Conference Agenda

In recent years, ESA, following a proposal of the UK Space Agency, USA, and China have started to implement the concept of creating an SI traceable Satellite – SITSat. The main idea is to establish in-orbit reference radiometric calibration of other sensors based on a SITSat. In this concept the SITSat’s are must have the property that they have a robust documented uncertainty to SI in-flight and can be considered a benchmark for radiation measurement and in this case only a few such satellites need to exist to provide the space based calibration reference for transference to other satellites. However, as the high-resolution spaceborne sensor, with small swath is concerned, the cross-points (between the benchmark satellite and monitored satellite) is rarer to find if very strict matching conditions are required. So, this project explores a complimentary method of benchmark transfer calibration for the high-resolution space-borne sensor, which uses RadCalNet sites measurements as the intermediate radiometric reference value. It will benefit to solve the problem of increasing cross-calibration uncertainty and limited cross-calibration frequency caused by the relaxation of matching constraints. In the past year, great achievement has been done in designing overall research scheme and analyzing the sources of uncertainty, exploring preliminary cross-calibration of optical satellite with high-precision radiometric reference satellite over Baotou site in China as demonstration.

(1) Transfer the benchmark from the SI-traceable sensor to the RadCalNet TOA reflectance. The TOA reflectance model of ground target (e.g. Baotou site) was constructed using satellite observation data with high radiometric calibration accuracy. Then the model was used to correct the RadCalNet standard TOA reflectance products. The corrected RadCalNet TOA reflectance was used as a radiometric reference benchmark, which can be traced back to reference satellite sensor. Finally, the corrected RadCalNet TOA reflectance was used to calibrate the to-be-calibrated satellite sensors. Through this method, the uncertainty of cross-calibration between the reference satellite and the satellite to be calibrated caused by the relaxation of the time matching constraints can be reduced.

(2) The proposed method was validated and analyzed by taking Landsat8/OLI as the radiometric reference satellite, TOA reflectance products of sand target in Baotou site as the ground target and Sentinel-2A/2B and SV-01 satellite as the be-calibrated satellite. The results showed that the accuracy of Baotou sand target TOA reflectance model established in this study is quite good, which the average relative difference between the predicted values of the model and the observed values of Landsat8/OLI satellite is less than 1% (the band 4 is less than 2%). By using this model, the relative difference between TOA reflectance product of Baotou site and the actual TOA reflectance observed by Sentinel-2 and SV-1 can be reduced effectively from 6% to less than 3%. These experiments and results validated the effective of proposed method.

(3) This project will develop transfer calibration for ESA and TPM satellites: using Landsat-8 and sentinel-2A/B as reference satellites, and Chinese satellites (such as GF series, ZY series and SV series satellites) as to-be-calibrated satellites, carry out transfer calibration demonstration based on RadCalNet sites. At present, 1 postgraduate student will get Master Degree in 2021, and 2 postgraduate student students and 2 young scholars already participate in the Dragon 5 program. And the in-situ data measurements of Baotou site already provide standard calibration product to support this research. In the future, the data of other sites in China and other RadCalNet sites in Europe will provide radiometric calibration data, to improve and guarantee the radiometric calibration accuracy and data quality of Chinese and European satellites by carrying out application demonstration of automated radiometric calibration based on RadCalNet and the proposed method.

Surface reflectance is the fundamental quantity required in the majority of optical Earth Observation analyses, and as an essential input to derive biophysical products. These products, which include essential climate variables (ECVs) such as leaf area index (LAI) and the fraction of absorbed photo synthetically active radiation (FAPAR), in addition to parameters such as the fraction of vegetation cover (FCOVER) and Canopy Chlorophyll Content (CCC) , provide insight into the state and function of the terrestrial environment. In turn, they are crucial in understanding vegetation productivity/yield, biogeochemical cycles, and the weather and climate systems. Therefore, validation of such products are of great importance to ensure they meet the accuracy requirements for specific applications.

The aim of this project is to evaluate the capability of UAVs as a source of reference data for validating decametric surface reflectance and vegetation biophysical products, with a specific focus on the European Sentinel-2 and Chinese Gaofen-6 missions. The project will provide an opportunity to transfer knowledge gained from existing ESA-funded projects on fiducial reference measurements (FRM), which focus on traceability and uncertainty evaluation in Earth Observation validation efforts.

In-situ measurements shall be collected using a combination of instruments, LAI-2200C plant canopy analyser and digital hemispherical photography for obtaining LAI, FAPAR and FCOVER data. Leaf chlorophyll content (LCC) obtained with the Minolta SPAD-502 chlorophyll meter shall be combined with LAI data to derive in-situ measurements of CCC.

In the first year of the project, existing ground measurements obtained during previous field campaigns at three sites (Wytham Woods, UK (51.774°, -1.338°) and Taizi Mountain and Wangmang Cave, China (30.916°, 112.866°) , will be used to calibrate and validate a prototype processor for deriving biophysical variables from Gaofen-6 data. These products will be compared with the biophysical variable derived from Sentinel 2 L2 processor to evaluate their similarity and differences, with a view to exploit the complementary from both satellite sensors. In addition, the suitability of drone images collected over the Chinese sites will be evaluate to bridge the scale gap between the ground measurements and Sentinel 2 image.

The schedule for the project, detailing the field campaigns, processing chains and planned academic exchange activities shall be presented.

In August 2018, ESA’s Earth Explorer mission Aeolus has been successfully launched to space. Since then Aeolus has been demonstrating its capability to accurately measure atmospheric wind profiles from the ground to the lower stratosphere on a global scale deploying the first ever satellite borne wind lidar system ALADIN. In order to validate Aeolus wind products several airborne campaigns were performed over Central Europa and the North Atlantic region (most recently in autumn 2019 in Iceland), employing the ALADIN Airborne Demonstrator (A2D) developed by DLR (Deutsches Zentrum für Luft- und Raumfahrt). Ground-based direct-detection and heterodyne Doppler wind lidar and ocean lidar are developed by the Ocean University of China (OUC) and deployed during several field campaigns, including the sailing competition within the Olympic Games in 2008 in Qingdao and the atmospheric explorer in Tibetan Plateau Experiment of Atmospheric Sciences (TIPEX III). The Shanghai Institute of Optics and Fine Mechanics (SIOM) of the Chinese Academy of Sciences (CAS) developed a ground based direct-detection wind lidar in 355nm and a airborne coherent Doppler wind lidar. SIOM is responsible for several ground validation stations for future spaceborne atmospheric lidar in China, which may provide useful aerosol and wind profiles data for Aeolus validation. The National Satellite Meteorological Center (NSMC), China Meteorological Administration (CMA) is responsible for receiving, processing the data of Chinese FY meteorological satellites, and distributing the data and information products to users for application. Apart from that, it is envisaged to investigate the capability of measuring the marine boundary layer with Aeolus and to measure marine optical properties with co-located shipborne ocean lidar systems during overpasses of Aeolus. The first part of this proposal covers the validation of Aeolus wind and aerosol data products by means of ground and airborne observations with the objective to improve the quality of Aeolus operational data products. Global observations of column carbon dioxide concentrations and aerosol extinction profiles are important for climate study and environment monitoring which is why China decided to implement the lidar mission ACDL (Aerosol and Carbon dioxide Detection Lidar) to measure CO2 and aerosol from space - currently scheduled for 2021. Within this framework a spaceborne engineering prototype of the ACDL lidar is being developed and an airborne lidar prototype for column carbon dioxide concentration measurements was developed by Shanghai Institute of Optics and Fine Mechanics (SIOM) of the Chinese Academy of Sciences (CAS). The second part of the proposal covers the preparation of the ACDL mission with the objectives to analyse requirements for column carbon dioxide concentration and aerosol extinction profile measurements of the ACDL lidar for science applications and to validate the retrieval algorithms for carbon dioxide and aerosol parameters for the future space mission.

Remote sensing of ocean color over coastal waters is challenging and these difficulties can be placed in 3 categories: i) adverse atmospheric conditions associated with the presence of thin clouds or thick aerosol plumes (sometimes biomass burning), ii) challenging environments found over or around the water target (boundary conditions); iii) extreme conditions associated with the water content in optically active constituents (high concentrations of sediments). Evaluation and improvements of the estimation of bio-optical and biogeochemical parameters is an indispensable task for accurately monitoring the dynamics and the quality of coastal waters through the use of ocean color remote sensing. Especially, with the improvement of sensor ability and the advent of novel retrieval algorithms/models, ocean color is playing a more and more important role in understanding, the utilization, protection and management of coastal environments. Ocean color data can thus provide biogeochemical data with known uncertainty, which is of great importance for quantitatively characterizing variation of key elements in coastal ecosystem and is required for input in modelling.

Our project aims at tackling those issues over European (mainly French) and Chinese coastal waters. The main scientific objectives concern the monitoring of the quality of the French and Chinese coastal waters using OLCI and COCTS/CZI space-borne sensors. The project is divided into different tasks: (1) Characterization of uncertainty of OLCI and COCTS/CZI ocean color products in coastal waters; (2) Development of novel regional EO datasets in coastal waters. The first task aims at evaluating the atmospheric correction and bio-optical algorithms of OLCI and COCTS/CZI in our two areas of interest using in-situ measurements collected by both teams and the second task aims at developing regional bio-optical algorithms for the Chinese/French coastal waters according to specific spectral configuration of COCTS and OLCI.

During the symposium, we will present the objectives of the project with detailed description of each task, the in-situ measurements collected by both teams that will be used to validate the different algorithms and the plan for training young scientists.

Project name: All-Weather Land Surface Temperature at High Spatial Resolution: Validation and Applications.

Project’s objectives: The main objective is to inter-compare and validate two new LST products, which provide (nearly) gap-free all-weather LST at high spatial resolution. The two all-weather LST products utilise different retrieval approaches, namely the method by
- Zhang et al. (2019): temporal component decomposition and merging of TIR LST with passive microwave (PMW) LST.
- Martins et al. (2019): merging of clear-sky MSG/SEVIRI LST with LST generated by a Soil-Vegetation-Atmosphere (SVAT) model under cloudy conditions.

Further objectives: i) Generation of long-term (global) all-weather LST data set; ii) Setting up an LST validation station in China to provide Fiducial Reference Measurements (FRM); iii) Employing all-weather LST data to simulate and study freeze/thaw on the TP.

Major progress: Land Surface Temperature (LST) is an indicator for the exchange of energy in the process of atmosphere-ground interaction. The all-weather LST at high spatial resolution is required for understanding and simulating regional processes of meteorology, hydrology, and ecology. The project team has completed a series of validation, algorithm development, and product generation.

Due to considerable temporal gaps between AMSR-E and AMSR2 observations from November 2011 to May 2012, the current version of integrated LST based on MODIS-AMSR-E/2 data is not really an all-weather product. To solve this problem, the project team used Chinese Fengyun-3B MWRI brightness temperature (BT) to reconstruct a spatial-seamless (i.e. without the two major gaps) AMSR-E/2-like microwave (MW) BT based on MWRI data for 2011–2012 over the Tibetan Plateau (TP) and to estimate a realistic 1-km all-weather LST by integrating reconstructed MW BT with Aqua-MODIS LST. Based on the in-situ measurements from Heihe Watershed Allied Telemetry Experimental Research (HiWATER) and Watershed Allied Telemetry Experimental Research (WATER) in the Heihe River basin, and networks operated by other Chinese groups on the TP, the project team validated the estimated all-weather LST with RMSEs of about 1.45–3.36 K.

Passive microwave (PMW) is an effective means to obtain surface temperature under clouds, thus the PMW-LST accuracy is critical for all-weather LST. The project team used a convolutional neural network (CNN) to estimate LST from the AMSR-E and AMSR2 data over the Chinese landmass. The intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s Glob Temperature AMSR-E LSTs. Validation against in-situ LSTs showed that the CNN LSTs yielded RMSEs of 2.10–4.72 K for forest and cropland sites.

Reanalyses data from Global Circulation Models (GCM) have the advantage to be spatiotemporally continuous: therefore, they offer a promising alternative to be merged with TIR data in order to reconstruct an all-weather LST product. Based on the decomposition model of LST time series, the project team proposed a novel method to reconstruct a 1-km all-weather LST, which is termed ‘reanalysis and thermal infrared remote sensing data merging’ (RTM). RTM was applied to merge (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the TP and the surrounding area. Validation results based on in-situ LST show that the RTM LST has RMSEs of 2.03–3.98 K.

Based on the method of Zhang et al. (2019) and RTM, the project team has produced and released two all-weather LST products: i) Daily 1-km all-weather land surface temperature dataset for Western China V1 (2003-2018) and ii) Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST-China (TRIMS LST-China; 2000-2019).

At the EUMETSAT LSA-SAF, the operational “All-Sky LST” production and distribution is now underway. The product is based on optical observations by SEVIRI (onboard MSG), delivering data every 30 min with a 3 km resolution at nadir, for the whole SEVIRI disk encompassing Europe, Africa and part of South America. The product has been thoroughly validated against in-situ data collected from 33 stations located over a wide range of biomes, distributed by global networks (e.g. BSRN, SURFRAD, KIT and EFDC), with comparable RMSEs between clear and cloud conditions (2.8 K and 2.9 K, respectively). Comparisons with AMSR-E (Martins et al., 2019) and with ERA5-Land (MLST-AS Validation Report) have highlighted that most satellite to in-situ discrepancies may be explained by land surface heterogeneities, directional effects, the presence of deep/opaque clouds and high desert aerosol loads. This information will be useful to contrain the product uncertainty, which will be one of the outcomes of this project.

Based on a highly standardized instrument package for LST validation developed for Copernicus LAW (https://law.acri-st.fr/home), the project team adapted and built an instrument package to be deployed within this Dragon 5 project on a suitable Chinese validation site. The package’s main instruments are two long-term stable, narrow-band TIR radiometers, which have been calibrated against KIT’s certified reference source; furthermore, the entire instrument package has been tested intensively. During an inter-comparison study performed in September 2020 on Lake Constance, an identical instrument package was inter-compared against the ISAR (Infrared Sea Surface Temperature Autonomous Radiometer), which is constantly calibrated against two internal blackbodies: the in-situ LST obtained with the standard instrument package only had a bias of -0.09 K and a standard deviation of 0.06 K w.r.t. the ISAR.

The next schedule: i) The project team will further inter-compare TRIMS-LST with the original two products. ii) Based on existing infrastructure, the Chinese team will make efforts to set up a new LST validation station on the TP (or its nearby areas). The station will provide highly accurate in-situ LST and can draw on KIT's technical and scientific support. iii) the project team will use all-weather LST to calibrate and evaluate the hydrological model on Tibetan Plateau. iv) the project team will inter-compare co-located all-weather LST based on the Martins et al. (2019) and Zhang et al. (2019) methods and validate both products with in-situ LST from KIT’s permanent validation station at Gobabeb, Namibia. Other new methods will also be considered.

With the support of Dragon-5 project, the Chinese team's Ph.D. student, Jin Ma, went to KIT for a one-year exchange and has now returned to China after completing the exchange.

The monitoring and forecasting of global water cycle under climate changes indeed require enhancement of satellite remote sensing products in both of spatial resolution and accuracy. To strengthen the ability of microwave remote sensing in global water cycle studies and seek for new opportunities of satellite missions, we put forward research contents as follows in the first year of project implementation:

(1) Refinement of the SMOS Multiangular Brightness Temperature with the Adoption of SMAP Observations

The Soil Moisture Ocean Salinity (SMOS) was the first mission to provides L-band multiangular brightness temperature (TB) at a global scale. However, radio frequency interference (RFI) and aliasing issues jeopardize part of its scientific applications in certain areas of the world. The Soil Moisture Active Passive (SMAP) mission provides the L-band brightness temperature at a fixed incidence angle of 40° with the RFI effects being well detected and filtered. In this study, we proposed a method called three-step regression to refine the SMOS multiangular TB with the adoption of SMAP observations through two options of anchor regression and translation transform, resulting in a multiangular TB dataset which is highly consistent with the SMAP TB. Results show the three-step regression can represent the multiangular L-band TB even for areas strongly affected by RFI, and improve the spatial and temporal coverage of SMOS. The evaluation results with 12 soil moisture networks show that the R2 between the refined TB dataset and in situ soil moisture has a significant improvement at strong RFI contaminate regions as compared with that of Centre Aval de Traitement des Données (CATDS) L3 daily TB product. The cumulative density function (CDF) of the refined TB at 40° are consistent with that of SMAP at a global or regional scale, which would promote the development of a consistent SMOS-SMAP TB and soil moisture products. (Submitted to IEEE TGRS)

(2) Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm

We explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended. (Published in Remote Sensing of Environment)

(3) A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019)

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently lunched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002-2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m3/m3. NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable though the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability. (Published in Scientific Data)

Soil moisture plays an important role in land surface processes by controlling the partitioning of global radiation into latent and sensible heat fluxes and the partitioning of precipitation into surface runoff and infiltration. Acquiring accurate soil moisture information over large areas remains a challenge. Assimilation of remotely sensed soil moisture into land surface models has been proven an effective way to generate more accurate soil moisture data products, but there are still several limitations. For example, it is observed that evapotranspiration estimates are hardly improved by soil moisture assimilation. Land surface models have in general an over-simplified representation of groundwater dynamics. In this work, we investigate the assimilation of soil moisture information from the SMAP satellite into the coupled land surface-subsurface model CLM-ParFlow, components of the Terrestrial Systems Modeling Platform (TSMP). CLM-ParFlow solves the 3D Richards´ equation for water flow in the subsurface, as well as overland flow by routing. We investigated whether this mechanistic representation of subsurface flow processes in combination with data assimilation results in a better characterization of soil moisture and evapotranspiraton than with the stand-alone land surface model CLM. A study was carried out over parts of western Germany, for the years 2017 and 2018. The SMAP soil moisture data is assimilated with the Ensemble Kalman Filter (EnKF), in some simulation scenarios including hydraulic parameter estimation. The simulated soil moisture and evapotranspiration (ET) time series are evaluated with in-situ measurements from Cosmic Ray Neutron Sensors (CRNS) and Eddy Covariance (EC) stations. Simulations illustrate that there is no systematic bias between soil moisture from SMAP and CLM-ParFlow. The soil moisture characterization improves with data assimilation and CLM-ParFLow captures better spatial patterns than CLM stand-alone.

The objective of this project is to assess the feasibility of remotely sensed products of key cryospheric and hydrological elements (snow, evapotranspiration, soil moisture and precipitation) in representative regions across the Third Pole region and the Heihe River Basin of China and selected test sites in other regions, e.g. northern Finland. The in-situ measurements used to validate remotely sensed products have been collected from several ground-based observation networks including the Finnish Meteorological Institute (FMI), the TERrestrial ENvironmental Observatories (TERENO), the Agrosphere institute (IBG-3) and The Qilian Mountain Observatories (QMO). Essential remote sensing products e.g. the GlobSnow data sets covering northern hemisphere and the soil moisture data set from SMOS, were evaluated by referencing ground-based observations in representative regions. The upscaling methods were developed to improve the representativeness of ground-based observations to remote sensing pixels. The validated products were also inter-compared with other gridded products, and the spatiotemporal trends were diagnosed by statistical indexes, e.g., RMSE and correlation coefficient. The performance of each product will be further evaluated in different landscapes, topographic conditions in the representative regions selected in China and Europe. The research results have been submitted to or published in international journals such as Remote Sensing and the Cryosphere. In addition, young scientists on this project made considerable efforts to observe snow, evapotranspiration, soil moisture and precipitation. They also assist with the validation of remotely sensed products on preprocessing data, developing validation algorithms and writing validation reports.

In the context of global climate change, drought, flood and wetland vegetation change caused by hydrometeorological change have caused great variations to the food, water, soil resources and ecological environment on which human beings depend for survival. The hydrometeorological characteristics in middle and lower Yangtze River Basin have significant spatiotemporal heterogeneity. A series of in-depth study of the hydrometeorological changes in middle and lower Yangtze River Basin and its impact on wetland vegetation is of great significance to natural, economic and ecological environment constructions, and satellite remote sensing data, meteorological observation data, hydrological observation data, and statistical yearbook data were collected for this research.

Soil moisture and reference crop evapotranspiration (ET0) are of great importance in assessing the potential impacts of climate changes on energy and water cycles, and they are key indicators of drought assessment. The history and future drought conditions were studied. The applicability of ESA CCI soil moisture data in Yangtze River Basin was verified, and a concept of lag time was proposed to quantify the hysteresis between soil moisture and meteorological elements, such as precipitation, temperature and evapotranspiration, under different climatic conditions and timescales. A novel Comprehensive Agricultural Drought Index (CADI) was then constructed to reflect the feedback of time lag effects in drought assessment. Results showed that the climate generally regulated the lag times, and the lag time in arid region is shorter than that in humid region. The CADI was able to effectively monitor the annual and seasonal variations and spatial pattern of agricultural drought, particularly better identify summer droughts, from which the crop phenology related agriculture drought monitoring can benefit. The spatiotemporal change of ET0 and the drought response over Poyang Lake watershed from 2011 to 2100 were investigated based on the meteorological data and the output of the general circulation model (GCM) from the CMIP5. We found that ET0 will increase in the future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, and the spatial distribution of ET0 is generally high in the east and low in the west. The drought index (DI) of the watershed showed an increasing trend, the seasonal distribution of DI is fall >summer >spring >winter, and the Ganjiang River Basin of Poyang lake will suffer high risks of future drought.

In 2020, Poyang Lake suffered the most serious flood hazard since the 21st century, which presents the characteristics of sharp shift from drought to flood. A multi-criteria model combining the analytic hierarchy process and Entropy weight method (AHP-Entropy) was proposed to assess the long and short flood risk. Validation of the flood risk assessment results shows that the flood risk assessment model has a great consistency with Sentinel-1 synthetic aperture radar data, which indicated that the presented flood risk model is reliable. Over all, the northeastern parts of the Poyang Lake basin are prone to floods and the risk of floods gradually decreased from the Poyang Lake area towards the surrounding areas. The sharp rise in water level and long-term, high-intensity precipitation are important causes of the flood. The water level in 2020 from July to October were at least 17% higher than the same period from 2010 to 2019 on average, and the average precipitation from June to September were all higher than the same period in previous years. The cropland areas were the most heavily inundated compared with wetland, grassland, impervious surface, forest and bare land.

Hydrology is a critical environmental condition for the evolution of wetland ecosystems. The hydrological influences on wetland cover distribution and transition in a large complex lake-floodplain system, Poyang Lake were then investigated. The statistical results of annual inundation conditions for different wetland cover types indicated that vegetation communities were preferential to hydrological environments with shorter annual inundation than water and mudflats, and different vegetation communities were distributed in areas with considerable variations in annual inundation, which suggested a substantial hydrological influence on the distribution of wetland cover in Poyang Lake. The spatial analysis indicated that hydrological changes were probably the dominant factor for the wetland cover evolution in the floodplain areas of the northern and central parts of Poyang Lake, but not the unique determined factor for wetland cover transitions in the shallow floodplains near the sink of the inflows in the eastern and southwestern parts of Poyang Lake.

Remote sensing (RS) data is successfully applied since decades for the identification and monitoring of landslide phenomena at different spatio-temporal scales. However, limitations associated with data availability/accessibility (e.g. spatial coverage, low temporal revisit time, high costs) might hampered the development of operational tools.

The results and analyses retrieved in the framework of D4 project 32365 have shown the great benefits of RS in monitoring multi-hazards. The wide spatial and temporal data availability allowed a detailed description of landslide histories even of remote regions. Therefore, the continuous monitoring of such hazards, namely large landslides, is of fundamental importance to minimize and prevent the actual and future risks. In this Dragon-5 proposal, we foresee to continue the monitoring activities started with the Dragon-4 project mainly by means of multi-source RS data at diverse areas located in different countries.

In this first year we applied the InSAR Stacking technique to process and analyze the sentinel-1 data covering the Gilgit research area from October 2019 to October 2020. The research results show that the absolute value of the deformation rate in most areas is less than 10 mm/yr, which is relatively stable. The maximum sedimentation rate of each image frame is 347.6mm/yr, 525.3mm/yr, 455.4mm/yr, 284.5mm/yr, respectively. The deformation results were graded and colored, and displayed in a three-dimensional scene. Several highly suspected landslides located near human settlements in the area were identified. To compensate for the geometric distortion caused by a single imaging geometry a combination of different orbit data was use to effectively avoid "monitoring loopholes". Therefore, the research data adopted the method of combining ascending and descending orbits allowing for comprehensive and accurate early identification of landslide hazards. This work is of great significance for understanding the geological deformation of Gilgit area, especially the identification of some slopes with obvious slip phenomenon, which has great reference value for the follow-up disaster investigation work.

Multi-temporal landslide detection through optical imagery time-series analysis is a second goal of this project. Building upon the results of our Dragon 4 project, we investigate further in automated landslide detection approaches using high-resolution optical imagery (i.e. Senintel-2). Time-series analysis has shown to be an efficient technique for identifying major landslide events both spatially and temporally. Multi-temporal change detection demonstrated to minimize false-positives e.g. through artefacts or agricultural activity that result in bi-temporal change-detection approaches. The next step of our work will consist on the investigation of landslide predisposing conditions through the recognition of preceding land-cover changes and on the explotation of triggering causes by linking landslide events with rainfall data or seismic activity records.

Finally, in the scope of this project we also intend to explore the availability of SAR data with spatial and temporal resolutions at an unprecedented level, associated with the new methods of SAR time series processing to develop an active system for structural risks detecting and alerting. However, only the use of Artificial Intelligence (AI) techniques will allow to deal with the huge amount of data that will be generated. The Vilariça Valley, located in the north of Portugal is crossed by an active fault and will be used as test site to develop the AI-based risk alert system. In this region there is a high number of buildings with historical and patrimonial interests that may be at risk. In this first year, we download all Sentinel-1 data available and start the small and large area processing. In parallel we are also designing the platform to be developed, integrating different data sources and AI techniques.

The seismic deformation monitoring efforts using InSAR in the past 16 years gain fruitful achievements under the Dragon 1-4 cooperation projects. The seismic-related works using InSAR method include interseismic deformation monitoring along big faults, regional-scale deformation detection, major earthquake deformation measurements and postseismic deformation analysis for rheology studies. In recent years, induced seismicity monitoring is also another important task to do for mines or shale gas production.

In Dragon 5, we continue our Dragon 1-4 works on seismic deformation monitoring, in conjunction with detecting abnormal changes of electromagnetic field in the lithosphere. However, new challenges appear on SAR data analysis itself and integration with electromagnetic field to interpret the mechanism of causing seismic deformation. In the past 5 years, Sentinel-1 satellites acquired high-quality data and are still accumulating with fast rate and require high capability for InSAR data processing. To overcome the issues, we developed parallel computation systems for this purpose, which also has a great storage system attached to it. Moreover, with the big forward on artificial intelligence (AI) and machine learning algorithms developed in recent years, we hope to integrate them into data processing system to improve deformation detection precision and data analysis process in aggregation with electromagnetic data. Another piece of work is to deal with the atmospheric delays on InSAR time-series analysis because the current methods all have various kinds of difficulties in the analysis, and prevent further improvements on precisions. The project proposes to use machine learning methods to construct models that could be used to accurately make predictions or simulations of atmospheric delays, as shown by some of the recent tries.

The tectonic environment of China and surrounding regions depend mostly on the collision of Indo and Eurasia plates. The Dragon 5 project will still focus on faults, such as the Haiyuan, Kunlun, Altyn Tagh, Xianshuihe, Tianshan fault systems etc. In addition, we will also integrate InSAR and GPS data to develop an inversion model for regional strain distribution in particular regions such as Tibet, North China Plain, to prepare for seismic hazard mitigation, and assess the risk for national key projects, such as the Sichuan-Tibet railway construction project. Furthermore, the recent hot topic on induced seismicity is the new field for InSAR working with other traditional approaches, in particular for the Sichuan basin, so we will also address this new topic in our Dragon 5 project.

The project aims to develop and apply innovative data analytic methods underpinned with machine (deep) learning technology to analyze and detect seismic anomalies from electromagnetic data observed by the SWARM and CSES satellites along with CSELF network.

Both the ground-based and satellite-based observations have shown that a range of frequency band of electromagnetic signals had been recorded in ionosphere around strong earthquakes. These phenomena conjectured that when earthquakes occurring electromagnetic waves generated could penetrate from lithosphere around the epicentre areas of earthquakes into ionosphere, which could be supported by the simulated results of penetrating process of electromagnetic wave from ground to ionosphere.

Since the DEMETER satellite launched by the French CNES on 29 June 2004, a large number of papers have been published in respect of ULF/ELF/VLF/LF electromagnetic perturbations in topside ionosphere, and they were inferred to be possibly related to earthquakes. On 22 November 2013, the SWARM satellite constellationbegan to operate, mainly focusing on observing geomagnetic field in ULF band. Some interesting phenomena around earthquakes have been reported using the SWARM satellite data. The first Chinese Seismo-Electromagnetic Satellite (CSES) was launched on 2^nd February 2018, some perturbations related to earthquakes were also detected in electromagnetic field. The DEMETER operation ended in 2010, but SWARM and CSES both are still in orbit at present. SWARM has delivered data for more than 7 years, and CSES for longer than 3 years, thus the measurements obtained by these satellites provide an unprecedented opportunity for conducting stereo investigations into earthquakes at different altitudes and local times, especially on the electromagnetic waves at different frequency bands.

In China, a new CSELF network was constructed with more than 30 stations since 2012, dedicated to record electromagnetic waves below kHz. All these ground electromagnetic observations can be compared with the satellite data at the respective frequency bands, and the same frequency signals could be distinguished and traced from the ground to the ionosphere. By accounting for the multi-frequency bands and a large amount of data, machine learning-based approaches will help scan and extract all the disturbed signals and construct prediction models by incorporating the relation between electromagnetic signals and earthquakes.

In Dragon 3 and 4, five algorithms for anomaly detection have been developed, including Wavelet Maxima (WM), Geometric Moving Average Martingale (GMAM) based on the Martingale theory, the integration of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) called CUSUM-EWMA, a Fuzzy Inspired Approach to Seismic Anomaly Detection (FIAD), and Enhanced Martingale. These algorithms have been used to analyze NOAA, SWARM and CSELF data for distinguishing anomalies in relation to the Wenchuan, Lushan, Puer, Jinggu, Taoyuan, Ludian and Peloponnese earthquakes occurred in China and Greece, and the preliminary results have been achieved.

In this report we will present a brief summary of the results obtained from the Dragon 3 & 4 projects, and then introduce the aims and objectives of this Dragon 5 project, particularly emphasizing on the challenges head when addressing possible approaches for verifying the relationship between electromagnetic disturbances with earthquakes, and developing pragmatic and sophisticated anomaly detection algorithms underpinned with Deep Neural Networks, we will report the preliminary results achieved to date.

Precipitable water vapor (PWV) from numerical weather models, such as the latest generation of European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and the ECMWF High RESolution (HRES) models, are important to meteorological studies and to error mitigation of geodetic observations such as Interferometric Synthetic Aperture Radar. In this study, we provide global validations of these new weather models with respect to Global Positioning System (GPS, ∼13,000 stations) and Moderate Resolution Imaging Spectrometer (MODIS, ∼1 km resolution) using data from January 2016 to December 2018 of every 1 h. The global standard deviations of the Zenith Tropospheric Delay (ZTD) differences (DSTDs) between weather models and GPS are 1.69 cm for ERA5 and 1.54 cm for HRES. The global PWV DSTDs between weather models and MODIS are 0.34 cm for ERA5 and 0.32 cm for HRES. The two weather models generally perform better in western North America, Europe, and Arctic by having low ZTD DSTDs (<1.3 cm) or PWV DSTDs (<0.3 cm). HRES also has a low ZTD DSTD of less than 1.3 cm in Antarctic, Japan, New Zealand, and Africa and outperforms ERA5 in most regions of the world, despite the fact that 83% of the HRES PWV values are temporally interpolated (from 6 to 1-h). However, under extreme weather conditions, ERA5 performs better owing to its high temporal resolution (1 h). We have developed a new generation of the Generic Atmospheric Correction Online Service for InSAR (GACOS) which can utilize ERA5, HRES and GNSS products to generate high resolution tropospheric delay maps for InSAR atmospheric correction. In this study, we also demonstrate some successful applications of the GACOS to a variety of geophysical studies.

References:

Yu, C., Z. Li, and G. Blewitt (2021), Global Comparisons of ERA5 and the Operational HRES Tropospheric Delay and Water Vapor Products With GPS and MODIS, Earth and Space Science, 8(5), e2020EA001417, https://doi.org/10.1029/2020EA001417.

Yu, C., Z. Li, L. Bai, J.-P. Muller, J. Zhang, and Q. Zeng (2021), Successful Applications of Generic Atmospheric Correction Online Service for InSAR (GACOS) to the Reduction of Atmospheric Effects on InSAR Observations, Journal of Geodesy and Geoinformation Science, 4(1), 109-115.

In the framework of Dragon 5 project, Northeastern University (NEU) from China, the National Institute of Geophysics and Volcanology (INGV) from Italy and Changbaishan Volcano Observatory of China have been collaborating to analyze the multiple geohazards over the heavy industrial base and Changbaishan Volcano in Northeast China using multi-source remote sensing data provided by ESA and third party missions.
The heavy industrial base in Northeast China has been playing an important role in the economic development of China. However, the hard mining activities have a strong impact on local environment due to continuous ground excavations for coal and iron ore. Therefore, mining areas in Northeast China are subject to a multi-hazard exposure including subsidence, landslides, ground fissures and building inclination. In order to keep safety of local environment and mining activities, continuous monitoring of the spatial and temporal variations of multiple geohazards are carried out for early-warning and risk assessment by the research team.
The Changbaishan active volcano region (Jilin Province, about 300 km east from Shenyang city) is also affected by landslides, earthquakes and ground deformation related to volcanic and hydrothermal processes. The last deformation related to such phenomena occurred during the 2002-2006 unrest episode, in 2011 and in 2017, when a nuclear test explosion in North Korea triggered landslides on the steep slopes surrounding the caldera lake. The frequency of earthquake swarms has also increased since December 2020. The multi-hazard exposure of Changbaishan is also high, because a population of about 135000 in China and 31000 in North Korea lives within 50 km distance from the volcano. In addition 2000000 tourists visit the Changbaishan Volcano National Reserve, a part of the UNESCO Man and Biosphere program each year. Therefore, surveillance of the dynamics of Changbaishan Volcano is also crucial for disaster prevention and risk mitigation.
In the framework of Dragon 4 and Dragon 5, the research team has collected more than 300 COSMO-SkyMed images, hundreds of Sentinel-1 images, 30 TerraSAR-X images and 19 ALOS-2 PalSAR images over the study areas. We focused investigation over some of the most important sites and towns located in Northeastern China, such as Shenyang city, Anshan and Fushun open pit mines, and Changbaishan volcano as well. Time Series InSAR analysis is carried out over the study areas with assistance of TanDEM DEM and precise LiDAR DEM using both the Persistent Scatterers and the Small Baseline Subsets technique. The results from multiple stacks, covering different temporal interval and operating in different frequency bands, have shown a very good consistency over each study area. The results are compared with terrestrial measurements, precipitation data and geological data for the purpose of accuracy assessment as well. Our research results have demonstrated that displacement time series retrieved through the advanced InSAR technologies can be used as a routine tool for deformation monitoring of multiple hazards and provide nowadays a fundamental technical support for disaster prevention and mitigation.

Archaeological prospection and protection of cultural and natural heritage sites are important applications of remote sensing. The key goal of the Dragon-5 project SARchaeology is to exploit satellite SAR imagery and multi-sensor approaches to detect objects of archaeological significance, and monitor the status and stability of cultural and natural heritage sites and their assets.

The project focuses on arid areas, e.g. paleo-channels around Lop-Nor in China, kurgans (Iron Age burial mounds) in the Altai mountains in China and Tuva region in Russia, and partly buried archaeological ruins in the larger province of Rome in Italy, as well as natural heritage of the Jiuzhaigou site in China.

Image analysis methods that are used in the project include – but are not limited to – feature extraction, image classification, change detection, and multi-temporal Interferometric SAR (InSAR). The latter is essential to address the goal of site protection, via estimation and monitoring of surface deformation due to geological processes (e.g. subsidence, landslides), which can endanger natural and cultural heritage sites.

SAR datasets exploited for archaeological prospection include ALOS-1 L-band, as well as shorter wavelengths, namely ERS-1/2, ENVISAT, RADARSAT-1/2 and Sentinel-1 C-band, and TerraSAR-X, potentially Iceye and Paz X-band data, in order to test signal penetration capabilities at the different wavelengths and spatial resolutions. Thanks to the upcoming wider availability of long-wavelength data from various L-band missions and BIOMASS P-band mission, sub-surface target detection is also becoming possible, thus opening new perspectives for the use of SAR for archaeological prospection and identification of hidden paleo-channels and linear structures. Long-term surface motion monitoring and site surveillance are guaranteed with Sentinel-1 SAR data and their abundant stacks acquired over the study sites since 2014. Optical imagery from Deimos-2, WorldView, GeoEye, QuickBird, CBERS-4 and Jilin-1 will be used to provide very high resolution basemap layers to aid the SAR image interpretation and identify the main archaeological features. SPOT, Pleiades and RapidEye imagery from ESA collections will also be used.

Where possible in-situ measurements will be collected at the times of SAR satellites passes.

During the first year, the project has been setup and preliminary work has started. Collaboration between the European and Chinese teams in the framework of other projects, such as the long-term monitoring of surface deformation in Wuhan and research on kurgans, has formed the basis for kicking-off a much stronger partnership in Dragon 5.

The focus of the initial project activities has been on study site selection based on existing literature review, consolidation of scientific objectives for each heritage site, EO datasets identification and access.

For the research on burial mounds, the work has also been focused on improving the methodologies and better monitoring the sites with respect to climatological factors. This is important as the most valuable burial mounds are to be found in or close to permafrost areas. Global warming and thawing of permafrost endangers the organic remains in some of the sites in question that are currently still frozen and therefore extremely valuable for archaeological analysis. Learning more about the current extent of permafrost, monitoring spatial changes and hopefully being able to predict the spatio-temporal patterns of future changes will be highly important for the planning and prioritization of archaeological excavations.

Regarding dissemination and teaching activities, Prof. Fu organized the International Workshop on Space Technologies for Disaster Mitigation of World Heritage on 13-16 October 2020, in Jiuzhaigou, China, and Prof. Balz gave lectures on SAR remote sensing.

Field data collection and ground truthing for the main sites in Central Asia has been postponed due to the pandemic situation, though site observations from previous cooperation in the field of the detection and mapping of kurgans and other burial mounds in Central Asia will be exploited to address the current difficulties, with a view to future dedicated field work in Russia and Mongolia when the situation will allow.

The second year of the project will be focused on intensive EO data processing and initial analysis and interpretation, for both archaeological prospection and heritage protection. Moreover, it is planned to conduct field work in the area of Rome, and to resume the work in the Altai. Furthermore, a plan to extend the use of data from different sources, especially the combination of European and Chinese remote sensing data sources, is also in place.

Regarding the level of training and involvement of young scientists in the project, a PhD student from the Chinese team is currently preparing an application to submit to the Chinese Scholarship Council to support a research visit in Rome to work with the European team on long-term monitoring of heritage sites with multi-temporal InSAR and ground truthing, tentatively in 2021-2022 for a period of 1.5 years. On the other hand, the European team is assessing opportunities to identify and recruit MSc students and graduates to work on the project starting from 2022.