campaign carried out in 2018. As in (Zarco-Tejada et al. 2018), common multispectral vegetation indices, did not differ significantly between asymptomatic and symptomatic trees, so unable to detect non-visual symptoms of Xf infection. In the present study, despite it was carried out an identification and masking of background soil, some differences could be mainly associated to changes on soil background more than with tree anomalies, since spatial resolution of satellite imagery seems to be scarce to be used for a tree focused study but for an analysis at a parcel level, where at the same time the heterogeneity of trees status could differ. Detailed analysis of multi-temporal vegetation indices profiles showed that remote sensing observations, can help to identify unexpected phenology patterns or anomalies that could be related to tree pests. Despite the capability detect individual trees vary according image spatial resolution, remote sensing techniques help to put into evidence a particular parcel where a further analysis need to focus.

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

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

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

Satellite and Argo observations of dissolved oxygen responses to “Wind Pump” in the Bay of Bengal Huabing Xu1, 3, Danling Tang1, 3*, Jinyu Sheng2, Yupeng Liu1, 3, Yi Sui2   Effects of tropical cyclones (TCs) “Wind pump” on dissolved oxygen (DO) in subsurface waters (20-200 m) over the Oxygen Minimum Zones (OMZs) in the Bay of Bengal (BoB) are examined based on Argo and satellite data. Five TCs (Hudhud, Five, Vardah, Maarutha and Mora) sweeping the central BoB during 2013-2018 are considered. Our analyses reveal three types of DO temporal variability caused by the storm-induced mixing and upwelling. The first type occurred during TC Hudhud features temporal increases of DO in subsurface waters (37-70 m) caused mainly by intense vertical mixing and downwelling. The second type features DO reductions in subsurface waters respectively after four TCs (Hudhud, Five, Maarutha and Mora) attributed to storm-induced upwelling. The third type occurring during TC Vardah features temporal increases of DO at depths between 40-79 m and decreases at depths between 80-150 m due to the combined effect of strong vertical mixing and upwelling. These three types of DO responses can occur in different areas, depending on TC intensity, translational speed and Ekman pumping. The temporal DO variability is also influenced by the shallow oxycline (58.3±16.7 m), mesoscale eddies and biochemical processes. Due to TC intensification, a pre-existing oceanic cyclonic eddy produced a large upwelling and induced a long time of DO decrease in the subsurface layer. This study suggests three different types of DO responses along TC track in the OMZ, which would help us to evaluate the influence of TC on OMZ. 

In studies of upwelling usually data from infrared and optical sensors are used which provide information on the sea surface temperature (SST) and the chlorophyll-a (Chl-a) concentration. In this paper, we show that also synthetic aperture radars (SAR) images can also give valuable contribution to such studies. Upwelling regions become detectable by SAR because they are associated with a reduction of the radar backscatter due to 1) a change of the stability of the air-sea interface or/and 2) the presence of biogenic slicks. While the change of the stability of the air-sea interface due to the presence of cold surface water in the upwelling region causes only a small reduction of the radar backscatter, biogenic cause a very strong reduction, usually of more than 10 dB. In areas of strong upwelling, the biological productivity is high due to increased nutrient supply from lower water levels. The biota living in this area secrete surface active material that ascends to the sea surface and forms there biogenic slicks, which often cover large areas in the form as monomolecular layers. The biogenic slicks damp the short-scale surface waves, which are responsible for the radar backscattering, as strongly as mineral oil films, and thus areas covered with the surface films become visible on SAR images as areas of strongly reduced radar backscatter (dark areas). Biogenic slicks often are entrained in the surface current field of eddies and thus render the structure of eddies visible on SAR images. This paper focuses on upwelling events off the coast of Namibia (caused by northward directed coastal winds), off the coast of South Africa (caused by  intensification of the  meandering Agulhas Return Current), off the southern coast of Sicily (caused by strong southeastward directed coastal winds, in particular by  the Mistral), and off the north coast of Taiwan (caused by the interaction of the Kuroshio Current with shallow bottom topography). These upwelling events are studied using Sentinel-1 and GF-3 SAR images, Modis SST and Chl-a maps and model data of geostrophic surface currents. We show that this synergism yields new insights into upwelling mechanism. In particular, we show that upwelling events are often associated with the generation of filaments, internal waves, and small-scale eddies, which are detectable by SAR. 

Rain is one common phenomenon usually observed on SAR images. Its signatures on C-band images are often composed of very bright patches and adjacent dark patches. It is caused by the radar backscattering or attenuation from rain-induced structures on the sea surface (ring waves, splash products, and turbulence) and the hydrometeors in the atmosphere (liquid water or ice). Although some models have been developed and laboratory experiments have been conducted to explain rain impact at C-band, the vertical non-uniform distribution of raindrops and the presence of ice aloft are often ignored. In this study, we co-analyze Sentinel-1 C-band SAR data together with high temporal and spatial resolution weather radar NEXRAD. NEXRAD provide different precipitation products, including rain rate (1 hour/0.25 km), basic reflectivity (less than 5 min /0.5 km) and hydrometeor classification (less than 5 min/0.25 km). More than 747 SAR images in both co- and cross-polarization have been collocated to provide statistics of NRCS under rain, at 1 km resolution and less than 5 min between SAR and NEXRAD. Our results evidence that NRCS at both VV and VH increase with rain rate, for low to moderate wind regimes. In addition, the very bright patches obtained at both polarizations are found to be in relation with melting ice particles in the atmosphere, by examining the NEXRAD hydrometeor classification. Our analysis also reveals the possible importance of rain impact after the rainfall, with a decrease of the backscatter observed after intense rainfall. This indicates the need to analyze the rain event history to take into account rain effect persistency after the rain event. Finally, examples in the specific case of hurricane are discussed.

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A semi-automated pocket FerryBox monitoring system was installed on a cargo ship in October 2015 to continuously record in situ chemical oceanographic data along a transect between two cities across the Bohai Strait, China for one year. In 2016, 2 cruises by research vessels that had an automated FerryBox system installed covered the Bohai Strait in the summer and winter. This report summarizes the outcome of this one-year campaign that used FerryBox as a water quality monitoring tool in the Bohai Strait. Temperature, salinity, dissolved oxygen, turbidity and saturation were the 5 parameters selected to describe the seasonal patterns in the Bohai Strait during the one-year period. Evidence of a short-term spring bloom event was found between the FerryBox transects, and the hydrodynamic factors that are the main controlling forces of the water quality in the strait are discussed. The importance of using FerryBox as a valuable and robust tool in future coastal operational observation networks is emphasized

Sand excavation can transport sediments into surrounding waters and thus raise the suspended sediment concentration. However, assessment of the area that is influenced by sand excavation and the strength of this influence is not easy due to the temporal and spatial variability of the background suspended sediment concentrations. Remote sensing can provide data before and after sand excavation activities and thus provides a possibility to estimate the variation in suspended sediment resulting from sand excavation. Sand excavation generally occurs in rivers or estuaries. The medium resolution of ocean color satellite data makes it difficult to obtain appropriate information in estuaries or rivers because of the spatial resolution of the sensors, the narrow spans of the rivers and the effects from the adjacent land. Sand excavation in the Pearl River Estuary has become frequent in recent years due to the development of urbanization in China. The Hong Kong-Zhuhai-Macau Bridge crosses the Pearl River Estuary and is the largest bridge and tunnel project in the world. The suspended sediment generated by upstream sand excavation was doubted to have a significant impact on the suspended sediment in the tunnel region. In this paper, we assessed the impact of upstream sand excavation on the suspended sediment in the Hong Kong-Zhuhai-Macau Bridge construction area using Landsat OLI, ETM+ and TM data. Regional suspended sediment algorithms were developed for Landsat using a symbolic regression method based on data from 25 cruises in the study area from 2003 to 2014. A band shift was conducted on the remote sensing reflectance data from Landsat ETM+ and OLI to produce a time series of suspended sediment that was internally consistent with that of Landsat TM data. The suspended sediment distribution was extracted and used to compare two different conditions, with and without sand excavation. The correlation of suspended sediment in different regions in the surrounding waters, including the correlation between the construction regions and the sand excavation regions, was calculated. Our results indicate that the sand excavation in the upstream region of the Pearl River Estuary has limited impact on the surface suspended sediment concentrations in the Hong Kong-Zhuhai-Macau Bridge tunnel area.

Several decades of glacier recession has led to the formation of large populations of glacial lakes in most glacierised high mountain regions of the world. In many of these regions, enhanced glacier retreat and mass loss has been observed from glaciers which remain in contact with a glacial lake over a prolonged time period. Such negative glacier-lake interactions may heavily influence the long term ice mass loss budget of high mountain regions, and heighten the threat posed by glacial lake outburst floods (GLOFs). Glacier-lake interactions remain scarcely studied in the Himalaya, thus our aim of this work is to quantify the impact of lake expansion on glacier mass loss and glacier retreat across the main Himalayan arc. We generated geodetic mass balance estimates for two time periods (1970s-2000 and 2000-2016) over several regions of the main Himalayan arc, and observed 39-51% greater ice mass loss from lake-terminating glaciers when compared to land-terminating glaciers. Lake-terminating glaciers contributed ~19% of the total ice mass loss across the region, across both time periods, despite comprising only 9% of the glacier population. The mapping of glacier terminus positions over coincident periods shows land-terminating glacier retreat rates of 6.6-12.4 m a-1, and 16.5-26 m a-1 for lake-terminating glaciers. Over the later time period, land-terminating glacier length reduced by 8.5%, whereas lake-terminating glacier length reduced by 19.2%. Our results emphasise the role of lake expansion in glacier evolution across the Himalaya, and the requirement to consider glacier-lake interactions in future assessments of glacier recession in the region.

There are more than 5000 glacial lakes > 0.003 km2 in the Third Pole region including the Pamir-Hindu Kush-Karakoram-Himalaya and the Tibetan Plateau. Around 30 lakes were identified as being potentially the most dangerous across the Tibetan Plateau. This assessment was based on four core determinates of GLOF hazard, including lake size, watershed area, dam steepness, and topographic potential for ice/rock avalanching. This study found that the potentially most dangerous lakes are located in the central Himalaya, the region where past glacial lake outburst floods (GLOFs) have occurred most frequently. Despite this fact, in-situ measurements relating to lake bathymetries, dam stability, and mother glacier dynamics are still very limited. We selected the most dangerous lakes in the central Himalaya (mainly in Poiqu basin), and mapped lake extents,  glacier outlines, their frontal positions and ice flow from optical remote sensing data, and calculated glacier surface elevation change from digital terrain models between 1970s and 2018.  Measurements of bathymetries, ground temperature, moisture and heat fluxes at different depths in the lake dam and outwash areas are ongoing. The stability of moraine dams, eventual failure and possible GLOF impacts will be modelled, and observations will be extended over long timescales. The glacial-lake change analysis presented in this study can significantly improve our knowledge of past lake evolution in the central Himalayas and the future GLOF threat.

Meltwater from rock glaciers could provide a relevant contribution to water supply especially in dry regions. Moreover, rock glaciers could have serious hazard potentials when located at or above steep slopes or when damming lakes. Existing investigations about rock glaciers in High Mountain Asia indicate that the landforms are abundant, but information is rare for the Tibetan Plateau and the northern slopes of the Himalaya. We compiled a rock glacier inventory for the Poiqu basin (~28°17´N, 85°58´E) – central Himalaya/Tibet. The mapping was mainly based on optical Pleaides imagery with 0.5m resolution. Rock glaciers were identified based on their characteristic shape and their surface structure. In addition, we generated a Pleiades DEM and used it for a) creating a hillshade to support rock glacier identification and b) to derive their topographical parameters. Additional information on the occurrence and activity of the rock glaciers was provided by the InSAR technique using ALOS-1 data. The results of the inventory reveal 370 rock glaciers covering an area of about 21.2 km2. The largest one has an area of 0.5 km2 and three have an area of more than 0.3 km2. The rock glaciers are located between ~3715 m and ~5850 m with a mean altitude of ~5075 m a.s.l.. The mean slope of all rock glaciers is close to 17.4° (min. 6.8°, max. 37.6°). Most of the rock glaciers face towards the Northeast (19%) and West (18.5%). Our study indicates that 147 rock glaciers can be classified as active. We also found rock glaciers damming lakes and rock glaciers located above roads which could threaten the infrastructure in case of instability. Preliminary results of rock glacier mapping of the same region, which were based on Sentinel 2 images with 10 m resolution and the 8 m High Mountain Asia DEM revealed slightly less rock glaciers (362) in numbers and but indicated a much larger rock glacier area (>40 km2). We conclude, that high resolution data is of utmost importance when creating a rock glacier inventory.

  Active rock glaciers (ARGs) and protalus lobes (PTLs) are characteristic periglacial landforms indicating the presence and creeping process of permafrost underground in an alpine environment. However, little information about such landforms has been provided in mountainous western China. In this work, we compiled an inventory including ARGs and PTLs in part of the West Kunlun Shan based on satellite Synthetic Aperture Radar (SAR) interferometry and optical images from Google Earth. Fifteen interferograms generated from ALOS-1 PALSAR images were used for identifying ground movements. Their geomorphic parameters such as aspect, area, altitude, and slope angle, were quantified using the SRTM digital elevation model. Within the 70000 km2 study area, we identified 67 ARGs and 22 PTLs. The preliminary results reveal that the mean downslope velocities of the ARGs and PTLs are 79 cm/yr and 25 cm/yr, respectively. The maximum downslope velocity for the ARGs is about 200 cm/yr. The aspects of the landforms vary significantly: 45% of the ARGs are located on northeast-facing slopes while 50% of the PTLs are located on southeast-facing slopes. Our inventory shows the total areas covered by ARGs and PTLs are 14.4 km2 and 1.2 km2, respectively. The largest ARG has an area of 0.7 km2. The ARGs are located between 4100 m and 5600 m and have a mean slope angle of 13°. While the PTLs are in a narrower band, between 4600 m and 5300 m and have a slightly steeper angle of 18°.Compared with the existing rock glacier inventories in High Mountain Asia such as the northern Tien Shan and the Hindu Kush Himalaya, the distribution density of ARGs and PTLs in the western Kunlun Shan is much lower. 

Approaches of monitoring earthquakes have evolved from conventional ground-based networks to include space. In the areas of seismology, geology and geophysics, scientists believe that the events leading up to earthquakes goes through a complex process and the process is somehow chaotic. Understanding earthquakes requires a breakthrough from traditional approaches to utilizing advanced technology. In fact, the seismology discipline has expanded the scope of earthquake study from conventional ground-based observations to space. In particular, since the Swarm satellite mission lunched in 2013, they have paved a way to provide a wide range of measurements in space by Vector Field Magnetometer, Absolute Scalar Magnetometer, Electrical Field Instrument, etc. instrumental sensors.  The measurements delivered by the three satellite are very valuable for a range of applications, including earthquake prediction study. However, for more than 5 years, relatively little advancement has been achieved on establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes before they occur. This report presents a continuous effort, describing essential functional components of a system for automatic anomaly detection. Through a case study we demonstrate the functionality of the system in detecting anomalies, and the process of data processing and analysis along with experience in developing a viable tool for precisely discovering seismic anomalies from the observed data by the Swarm satellites.

Earthquake is one of the most severe natural disasters. More than half of lives lost is caused by earthquake among natural disasters in the world. The earthquake also causes huge economic losses, e.g., about 800 billion RMB were lost during the Wenchuan EQ (Mw=7.8, 2008). Therefore, the governments and scientist of many countries, especially of those with frequent earthquakes, pay great attention to the study of earthquake prediction. It was in the 1960s that the study on the earthquake prediction started and national projects for EQ prediction designed in several countries e.g., China, Japan, USSR and USA (Chen, et al, 2000, Uyeda, 2015).   As well known, the earthquake prediction is a difficult scientific problem in the world. It is often debated and doubtful about that earthquake could be predicted and about whether there is any observable anomalous precursor prior to the earthquake. During the last decades, a lot of example of observable short-term precursors was published letting more scientists considering that there indeed exists precursor before an earthquake and it can be observed only if the reasonably effective method is used. The electromagnetic (EM) method is believed by scientists to be one of the methods that can be used to first reach success in short-term prediction. In this report, we will introduce the first EM observation network built recently using the alternate EM field and its exploitation for monitoring earthquakes. As an example, we will present how to use natural EM source to capture the precursors before the 5.1 Ms Yangbi earthquake in Yunnan province, China, along with a comparative study with the result detected from the Swarm electromagnetic data in the corresponding period of time. The study is supported by NSFC (41674081,41374077）and NDICC (15212Z0000001). The members of EM group of Institute of Geology, China Earthquake Administration and colleagues in the Yunnan Earthquake Administration joined the construction of the network and data observation.  

Since the Swarm satellite constellation launched November 2013, three satellites have delivered immense amounts of geomagnetic field measurements. Currently it is very extremely difficult for researchers to track observed data based on the instrumental sensors, such as Vector Field Magnetometer (VFM), Absolute Scalar Magnetometer (ASM), and conduct anomaly detection, it is imperative to develop more effective data analytics for detecting and discovering abnormalities in the electromagnetic time series data for earthquake studies. In this report, we propose a Martingale framework which could be adequate for assessing abnormal changes within electromagnetic data streams. The martingale method becomes essential as traditional statistical approach are inappropriate for the high dimensional electromagnetic dataset (Vapnik, 1998). The first step using the framework is to categorically obtain a practical data model in the machine learning standard scenarios through the use of strangeness measures. The strangeness measures establish a way of testing the exchangeability assumption of the dataset using a hypothesis test which drives the martingale process. The Martingale model will also involve the use of machine learning smoothing techniques to reduce noise and other interference efficiently making the framework more sensitive in detecting change point/anomaly. And finally, the model will be evaluated over Swarm electromagnetic data based on the two selected earthquakes, compared against a benchmark method and studied on its effectiveness in detecting abnormal changes before the earthquakes.

The pace of urbanization has been unprecedented. Today, 55 per cent of the world’s population live in cities and another 2.5 billion people is expected to move to urban areas by 2050 (UN, 2018).  The UN 2030 Agenda for Sustainable Development gives a prominent role to monitoring the urbanization process. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has been playing a crucial role in urbanization monitoring at regional and global scale. Several methodologies have been developed using Synthetic Aperture Radar (SAR)  and/or multispectral imagery to map urban extent globally including the Global Urban Footprint (GUF) and the Global Human Settlement Layer (GHSL). These datasets provide a reliable global map of the urban areas, but they are characterized by low temporal resolution (i.e. every five years) which highlights the need of further research and method development   The objectives of this research are two folds, one is to develop a globally applicable and entirely automatic method to monitor urban footprints using Sentinel-1 SAR and Sentinel-2 MSI dense time series exploiting the Google Earth Engine (GEE) cloud platform, and other is to evaluate derived urban extent for the monitorin of the UN Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate. The innovative aspects of the developed method is to integrate Sentinel-1 and Sentinel-2 dense time series using a totally unsupervised approach. The estimation of the selected urban footprint is performed in several progressive steps. First, the area of interest is divided into mountainous and non-mountainous areas using an available DSM (i.e. SRTM or ALOS World 3D) to take into account the layover and foreshortening of SAR geometric distortions. Then, Sentinel-1 ascending and descending time series are processed in order to enhance the backscatter of stable urban areas and to compute the Sentinel-1 Urban mask using an automatic thresholding procedure. The latest step is to compute a probability urban map combining the Sentinel-1 Urban mask with the Sentinel-2 multi-spectral time series. All available Sentinel-2 images, acquired during the selected sensing period, will be used to compute a cloud-free Sentinel-2 image composite, subsequently we applied a segmentation algorithm to the Sentinel-2 composite, and for each object, we compute several multitemporal spectral indexes statistics (i.e. Min/Max NDVI, Median NDBI, Mean NDWI). Finally, we use a ruleset to estimate the probability urban map combining the Sentinel-1 Urban mask with the computed Sentinel-2 indexes statistics.    To ensure its global applicability, we tested the developed approach in several cities worldwide (i.e. Beijing, Lagos, Milan, Mumbai, New York, Rio and Stockholm) characterized by different urban density and morphologies. We computed the urban footprint in different periods to evaluate the temporal stability of the method and to produce urban footprint time series. The results show that through this method it is possible to obtain high accuracy (kappa higher than 0.85) with respect to the reference data acquired within the EO4Urban project in all cities and in different periods [5]. The developed method obtains equal or higher accuracy than GUF and GHSL data in the same area, and a visual comparison shows that the integration of the Sentinel-1 SAR and Sentinel-2 MSI data leads to achieving highly detailed information. Based on the methodology defined by the UN Habitat, the urban extraction results are being used in the monitoring of Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate in the selected cities. The preliminary results show that timely and reliable urban extraction is essential for the definition of cities and for monitoring Urban SDG indicator 11.3.1. 

In the last ten years, the harmonization of rapid urbanization and extremely prosperous economic activity with the air quality and urban land use is the most concerned issue in Chinese urban development policy [1][2]. Accordingly, an urgent and challenging task is to improve the knowledge and understanding of change patterns in human settlements for fast-urbanized megalopolis in South and East Asia. Thanks to the availability of time-series of heterogeneous remote sensing data, it is now possible to explore these changes decoupling those due to urban expansion and those due to increasing economic activities [3]. In this work, we combine multi-temporal the sentinel-1 SAR C-band sensor and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime sensor (also called the Day/Night Band, or DNB) to explore urban change patterns at the geographical scale of Chinese Megalopolis. The joint use of heterogeneous sensor allows discovering more spatial-temporal features and deeper relationships between urban construction and nighttime-based changes, which indirectly reflect the connections between urbanization and economic development. Three megalopolis, namely the Jingjinji, the Yangtze River Delta and the Pearl River Delta have been selected, which correspond to the currently most developed and the most densely populated portions in P.R.China. First, Sentinel-1 SAR is used to extract urban extents that ensure the focus of our analysis is in built-up areas at the finest spatial resolution for freely available data sets. To handle big-size data over each Megalopolis, defined as a chain of roughly adjacent metropolitan areas, which may be somewhat separated or may merge into a continuous urban region, the critical preprocessing steps and computations are performed in Google Earth Engine (GEE). Then, data-driven unsupervised classification is used to explore change patterns according to a feature space joining the base and the change images. In this way, both the initial state and the temporal change pattern are considered. To ensure the reliability of unsupervised clustering, GMM, K-method, and DBSCAN are adaptively applied to the same feature space. At last, the 2-dimensional vector analysis are given to interpret the clustering results. Consider the resolution difference between the Nighttime light sensor and Sentinel-1 SAR, upscaling is applied to the SAR images to match VIIRS images at 500-meter resolution. The vector analysis according to the extracted clusters shows that these clusters are interpretable as meaningful temporal and spatial patterns, although the interpretability of the extracted cluster patterns depends on the classifier performance to different feature spaces. According to the most stable clustering results, change patterns of urban construction and nighttime fundamental facilities are clearly differentiated between core urban and suburban areas, and very new development city zone are singled out and highlighted.ß Ref: [1] Wang, Shuxiao, et al. "Effectiveness of national air pollution control policies on the air quality in metropolitan areas of China." Journal of Environmental Sciences 26.1 (2014): 13-22. [2] Wu, Yanyan, Shuyuan Li, and Shixiao Yu. "Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China." Environmental monitoring and assessment 188.1 (2016): 54. [3] Frolking, Steve, et al. "A global fingerprint of macro-scale changes in urban structure from 1999 to 2009." Environmental Research Letters 8.2 (2013): 024004.

Urban areas have developed mainly against a socio-economic paradigm ignoring to a large extent the environmental impacts Of particular concern for cities in our days, is the lack of balance between the natural, built and socio-economic environments, leading among others, to the degradation of their thermal environment, in particular overheating. Heat islands/spots within the urban ecosystems have negative impact to human health especially for vulnerable groups, increase energy use for cooling and lead to poor city energy efficiency, intensify energy poverty, deteriorate air quality and result in socio-economic problems in general. While in vegetated areas, evaportranspiration transfers most of the incoming radiation into latent heat, in built up areas sensible heat is generated, which leads to the strong heat load of urban areas. In addition buildings strongly affect the flow patterns of wind and heat, practically keeping the heat close to the ground.  Mitigation plans to counteract overheating are now developed by several cities around the world, in line to the Sustainable Development Goals of the United Nations and the new Covenant of Mayors for Climate and Energy, which recognizes the role of ecosystem-based mitigation in enhancing urban resilience and providing multiple benefits. Plans also take advantage of Nature Based Solutions (NBS) in an effort  to restore the urban ecosystems and achieve the needed balance between  the natural, the built and the socio-economic environments.   The scope of this paper is to assess the factors in support of a Planning Support System (PSS) for the design of the appropriate, science and policy wise, NBS so as to restore urban ecosystems, counteract overheating and improve thermal resilience.  

Regional land subsidence is an integrated systematic issue related to multidisciplinary and being of global focus, and has been being a serious threat to the urban infrastructure, high-speed railway and the utilization of underground space, and restricting the sustainable development of society. The study of the regional subsidence evolution in Beijing Plain is of great significance: it is necessary to reveal the regional land subsidence evolution pattern under the background of Integration of Beijing-Tianjin-Hebei and the South-to-North Water Diversion. Furthermore, it can help to realize the scientific regulation of regional subsidence and ensure the sustainable development of regional economy and society, which has a special significance and application prospect. Therefore the MT-InSAR method is used to obtain the regional ground subsidence time series information of the study area in three periods: Jun. 2003 ~ Aug. 2010, Oct. 2010 ~ Nov. 2015, and May. 2015 ~ Jun. 2018. Then equations are established based on the time-overlapping information to complete the fusion of multi-platform time series, the inconsistence between different reference points is solved, simultaneously. The results show that, the maximum subsidence values in Beijing Plain are 690.6 mm, 649.2 mm and 411.7 mm during the three periods, with maximum deformation rates of 100.6 mm/a, 130.0 mm/a and 142.3 mm/a, respectively. For the spatial distribution and the evolution of the land subsidence field, the weighted spatial kernel density analysis, profile analysis, trend-surface analysis and profile-gradient analysis are used to analyze the spatial-temporal evolution characteristics of the land subsidence field. In this case, land subsidence in Beijing Plain are thoroughly analyzed overall distribution characteristics and evolution process. Nine subsidence centers are identified and the subsidence centers are connecting to form a main subsidence area in the northern part of Beijing Plain. The spatial clustering degree of the subsidence in the Beijing Plain indicates an overall heterogeneity in spatial. Moreover, the northern subsidence areas spread along the Nankou-Sunhe fault, and is cut into several subsidence centers by active faults, indicating that the regional geological structure has obvious control effect on the spatial distribution of land subsidence areas. The evolution of the subsidence field: the northern subsidence areas spread along the northwest-southeast direction, and then expands to both the east and west sides. Then through the distribution of subsidence areas and groundwater funnel, the InSAR based time series and the monitoring well based groundwater level changes, the correlations in spatial and responses between land subsidence field and groundwater flow field are analyzed. The results show that the subsidence center in the northern Beijing Plain is consistent with the groundwater drop funnel in spatial, with a similar downward trend over the whole observation time. Through the analysis of well based results located in different areas, the long term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction; while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics as the groundwater level changes.

Cities worldwide experience enhanced heat stress, as a result of the impact of their surface properties and geometry on the surface energy balance. Land surface temperature (LST) from satellite thermal imagery can provide an overall view of extended urban areas, assisting in the identification of thermally vulnerable sites for mitigation responses. Several studies have demonstrated the relationship between LST and vegetation fraction within cities. Here, the Sentinel-3 SLSTR Level-2 LST product is used to examine the synergy between several two- and three-dimensional properties of the urban morphology (e.g. building height, canyon aspect ratio) and LST, for summer conditions. Results, demonstrate that at daytime open impervious urban areas experience higher temperatures than densely built neighborhoods. The contrasting thermal patterns of nighttime imagery reveal the surface heat island development cycle, and indicate the potential caveat of using solely LST as a map based planning tool, due to its dependence on satellite overpass timing. Furthermore, the surface temperature distribution is examined in the context of Local Climate Zones (LCZs); in general, a considerable differentiation among LCZs is found, although local circulations exhibit a stronger control on coastal zones

Benefit from steering antenna beam, TOPS (Terrain Observation by Progressive Scans), the default imaging model of Sentinel-1, can obtain wide coverage with azimuth-invariant SNR and at the same time avoid scalloping. Meanwhile, the introduced Doppler centroid difference higher than 5000 Hz requires a stringent co-registration accuracy to prevent phase discontinuities over burst boundaries. For multi-temporal analysis, it is even harder to achieve the required accuracy. The residual mis-registration can bias the extracted geophysical inversion parameters such as surface deformation. To improve the co-registration accuracy, a co-registration approach based on Floyd-Warshall algorithm and Enhanced Spectral Diversity is proposed. For further improvement in low coherence regions, we present a coherence estimator by combining two consecutive bursts SLC samples. The performance of the presented approach is validated by two low coherence scenes.

PolSAR data has become the significant data source in urban research. However, the widely used methods extract features at the expense of spatial resolution loss. A PolSAR feature optimization approach is addressed. The new method relies on the adaptive selection of homogeneous samples, both polarimetric and spectral characteristics are taken into account. Those homogeneous samples are first applied to suppress the speckle and to refine the feature estimators afterwards. The comparison with the state-of-the-arts using real-world datasets shows that more accurate PolSAR features with well-preserved edges can be obtained over textural regions.

Urbanization is one of the main factors to cause the land use change in the world, the urban area is rapidly expanding with the accelerated process of urbanization, so the knowledge of urban distribution can provide a reliable technical and decision-making basis for urban planning and development potential evaluation. Remote sensing technology has played an active role in the extraction of urban land, and the urban light at night can be used to analyze the city expansion trend and development potential through light intensity and change rate. In this study, cities in Southeast Asia were taken as the research objects, and landsat8 OLI remote sensing data was taken as the main data source. Firstly, spectral characteristics of different objects were analyzed, and the thresholds of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were determined to remove the vegetation and water. The difference between the remaining objects were enlarged after the removal of vegetation and water, the normalized difference building index (NDBI) and the minimum distance classification method were adopted to eliminate the unplanted farmland and bare land. The results showed that this method was simple and capable to extract the impervious land step by step and the accuracy was above 85%. The slope of light change was calculated based on the light data of Defense Meteorological Satellite Program (DSMP) between 2000-2013, in order to analyze the development trend and potential of urban and its surrounding area. The results showed that the urban light index increased at an average annual rate of 0.75 in the built-up area and the surrounding area grew at a high rate of 1.03 per year in the past years from 2000 to 2013, which showed a high density and quick growth rate. It still maintained a strong growth trend and had great potential and space for development.

Over the past two decades, there has been substantial urban growth in Stockholm, Sweden. As a result of accelerating urbanization, Stockholm is now the fastest-growing capital in Europe and it is expected that the Greater Stockholm area will more than double in population by 2045, to 4.5 - 5 million people. The Swedish government has recently taken steps to ensure sustainable management of its green and blue resources in urban areas by requiring all counties to draw up regional plans for their green infrastructure. Using Sentinel-2 and SPOT 5 images, this research investigates the evolution of land cover change in Stockholm County between 2005 and 2015 with a particular focus on what impact urban growth has had on protected green areas, green infrastructure and urban ecosystem service provision. One scene of Sentinel-2A MSI imagery from 2015 and ten scenes of SPOT 5 imagery from 2005 over Stockholm County were selected for this study. These images are classified into 10 land cover categories using an object-based SVM classifier with spectral, shape and texture features as inputs. The classifications are then used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2005 and 2015, including generation of land cover change statistics, urban ecosystem service provision bundles which include spatial configuration information and evaluation of impact on legislatively protected areas as well as ecologically important habitat networks. Preliminary results indicate that Urban areas increased by15% or approximately 116 km2 while non-urban land cover, mainly agricultural areas and green structure, decreased by just under 4%.   The increase in urban areas is just over 2% of the total county land area.  More specifically, the results suggest that urban areas may soon overtake agricultural areas to become the second largest land use/cover category in the county landscape after forest. The largest increases in urban areas and significant losses of green structure occurred mainly in the northern and southern outskirts of the county in the rural-urban fringe, with the exception of two municipalities close to Stockholm city which also experienced significant urban growth. In terms of ecosystem service provision, notable decreases occurred in temperature and global climate regulation, air purification, noise reduction and recreation, place values and social cohesion. Urban areas within a 200m buffer zone around the Swedish EPA’s nature reserves in Stockholm County increased by 16% over the decade, with several examples of new urban areas constructed along the boundary of nature reserves. Further research will include evaluation of important ecological networks in Stockholm county, such as its regional green wedges and broadleaved forest distribution, to see how these have been affected by the urban growth. The results of this study can assist policymakers and planners in their efforts to ensure sustainable urban development and natural resource management for the Stockholm region.

Over the past several decades, the unprecedented pace of urbanization and socioeconomic development in China has placed great anthropogenic pressures on inland surface water quality. In recent years, continuously increasing environmental investments have been undertaken to control pollutant discharge and improve inland water quality across the entire country. However, the quantitative response of water quality to both increasing human pressure and effort is less well understood, particularly for a large-scale region with diverse driving factors. In this study, we use satellite-derived nocturnal radiance signals as proxy measures for both the negative (using the product of lit area and population size) and positive (using the area-weighted magnitude of nighttime lights) effects of human activity to evaluate how water quality changes over time in response to anthropogenic disturbances. Our method clearly demonstrates the extended application of remotely sensed data of nighttime lights with dual actions, particularly in the absence of direct observations of socioeconomic variables due to their consistent, timely and spatially explicit proxy measures for diverse human activities.

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