Conference Agenda

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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 frequencies
and provide different imaging modes, there is a need to re-evaluate strategies for operational sea
ice 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 different
radar frequencies, different polarizations, and spatial resolutions. Examples will be shown that
demonstrate how the anaylsis of sea ice conditions can be improved for a given problem such
as thin ice detection and classification or identification of ice deformation structures. For this purpose
we 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 sources
such 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 k_Δgray-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 6^th 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.

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[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.

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[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.

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[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.

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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 2^nd 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 k_s are therefore estimated by subtracting double-bounce contributions k_st+k_sv from total ground response, i.e. k_s = k_g − (k_st+k_sv).

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