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Project Summary PI Europe PI China Domain Full text
3-D CHARACTERIZATION AND TEMPORAL ANALYSIS OF FORESTS AND VEGETATED AREAS USING TIME-SERIES OF POLARIMETRIC SAR DATA AND TOMOGRAPHIC PROCESSING Based on the experience accumulated during the DRAGON-1 to -4 projects, we intend, under the DRAGON-5 project to strengthen the established fruitful collaborations between European and Chinese partners and experts in PolSAR, PolInSAR and [...] Dr. Laurent Ferro-Famil, University of Rennes 1, FRANCE Prof.. Erxue Chen, Institute of forest resources information technique, CHINA Ecosystems Based on the experience accumulated during the DRAGON-1 to -4 projects, we intend, under the DRAGON-5 project to strengthen the established fruitful collaborations between European and Chinese partners and experts in PolSAR, PolInSAR and PolTomo-TomoSAR, for the 3-D characterization and temporal analysis of forests and vegetated areas using time-series of polarimetric SAR data and tomographic processing. This project aims to promote the use of existing spaceborne SAR sensors with polarimetric and interferometric diversities, for the temporal monitoring of forested and vegetated areas and to pave the way for future spaceborne missions and concepts. The proposed project contains 4 main scientific topics with the following objectives: 1) Development of physical parameter retrieval methods for the quantitative 3-D characterization of vegetated areas using low frequency sensors, whose penetration properties are well suited to the imaging of the intrinsic properties of natural volumes. This topic aims at developing vegetation parameters extraction methods based on the complementary aspects of PolSAR and PolTomSAR, for volumetric land-cover characterization. Among the many descriptors of vegetated areas, key ecosystem parameter for biomass stock successions, and growth dynamics, such as forest structure and Above Ground Biomass will be addressed, as well as classical indicators like tree height. Special attention will be dedicated to the estimation of the underlying ground dielectric and roughness properties, over both wild and cultivated areas. 2) Development of innovative vector signal processing techniques for high-resolution 3-D imaging. The recent history of SAR tomography shows that the possibilities for characterizing 3-D environments using Multi-Baseline Pol-inSAR data are highly linked to both the quality of the signal processing techniques used to perform 3-D focusing and to the acquisition configuration. During this project, several options, related to original CS- and Wavelet-CS based imaging solutions will be tested, and original configurations, like multi-temporal Tandems, Bistatic Tomographic pairs, will be analyzed and explored at various application scales. 3) Temporal monitoring of forested and vegetated areas using time-series of acquisitions. Time series of Sentinel 1 or ALOS sensors will be used to detect and monitor Forest and grassland disasters, forest mapping, AGB estimation, as well as nearly real-time deforestation mapping over 3 continents. 4) PolSARpro Software v6.0 is a polarimetric SAR data processing and educational tool developed under contract to the European Space Agency. It is proposed in this project to include all the new algorithms and scientific procedures that will be developed during the DRAGON-5 project. It will thus increase the great collection of well-established algorithms and tools designed to handle PolSAR and Pol-InSAR data from airborne and spaceborne sensors. The PolSARpro software could thus become also an important communication tool, advertising the international Geoscience and Remote Sensing community for promoting the most important scientific developments conducted during the DRAGON-5 project. The funding of the project in general and of the Young Scientists in particular is not problematic at all since both European and Chinese partners are used work on the topic proposed in this project and can, through national or regional funding, guarantee that the project will not suffer from a lack of human or material resources
ABSOLUTE CALIBRATION OF EUROPEAN AND CHINESE SATELLITE ALTIMETERS ATTAINING FIDUCIAL REFERENCE MEASUREMENTS STANDARDS This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3 and Sentinel-6 and the Chinese HY-2 satellite altimeters based upon two permanent Cal/Val facilities: (1) the Permanent Altimetry [...] Prof.. Stelios Mertikas, Technical University of Crete, GREECE Prof.. Mingsen LIN, National Satellite Ocean Application Service (NSOAS), CHINA Calibration and Validation This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3 and Sentinel-6 and the Chinese HY-2 satellite altimeters based upon two permanent Cal/Val facilities: (1) the Permanent Altimetry Calibration Facility (PFAC) established by ESA in Crete, Greece and (2) the National Cal/Val facility at the Wanshan islands, South China Sea. Other satellites, such as Guanlan, CryoSat-2, CFOSAT, CRISTAL, etc., may be also supported by these Cal/Val services. Satellites will be calibrated and monitored using uniform, standardized procedures, protocols and best practices and also built upon trusted and undisputable reference standards at both Cal/Val infrastructures in Europe and China. Altimeters will be thus monitored in a coordinated, absolute, homogeneous, long-term and worldwide manner.Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere) all defined by Cal/Val sites. In this work, final uncertainty for altimeter bias will be attributed to several individual error sources, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc. Absolute calibration of altimeters has also been carried out on land with microwave transponders. This is a unique technique that calibrates the altimeter range directly with the transponder. At the moment, the PFAC in Crete hosts the only operational transponder. A second transponder is currently under construction to be installed at the Gavdos Cal/Val facility and will support Sentinel-6 and Sentinel-3 calibrations. If future HY-2 Chinese missions, such as HY-2C, overpass this European Cal/Val facility, and when operationally supported. The project will implement the action plan established by ESA for Fiducial Reference Measurements for Altimetry (FRM4ALT) calibration. At present, the PFAC is the only facility in the world that reports its Cal/Val results along with their FRM uncertainty. Through this project, the procedures, protocols and best practices, developed in the course of the ESAÔÇÖs FRM4ALT project, will be updated, upgraded and followed at both Cal/Val facilities in Europe and China. This proposed collaborative project will contribute to an FRM4ALT calibration of Sentinel-3 and HY-2B missions during their operational phase, but also to upcoming Sentinel-6 and HY-2C and possibly to the Quanlan satellite of China. As such, it will promote the FRM4ALT strategy of ESA to Chinese stakeholders and scientists. The main outcomes of the proposed project will be (1) the standardization of operations and data processing followed at both Cal/Val infrastructures in Europe and China, (2) the exchange of knowledge and training on properly implementing FRM4ALT in practice, (3) the absolute calibration of European and Chinese satellite altimeters along with unified reporting of FRM uncertainty for the produced Cal/Val results, and (4) inter-comparison of the Cal/Val results obtained at the two main Cal/Val facilities and investigation of any of deviations. Calibration and validation activities at the PFAC in Greece are currently supported by two ESA projects namely SeRAC and FRM4S6. Further funding is provided by international space agencies (i.e., CNES, France) and institutional funds (TUC and Space Geomatica). Similar support has been provided by the Chinese government for the Wanshan Cal/Val facility in South China Sea.
ALL-WEATHER LAND SURFACE TEMPERATURE AT HIGH SPATIAL RESOLUTION: VALIDATION AND APPLICATIONS Problem statement: Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere. On the extensive Tibetan Plateau (TP), where in-situ observations are usually extremely sparse, [...] Dr. Frank Goettsche, Karlsruhe Institute of Technology, GERMANY Prof.. Ji Zhou, University of Electronic Science and Technology of China, CHINA Calibration and Validation Problem statement: Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere. On the extensive Tibetan Plateau (TP), where in-situ observations are usually extremely sparse, accurate knowledge of the land surface energy balance is crucial for understanding and simulating regional processes of meteorology, hydrology and ecology. More specifically, all-weather LST products are required for accurately simulating soil heat transfer, which provides insights into changes in TP permafrost / seasonally frozen ground and regional climate change. However, LST products based on thermal infrared (TIR) remote sensing are limited to clear sky conditions. Recently two all-weather satellite LST products became available, but they still require more extensive validation and assessment of their uncertainty. Objectives: The main objective is to inter-compare and validate the 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: – Generation of long term (global) all-weather LST data set – Setting up an LST validation station in China to provide Fiducial Reference Measurements (FRM) – Employing all-weather LST data to simulate and study freeze / thaw on the TP Method: The two new all-weather LST products and LST extracted from ERA5-Land data, which are provided by Copernicus Climate Change Service (C3S), will be inter-compared over selected regions in China, Europe, and Southern Africa. All inter-comparisons will utilise ESA GlobTemperature (GT) harmonised data format (netCDF) and their relative performance will be assessed to provide insights into their respective strengths and limitations. The three LST products will be validated against in-situ measurements from the following station networks: 1) Karlsruhe Institute of Technology (KIT), 2) Baseline Surface Radiation Network (BSRN), 3) European Fluxes Database Cluster (EFDC) initiative, 4) Heihe Watershed Allied Telemetry Experimental Research (HiWATER) and Watershed Allied Telemetry Experimental Research (WATER) in the Heihe River basin, and 5) networks operated by other Chinese groups on the TP. Based on experience and an instrument package provided by KIT, the Chinese partners will set up a new LST validation station in China. Since the thermal sampling depth correction (TSDC) between TIR LST and PMW LST is larger for dry soil, all-weather LST determined over the arid validation site Gobabeb (Namibia) will be compared with results from the Zhou et al. (2017) method, which explicitly models soil heat conduction. The main causes of differences between the three LST products will be identified and used to improve the estimates of LSA SAF all-weather LST uncertainty. The all-weather LST retrieved from merged TIR and PWM data will serve as input for model simulations of freeze / thaw on the TP. Deliverables: – Inter-comparison and validation results for the two all-weather LST products – Assessment of all-weather LST product uncertainties – Results from simulating freeze / thaw on TP Source of funding: Frank-M. Göttsche is funded by Karlsruhe Institute of Technology (KIT). João P.A. Martins is funded by Instituto Português do Mar e da Atmosfera /Portuguese Institute for the Sea and the Atmosphere (IPMA). Ji Zhou is funded by the National Natural Science Foundation of China under Grant 41871241 and the University of Electronic Science and Technology of China (UESTC). Wenjiang Zhang is funded by the National Natural Science Foundation of China under Grant 41771112.
APPLICATION OF SINO-EU OPTICAL DATA INTO AGRONOMIC MODELS TO PREDICT CROP PERFORMANCE AND TO MONITOR AND FORECAST CROP PESTS AND DISEASES Starting from the outmost results of the previous Dragon4 (32275) initiative this new proposal intends to explore the application of Sino-EU optical data into agronomic models to predict crop performance and to monitor and forecast crop pests [...] Dr. Stefano Pignatti Morano, Istituto di metodologie per l'analisi ambientale, CNR IMAA, ITALY Prof.. Wenjiang Huang, Aerospace Information Research Institute, CHINA Sustainable Agriculture and Water Resources Starting from the outmost results of the previous Dragon4 (32275) initiative this new proposal intends to explore the application of Sino-EU optical data into agronomic models to predict crop performance and to monitor and forecast crop pests and diseases.The team, as in the previous project, is composed by the Italian team with the National Research Council and two Universities, while the Chinese one with the Chinese Academy of Science and the Beijing Research center for Information Technology in Agriculture. The project will explore and verify pre-operative algorithms and processing chains using ESA/Chinese multi-frequency EO data to (a) develop innovative and advanced methods for crop plant key parameters retrieval at different growth stages (plant pigment, equivalent water thickness, dry matter, nitrogen and biomass); (b) estimate crop yields and grain quality using agronomic models by integrating multi-source information and by different assimilation techniques; (c) identify early crop stress both at the leaf and at the canopy level also by inferring the agricultural soil properties; (d) develop new dynamic methods and models to monitor and forecast crop pests and disease. This will be achieved by using ESA Products (Sentinel-2), TPM (Landsat-8, SPOT 7, WorldView) and Chinese product (GF-6, GF-1), taking also advantage of the spectral resolution of the ASI PRISMA hyperspectral imagery and the next generation ESA FLEX and Sentinel-5 missions. Moreover, the project foresees to explore the possibility offered by the DIAS systems.The Sino-EU synergy, beside the use of the EO mission data, will be exploited on jointly selected agricultural test sites in China and in Italy. Firstly, the Team will jointly explore the capabilities of the operational mapping of vegetation variables through RTMs, which is a challenging task due to the ill-posedness of the inversion and to the influence of several hampering factors (e.g., the canopy structure, the influence of the atmosphere, the illumination conditions and the sun-sensor geometries). For these reasons, the use of regularization strategies (cost functions, multiple solution) to reduce the uncertainties in the quantitative estimation of vegetation variables. To this aim, approaches exploiting physically-based radiative transfer models (RTM), will be compared with other methods. Secondly, data assimilation of multivariate and multi-scale remotely sensed variables into agricultural models (i.e. crop growing, pest e disease) will be explored. The project proposes to advance those approaches that tackle multiple scales and multiple variables, i.e. employing concurrently two or more variables for the assimilation. Agricultural model uncertainties will be assessed using global sensitivity analysis methods. Different assimilation algorithms (deterministic and stochastic) based on the EnKF and PSO methods. These methods will update the state variables and/or parameters of the crop models, to estimate variables of agronomic interests, such as crop yield and grain protein quality.Finally, the integration of multi-source data to retrieve crop physicochemical parameters, monitor crop pests and diseases habitat and then forecast damaged areas and levels at both farm and national scales will be explored. Machine learning methods, such as decision tree, SVM and deep learning algorithms will be applied to learn from field samples and devise complex models to detect the relationship between pest/disease and features. New dynamic methods and models will be compared with traditional methods using ground samples. Cross cutting validation activities will provide the data and for the retrieval algorithms validation and for the data assimilation approaches. Field experiments will be implemented. Ground hyperspectral data, agronomic management data, agrometeorological data, and soil data will be collected. Crop yield and quality variables (e.g. protein content) will be measured at harvest.
ASSESSING EFFECT OF CARBON EMISSION REDUCTION WITH INTEGRATING RENEWABLE ENERGY IN URBAN RANGE ENERGY GENERATION SYSTEMS The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. The anthropogenic emissions of CO2 are primarily generated by human [...] Dr. Ming Jun HUANG, Ulster University, UK Dr. Xingying Zhang, China Meteorological Administration, CHINA Atmosphere The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. The anthropogenic emissions of CO2 are primarily generated by human activities, including fossil fuel combustion, energy used in transport sectors, etc. In the urban, energy used in domestic and transport sectors takes more than 80% of the total energy consumption in the UK. In the past decade, renewable energy (RE) technologies, such as solar and wind power, geothermal and hydro power, have gradually been deployed in domestic buildings for heating and electricity. However global fossil CO2 emissions are still more than 4% higher in 2019 compared with those in 2015. In the UK, the recent campaign of CO2 reductions has proposed a policy of the phase-out of coal, and by 2050, the gas boiler could be as obsolete as the coal fire in UK homes. Although many policies for decarbonisation, like the Paris Agreement and integrating REs into urban buildings have been introduced, it is not clear what is the contribution of REs to CO2 reduction. Therefore it is imperative to study the impact of REs integration with existing power generations on the CO2 reduction by using satellite monitoring, analysing the REs supply with on-site response and artificial intelligent technology. Since 1983, the World Meteorological Organization (WMO) has established various Global Atmosphere Watch stations worldwide in different latitudes and longitudes to continuously monitor changes of atmospheric CO2 and CH4 concentrations at near-surface level. To understand the transport mechanisms of global greenhouse gases (GHG), JAXA launched Greenhouse gases Observing Satellite (GOSAT) and GOSAT-2 in 2009 and 2018 for clarifying the sources and sinks of CO2. NASA put the OCO-2 and OCO-3 satellites to operation in 2014 and 2019 for quantifying variations in the column averaged atmospheric CO2 dry air mole fraction, namely XCO2. Chinese carbon dioxide observation satellite (TanSat) was launched on 22 Dec 2016. These satellites provide the ability to retrieve XCO2, and their XCO2 data products have been used to improve our knowledge of natural and anthropogenic CO2 sources and sinks. The synergistic use of complementary measurements is not only addressing the carbon cycles, but also opens a unique opportunity to address some of the main knowledge gaps in atmospheric CO2 for the urban with the prevision of integration of REs into buildings for electricity and heating. The project aims at exploiting the synergic measurements together with REs technology and advanced AI to quantify the effect of REs in the terrestrial carbon cycle. Specifically the key objectives include: • Develop fusion algorithms for combining measurements from different satellites on required spatial and temporal scales for the urbans. • Develop retrieve algorithms of CO2 from satellite and combined measurements • Validate and apply GHG products of satellites to estimate CO2 concentration and distribution • Investigate the energy demand and the energy contribution of RE integration in the urban regions for heating and transportation. • Develop the methods of studying the effect of integrating the REs into urbans to reduce CO2 emission. • Provide policy makers with the evidence of CO2 reduction over regions that have integrated REs as energy suppliers. The proposed project involves a collaboration between the Sustainable Technology Centre at Ulster University, UK and the National Satellite Meteorological Centre (NSMC) at China Meteorological Administration (CMA). Work conducted by both teams are part of their respective research commitments, hence limited funding will be used to support this cooperation research. It is also expected the Dragon 5 program would provide certain amount of funding to support EU partners for attending symposia and for young scientists to carry out the project research.
AUTOMATED IDENTIFYING OF ENVIRONMENTAL CHANGES USING SATELLITE TIME-SERIES Copernicus programme provide a huge amount of image data for many applications around the globe. Besides data being free and open, Sentinels also holds great potential to use dense observation time-series in various dynamical Earth environment [...] Dr. Mika Karjalainen, The Finnish Geospatial Research Institute, FINLAND Prof.. Yan Song, China University of Geoscience (Wuhan), CHINA Data Analysis Copernicus programme provide a huge amount of image data for many applications around the globe. Besides data being free and open, Sentinels also holds great potential to use dense observation time-series in various dynamical Earth environment monitoring applications. The amount of EO data is extensive, therefore it is of high importance to develop automated tools to process and analyse the data. In the FGI, we have developed a toolkit (EODIE) to automatically process Sentinel satellite images, to extract time-series data (NDVI or other index, or SAR backscattering values). When the EO time-series data are combined with ancillary information (training data set), it is possible to design intelligent classification tools to automatically identify causes for environmental changes. Our objective is to use machine learning techniques, Random forest and Neural Networks based methods, and design a classification tool to be used in automatic identification of environmental changes. We aim to test the classification toolkit in the field of agriculture (crop species classification in Finland), forestry (forest thinning mapping in Finland), and coastal area monitoring (China). The proposed research connects the complementary experiences of the European and Chinese partners to develop innovative techniques for analysing EO time-series data. The research directly aims at advancing the scientific careers of young researchers involved with the project by providing good opportunities to write articles for top journals in the field of remote sensing. For example, the use of EODIE toolkit (Earth Observation Data Information Extractor) is already applied in the demonstration projects in agricultural mapping in Finland, and likely of interest to the scientific community in Dragon 5 context. The proposed research is partially funded by the organizations of the reseach teams and other outside funded projects.
BIG DATA INTELLIGENT MINING AND COUPLING ANALYSIS OF EDDY AND CYCLONE We here propose a new algorithm for parallel identification of mesoscale eddies from global satellite altimetry data. And a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data.We [...] Dr. Wang Shuai, Imperial College London, UK Dr. Fenglin Tian, Ocean University of China, CHINA Data Analysis We here propose a new algorithm for parallel identification of mesoscale eddies from global satellite altimetry data. And a new hybrid mesoscale eddy tracking method to enhance the eddy tracking accuracy from global satellite altimeter data.We will build a tropical cyclone data set globally based on a state-of-the-art atmospheric reanalysis product. Combined with the long time eddy identification data products, we intend to calculate the probability of the encounter between cyclones and mesoscale eddies.
CALIBRATION AND VALIDATION OF THE FIRST CHINESE GNSS-R MISSION—BUFENG-1 A/B 1 Objectives On June 5 2019, following the UK TechDemoSat-1 mission and the US Cyclone GNSS (CYGNSS) constellation, BuFeng-1 A/B in-orbit demonstration satellites were successfully deployed in orbit by Chinese first-time sea platform launch. [...] Dr. Weiqiang Li, Institute of Space Sciences, CSIC, SPAIN Dr. Cheng Jing, China Academy of Space Technology (CAST)-XIAN, CHINA Calibration and Validation 1 Objectives On June 5 2019, following the UK TechDemoSat-1 mission and the US Cyclone GNSS (CYGNSS) constellation, BuFeng-1 A/B in-orbit demonstration satellites were successfully deployed in orbit by Chinese first-time sea platform launch. Now, it is one of the only two in-orbit operational GNSS-R mission (NASA CYGNSS and BuFeng-1 A/B). After months of full-time operation, the preliminary results reveal that the derived sea surface wind speed is impressively reliable at low-to-moderate wind speed range under fully developed seas condition. As a result, the GNSS-R technique have become a key component for China meteorological satellite observation system for the future numerical weather forecast and typhoon monitoring. The calibration of scattering coefficients for high sea surface wind speeds under heavy precipitation and the validations of the performances for the other GNSS-R applications (such as soil moisture and ocean altimetry) should be further studied, which can guide the design of future ESA and China GNSS-R satellite missions, such as BuFeng constellations, FFScat, G-TERN, Cookie, ORORO and HydroGNSS. The products of ESA SMOS mission, for soil moisture and ocean salinity, can also provide high ocean wind products, and are especially suitable for the calibration and validation of spaceborne GNSS-R measurements. It is noted that the Dragon 5 project can fully cover the life span of the BuFeng-1 A/B satellites. Regarding these requirements, the objectives are summarized as follows: 1) Collocation of integrated ESA-CHINA EO data products and BuFeng-1 data preprocessing 2) Calibration of the BuFeng-1 A/B main observables, including NBRCS, power DDM, and SNR 3) Validation of the calibrated results from BuFeng-1 A/B; 4) Optimization and improvements of future spaceborne GNSS-R instruments. 2 Methods With respect to the objectives, the accumulated first handed BuFeng-1 data from CMA are collected and collocated with other products from ESA and China EO missions, including SMOS, CRYOSAT-2, HY-1/2, and FY series. After that, spaceborne GNSS-R observations and auxiliary data will be analyzed to check the sensitivities of the GNSS-R observables (such as NBRCS, SNR, calibrated power DDM) to different spaceborne GNSS-R applications, such as sea surface winds, inland soil moisture, and sea surface height. Besides EO data, the validation methods also comprise the ECMWF reanalysis products on wind speed, soil moisture content, sea surface height, etc. Based on the preparation of the matchup datasets, the calibration and validation of objectives 2) and 3) will be carried on with the state-of-the-art big data analysis approaches, including Artificial Intelligence and Machine Learning to optimize the models of each application. In order to achieve the objective 4), the error budget of different geophysical measurements will be developed as the functions of different mission and instrumental parameters, which can guide the design of future GNSS-R instruments, such as the data acquisition methods, antennas design, and power calibration algorithms. 3 Deliverables The major deliverables expected from this project include the following: 1) New models, methods, and documents for instrument calibration and performance validation of BuFeng-1 GNSS-R satellite mission are developed and made available to the scientific community. 2) Peer-reviewed journal papers in Remote Sensing of Environment, IEEE TGRS, Remote Sensing, IEEE J-STAR, Geophysical Research Letters, IJRS, etc. 3) Presentations at major international symposium, such as Dragon 5 symposium, ESA Live Planet Symposium and IGARSS. 4) Interim project reports and final project report 5) Ph.D. thesis and M.Sc. thesis 4 Funding Chinese fund: National Major Projects of High-Resolution Earth Observation Systems;European fund: Sensing with Pioneering Opportunistic Techniques, Spanish Ministry of Economy and Competitiveness (RTI2018-099008-B-C22)
CEFO: CHINA-ESA FOREST OBSERVATION The key aim of CEFO proposal is to develop methods and data products which will support the sustainable economic development of the key forestry sector in China, thereby alleviating poverty among this community. The CEFO project will apply and [...] Dr. Juan Suarez, Northern Research Station, UK Prof.. Yong Pang, Chinese Academy of Forestry, CHINA Ecosystems The key aim of CEFO proposal is to develop methods and data products which will support the sustainable economic development of the key forestry sector in China, thereby alleviating poverty among this community. The CEFO project will apply and evaluate innovative remote sensing methods to improve sustainable forestry management for Chinese forests, in a close collaboration between Chinese forest researchers, stakeholders, and the UK research team. The research focuses on priority areas of application of remote sensing to change detection, yield and forest carbon sequestration, and improved detection of stress related to growth and forest health. It will develop the joint use and evaluation of Chinese and European satellites (Sentinel-2, Gaofen 1/2/6/7), the planned Chinese Terrestrial Ecosystem Carbon Monitoring Satellite and ESA BIOMASS mission. To achieve this, it will apply a set of spectroscopy, radiative transfer modelling and time series analysis methods to Chinese forests, recently developed within collaborative research projects with NASA, EU and MOST funding. In particular, the project will develop methods based on fundamental tree physiology that can be extended for the future monitoring of hazards affecting Chinese forests using remote sensing. It will also integrate remote sensing data for model and algorithm development to advance data visualisation and simulation techniques; to detect change using time series observations to inform policy, monitor vegetation condition, and provide growth model inputs to assess yield and to estimate carbon sequestration. The project will also contribute expertise and state-of-the art equipment towards capacity building for remote sensing field and lab analysis. Some funding sources include the joint project funded by the National Science Foundation of China (41871278) and China Gaofen Forest Application Project, Northern Research Station of UK and the Chinese Academy of Forestry. They will support the normal progressing of the Dragon 5 cooperation and the participating of the annual symposium. Some scholarships from ESA will be very helpful to have 1 or 2 graduate students focus on this project. We will try to apply some funding from the GFOI (Global Forest Observation Initiative) related activities.
COLLABORATIVE MONITORING OF DIFFERENT HAZARDS AND ENVIRONMENTAL IMPACT DUE TO HEAVY INDUSTRIAL ACTIVITY AND NATURAL PHENOMENA WITH MULTI-SOURCE REMOTE SENSING DATA The industrial district of Shenyang and Anshan plays an important role in the economic and social development of Northeast China. The mining activities strongly impact local environment due to ground excavations of coal and iron extraction. [...] Dr. Cristiano Tolomei, Ist. Naz di Geofisica e Vulcanologia - INGV, ITALY Dr. Lianhuan Wei, Northeastern University, CHINA Solid Earth The industrial district of Shenyang and Anshan plays an important role in the economic and social development of Northeast China. The mining activities strongly impact local environment due to ground excavations of coal and iron extraction. Anshan and Shenyang are subjected to multi-hazard including subsidence, landslides, and building damages. Results from the Dragon-4 project revealed landslides around open-pit mines, building instability and structural damages. In addition, the tunnel construction of underground lines at Shenyang has triggered surface fissuring, subsidence and sinkholes. The monitoring of such hazards is of fundamental importance to minimize and prevent the risks. In this proposal, we foresee to continue the monitoring activities started with the Dragon-4 project by means of multi-source remote sensing data at Shenyang and Anshan. Our Dragon-5 proposal will also consider a new study site, the Changbaishan active volcano (Jilin Province, ~300 km east from Shenyang). This volcano last erupted in 1903 and was responsible for the largest eruption of the last millennium in 946 CE. Changbaishan is affected by landslides, earthquakes and ground deformation. Deformation phenomena occurred during the 2002-2006 unrest episode and in 2017, when a nuclear test in North Korea triggered landslides. The multi-hazard exposure of Changbaishan is high because a population of ~135000 in China and 31000 in North Korea lives within 50 km from the volcano. In addition 2000000/yr tourists visit the Changbaishan volcano UNESCO National Reserve. We choose the topic ‘Solid Earth’ and the following sub-topics: 1.2-Monitoring of surface deformation and large landslides for the Shenyang, Anshan and Changbaishan sites 1.1-Seismic deformation monitoring for the Changbaishan site 1.4-Subsurface target detection for the Changbaishan hydrothermal and/or magma reservoir. The main goals of this proposal are to take advantage of the availability of remote sensing data to: 1) monitor and analyze the different hazards and environmental impact due to heavy industrial activity at Shenyang and Anshan areas and to natural phenomena at Changbaishan; 2) identification and modeling of single and multiple hazards, identifying the cross-related influence and causing factors; 3) forecast when and how hazards might happen, generate hazard scenarios, and provide support for disaster prevention and damage reduction to Authorities. The methodology to achieve the above objectives is the collaborative analysis of multi-source EO data, by means of InSAR time-series, VNIR optical data series, seismic, geochemical, laser scanning data and modeling. Time series InSAR allow to analyze the spatial and temporal deformation over large areas. Using both ascending and descending orbits, we will monitor such phenomena considering different sensors and different band frequency. We will decompose the LoS deformation into the Up and E-W directions to better constrain the deformation field. Volcanic deformation and landslide movements occur in both vertical and horizontal directions. We will adopt a multi-orbit InSAR time-series fusion approach with the assistance of high resolution DEMs generated by laser scanning. The deformation patterns will be validated with leveling and geodetic data. The volcanic source inversion will also be carried out by means of modeling. The hazards in traditional industrial regions and volcanic areas may be due to multiple causes. Instead of only monitoring a single hazard, this project aims to detect the spatio-temporal evolution of processes causative of multiple hazards via data modeling and assimilation techniques. The proposed research also foresees the exchange of young scientists. We apply for funding for the young scientist within this project and agencies in both Countries. Funding are also available from INGV and internal active projects.
CROSS-CALIBRATION OF HIGH-RESOLUTION OPTICAL SATELLITE WITH SI-TRACEABLE INSTRUMENTS OVER RADCALNET SITES Various global scientific issues (like climate change, environment monitoring, and ecological security) are making more and more strict requirements on the accuracy of remote sensing information products, which put forward very high accuracy and [...] Dr. Philippe Goryl, ESA-Esrin, ITALY Prof.. Chuanrong LI, Chinese Academy of Sciences, CHINA Calibration and Validation Various global scientific issues (like climate change, environment monitoring, and ecological security) are making more and more strict requirements on the accuracy of remote sensing information products, which put forward very high accuracy and stability demands on the on-orbit calibration of remote sensors. Nowadays, on-board calibration for spaceborne sensors cannot reach the level of actually traceable to the ground-based radiometric primary standard, whereas field vicarious calibration can only obtain limited calibration accuracy since it is likely to be influenced by scaling effect, atmospheric condition, environment variation, etc. In recent years, ESA, USA, and China have successively proposed the essential concept of spaceborne radiometric benchmark sensors. The main idea of radiometric calibration based on this benchmark sensor is: upload the SI-traceable radiometric benchmark instrument in a small number of radiation benchmark satellites, then transfer the traceable radiation values from the benchmark satellite to other satellites to be calibrated. However, as the high-resolution spaceborne sensor is concerned, the cross-points (between the benchmark satellite and monitored satellite) can hardly be found under the strict matching condition when performing cross-calibration, because of high-resolution satellite’s narrow swath. So, this project will propose a new method of benchmark transfer calibration for the high-resolution space-borne sensor, which uses RadCalNet site measurement as the ground reference value. The new method is to solve the problem of increasing of cross-calibration error due to unavoidable relaxation of matching constraints to improve cross-point opportunities between high-resolution satellites. In this project, Chinese and European researchers dedicated to radiometric calibration will collaborate in the transfer calibration technologies based on RadCalNet and the SI-traceable spaceborne reference instrument. On one side, new method of radiometric benchmark transfer calibration which adopts RadCalNet measurement as ground reference value will be cooperatively developed to break through important technical problems existed in the benchmark transfer chain (benchmark satellite -> standard TOA spectral reflectance provided by RadCalNet -> satellite to be calibrated (i.e., the monitored satellite)). On the other side, based on previous cooperative research on RadCalNet, both parties will make effort to further improve the accuracy of RadCalNet standard product and the inter-site product consistency, and incorporate more Chinese automated calibration sites into the RadCalNet framework if possible, to carry out demonstration applications of automated calibration & benchmark transfer calibration for Chinese and European high-resolution satellites. The main research contents of this project include constraint mechanism analysis of the radiometric benchmark transfer calibration; a new method of the radiometric benchmark transfer calibration; demonstration of radiometric calibration for Chinese and European high-resolution satellites. The expected achievements of this project include (1) constraint mechanism of the radiometric benchmark transfer calibration based on RadCalNet; (2) new method of the radiometric benchmark transfer calibration; (3) technical report on the demonstration of transfer calibration for high-resolution satellites; (4) academic papers and talent training. In the project executing process, a series of external projects can be available to effectively support the operation of this project. Relevant supporting projects include: (a) Spaceborne radiometric benchmark transfer calibration and its ground-based validation; (b) Global automated radiometric calibration network; (c) Land satellite calibration network; (d) Radiometric re-calibration of thermal infrared band of land satellite and surface temperature retrieval.
CRYOSPHERE-HYDROSPHERE INTERACTIONS OF THE ASIAN WATER TOWERS: USING REMOTE SENSING TO DRIVE HYPER-RESOLUTION ECOHYDROLOGICAL MODELLING This project seizes the opportunity offered by ESA and NRSCC to access high resolution satellite observations of Earth’s surface to provide novel understanding of the cryosphere and water cycle of key water towers of High Mountain Asia (HMA). [...] Dr. Francesca Pellicciotti, Swiss Federal Institute for Forest, Snow and Landscape Research,WSL, SWITZERLAND Prof.. Massimo Menenti, Aerospace Information Research Institute - CAS, CHINA Cryosphere and Hydrology This project seizes the opportunity offered by ESA and NRSCC to access high resolution satellite observations of Earth’s surface to provide novel understanding of the cryosphere and water cycle of key water towers of High Mountain Asia (HMA). Using a hyper-resolution ecohydrological model, fed by Earth System Observations, we will bridge the modelling gap between snow and glaciers, which generate the runoff that ultimately feeds major rivers, and downstream water cycle components such as vegetation, which buffer, delay or amplify that runoff. We will focus on blue (runoff) and green (evapotranspiration) water interactions in HMA, which are often examined separately, and integrate water supply changes due to a vanishing cryosphere with the effect of vegetation to dampen or amplify those changes, especially in periods of droughts. This new perspective will enable us to assess the vulnerability of selected High Asian water towers. The new model will afford a thorough assessment of all water budget components in 10 benchmark catchments representative of the climatic differences of HMA. This unprecedented synthesis effort is possible through the combined expertise in remote sensing (Chinese PI) and modelling (European PI), with synergies due to existing projects and support from partners in the region. Our main aim is to understand how green water processes affect the availability of blue water from glaciers, snow and precipitation across High Mountain Asia High-resolution satellite data of land-cover, surface albedo, vegetation phenology, surface water, glacier velocities, surface lowering and mass balance will guide model developments and support model calibration and validation in a systematic manner to ensure comparability across case studies, providing a holistic assessment of how ecosystems and vegetation can enhance or reduce glacier response to climate change in HMA. The 10 glacierized sites span a variety of climates, glacier conditions and mass balance regimes. For each catchment, field measurements of glacier melt, mass balance, runoff and meteorological variables are available. These will be used in combination with the diverse remote sensing observations generated by the Chinese PI group to drive the model and validate results. This key synergy is further strengthened by a partnership with Dr Tobias Bolch and his team. Dr Bolch leads a companion proposal to develop records of glacier shrinkage, thinning and motion for some of the same sites that we propose to study, maximising synergies. Our multidisciplinary team of European and Chinese scientists will thus collaborate to: i) provide an advanced characterisation of the main glacier and hydrological processes from remote sensing observations in the high elevation catchments of HMA; ii) resolve the altitudinal surface mass balance for all study glaciers; iii) apply a novel hyper-resolution earth-surface model to simulate the complexity of the high mountain water budget and quantify changes in streamflow. The proposed work is supported by, for the European PI: (1) ERC Consolidator Grant “RAVEN: Rapid mass loss of debris covered glaciers in High Mountain Asia”; (2) Royal Society Grant “Understanding glaciers and hydrological changes in the Tibetan Plateau using high resolution monitoring and modelling”; (3) Swiss National Science Foundation (SNSF) project “High Elevation Precipitation in High Mountain Asia (HOPE)”; (4) SNSF project “Understanding snow, glacier and rivers response to climate in High Mountain Asia (ASCENT)”; and (5) NERC Grant “Peruvian Glacier Retreat and its Impact on Water Security (Peru GROWS)”. For the Chinese PI, work is supported by: (1) Natural Science Foundation of China, grant number 91737205; (2) Strategic Priority Research Program of the Chinese Academy of Sciences (CAS), grant numbers XDA19030203 and XDA19070102; and (3) MOST High Level Foreign Expert program, grant number G20190161018.
DETAILED CONTEMPORARY GLACIER CHANGES IN HIGH MOUNTAIN ASIA USING MULTI-SOURCE SATELLITE DATA Glaciers are sensitive indicators of climate change and affect regional and global water circulation. High Mountain Asia (HMA) has the largest volume of glacier ice in mid-latitude regions and is considered as the water tower of Asia. HMA [...] Dr. Tobias Bolch, University of St Andrews, UK Dr. Lei Huang, Aerospace Information Research Institute, Chinese Academy of Sciences, CHINA Cryosphere and Hydrology Glaciers are sensitive indicators of climate change and affect regional and global water circulation. High Mountain Asia (HMA) has the largest volume of glacier ice in mid-latitude regions and is considered as the water tower of Asia. HMA glaciers do not only provide drinking and irrigation water for millions of people in and beyond the mountain ranges especially in drought-affected regions, but also provide water to ecosystems. Therefore, continued monitoring of glacier changes and its influences is essential. In this project, we plan to monitor contemporary glacier changes and influences in HMA using recently available satellite data with the focus on Sentinel-1 and 2 and ICESat-2 data but also very high-resolution stereo data. We will develop new methods to monitor glacier with unprecedented detail focusing on changes in area, thickness, velocity and accumulation area ratio (AAR), and reveal the most their recent trends in HMA. Area change. We aim to develop a novel method using repeat Sentinel-2 images on a cloud-computation platform to automatically map clean ice using spectral reflectance information based on composite of cloud-free and seasonal snow-free pixels. For the debris-covered parts which cannot be identified using spectral information alone we aim to develop novel decision tree and random forest algorithms using multisource information including besides spectral reflectance SAR coherence and surface velocity measurements. Thickness change. We will develop and apply an automated method to measure glacier thickness changes for whole HMA by newly launched ICESat-2 laser altimetry data. Moreover we aim to test the suitability (1) to calculate glacier mass balance using the generated outlines from a similar time and density estimates and (2) to measure seasonal mass balance. The accuracy of thickness change from ICESat-2 will be validated in different mountainous areas. Velocity change. Glacier surface velocity provides important information about glacier mass fluxes and allows to calculate surface mass balance using the thickness change data. Monitoring of glacier velocity provides also insights into glacier surging behaviour. We will develop an automated pipeline to derive glacier velocity based on feature tracking using both Sentinel-1 and 2 images. Using both SAR and optical data allows cross-validate glacier velocities and the changes. Accumulation area ratio (AAR). The AAR is a sensitive indicator of glacier mass balance. In late summer, the AAR can be estimated by the wet snow zone ratio using synthetic aperture radar(SAR). The late summer snow line can be delineated from the boundary of the accumulation area. The Glacier mass balance estimates based on the snow-line/AAR observations will be cross-validated using the thickness change data. The methods will be developed, validated and calibrated in selected benchmark sites located in different climatic regions by multi-temporal very high-resolution stereo satellite data such as TerraSAR-X, Pleiades, ZY3, GF7 and glaciological field measurements. In the next step it will then be tested with which accuracy the methods can be applied to whole HMA using especially S1, S2 and ICESat-2 data. Overall, this project will provide comprehensive information about heterogenous glacier characteristics and changes. The results will be analyzed in order to reveal detailed insights into the spatial heterogeneity of glacier mass balance, surface mass balance and velocity and observed annual and seasonal trends. The data and results will be of high value calibrate and validate the glacier component of glacio-hydrological models. It is foreseen that a partner project (led by F. Pellicciotti and M. Menenti) will use these data to better understand the importance of glacier to overall runoff and project future changes using different climate scenarios.
EARTH OBSERVATION FOR SEISMIC HAZARD ASSESSMENT AND LANDSLIDE EARLY WARNING SYSTEM Landslides are a major global hazard, controlled by geology, weather and land-use, and also a major secondary hazard in most continental earthquakes. Recent catastrophic landslides in China and elsewhere have demonstrated the importance of [...] Prof.. Zhenhong Li, Newcastle University, UK Prof.. Qiming Zeng, Peking University, CHINA Solid Earth Landslides are a major global hazard, controlled by geology, weather and land-use, and also a major secondary hazard in most continental earthquakes. Recent catastrophic landslides in China and elsewhere have demonstrated the importance of understanding this hazard and of developing early-warning systems. Developing and validating Earth Observation (EO) technologies for the detection and monitoring of landslide hazards meets the Sentinel mission objective of geological hazard mapping. EO allows hazard assessments to be made and enable improved planning, design and early warning systems. In our Dragon-1/2/3/4 projects, we have successfully employed InSAR to map a range of active landslides in different regions of China, e.g. the Badong, Xintan, Shuping, Heifangtai and Maoxian landslides. In this project, we aim to further develop advanced SAR and optical techniques to detect potential landslides across the whole Jinsha River region, and demonstrate EO-based landslide early warning system over selected landslides. The main objectives of the project are as follows: O1. Integrate various SAR/InSAR/Optical techniques to generate surface deformation maps for extremely-slow to very-slow moving to slow-moving landslides. O2. Combine various SAR and optical datasets to generate surface deformation maps for slow-moving to fast-moving landslides. O3. Utilise deep learning techinques to automatically detect landslides based on surface deformation maps. O4. Determine the geophysical mechanisms responsible for landslides and provide a quantitative risk assessment along the Jinsha River region. O5. Demontrate GNSS-based landslide early warning system on selected sites. We expect that this project will lead to: (1). A processing chain to integrate Conventional InSAR, pixel offset tracking of radar and optical amplitude measurements and a time series tool. (2). Optimized ways to combine satellite radar and optical images for automatic detection of fast-moving landslides (3). Demonstration of landslide early warning system (4). Around 15 young researchers in China and Europe trained in the landslide field by the end of this Dragon-5 project (5). Regular academic exchanges between China and Europe (6). Joint workshops with young researchers involved (7). Joint publications in high impact journals This project is a collaboration among eleven institutions in China and the EU/UK. We will take advantage of the opportunity offered by the Dragon framework for Chinese-European exchange. We plan joint workshops in China for young postdoctoral scientists and students. Young scientists will also take part in exchanges, e.g. visits by Chinese scientists to work on InSAR at the UK universities, field visits to China by UK scientists. This project will be supported by (1) National Natural Science Foundation of China (NSFC) [41571337] (PI: Qiming Zeng) (2) China Earthquake Administration [ZDJ2018-16] (PI: Jingfa Zhang) (3) National Natural Science Foundation of China (NSFC) [41874005] (PI: Chaoying Zhao) (4) National Natural Science Foundation of China (NSFC) [41941019] (Co-I: Wu Zhu) (5) UK Natural Environment Research Council [NE/K010794/1] (Newcastle PI: Zhenhong Li) (6) UK Natural Environment Research Council [COMET] (Newcastle PI: Zhenhong Li)
EARTH OBSERVATION SERVICES FOR CLIMATE FRIENDLY AND SMART CITIES The project addresses, mainly with the use of Earth Observation, two distinct themes with strong
interlink: climate change as this relates to the thermal resilience of cities and urban environment
in terms of smart cities. In the former theme, [...]
Prof.. Constantinos CARTALIS, National and Kapodistrian University of Athens, GREECE Prof.. Gong Huili, Capital Normal University, CHINA Urbanization and Environment The project addresses, mainly with the use of Earth Observation, two distinct themes with strong interlink: climate change as this relates to the thermal resilience of cities and urban environment in terms of smart cities. In the former theme, the aim is to draft climate change adaptation plan as far as urban heat is concerned; in the latter case, the aim is to detect and assess urban geological hazards. Areas of application will be the wider Beijing and Athens urban areas. In terms of climate change, the scientific objectives are: to assess the impact of climate change to the thermal environment of a city; to study the relationship between urban form and the state of the thermal environment of urban areas; to define a methodology for the detection of intracity thermal heat spots, to define and map urban heat risk and assess climate resilience; to use the above in support of climate change adaptation plans as far as urban heat is concerned and in view of improving climate resilience.
EO-AI4URBAN: EARTH OBSERVATION BIG DATA AND DEEP LEARNING FOR SUSTAINABLE AND RESILIENT CITIES The pace of urbanization has been unprecedented. Today, 55 per cent of the world’s population live in cities and another 2.5 billion people is expected to move to urban areas by 2050 (UN, 2018). Rapid urbanization poses significant social and [...] Prof.. Yifang Ban, Royal Institute of Technology, SWEDEN Prof.. Yunming Ye, Harbin Institute of Technology, Shenzhen, CHINA Urbanization and Environment The pace of urbanization has been unprecedented. Today, 55 per cent of the world’s population live in cities and another 2.5 billion people is expected to move to urban areas by 2050 (UN, 2018). Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services, and making cities more vulnerable to disasters. Therefore, timely and accurate information on urban changing patterns on both 2D and 3D is of crucial importance to support sustainable and resilient urban planning and monitoring of the UN 2030 Urban Sustainable Development Goal (SDG). Thanks to the fast growing of the satellite technology, we are moving forward to the new era of Earth Observation (EO). National and International space agencies as well as innovative companies have started various EO programs (e.g, ESA Copernicus, RADARSAT Constellation Mission, Planet, ICEYE) that are able to acquire massive amount of satellite imagery with higher spatial resolution and frequent temporal coverage. These EO big data represent a great opportunity to develop innovative methodologies for urban mapping and continuous urban change detection. The main challenge is the lack of robust and automated processing methods to extract valuable information from the massive amount of EO data. The overall objective of this project is to develop innovative, robust and globally applicable methods, based on Earth observation big data and AI, for urban land cover mapping and urbanization monitoring. The innovative aspects of this research include development of novel methodology through interdisciplinary research and supporting planning smart, sustainable and resilient cities. The proposed methodology includes the development of semantic segmentation with better generalization with weakly supervised and self-supervised training for urban land cover mapping, deep Siamese convolutional neural network for change detection, and unsupervised temporal anomaly detection for time series analysis. In addition, two SAR-based methods, i.e, SAR interferometry and radargrammetry, will be explored for 3D change detection as urban areas not only expend in 2D but also in the 3rd dimension. Open and free Earth observation big data will be used to demonstrate the new deep learning-based methods in Jing-Jin-Ji, Yangtze River Delta, Yellow River Delta and Pear River Delta in China plus ten cities around the world including Stockholm, Lagos, Mumbai. It is anticipated that detailed urban land cover information and their changes will be mapped detected in a timely and accurate manner. The urban change in 3D will be estimated to better understand urban density and environmental impact. This research is expected to contribute to 1) advance EO science, technology and applications beyond the state of the art, 2). timely and reliable updating of urban databases to support sustainable planning at municipal and regional levels, 3) the monitoring objectives of the national authorities and the UN SDG 11: make cities and human settlements inclusive, safe, resilient and sustainable. The proposal involves two topics and 4 sub-topics including: 7. Urbanization and Environment 7.1 Urban expansion 7.2 Urban land use structure and its change 7.4 Smart cities 10 Data Analysis 10.1 Big Data Analytics 10.2 Artificial Intelligence and Machine Learning The project will be partially funded by the projects that the team partners have been secured. Specifically, the EO-AI4ChangeDetection project funded KTH Digital Futures, Sentinel4Urban project is funded by SNSA, ESA CCI HR Landcover. The Chinese partners also have existing projects will apply for the funding from Natural Science Foundation of China and related provinces to support this project.
EXPLOITATION OF SATELLITE REMOTE SENSING TO IMPROVE OUR UNDERSTANDING OF THE MECHANISMS AND PROCESSES AFFECTING AIR QUALITY IN CHINA (EMPAC) This proposal addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical [...] Prof.. Ronald van der A, The Royal Netherlands Meteorological Institute (KNMI), NETHERLANDS Prof.. Jianhui Bai, Institute of Atmospheric Physiscs, Chinese Academy of Sciences, CHINA Atmosphere This proposal addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical influences. Detailed in situ measurements are combined with ground-based and satellite remote sensing which together provide complimentary information on the contributions from different sources and processes affecting AQ, with scales varying from the whole of China to local studies and from the surface to the top of the boundary layer and above. Different species contributing to air quality will be studied, i.e. aerosols, in AQ studies often represented as PM2.5 (mass of dry particles with in situ diameter smaller than 2.5 µm), trace gases such as NO2, NH3, Volatile Organic Compounds (VOCs) and O3. The primary source of information in these studies is the use of a variety of satellite-based instruments providing data on atmospheric composition using different techniques. Concentrations of atmospheric components are either retrieved by the EMPAC consortium or downloaded from public data services such as COPERNICUS. They are validated using reference data sets and evaluated by comparison with other satellite data over areas where no reference data are available. For the interpretation of satellite data and their use to AQ studies, a wide variety of data from other types of observations is used, providing complementary information on the species measured from satellites, as well as meteorological information and large-scale phenomena. In particular this proposal focuses on the relationship between satellite-based column-integrated properties and near-surface concentrations important for AQ which would allow for the use of satellites to provide AQ information on large spatial scales. To understand the satellite/surface relationships, detailed process studies will be undertaken, using ground/based in situ measurements, instrumented towers and drones, as well as remote sensing using lidar and Max-DOAS. A unique source of information on the vertical variation of NO2, O3, PM2.5 and BC is obtained from the use of an instrumented drone. The proposal is structured along three related topics: Aerosols, Trace Gases and biosphere/atmosphere interactions.
EXPLOITING UAVS FOR VALIDATING DECAMETRIC EARTH OBSERVATION DATA FROM SENTINEL-2 AND GAOFEN-6 (UAV4VAL) Surface reflectance is the fundamental quantity required in the majority of optical earth observation analyses, and as an essential input to biophysical variable retrieval algorithms, it forms the basis of many higher level products. These [...] Prof.. Jadu Dash, University of Southampton, UK Prof.. Yongjun Zhang, Wuhan University, CHINA Calibration and Validation Surface reflectance is the fundamental quantity required in the majority of optical earth observation analyses, and as an essential input to biophysical variable retrieval algorithms, it forms the basis of many higher level 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), provide insight into the evolution of the terrestrial environment. In turn, they are crucial in understanding vegetation productivity/yield, biogeochemical cycles, and the weather and climate systems. In the context of an increasing global population, the need to ensure food security, and environmental change, accurate estimates of these parameters are required to enable sustainable management of natural resources. To ensure their accuracy, validation of decametric surface reflectance and vegetation products is required, using independent ground reference measurements to verify product performance. However, the collection of ground reference measurements is time-consuming and resource intensive, limiting the extent of validation efforts in both space and time. Recently, the potential of unmanned aerial vehicles (UAVs) to reduce required resources and increase spatial and temporal coverage has been recognised. 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 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. The aim of the project will be achieved by the collection, processing, and analysis of ground measurements over a number of European and Chinese sites, coinciding with UAV acquisitions. The project will investigate the feasibility of using UAV data as an alternative to traditional ground measurements for validating Sentinel-2 and Gaofen-6 products.
GEOPHYSICAL AND ATMOSPHERIC RETRIEVAL FROM SAR DATA STACKS OVER NATURAL SCENARIOS The aim of this project consists in the development and application of processing methodologies to address two specific Sub-topics relevant for stack-based spaceborne applications. Sub-topic 1 concerns the internal structure of natural media, [...] Prof.. Stefano Tebaldini, Politecnico di Milano, ITALY Prof.. Mingsheng Liao, LIESMARS, Wuhan University, CHINA Atmosphere The aim of this project consists in the development and application of processing methodologies to address two specific Sub-topics relevant for stack-based spaceborne applications. Sub-topic 1 concerns the internal structure of natural media, and it is mapped to Dragon topic Solid Earth – Subsurface target detection. Sub-topic 2 concerns joint estimation of deformation and water vapour maps, and it is mapped to Dragon topic Solid Earth – Monitoring of surface deformation of large landslides. The topics above are of fundamental importance in the context of present and future spaceborne missions, which will allow increasingly more systematic use of multiple acquisitions thanks to improved hardware stability and orbital control. Indeed, the proposed activities are intended to support use of multi-pass data stacks from:o the upcoming P-Band mission BIOMASS. o future L-Band missions, such as the SAOCOM constellation, the upcoming Chinese L-Band bistatic Mission Lu-Tan1, and potentially Tandem-L and Rose-L.o the C-Band Sentinel Missions.Sub-topic 1 will consider as test sites a forested area in North-West Germany and a desert area in Namibia, which are under study in the context of the ESA campaigns TomoSense and DesertSAR. The activities will focus on processing SAR image stacks to extract information about forest structure and sub-surface terrain topography on forested areas, and about the internal structure of sand dunes and surface topography on desert areas. Estimation and compensation of ionospheric and tropospheric propagation effects will be considered as well. Given the availability of a large amount of reference data at both sites, the success of this study will be assessed by direct validation against reference data from in-situ measurements and products from airborne Tomography. During the first years of this study, the activities will proceed based on stacks of ALOS SAR images, which are currently available as archive products.Sub-topic 2 will consider two test sites: Kenya, of interest for retrieval of water-vapor and deformation over large scale, and Xilodou dam, characterized by on-going deformation, and at the same time, very humid and heavily vegetated conditions and rugged terrain. The objective is two-fold. For the generation of tropospheric products, for meteorological application, the synergic exploitation of distributed and permanent scatterers, is still an open issue, where the retrieval of absolute phase screen needs merging with GNSS and meteorological maps (ERA5, GACOS), where timeliness and efficiency is a must. The integration of DS and PS will in parallel by tested on difficult sites with fast and high resolution deformations in Xilodou.DeliverablesD1: Dragon symposium 2021: attendance and presentationsD2: Dragon symposium 2022: attendance, presentations, and journal papersD3: Dragon symposium 2023: attendance and presentationsD4: Dragon symposium 2024: attendance, presentations, and journal papersFundingThe activities are planned to start with two European Young Scientists from Politecnico di Milano:o Ing. Marco Manzoni, currently pursuing his PhD at PoliMi under ministerial funding granted until end of 2021. o Dr. Mauro Mariotti dÔÇÖAlessandro, working in the SAR group at PoliMi as Post-Doc research fellow under internal funding granted until end of 2021.A YS from University of Pisa, Dr. Claudia Zoppetti, Post-Doc collaborator under funding granted until January 2021, is also availableIt is important to note that the duration of the Dragon programme is significantly longer than the average time a University is able to grant funding for young researchers (PhD are given within a 3 year programme, research funding are typically granted on yearly basis). If the project is granted, future PhD students and Research Fellows from both PoliMi and University of Pisa will be involved in the proposed Dragon project in the next years.
GLOBAL CLIMATE CHANGE, SEA LEVEL RISE, EXTREME EVENTS AND LOCAL GROUND SUBSIDENCE EFFECTS IN COASTAL AND RIVER DELTA REGIONS THROUGH NOVEL AND INTEGRATED REMOTE SENSING APPROACHES (GREENISH) Coastal zones are essential for the socio-economic well-being of many nations. Coastal regions, which are the location of large population centres, have multiple uses, needs and opportunities, and are particularly exposed to extreme events and [...] Dr. Antonio Pepe, CNR - I. Rilevamento Elettroma. Ambiente, ITALY Prof.. Qing Zhao, East China Normal University, CHINA Oceans and coastal zones Coastal zones are essential for the socio-economic well-being of many nations. Coastal regions, which are the location of large population centres, have multiple uses, needs and opportunities, and are particularly exposed to extreme events and climate change. Many key sectors are affected by long-term effects in these zones, such as the monitoring of public/private infrastructures, cultural/natural heritage preservation, risk management, and agriculture. The combined effects of sea level rise (SLR), tidal evolution, modulated ocean currents and extreme events can have numerous impacts to coastal, river delta, and inland water zones, including water management, which in turn lead to cascading and unpredictable impacts on other sectors. The GREENISH project is the natural extension of the 32294 Dragon IV project, and aims to provide extensive research and development analyses of areas in Europe and China subject to climate change induced (e.g., SLR, flooding, and urban climate threats) and anthropogenic disasters (e.g., ground subsidence over reclaimed-land platforms), with the goal to improve the knowledge and develop new remote-sensing methods. Of great relevance is a detailed understanding of the combined risk of SLR, tidal evolution, storm surges, and ground subsidence in coastal areas and lake-river systems. Global sea-level is rising, and tides are also changing worldwide and these risks are accompanied by increasing concerns about the growing urbanization of the worldÔÇÖs low-lying coastal regions and related coastal hazards (e.g., flooding). Inland water bodies such as lake and river system also experience substantial degradation with rapid economic development.The use of optical, SAR, InSAR, and hyper-spectral data products will be fostered. Selected case-study areas include the Yangtze and Pearl river deltas, Poyang Lake, the Bohai Rim Region (China), the city of Istanbul (Turkey), the Po river delta and the Venice Lagoon (Italy). Flood hazards will be investigated by using satellite SAR and altimeter data, tide gauge data, and by developing proper hydrodynamic models. The results will help provide reliable information for improving the resilience of population centres to coastal disasters.The main goal of the project is the well-use of Earth Observation (EO) data and in-situ monitoring information, to detect the long-term evolution of coastal, deltaic and lake-river systems. More specifically, the project aims:- To study the ground deformation in coastal/deltaic regions with conventional and novel interferometric SAR approaches. – To monitor changes of urbanized areas via coherent and incoherent change detection analyses. – To study interactions between ocean currents and coasts, such as coastal erosion, using high resolution optical and SAR satellite images.- To properly assess SLR, tidal evolution, and hydrogeological risks in urban coastal areas.- To study the interactions between Poyang Lake and its connecting rivers.- To study atmosphere/surface interactions and develop atmospheric phase screen correction methods in multi-temporal SAR images. – To develop methods to integrate satellite- and ground-based RADAR systems to monitor public infrastructures in Shanghai- To develop interactive maps of coastal, urban, and inland zones susceptible to primary and secondary risks via GIS. – To train Young scientists (PhD and post-doc).The project deliverables are papers on peer-reviewed international journals, conference proceedings and new software algorithms to monitor and map coastal risks. The work will be financed from internal resources of the participants. Additional funding will come from National Natural Science Foundation of China, Research Grants of Ministry of Land and Resources of China, High-end Foreign Experts Recruitment Program of China, the Fundamental Research Funds for the Central Universities of China, EU projects and EU-China governmental cooperation.
GRASSLAND DEGRADATION DETECTION AND ASSESSMENT BY REMOTE SENSING Monitoring grassland degradation on a large scale has proven very difficult. Early estimates of the extent of degraded grasslands in dry areas put their number as about 3,050 million hectares (ha), or 94% of all degraded drylands, but relied on [...] Prof.. Alan Grainger, School of Geography, University of Leeds, UK Prof.. Zhihai Gao, Chinese Academy of Forestry, CHINA Ecosystems Monitoring grassland degradation on a large scale has proven very difficult. Early estimates of the extent of degraded grasslands in dry areas put their number as about 3,050 million hectares (ha), or 94% of all degraded drylands, but relied on subjective assessments. Attempts to base estimates on measurements, e.g. by means of the Normalised Difference Vegetation Index (NDVI) derived from low resolution satellite data, came in for much criticism. Continuing difficulties of this kind prevented the Third Edition of the World Atlas of Desertification from displaying maps of the extent of dryland degradation based on satellite data. So there is an urgent need to devise new methods for measuring grassland degradation using satellite sensors. This project aims to fill this gap by devising such methods. It will experiment with various combinations of optical and radar data with resolutions varying from as much as 30 m (medium resolution) to ≤ 1 m (very high resolution). The research will focus on grasslands in China, which cover 400 million ha, or 42% of national land area, and this enables it to take advantage of data collected by both Chinese and ESA satellites. Its findings will make it feasible to monitor the degradation of grasslands, and drylands generally, in a reliable way, and to tackle the even more challenging task of measuring the combination of degradation and restoration that is required for monitoring progress in achieving the goal of land degradation neutrality, which are included in one of the sustainable development goals. Without reliable measurements of human-induced degradation, catalysed by droughts, it will be impossible to estimate the likely impact of long-term global climate change. Topics and methods: (1) Mapping and dynamic monitoring of grassland types: Referring to the traditional grassland categorizing system, the remote sensing classification system of the study area would be firstly constructed on the basis of analyzing the charicteristics of grassland types in the study area and payloads of China and ESA’s EO satellites. Then methods on mapping and dynamic monitoring of grassland types will be studied by multi-source remote sensing, especially with the utilization of Very High Resoultuion (VHR) datasets. (2) Quantitative estimation of grassland ecological parameters:The estimation and dynamic monitoring technologies of grassland ecological parameters, including grassland vegetation coverage, NPP and grassland biomass, will be developed by means of the combination of multi-scale and multi-source remote sensing and field investigation, and a grassland ecological static and dynamic monitoring technological system is expected to build (3) Degraded Grassland detection and assessment: The regularity of grassland ecological change and its spatio-temporal coupling relationship with climatic factors, soil characteristics and intensity of grazing will be analyzied synthetically. A remote sensing method for detection of degraded grassland which can greatly eliminate the impact of climate fluctuation will be developed, and the degree of grassland degradation will be assessed scientifically by grading the vegetation productivity and soil characteristics within degraded grassland. Availability of funding to run the project: For the European side, the School of Geography has the necessary institutional capacity to run the project. In addition, during the 4 years research, two funding projects, sponsored by National Science and Technology Major Project (No. 21-Y30B02-9001-19/22) and Fundamental Research Funds for the Central Non-Prof.it Research Institution of CAF (CAFYBB2019ZB004), are being undertaking by the Chinese team members. They will support the normal progressing of the Dragon 5 cooperation and the participating of the annual symposium.
IMPACTS OF FUTURE CLIMATE CHANGE ON WATER QUALITY AND ECOSYSTEM IN THE MIDDLE AND LOWER REACHES OF THE YANGTZE RIVER The 2030 SDGs identify water and its management as crucial for providing the economic, social and environmental well-being of the present and future generations. Lakes in the basin of the Yangtze river, play a fundamental role in regional [...] Dr. Herve Yesou, University of Strasbourg, FRANCE Prof.. Xiaoling CHEN, LIESMARS, Wuhan University, CHINA Cryosphere and Hydrology The 2030 SDGs identify water and its management as crucial for providing the economic, social and environmental well-being of the present and future generations. Lakes in the basin of the Yangtze river, play a fundamental role in regional bio-geochemical cycles and provide major services to the communities, provisioning services (drinking water, fishing) and biodiversity keeping. However, the extreme temporal and spatial variability of these massive but extremely shallow ecosystems prevents a reliable quantification of their dynamics with respect to changes in climate and land use. To challenge this DRAGON5 project, successor of Dragon 4, EOWAQYWET, will provide: 1- water bodies extent and height monitoring, 2- water quality monitoring, 3- wetland ecosystem understanding, 4- regional interaction and global context. WP1: Water extent WEM and height WLM monitoring: � WP1.1: WEM. 1- continuity over Yangtze basin lakes. 2- assessment of new Full Pol SAR systems. � W1.2: WEL. 1- Insure the continuity of the monitoring; 2 -Integrate more virtual gauge stations, based on S3 and Jason-3, 3- integration of S6 and SWOT WP2: Water quality: 1- develop and validate processing protocols for multiple sensor systems, applied to algal blooms and black water events monitoring, macrophyte overproduction and lake stratification. 2- provide new insights, and decisional instructions for the analysis of water quality dynamics with respect to ambient water quality requirements and provisioning (SDG 6.1.1. and SDG 6.3.2). � WP21. advanced algorithms characterizing the optically complex waters. Experiment of fluorescence and LWST to quantify the timing and extension of surface algal blooms exploring in situ and new sensor systems (FLEX). � WP22: Multi scale – temporal retrieval of lake water surface temperature (LWST). From HR to LR sensors with TIR innovative approach to define high temporal resolution sequences � WP2.3: Novel methods to determine and model the dynamics of particulate and dissolved carbon and nutrients (N and P), with reference to primary productivity, incorporating information on wind, wave dynamics and LWST. WP2.4: biogeochemical modelling of shallow lake systems, integrating satellite estimated bio-optical, LWST, and WEL, in-situ measurements and controlled (micro and mesocosm), experiments to determine the links between catchment-related (LUCC), climatic (precipitation, evaporation) and hydrological (soil moisture.. ) conditions and lake carbon, nutrient and bio-optical dynamics WP3: Wetland mapping and biodiversity values analysis focus on the interaction between vegetation resources, water cycle analysis and human (dikes, tree planting, fishes� farms / traps), interactions and biodiversity. HR and superspectral imagery will be exploited for mapping the vegetation, floating and submerged, phenology and quality (as feeding resources for birds).. Final aim is to model, map and explain the distribution of biodiversity and their associated habitats, explaining spatio-temporal changes in biodiversity caused by biotic and abiotic factors. WP4: Regional and global interactions1- better understanding of the monsoon lakes behaviors in a regional and global change context, enhancing potential drought tendency , with also a more and more earlier draw off of the water thanks to time series analysis of rainfall, evaporation, river flows will be taking in account within component multi-scale analysis (wavelet�) and modeling , of course attention will be paid to the influence of management of the 3GD as well as sans dragging in the lakes. This works will exploitation of multi-mission data, Sentinel1, 2 &3 as core , TPM and Chinese missions (Beijing 1, HJ-1AB, GF 1, 3 &5) plus new sensors FLEX, SWOT, J-CS.
INNOVATIVE USER-RELEVANT SATELLITE PRODUCTS FOR COASTAL AND TRANSITIONAL WATERS The Earth's coastal and transitional waters are fundamental resources and encompass a broad
range of ecosystems that are core to global biogeochemical cycling, food and energy production.
The mounting conflicting pressures from the number of [...]
Dr. Spyrakos Evangelos, University of Stirling, UK Prof.. Junsheng Li, Aerospace Information Research Institute, Chinese Academy of Sciences, CHINA Oceans and coastal zones The Earth’s coastal and transitional waters are fundamental resources and encompass a broad range of ecosystems that are core to global biogeochemical cycling, food and energy production. The mounting conflicting pressures from the number of users and uses, coupled with population growth, industrialization, land use intensification and climate change bring into focus the urgent need for the sustainable management of our aquatic resources and space. The increasing availability of satellite data from the new missions in this decade has radically transformed the approaches to monitor and sustainably manage coastal and transitional systems and has stimulated rapid growth in the development of downstream services. This proposal aims to develop and validate innovative user-relevant satellite products in regards to the biogeochemical properties of coastal and transitional waters to improve the management and sustainable exploitation of these zones by exploring the capacity offered by latest generation of satellite data (e.g. Sentinel-2 MSI, Sentinel-3 OLCI, HY-1, GF-1, GF-6, Jilin-1, and Planet Dove) from Europe and China. In particular, we will develop and validate innovative products for phytoplankton size classes (PSC, as an important parameter for shellfish aquaculture), primary production (PP, leading to carrying capacity estimates for aquaculture and policy making), specific ecosystem threatening harmful algae blooms (HABs) (e.g. caused by Pseudo-nitzschia spp., including macroalgal blooms due to the macroalgae Ulva prolifera) and marine oil spills (MOS). Our study areas will include and the Danube Delta & Black Sea coast, Galician coast (NW Iberian Peninsula), Shandong Peninsula coast and Northern South China Sea. This proposed work is under the topic of “Oceans & Coastal zones” and spans over the subtopics “Algae and phytoplankton blooms” and “Marine dynamic environment”.
INTEGRATION OF MULTI-SOURCE REMOTE SENSING DATA TO DETECT AND MONITORING LARGE AND RAPID LANDSLIDES AND USE OF ARTIFICIAL INTELLIGENCE FOR CULTURAL HERITAGE PRESERVATION 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 (spatial [...] Prof. Joaquim Sousa, University of Trás-os-Montes and Alto Douro, UTAD, PORTUGAL Prof.. Fan Jinghui, China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, CHINA Solid Earth 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 (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 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 D5 proposal, we foresee to continue the monitoring activities started with the D4 project mainly by means of multi-source RS data at diverse areas located in different countries.Our D5 proposal would also consider monitoring structures of great heritage and historical value, more quickly and effectively, as these structures are continuously subject to deformations caused by internal and external factors, especially when located in high risk areas. The availability of SAR data with spatial and temporal resolutions at an unprecedented level, associated with the new methods of SAR time series processing, allow us to think in the development of active systems for structural risks detecting and alerting. However, only the use of Artificial Intelligence techniques will allow to deal with the huge amount of data that will be generated. The Vilari├ºa 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 order to cover most situations, the following Chinese sites will be also included: (1) Hani Rice Terraces; (2) Fushun West Open pit Coal Mine and (3) Shuping-Fanjiaping. Besides, the Central Karakoram Range in the Northern Pakistan, exposed to a variety of natural hazards including devastating landslides, would be an important study area on the Belt and Road.Based on the above selected areas of investigation and their multi-hazard exposure, we refer to the ÔÇÿAcross topicsÔÇÖ option. We choose the topics ÔÇÿ1. Solid EarthÔÇÖ and ÔÇÖ10. Data AnalysisÔÇÖ, with the following sub-topics:ÔÇó 1.2 – ÔÇ£Monitoring of surface deformation of large landslidesÔÇØÔÇó 1.3 – ÔÇ£Infrastructure health diagnosis and safety monitoringÔÇØÔÇó 10.2 – ÔÇ£Artificial Intelligence and Machine LearningÔÇØMain goals:- detect and analyze recent rapid landslide events with satellite and ground based EO data- evaluate the stability of landslides, forecast when and how hazards might happen in future, generate future hazard scenarios and provide support for disaster prevention and damage reduction to authorities- use latest EO technologies for monitoring historical structures leading to the early detection of potential risks and thus making it possible to increase security and significantly reduce maintenance costs- develop an AI system to process and analyze huge amount of dataThe methodology to achieve the previous objectives is the collaborative analysis of multi-source EO data, by means of satellite SAR and InSAR time-series, S2 optical images, VHR and stereo optical images, GBSAR data, GNSS and CR measurements, geological and environmental data, and data modeling.The proposed research also foresees the exchange of YS in the scope of projects 41941017 and 41877522 funded by National Natural Science Foundation of China, 07-Y30B03-9001-19/21 funded by State Administration of Science, Technology and Industry for National Defense of China´╝îDD20190514 funded by China Geological Survey, and 1/SAMA/2020/2019 (POCI-62-2019-04) funded by AMA. We also apply for funding for the YS within this project and national agencies
INVESTIGATION OF INTERNAL WAVES IN ASIAN SEAS USING EUROPEAN AND CHINESE SATELLITE DATA The East and South China Seas, the Sulu Sea, and the Andaman Sea the sea areas, where the most intense internal wave are encountered in the WorldÔÇÖs ocean. Internal waves are of relevance, among others, for off-shore activities since they can [...] Prof.. em. Dr. Werner Alpers, University of Hamburg, GERMANY Dr. Kan Zeng, Ocean University of China, CHINA Oceans and coastal zones The East and South China Seas, the Sulu Sea, and the Andaman Sea the sea areas, where the most intense internal wave are encountered in the WorldÔÇÖs ocean. Internal waves are of relevance, among others, for off-shore activities since they can disrupt offshore exploration and drilling operations, for the propagation of sound in the ocean, since internal waves can disturb the propagation of acoustic signals, and for the transport of nutrient-rich water to the sea surface in coastal zones causing there plankton growth. Although much research was carried out in the last years on the generation, propagation, refraction, interaction, and breaking of internal waves in these Asian seas, we aim at pushing this research further by using data from different sensors flown on recently launched satellites, in particular from the European satellites Sentinel-1 and Sentinel-3, and the Chinese satellite GF-3. These satellite data will be compared theoretical models, in particular, with the model developed by the Kan Zeng (the Chinese PI of this project) and co-workers on the relationship between internal wave amplitude and half-width of the internal solitary wave. Furthermore, we will explore more in more how internal solitary waves are detectable by the new high-resolution SAR altimeter SRAL onboard the Sentinel-3 satellites.
LARGE-SCALE SPATIAL-TEMPORAL ANALYSIS FOR DENSE SATELLITE IMAGE SERIES WITH DEEP LEARNING The latest decade has witnessed a great development of satellite remote sensing sensors, Earth Observation (EO) Big data with huge quantities of satellite images with high spatial and temporal resolution are available. The successive observation [...] Prof.. Daniela Faur, University Politehnica of Bucharest, ROMANIA Dr. Weiwei Guo, Tongji Universtiy, CHINA Data Analysis The latest decade has witnessed a great development of satellite remote sensing sensors, Earth Observation (EO) Big data with huge quantities of satellite images with high spatial and temporal resolution are available. The successive observation for long periods of time of a large area with short revisit interval from space is resulting in dense satellite image time series (SITS). SITS are a new type of EO product allowing us to analyze and mine not only spatial but spatio-temporal dynamic evolution information content about the scene structure and objects. This need to exploit spatial and temporal information contents from SITS is increasing with a wide range of applications, including urban development, target dynamic monitoring, precise agriculture, forest, and etc. Meanwhile, the open and free data access from many EO missions as in the Copernicus program or ChinaÔÇôBrazil Earth Resources Satellite 4 opens new fantastic perspectives for research and applications of Artificial Intelligence for EO A4EO. With a significant progress of AI, especially the major breakthrough of deep learning has proven to be an extremely powerful tool in many fields including computer vision, speech recognition, natural language processing, etc, and has also been enjoyed into the geoscience and remote sensing community for remote sensing big data analysis. However, contrary to multimedia image analysis which has been boosted by advanced deep neural networks and easily available big multimedia training data, the deep learning techniques and automatic tools for scale dense SITS analysis are limited, and large-scale benchmark dataset is not exist. Moreover, dense SITS analysis raises some specific challenges. Novel network architectures and datasets have to be developed exploiting the temporal information jointly with the spatial and spectral information of the data, but cope with multi-modal, multi resolutions and multi sensors data in a synergistic way. In this project, we intend to develop hybrid explainable AI, deep learning and big data analytics techniques and tools for large-scale dense SITS analysis and address the challenges to advance the state of the art in this area. The overall objective of this project is to provide an effective solution for large-scale dense SITS analysis, being capable of automatic discovery of regularities, relationships, and dynamic evolution, leading to a better and easier understanding of the underlying processes of specific scenes and targets. Specifically, the objectives are:(1) Develop weakly supervised deep learning techniques for object extraction and semantic classification for remote sensing images. (2) Develop deep spatial-temporal network techniques for large dense SITS clustering, classification and prediction. (3) Exploit deep change detection techniques for multi-temporal satellite images. We will compile a large-scale spatial-temporal data sensor for spatial-temporal change detection both for optical remote sensing images and for SAR images and develop novel deep neural network architectures and learning techniques for our specific data tensor and change detection task. (4) Develop spatial-temporal fusion and synergic computation techniques of Multi-modal, Multi-resolutions and Multi-sensor images for SITS mining, classification, and change analysis. The scientists of CEOSpaceTech – the research center within Politehnica University Bucharest – Romania, Tongji University and Shanghai Jiaotong University – China will tightly collaborate to advance these innovative techniques. For the evaluation and validation process we consider 2 use cases, targeting areas of Romania – EU for ecosystem monitoring of an UNESCO protected area and Shanghai – China for urban evolution in support of smart and sustainable urban information services. A list of related funding projects able to co-financing this project is available in the Annexes.
LIDAR OBSERVATIONS FROM ESA´S AEOLUS (WIND, AEROSOL) AND CHINESE ACDL (AEROSOL, CO2) MISSIONS: VALIDATION AND ALGORITHM REFINEMENT FOR DATA QUALITY IMPROVEMENTS. n 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 Prof.iles from the ground to the lower stratosphere on a [...] Dr. Oliver Reitebuch, DLR-German Aerospace Center, GERMANY Prof.. Songhua Wu, Ocean University of China OUC - Ocean Remote Sensing Institute OSRI, CHINA Calibration and Validation n 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 Prof.iles 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 Prof.iles 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 Prof.iles 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 Prof.ile 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.
MAPPING FOREST PARAMETERS AND FOREST DAMAGE FOR SUSTAINABLE FOREST MANAGEMENT FROM DATA FUSION OF SATELLITE DATA This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring with the objective to develop new methods for the respective areas. The forest [...] Dr. Johan Fransson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SWEDEN Prof.. Xiaoli Zhang, Beijing Forestry University, CHINA Ecosystems This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring with the objective to develop new methods for the respective areas. The forest quality parameters include biomass, tree species and new quality parameters. For biomass, new tools will be developed to map forest state and change from satellite-borne radar with support of airborne laser scanning data. The deliverables are algorithms for correction of synthetic aperture radar (SAR) data for inconsistent weather conditions, decreasing stationary map uncertainties of forest estimates due e.g. fluctuating model uncertainty, wavelength dependence, and improper reference data, and using forest change estimations and auxiliary data to derive forest site-index (SI) maps. For tree species, the methods will be based on a combination of remotely sensed data types: medium-resolution satellite imagery, satellite SAR data, high-resolution images and LiDAR data to derive information both from spectral information structural information. The deliverables are algorithms for tree species identification from satellite images, SAR data, combining satellite images and ALS data, as well as a tree species map for a study area in Sweden. The new quality parameters are related to tree retention elements in the landscape, aiming to characterize and quantify forest structures that are key elements for ecological biodiversity. This objective will be addressed using several high-resolution data sources, such as multispectral satellite imagery and LiDAR data. The deliverables here are algorithms for mapping tree retention elements in the landscape from high-resolution multispectral satellite imagery and LiDAR data. The method for collaborative estimation of forest quality parameters includes the following steps: (1) extraction of features from Remote Sensing (RS) data, (2) linking RS data to reference data on sample plots, (3) training of estimators and classifiers using the reference data, and (4) prediction or supervised classification. For storm damage, the objective is to develop methodology and algorithms and to perform a scientific evaluation of SAR data from two or more satellite sensors for detecting and mapping changes in boreal forests. The approach for mapping storm-felled forest is to use change detection based on backscatter SAR images before and after the changes. Simulated wind-thrown fellings offer a unique in situ dataset with a setting very similar to a natural storm damaged forest. The deliverables here are algorithms for detecting and mapping changes in boreal forests, in particular storm damage, from SAR data from two or more satellite sensors. Two kinds of forest insect damage will be studied: (1) The European spruce bark beetle (Ips typographus [L.]), (2) pine wood nematode (Bursaphelenchus xylophilus). The objectives here are two-fold: (1) Developing the methods for forest insect damage detection at an early stage. (2) Analyzing distribution and spreading patterns of the forest insect damage. The deliverables here are methods for early detection, forest infestation spreading patterns and forecasting models, and prediction maps for large area application. The joint teams currently own 12 ongoing projects (2020 – 2022) with 4 339 700 EUR funding and 4 planned/submitted applications (2021 – 2023) with 1 078 740 EUR that are related to this proposal for Dragon 5 collaboration. For a coherent and comprehensive scientific research, field inventory and other remote sensing data have been collected in 2018 and 2019, and planned more data acquisition in 2020 – 2024. Together with satellite images provided by the Dragon 5 project, the large amount and various datasets will support our studies for forest information extraction.
MARINE DYNAMIC ENVIRONMENT MONITORING IN THE CHINA SEAS AND WESTERN PACIFIC OCEAN SEAS BY SATELLITE ALTIMETERS Satellite altimeter is one of important global ocean remote sensing technique to monitor the
marine dynamic environment. Sentinel-3A/3B satellite equipped with SRAL have been launched
on 16 Feb. 2016 and 25 Apr. 2018 in Europe, and HY-2A/2B [...]
Dr. Ole Andersen, Technical University of Denmark, DENMARK Dr. Jungang Yang, The First Institute of Oceanography, Ministry of Natural Resources of China, CHINA Oceans and coastal zones Satellite altimeter is one of important global ocean remote sensing technique to monitor the marine dynamic environment. Sentinel-3A/3B satellite equipped with SRAL have been launched on 16 Feb. 2016 and 25 Apr. 2018 in Europe, and HY-2A/2B satellite equipped Radar Altimeter were launched on 16 Aug. 2011 and 25 Oct. 2018 in China. The CFOSAT was launched in 2018. The combinations of European and Chinese altimeters will improve the data application ability of these altimeters. As the continuance of Dragon 4 project (ID.32292), the objectives of this research topic are to improve the retrieval of SSH and SWH of Sentinel-3 and HY-2 series altimeters in the Chinese seas by the waveform retracking method in the coastal areas, to combine Sentinel-3 and HY-2 series altimeters data into high spatial resolution grid data in the China seas and western Pacific Ocean, to develop the retrieval method of sea surface current by combining the altimeter, sea surface wind and SST data in the Chinese seas and western Pacific Ocean, and to analyze the spatial-temporal variation characteristics of ocean waves, ocean current and mesoscale eddies in the Chinese seas and the western Pacific Ocean. In this study, the deliverables of the investigation include the time series grid data of SWH, SLA (Sea Level Anomaly) and sea surface current with the high spatial resolution in the China seas and western Pacific Ocean, the spatial-temporal characters of marine dynamic environment, such as ocean wave, ocean circulation and mesoscale eddies in the China seas and western Pacific Ocean. The funding to support this project includes the National Natural Science Foundation of China (No.51839002) and National key research and development program of China (2016YFC1401801). Last name
MONITORING AND INVERSION OF KEY ELEMENTS OF CRYOSPHERE DYNAMIC IN THE PAN THIRD POLE WITH INTEGRATED EARTH OBSERVATIONS AND SIMULATION The objective of this project will be concentrated on two parts. First, this project will monitor glacier and frozen ground dynamics in the Pan Third Pole region (PTP) by the synergistic use of multi-platform earth remote sensing as well as [...] Dr. Andrew Hooper, School of Earth and Environment, University of Leeds, UK Prof.. Hui Lin, The Chinese University of Hong Kong, CHINA Cryosphere and Hydrology The objective of this project will be concentrated on two parts. First, this project will monitor glacier and frozen ground dynamics in the Pan Third Pole region (PTP) by the synergistic use of multi-platform earth remote sensing as well as in-situ observations. Second is to establish multi physical-based distributed models to inverse other key elements of cryosphere dynamic in PTP region, which also aims to analyze the impacts of different cryospheric component changes including exorheic region and upper basin of great rivers basing on multi-mission observations on glaciers, frozen ground and surface runoffs. Cryosphere over PTP is the largest component outside the polar regions, it dynamic and impacts on global changes are essential. In the last few decades, glaciers over PTP generally suffered from quick and heterogeneous degradation at different sub-regions and contribute greatly to sea level risings. Evidence from satellite geodesy presented that glaciers mass loss rate were accelerating in the past few decades along Himalaya. PTP is also called as Asia Water Tower because several great rivers rise from this region, its water supply safety is essential to billions of people. Water volumes for endorheic plateau lakes and surface runoffs experienced quick changes in recent decades. All these indicate the importance of monitoring cryosphere status and dynamic over the PTP and analyzing its impacts to surface hydrology. In the cryosphere key elements monitoring part, status and dynamic of the glacier and frozen ground in each sub-region of PTP including Eastern Nyainqentanglha, Himalayan, Hindu Kush, Karakoram, Pamir, Tien Shan and Inner Tibetan Plateau will be monitored with integrated earth observations including optical and microwave remote sensing as well as in-situ observations. Several new algorithms will be designed for new satellite datasets for deriving cryosphere features. Glacier equilibrium line altitude (ELA), flow rates and mass balance, frozen ground active layer thickness and ice-rich layer lost rates will be derived quantitatively at different sub-regions over PTP with various methods. In the cryospheric key elements modelling and inversion parts, we seek to employ and/or modify several empirical and/or physical-based models for simulating the key elements that can hardly be monitored by either in-situ observation or remote sensing. Afterward, we will perform GCMs to different scenarios of emission (RCPs) to project the fates of the cryosphere over the PTP and evaluating its impacts to the hydrological process in the future. Water supply safety at important irritation systems such as Indus and Yarlung Zangbo River will be analyzed. The primary goals will be: (1) A synergistic analyzing and interpretation of multi-source of optical and SAR images for the purposes of monitoring glacier outlines, summer end snowline altitudes, flow velocities, and height changes over the PTP in multi-temporal scale. (2) Applying multi-mission SAR images to monitor seasonal and decadal frozen ground changing associate with in-situ observations. (3) Inversion of glacier ice thickness, precipitations on glaciers, glacier melting, the albedo of glaciers, frozen groundwater lost rates, and their hydrological effects. (4) Simulate cryosphere fate over PTP and analyze its impacts to surface runoffs with different scenarios of radiative forcings. (5) An integrated OVGE platform for multi-dimensional visualization, geospatial analysis, dynamical modeling and decision-making for geological and environmental processes. Under the funding support from the National Basic Research Program of China (973), National Natural Science Foundation of China (NSFC), Hong Kong General Research Funding, European ERC Consolidator Grant, and Horizon 2020, this project will be implemented based on the planned schedule. The potential deliverables will include new developed methodologies and an integrated OVGE analysis prototype.
MONITORING AND MODELLING CLIMATE CHANGE IN WATER, ENERGY AND CARBON CYCLES IN THE PAN-THIRD POLE ENVIRONMENT (CLIMATE-PAN-TPE) Executive Summary:The Third Pole Environment centred on the Tibetan plateau and the Himalayas feeds AsiaÔÇÖs largest rivers which provide water to 1.5 billion people across ten countries. Due to its high elevation, TPE plays a significant role [...] Prof.. Bob Su, Inst Geo Inform Science and Earth Obs., NETHERLANDS Prof.. Yaoming Ma, Institute of Tibetan Plateau Research (ITP/CAS),, CHINA Climate Change Executive Summary:The Third Pole Environment centred on the Tibetan plateau and the Himalayas feeds AsiaÔÇÖs largest rivers which provide water to 1.5 billion people across ten countries. Due to its high elevation, TPE plays a significant role in global atmospheric circulation and is highly sensitive to climate change. Intensive exchanges of water and energy fluxes take place between the Asian monsoon, the plateau land surface (lakes, glaciers, snow and permafrost) and the plateau atmosphere at various temporal and spatial scales, but a fundamental understanding of the details of the coupling is lacking especially at the climate scale. Expanding westward from the Third Pole, the Pan-Third Pole region covers 20 million km2, encompassing the Tibetan Plateau, Pamir, Hindu Kush, Iran Plateau, the Caucasians, the Carpathians, etc. and is home to over 3 billion people. Climate change is expected to dramatically impact the water and energy as well as carbon cycles and exchanges in the Pan-TPE area and consequently alter the water resources, food security, energy transition and ecosystems as well as other related societal challenges. Monitoring and modelling climate change in Pan-TPE reflect key societal issues and contribute to the science component to other international initiatives, e.g. UN sustainable development goals (SDG), GEO societal benefit areas and the ESA EO science for society strategy.Thus the objective of this CLIMATE-Pan-TPE project is: To improve the process 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, energy and carbon budgets; To assess and monitor changes in cryosphere and hydrosphere; and to model and predict climate change impacts on water resources and ecosystems in the Pan-Third Pole Environment. A core innovation of the CLIMATE-Pan-TPE project is to verify or falsify recent climate change 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 heating on the Tibetan plateau as precursors of monsoon pattern changes and glaciers retreat, and their impacts on water resources and ecosystems. Method: We will use earth observation, in-situ measurements and modelling to advance process understanding relevant to monsoon scale predictions, and improve and develop coupled regional scale observation and hydroclimatic models to explain different physical links and scenarios that cannot be observed directly.Deliverables: The deliverables will be scientific outputs in terms of peer reviewed journal publications, PhD theses and data sets in terms of novel data records and modelling tools of essential climate variables for quantification of water, energy and carbon cycle dynamics in the Pan-Third Pole Environment.Funding: The sub-projects described in the work packages will be performed by funded research projects by PhD and postdoc researchers of the participating partners.
MONITORING EXTREME WEATHER AND CLIMATE EVENTS OVER CHINA AND EUROPE USING NEWLY DEVELOPED CHINESE AND EUROPEAN REMOTE SENSING DATA Extreme weather and climate events are events in which the state of weather and climate
deviates seriously from its mean state, and they are typically rare. One of the most visible
consequences of climate change leads to changes in the [...]
Dr. Abhay Devasthale, Swedish Meteorological and Hydrological Institute (SMHI), SWEDEN Prof.. Fuxiang Huang, National Satellite Meteorological Center, China Meteorological Administratration, CHINA Climate Change Extreme weather and climate events are events in which the state of weather and climate deviates seriously from its mean state, and they are typically rare. One of the most visible consequences of climate change leads to changes in the frequency, intensity, spatial extent, and duration of extreme weather and climate events. In recent years the severe disasters resulted from heat waves, heavy downpours, severe storms, and wild fires are frequently reported in the media with astonishing economic losses all over the world. For examples, the extreme cold wave occurred in Beijing-Tianjin-Hebei region of China in Jan 2016 was rare in meteorological record and called the ‘century cold wave’, frequent Arctic sudden warming events are regarded as extraordinary, and the extreme heat wave happened in north Europe in July 2018. This project will focus on monitoring extreme weather and climate events over Europe and China using Chinese and European newly developed remote sensing data. Specific scientific focuses of the proposed project are: (i) monitoring winter extreme warming or cold events over China and Europe; (ii) monitoring severe ozone depletion events over China or Europe; (iii) monitoring extreme summer heat waves over China and Europe; (iv) monitoring extreme precipitation events over China and Europe. This research is a joint project between National Satellite Meteorological Center (NSMC), China Meteorological Administration (CMA) and Swedish Meteorological and Hydrological Institute (SMHI). SMHI’s participation will be partly covered by the on-going project “Simulating Green Sahara with Earth System Model” supported by Swedish Research Council VR. The SU and SMHI team will apply for additional funding from the Swedish National Space Agency (SNSA) for more detailed scientific research on extreme events based on the data obtain within this project. The NSMC’s work will be partly supported by the on-going project funded by the National Natural Science Foundation of China and the Ministry of Science and Technology (MOST) of China. The NSMC team will also apply for additional funding from Chinese Academy of Sciences. The ozone sounding data over the Tibetan Plateau will be available for the project. Last name
MONITORING GREENHOUSE GASES FROM SPACE EarthÔÇÖs climate is influenced Prof.oundly by anthropogenic greenhouse gas (GHG) emissions. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The forecasts require accurate information about the [...] Prof.. Hartmut Boesch, University of Leicester, Department of Physics and Astronomy, UK Prof.. Yi Liu, Institute of Atmospheric Physics, Chinese Academy of Sciences, CHINA Atmosphere EarthÔÇÖs climate is influenced Prof.oundly by anthropogenic greenhouse gas (GHG) emissions. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The forecasts require accurate information about the sources and sinks of natural and anthropogenic GHGs, in particular, carbon dioxide (CO2) and methane (CH4). Presently, GHG concentrations are observed using ground-based and satellite observations. While local sources can be observed using accurate in-situ measurements, remote sensing methods from satellites are needed to obtain global and regional coverage, which are important for climate research. A number of studies have indicated that uncertainties in regional CO2 and CH4 surface fluxes can be significantly reduced with global, unbiased, precise space-borne measurements which can lead to a more complete understanding of the CO2 and CH4 budget. The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, GHGs are challenging. Validation of measurements and their uncertainties and continuous development of retrieval methods are important for the success of satellite remote sensing systems, especially for GHGs where error requirements are demanding. Furthermore, sophisticated data assimilation methods and atmospheric transport models are needed to link atmospheric concentration to the underlying surface fluxes. The main objectives of this research project is to use a combination of ground-based measurements of CO2 and CH4 and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval intercomparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO2 and CH4. The main geographic focus will be China but we will also take advantage of our global view provided by the space-borne data. Furthermore, we will look towards future observing systems from Europe and China. Specifically, we will use TCCON and Chinese ground-based measurements and extend the validation previously applied. AirCore Prof.ile observations of GHGs at Sodankyl├ñ will be used to support the validation at high latitudes. We will apply two independent retrieval algorithms available at University of Leicester and IAP to intercompare and advance retrieval methods. We will use the GEOS-Chem atmospheric transport model combined with Ensemble Kalman Filter to infer flux estimates of Chinese CO2 and CH4. No extra funding for this project is presently available. Work by both teams will be part of the normal scientific research. The Chinese team is funded by MOST and CAS, from which a limited amount of funding could be used to support international cooperation research. University of Leicester and University of Edinburgh will carry out the work using existing resources and by synergies with ongoing research projects funded by the National Centre for Earth Observations NCEO, NERC, ESA and the EU and the Finnish Meteorological Institute will use existing resources from the projects funded by the Finnish Academy, ESA and the EU.
MONITORING HARSH COASTAL ENVIRONMENTS AND OCEAN SURVEILLANCE USING RADAR REMOTE SENSING (MAC-OS) The proposed project aims at demonstrating the benefits of radar products for coastal area monitoring and, therefore, it is framed into the ÔÇ£Ocean & coastal zoneÔÇØ Dragon-5 thematic area. The various sub-topics addressed within this domain [...] Prof.. Ferdinando Nunziata, Univ degli studi di Napoli Parthenope, ITALY Prof.. Xiaofeng Yang, State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Ac, CHINA Oceans and coastal zones The proposed project aims at demonstrating the benefits of radar products for coastal area monitoring and, therefore, it is framed into the ÔÇ£Ocean & coastal zoneÔÇØ Dragon-5 thematic area. The various sub-topics addressed within this domain include the understanding of ÔÇ£marine dynamic environmentÔÇØ, the analysis of ÔÇ£sea surface characteristicsÔÇØ and the EO support for ÔÇ£marine disastersÔÇØ management. These applications are of paramount importance for both the scientific and the end-user communities. In particular, the project aims at exploiting Synthetic Aperture Radar (SAR) satellite measurements to generate innovative added-value products to observe coastal areas characterised by harsh environments, even under extreme weather conditions. The project relies on a deep co-operation between Chinese and European (including Italy, UK and Spain) partners that call for a complementary expertise which is a key resource to publish co-authored results and to train European and Chinese YSs. This co-operation has been already successfully experienced during the Dragon-4 project 32235. Master and PhD students will actively take part in the modeling and analysis of the phenomena under study, i.e. coastal water pollution, coastal erosion, in-land water body observation, metallic target detection, typhoon/cyclone monitoring, etc., as well as in the development of effective and reliable algorithms for the generation of added-value products from remotely sensed measurements. The project also aims at stimulating the use of complementary microwave satellite instruments, including scatterometer and radiometer on-board of operational and planned missions operated by ESA, ESA TPM and Chinese EO.The proposed piece of research will involve the development of tailored models of the processes under study combined with Artificial Intelligence (AI) methodologies that allow the interpretation and the processing of multi-pol (MP) SAR measurements, collected under different imaging modes, in order to derive the above-mentioned user friendly added-value products. The latter include, but are not limited to, maps of metallic targets at sea, e.g., ships and wind farms, as well as aquacultures, wetland coastal erosion/accretion trends due to both anthropogenic and natural phenomena, mapping marine pollutants, modeling, tracking and forecasting extreme weather events as cyclones/typhoons.Summarizing, this research project will address the following main assets:a) Promoting an “intelligent”, i.e., physically-based, exploitation of MP SAR measurements for generating end-user friendly added-values products in the context of harsh coastal zone management; b) Developing new AI-based models/methods to deal with the synergistic exploitation of microwave satellite measurements to address key issues for coastal area monitoring; c) Boosting the co-operation between Chinese and European partners by taking full advantage of their respective expertise, including the training of YS.ProjectÔÇÖs outcomes will be disseminated through publications to be submitted to Dragon 5 symposia at mid-term and final stages, as well as annual progress reports on the status of the projects will be provided during Dragon 5 symposia. The main achievements will be also presented at dedicated workshops and international symposia and will be submitted on peer-reviewed journals and refereed conferences. The proposed project is financially backed on Sino-European funds, e.g. ÔÇ£Development of algorithms to analyse and processing signals and images in the complex domainÔÇØ, funded by the European Fund for Regional Development (DING127), “On-board processing and remote sensing for meteorological and marine disasters”, funded by the National Key R&D Program of China (2017YFB0502803) and “Objective Tropical Cyclone Intensity Estimation from Remotely Sensed Passive and Active Microwave Observations”, funded by the National Natural Science Foundation of China (41871268).
MONITORING OF GREENHOUSE GASES WITH ADVANCED HYPER-SPECTRAL AND POLARIMETRIC TECHNIQUES Greenhouse gas emissions lead to global warming, frequent extreme weather and increasing natural disasters. It has become a global focus to clarify the impact of carbon emissions and reduce carbon emissions. As the dominant technology of carbon [...] Dr. Jochen Landgraf, Netherlands Institute for Space Research (SRON), NETHERLANDS Dr. Hailiang Shi, Hefei Institutes of Physical Science(HIPS),Chinese Academy of Sciences(CAS), CHINA Atmosphere Greenhouse gas emissions lead to global warming, frequent extreme weather and increasing natural disasters. It has become a global focus to clarify the impact of carbon emissions and reduce carbon emissions. As the dominant technology of carbon emission and carbon cycle monitoring, satellite remote sensing plays an important role in global climate research, but there are still large errors in the inversion results of high value aerosol, cloud coverage and other areas, which greatly reduces the effective detection data rate. Therefore, it is an inevitable trend to develop new satellite remote sensing monitoring and inversion technology which can provide more precise carbon information. The inversion of greenhouse gases requires not only high-resolution atmospheric absorption spectrum, but also information such as AOD and cloud parameters at the same time. Therefore, GOSAT and TanSat satellites are equipped with hyper-spectral instruments and aerosol instruments. The first greenhouse gas payload GMI based on spatial heterodyne spectroscopy (SHS) was built on the Chinese GaoFen-5 satellite launched in May 2018, which includes two carbon dioxide bands, one methane band and one oxygen band. In addition, The Directional Polarimetric Camera (DPC) is the first Chinese multi-angle polarized earth observation satellite sensor, which is synchronously carried on this satellite for cloud and aerosol product acquisition. DPC employed a charge coupled device detection unit with 512×512 effective pixels from the 554×512 useful pixels, realizing spatial resolution of 3.3 km under a swath width of 1850 km. Meanwhile, DPC has 3 polarized channels (490, 670 and 865 nm) together with 5 non-polarized bands (443, 565, 763, 765 and 910 nm) and can obtain at least 9 viewing angles by continuously capturing series images over the same target on orbit. This proposal is mainly based on the new hyper-spectral payload GMI and polarization payload DPC to carry out the greenhouse gas inversion research. We can use DPC observation to invert high precision aerosol optical and physical properties to calculate the air mass factor (AMF) for the greenhouse retrieval with the hyper-spectral sensor GMI. By adopting an optimal estimation (OE) algorithm combined with the aerosol high-precision microphysical and optical characteristics from the DPC sensor to perform the inversion of greenhouses gases, we can improve the accuracy at low AOD loading and file the current gaps inversion in satellite retrieval in the high-value area of AOD. Finally, the inversion results of TanSat, GOSAT, OCO-2 and other satellite are used as verification data. This proposal can also be used to provide a wealth of real measured observation data for the design of future greenhouse gas sensor incorporating aerosols. This proposal is a joint project of Hefei Institutes of Physical Science, Chinese Academy of Sciences (HIPS-CAS), Space Research Organization Netherlands (SRON). No extra funding for this project is presently available. China is jointly funded by the State Administration of science, technology and industry of national defense (Civil Aerospace pre research project: main greenhouse gas monitor of multimodal atmosphere, with a fund of 8.6 million yuan) and the Chinese Academy of Sciences (key deployment project of the Academy: high spatial-temporal resolution greenhouse gas detection and application technology, with a fund of 7 million yuan), some of which can be used for international cooperative research. The European side is mainly funded by projects that have been approved for research in progress, which are funded by the Netherlands space research center.
MONITORING OF MARINE ENVIRONMENT DISASTERS USING CFOSAT, HY SERIES AND MULTIPLE SATELLITES DATA As global climate change intensifies, many countries are facing increasing marine environment disasters. These disasters, such as typhoons, giant waves, macroalgal blooms and decrease in sea ice cover pose a serious threat to coastal areas, [...] Dr Daniele HAUSER, Centre d'étude des environnements terrestres et planétaires - CETP/CNRS, FRANCE Prof.. Jianqiang Liu, National Satellite Ocean Application Service - NSOAS, CHINA Oceans and coastal zones As global climate change intensifies, many countries are facing increasing marine environment disasters. These disasters, such as typhoons, giant waves, macroalgal blooms and decrease in sea ice cover pose a serious threat to coastal areas, aquaculture and maritime transportation. Therefore, it is essential for governments to respond quickly and reduce the loss and damage of these disasters.Satellite observation plays an important role in monitoring these marine environmental disasters by its unique advantages. However, monitoring of marine disasters with a single satellite data is extremely difficult due to the limitations of observed parameters, resolution, and revisit time etc. Consequently, utilization of multiple satellites data is inevitable and superior. Taking advantage of the multiple satellites data such as CFOSAT, HY-1, HY-2 and multiple satellites, this proposal aims to provide the combined satellite monitoring of marine environment disasters. Three aspects of research will be carried out. Firstly, the validation of CFOSAT and multiple satellite data will be executed. Secondly, the validation and merging of HY-1 and multiple satellite data will be executed. Thirdly, the multiple satellite data are combined to present the monitoring results of marine environment disasters. Meanwhile, data processing methods regarding the above three aspects, interactions of observed parameters and evolution mechanisms of marine environment disasters will also be investigated. This proposal has across subjects in Marine Disasters & Coastal zones including algae and phytoplankton blooms, marine dynamic environment and marine disasters. The studied areas include global ocean and polar region.The activities to be undertaken are financially supported by the following Chinese and European projects: CNSA project ÔÇ£CFOSAT technical research projectÔÇØ; NSFC project ÔÇ£High-resolution coastal wind retrieval from the rotating fan beam scatterometerÔÇØ ÔÇô Grant No. 41706197).
MONITORING WATER PRODUCTIVITY IN CROP PRODUCTION AREAS FROM FOOD SECURITY PERSPECTIVES Climate change and its subsequent implication of water availability is having a major impact on the crop production globally. Feed a growing population while minimizing water consummation for agriculture are twin challenges directly related to [...] Dr. Qinghan Dong, Flemish Institute for Technological Research (VITO), Belgium Dr. LIANG ZHU, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, CHINA Sustainable Agriculture and Water Resources Climate change and its subsequent implication of water availability is having a major impact on the crop production globally. Feed a growing population while minimizing water consummation for agriculture are twin challenges directly related to food security in many parts of the world. Water productivity is considered as a robust measure of the ability of agricultural systems to convert water to food. Focusing on two test sites, one in Europe and one in China, the proposed project will try to explore the methodology using remote sensing to generate water productivity and to examine variability of this measure in two different geographic locations, for different crop types or different cropping conditions. The research is planned in four steps involving the crop distribution mapping, crop productivity prediction, estimation of water use through evapotranspiration and water productivity mapping. The output of this proposed project would contribute to elucidate the driving factors for water productivity and ultimately improve the agricultural water management in drought threatening regions. The proposed research takes advantage of outcome from Dragon 3 (crop yield estimation) and Dragon 2&4 (crop distribution mapping) programmes, adding a dimension of water use efficiency in the theme of food security, contributing hopefully the development of the current ESA’s Food Security Thematic Exploitation Platform (FS-TEP, https://foodsecurity-tep.net/). Finally, it is worth to underline the multidisciplinary character of this proposal, which encompasses several the Dragon 5 program’s topics or sub-topics including food security, sustainable water use, crop monitoring as well as climate change. The deliverables consist of the databases for crop productivity maps, water use maps and water productivity maps relating to two study regions, as well as the presentations at the Dragon symposia and related publications acknowledging the contribution of Dragon Programmes. The proposed project would be partially supported by H2020 SIEUSOIL project co-funded by EU and Chinese MOST.
MULTI-FREQUENCY MICROWAVE REMOTE SENSING OF GLOBAL WATER CYCLE AND ITS CONTINUITY FROM SPACE? Multiple global water cycle related satellite data products (soil moisture, vegetation optical depth, landscape freeze/thaw, snow water equivalent, ocean salinity, precipitation etc.) are available and explored by a growing community. However, [...] Prof.. Yann Kerr, CNES/Centre d'Etudes Spatiales de la Biosphère (CESBIO), FRANCE Prof.. Jiancheng Shi, Aerospace Information Research Institute, Chinese Academy of Sciences, CHINA Cryosphere and Hydrology Multiple global water cycle related satellite data products (soil moisture, vegetation optical depth, landscape freeze/thaw, snow water equivalent, ocean salinity, precipitation etc.) are available and explored by a growing community. However, very significant discrepancies among these different satellite products have been reported. The space observation of the Cryosphere needs new instruments and tools, while the global mapping of soil moisture and ocean salinity needs to be continued. In addition, the temporal-spatial resolution and accuracy of different satellite data, including the ESA Soil Moisture Ocean Salinity (SMOS, single L-band and multiple incidence angles) and the Chinese Fengyun series satellites (multi-frequency at a single incidence angle), needs to be refined for a wider global water cycle study.This project is dedicated to improving the accuracy and temporal-spatial resolution of current remote sensing products related to water cycle, including soil moisture, vegetation optical depth, landscape freeze/thaw, snow wetness and water equivalent etc., through the synergy use of multi-sources satellite observations from European and Chinese Earth observation data. It is aimed to enhance the retrieval performance through the development of radiative transfer modelling and new algorithms. Meanwhile, new satellite missions should be studied to combine the advantages of current satellite design, and continue the multi-frequency microwave observation from space.This work is under the funding grant of CNES TOSCA ÔÇ£SMOS-HRÔÇØ and CNSA ÔÇ£Terrestrial Water Resources MissionÔÇØ.
PACIFIC MODULATION OF THE SEA LEVEL VARIABILITY OF THE BEAUFORT GYRE SYSTEM IN THE ARCTIC OCEAN It is crucial to monitor and understand regional sea-level changes that can differ from Global Mean Sea Level (GMSL) both in terms of magnitude as well as governing forcing and mechanisms (Stammer et al., 2013). For instance, while changes in [...] Prof.. Johnny Andre Johannessen, Nansen Environmental and Remote Sensing Center, NORWAY Prof.. Jianqi Sun, Institute of Atmospheric Physics, CHINA Climate Change It is crucial to monitor and understand regional sea-level changes that can differ from Global Mean Sea Level (GMSL) both in terms of magnitude as well as governing forcing and mechanisms (Stammer et al., 2013). For instance, while changes in salinity can have significant distinct impact on regional sea level change, such as in the Arctic Ocean, it has minor effect on GMSL. Quantifying the natural variability in the regional sea level change is also urgent in order to distinguish it from a potentially forced (anthropogenic) signal. Furthermore, the role of remote impact of climate variability in one region on the other needs to be well-understood. Climate change in the Pacific can, for instance, impact Arctic warming and the sea ice (Li et al., 2015; Svendsen et al., 2018; Yang et al., 2020). How this translates to sea level change remains unclear. The aim of this study is to examine and relate the sea level variability of the Beaufort Gyre (BG) in the Arctic Ocean to natural climate variability of the Pacific Ocean. The sea level variability of the Beaufort Gyre (BG) is influenced by the changes in steric height and ocean mass. Hence the freshwater and heat stored in the BG can have significant impact on the sea surface height. The anticyclonic circulation of BG is driven by a semi-permanent atmospheric circulation pattern, the Beaufort High (BH). The resulting Ekman convergence associated with BH advects and stores freshwater and sea-ice in the Beaufort Sea and can contribute to halosteric changes in sea-level. Since the altimetry era the sea-level in the Beaufort Sea basin has increased much faster than the average rate of increase in the Arctic Ocean sea-level (Zhang et al., 2016). Although many studies (e.g., Armitage et al., 2016, 2017; Zhang et al., 2016) have investigated this there are still many unanswered questions. The project objectives are to: (i) assess the role of variability of atmospheric modes in the Pacific Ocean and the Arctic Ocean on the BH and on the sea level variability of the region; (ii) advance the current understanding of the different mechanisms influencing the sea-level variability in the BG; (iii) validate climate models using observations and assess the sea level change with respect to natural versus anthropogenic origin; A suite of satellite data, ocean (TOPAZ, GREP) and atmospheric reanalysis data (ERA-I, ERA-5) together with climate model outputs (CMIP5, CMIP6) will be used to address these objectives. Methodologies include: Spatio-temporal analysis of observed sea level in conjunction with atmospheric forcing; EOF and composite analysis of regional atmospheric data to capture seasonal to decadal changes in the dominant atmospheric forcing; statistical analysis (e.g., correlation analysis) of monthly ocean model data and satellite data to understand the changes in the sea ice and halo steric and thermosteric variability; use of monthly climate model output (CMIP5 models and CMIP6) from control as well as historical simulations to assess whether the observed changes can be attributed to natural variability (in a first step by using a moving window analysis to detect the observed pattern in the simulations). Outcomes include: Advanced understanding of the mechanisms governing observed sea-level variability in the BG; The role of teleconnection between Pacific and the Arctic sea level; An assessment of the observed variability reproduced by CMIP5 (and eventually CMIP6) models, setting the stage for computing model weights by developing a performance metric and eventually decrease uncertainty in projections of sea level in the considered regions. Deliverables: Technical report; 3 publications in high impact journals; training of young scientists. Funding Sources: Bjerknes Center for Climate Research, Norway and internal funding from NERSC. Support from Chinese resources. Possible future funding from RCN, ESA, and Horizon Europe projects.
PROTOTYPE REAL-TIME REMOTE SENSING LAND DATA ASSIMILATION ALONG THE SILK ROAD ENDORHEIC RIVER BASINS AND EUROCORDEX-DOMAIN Objectives:The main objective is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a [...] Prof.. Harry Vereecken, Julich Research Centre, GERMANY Prof.. Xin Li, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, CHINA Cryosphere and Hydrology Objectives:The main objective is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale.The objective will be achieved through the following sub-objectives:ÔÇó Retrieval of key water cycle variables from multi-source RS data (WP1);ÔÇó Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2);ÔÇó Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3);ÔÇó Parameter estimations for terrestrial system models based on the LDAS (WP3);ÔÇó Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).Methods:Two LDAS will be developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and one for EUROCORDEX-domain (LDAS_EU). LDAS_Silk will be based on the recently developed watershed system model (Li et al., 2018a,b) and a common software for nonlinear and non-Gaussian land data assimilation (ComDA, Liu et al., 2020). The watershed system model is mainly composed of i) a distributed eco-hydrological model that integrates the glacier, snow, and frozen soil processes (GBEHM) (Yang et al., 2015), and ii) a Hydrological-Ecological Integrated watershed-scale FLOW model (HEIFLOW) (Tian et al., 2015). LDAS_EU will be based on the recently developed Terrestrial System Modeling Platform (TSMP) (Shrestha et al., 2014) and Parallel Data Assimilation Framework (PDAF) (Nerger and Hiller, 2013). The TSMP-PDAF is a modular high-performance data assimilation framework for an integrated subsurface-land surface-atmosphere model (Kurtz et al., 2016). TSMP comprises three component models: i) the Consortium for Small-scale Modeling (COSMO) atmospheric model (Baldauf et al., 2011), ii) the Community Land Model (CLM) (Oleson et al., 2008), and iii) the hydrological model ParFlow (Kollet and Maxwell, 2006). The TSMP can be run in three modes: fully coupled (COSMO + CLM + ParFlow), partly coupled (CLM + ParFlow/CLM + COSMO) or uncoupled. Comparison will be made between the performances of the LDAS_Silk and LDAS_EU.Multi-source RS data, from visible to thermal infrared and microwave, will be used to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. These data will be used as forcing data, calibration and validation data, and for assimilation into the two LDAS. The developed LDAS will estimate the different components of the water cycle (ET, SM, streamflow, SWE and groundwater dynamic) at the watershed/regional scale on a daily basis and a spatial resolution of 10 km.Deliverables: ÔÇó Retrievals of key ecohydrological variables, including vegetation parameters, SM, SWE, irrigation, lake and glacier extents from RS data (WP1);ÔÇó Development of LDAS_Silk for the silk road endorheic river basins (WP2);ÔÇó Development of LDAS_EU for the EUROCORDEX-domain (WP2);ÔÇó Calibration/validation procedures for the LDAS_Silk and the LDAS_EU (WP3);ÔÇó Estimation of different components of the water cycle for the silk road endorheic river basins and EUROCORDEX-domain produced by the developed LDAS (WP4).Source of Funding:ÔÇó Strategic Priority Research Program of Chinese Academy of Sciences (Grant numbers: XDA19070104 and XDA20100100);ÔÇó Research unit FOR2131 (Data Assimilation for Improved Characterization of Fluxes Across Compartmental Interfaces) of the German Science Foundation;ÔÇó European Union H2020 project EoCoE (Energy oriented center of excellence).
REMOTE SENSING OF CHANGING COASTAL MARINE ENVIRONMENTS (RESCCOME) Coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use, coastline changes and recreational activities. A [...] Dr. Martin Gade, University of Hamburg - ZMAW, Germany Prof.. Xiaoming Li, CAS Inst. of Remote Sensing Applications, CHINA Oceans and coastal zones Coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use, coastline changes and recreational activities. A continuous monitoring of those environments is of key importance for the identification of natural and manmade hazards, for an understanding of oceanic and atmospheric coastal processes, and eventually for a sustainable use of those vulnerable areas. The proposed project, “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME), will address research and development activities that will focus on the way, in which the rapidly increasing amount of high-resolution EO data can be used for the surveillance of marine coastal environments, and how EO sensors can detect and quantify processes and phenomena that are crucial for the local fauna and flora, for coastal residents and local authorities. ReSSCoME will consist of five research packages (RP), each addressing a relevant aspect of changing coastal marine environments: the state of vulnerable coastal regions and their changes (addressed in the RP on intertidal regions and coastline changes), the impact of growing economic use on coastal environments (offshore wind farms and oil pollution), and the growing threat of plastic debris and green tides (coastal pollution). The project consortium is formed by internationally renowned experts in each of the research fields. In order to ensure a high degree of cross-fertilization and synergy effects among the partners, five cross-cutting themes have been identified, the synergism of EO data, handling and processing of Big Data, identification of coastal stress factors, support of Young Scientists, and dissemination and outreach. Responsibilities for each RP and cross-cutting theme are equally distributed among all partners. The partner affiliations are based on, or close to, five European (Norwegian, North, Baltic, Black, and Mediterranean Sea) and three Chinese marginal seas (Bohai, Yellow and South China Sea). These marginal seas host five areas of interest, of which large quantities of EO data will be analysed, and in which complementing in-situ campaigns will be run. In addition, the western Java Sea will serve as a test and validation area for newly developed algorithms. Intertidal regions are particularly sensitive to natural and anthropogenic hazards. RP ‘Intertidal regions’ will focus on an optimization of the monitoring of those regions by including multi-modal SAR data into existing monitoring schemes that are based on optical EO data and in-situ observations. China and Northern Europe are hot-spots for future developments of offshore wind energy. RP ‘Offshore wind farms’ will provide information on wind resources, wake effects and environmental impacts, which are needed by wind energy industries during the entire lifecycle of a wind farm. The detection and quantification of marine oil pollution and the identification of its sources are crucial for the pollution monitoring in coastal marine waters. RP ‘Offshore oil pollution’ will address these tasks through a synoptical use of EO data and the automated processing of large quantities of SAR data (Big Data). Floating marine litter is a global problem, with millions of plastic items ending up in the sea. In addition, harmful algal blooms such as green tides are posing a threat to coastal marine environments. RP ‘Coastal pollution’ will address both aspects and will help in both optimizing the detection and quantification of marine litter, and understanding the dynamics of green tides. Coastlines are changing rapidly worldwide as a result of both (quasi-) natural and anthropogenic pressures. RP ‘Coastline changes’ will demonstrate the use of EO data for an accurate long-term quantification of coastline changes, which is needed by coastal managers for a sustainable development of coastal environments.
RESEARCH AND APPLICATION OF DEEP LEARNING FOR THE IMPROVEMENT AND ASSIMILATION OF SIGNIFICANT WAVE HEIGHT AND DIRECTIONAL WAVE SPECTRA FROM MULTI-MISSIONS Remotely sensed ocean waves from European and Chinese space missions have significantly supplemented the insufficient coverage of traditional wave observations such as buoys. The accuracy of the wave remote sensing is not only important for the [...] Dr. Lotfi Aouf, Meteo-France, Division Marine et Oceanographie, FRANCE Dr. Jiuke Wang, National Marine Environmental Forecasting Center (NMEFC) , CHINA Data Analysis Remotely sensed ocean waves from European and Chinese space missions have significantly supplemented the insufficient coverage of traditional wave observations such as buoys. The accuracy of the wave remote sensing is not only important for the wave forecast, but also critical to the air-sea interactions, which impact significantly weather and climate projections. Therefore, it is of great importance to improve the accuracy of retrieved wave products before their usage. The assimilation technique is known by its efficiency to improve the forecast accuracy by using the observations. Beside of good quality observations, a proper method of assimilation is also needed to make the best use of the observations, which will greatly impact the assimilation effects. Deep learning, which is based on artificial neural networks, has proved its efficiency and effectiveness in computer vision, speech recognition and many fields. It is innovative to apply this powerful method into both improvement and assimilation of wave products retrieved from several sensors such as SAR, wave spectrometer and altimeters available on European and Chinese satellites. Based on the satellite missions of Sentinel-1A/B SAR (Synthetic Aperture Radar), ENVISAT ASAR, Sentinel-3A/B altimetry, HY-2A/B altimetry, CFOSAT wave spectrometer SWIM (Chinese-French Oceanic Satellite, Surface Waves Investigation and Monitoring), this proposal will focus on:1) Finding the key factors which affect the accuracy through the detailed assessments of wave observations from each mission. This is critical to decide which parameter should go into the deep learning network. 2) Designing and building Deep Neural Networks (DNN) to improve the accuracy of altimetry missions (delay Doppler altimetry mode) by doing related researches and experiments. The structure and hyper-parameter will be carefully tuned and optimized to obtain a robust correction model. 3) Developing a model to correct the spectra from SAR and SWIM missions based on deep learning. Processing the wave directional spectrum as an image, techniques such as CNN (Convolutional Neural Network) will be applied in the correction. 4) Bringing out and verifying a new method of assimilation by combining techniques from deep learning, including those used for the quality control procedure before the assimilation and the method of data merging.5) Reprocessing the data from multi-missions using the deep learning correction. By using the corrected wave data in the assimilation, a global wave reanalysis from the model MFWAM will be produced and will highlight the use of the new deep learning assimilation method.6) Providing a better estimate of Modulation Transfer Function (MTF) of SAR and SWIM by using deep learning technique from model wave spectra. To demonstrate the feasibility of these objectives above, a series of exploratory experiments have been done by our teams. According to the comparisons between the corrections and buoy observations, the DNN has removed 77% of bias and improve 22% of SI (Scatter Index) for the SWH (Significant Wave Height) from Sentinel-3A. The DNN also removes 98% of bias and 26% of SI for the SWH of CFOSAT SWIM Nadir observation. The directional spectra from CFOSAT SWIM can be also improved by the combination of CNN and DNN. The deep learning can total decrease 45% of bias and lower the SI by 35%. Therefore, the feasibility experiment results have proved that deep learning is very promising when applied in the improvement of wave products retrieved from different sensors such as SWIM, SAR, and altimeters. So it should now be extended to other European and Chinese satellite missions. Furthermore, the study by using the deep learning in assimilation system will also be investigated. This proposal is funded by National Key R&D Program of China on Global high-quality marine data assimilation research and operational application and other relevant programs.
RETRIEVING THE CROP GROWTH INFORMATION FROM MULTIPLE SOURCE SATELLITE DATA TO SUPPORT SUSTAINABLE AGRICULTURE Remote sensing community has entered into a new era with the huge volume of satellite images at around 10 to 30 meter resolution fully and open available, including the sentinel series satellite in Europe and GF series satellite in China. These [...] Prof.. Pierre Defourny, Université catholique de Louvain, BELGIUM Dr. Jinlong Fan, National Satellite Meteorological Center, CHINA Sustainable Agriculture and Water Resources Remote sensing community has entered into a new era with the huge volume of satellite images at around 10 to 30 meter resolution fully and open available, including the sentinel series satellite in Europe and GF series satellite in China. These satellites brought more data options for the application in agricultural monitoring. The capability of agricultural monitoring in general is expected to be enhanced and improved with these satellite data in term of the monitoring spatial extent and the quality of the retrieved crop growth information. However, the agricultural cultivation is diverse in the world. There are existing large fields with mono crop and small fields with multiple strips of various crop types. This fact is impacting on the application of satellite data for agricultural monitoring. In order to support sustainable agriculture practices, it is important to find the best trade-off between the field size which can be assessed and the quality of the EO-derived information. Indeed EO-derived information for small fields should not be misleading when monitoring cropping practices towards a more sustainable agriculture.In general, the field size is quite small in many parts of farm land in China in comparison with that in Europe. The fine resolution satellite data are always expected to be used in the agricultural monitoring in China. Taking advantage of the operational availability of Sentinel and GF, how or to what fine extent can the remote sensing information support the framer in the agricultural management? In this project, 8 study sites are selected representing the major cropping systems, 3 sites for winter wheat and maize and another 3 for rice. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the north or south. The Sentinal1/2 and GF1/3/5/6, CBERS data will be mainly data sources to support this study. The remote sensing parameters, like LAI/FPAR/FCOVER/NDVI will be retrieved with the adapted algorithm. The crop classification algorithm will be applied to make crop type maps. Crop specific N retrieval algorithm will be developed. Finally, the retrieved information in the field level will be communicated with the farmers and jointly come up with the management suggestions for the nutritional application, irrigation and other practices. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm will be exchanged and the objective of this project will be fulfilled as the task team brings a step forwards to support agricultural monitoring at fine scale.
SARCHAEOLOGY: EXPLOITING SATELLITE SAR FOR ARCHAEOLOGICAL PROSPECTION AND HERITAGE SITE PROTECTION Archaeological prospection and the protection of cultural and natural heritage sites are important applications of remote sensing. In the past, they have been underrepresented in the Dragon programme. In our proposed project, we intend to work [...] Dr. Francesca Cigna, National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), ITALY Prof. Timo Balz, Wuhan University, CHINA Solid Earth Archaeological prospection and the protection of cultural and natural heritage sites are important applications of remote sensing. In the past, they have been underrepresented in the Dragon programme. In our proposed project, we intend to work on archaeological prospection and heritage protection with SAR remote sensing. With the upcoming wider availability of long-wavelength data from various L-band missions and ESA’s BIOMASS P-band mission, sub-surface target detection is becoming possible. This opens new perspectives for the use of SAR for the support of archaeological prospection, and this will be the main research focus of this project. Although the spatial resolution of these long-wavelength sensors will be too low for many archaeological applications, we expect the data to be useful for landscape archaeological analyses, especially with respect to hidden paleo-channels and hidden linear structures. This research will focus on arid areas in China, e.g. paleo-channels around Lop-Nor, as well as the larger province of Rome, including sub-urban and rural expanses with partly buried archaeological ruins. SAR data to be exploited for archaeological prospection will include ALOS-1 L-band, as well as shorter wavelengths, namely ERS-1/2, ENVISAT, RADARSAT-1/2 and Sentinel-1 C-band, and potentially Iceye and Paz X-band data, in order to test signal penetration capabilities at the different wavelengths and spatial resolutions. The identification of objects of archaeological interest from SAR is also an on-going research hotspot. Based on the team’s previous research on Kurgans (Iron Age burial mounds), we plan to work on the detection of Kurgans in Copernicus Sentinel-1 images. Generally, the resolution of Sentinel-1 is too low for the clear identification of Kurgans. However, using multi-temporal despeckling, Kurgans can become distinguishable in Sentinel-1. Even more so, with the correct combination of seasonal images, e.g. only summer images, and polarization.Subsidence can damage cultural heritage sites and various surface motion related disasters, e.g. landslides, can endanger natural and cultural heritage sites. Measuring surface motion with SAR is therefore an important part of heritage protection. In this regard, we focus our research on the long-term surface motion monitoring. Due to changes in the environment, as well as changes in the availability of sensors, a true long-term surveillance, covering decades, is a challenging task. The research in this project will therefore focus on the long-term surveillance, mainly with Sentinel-1. Looting is another on-going problem in archaeology and in the protection of our heritage. Remote sensing can play an important role in the detection of looting and we intend to further investigate looting detection with SAR data. In the proposed project, we present a new research direction for Dragon, with a new team, while keeping a degree of continuity with previous Dragon programs as well. The European partners will support the project with in-kind contribution of their work time and in-house computing resources. The funding from China will be coming from internal funding of LIESMARS and Wuhan University. The exchange of the Chinese students will be supported by the Chinese Scholarship Council.
SATELLITE OBSERVATIONS FOR IMPROVING IRRIGATION WATER MANAGEMENT – SAT4IRRIWATER The project objective is to assess high resolution irrigation water needs and crop water productivity based on the integrated use of satellite data, ground-hydro meteorological data and numerical modelling suitable for agricultural farms as well [...] Prof.. Marco Mancini, Politecnico di Milano, ITALY Dr. Li Jia, Aerospace Information Research Institute, Chinese Academy of Sciences, CHINA Sustainable Agriculture and Water Resources The project objective is to assess high resolution irrigation water needs and crop water productivity based on the integrated use of satellite data, ground-hydro meteorological data and numerical modelling suitable for agricultural farms as well as large un-gauged agricultural areas. The project responds to the call Topic 5. Sustainable Agriculture and Water Resources – 5.3 Water resources and its utilization. This satellite data driven integrated approach is a necessary support to improve water management in intensive irrigated areas. In fact, agriculture is the largest consumer of water worldwide and at the same time irrigation is one of the sectors with the hugest differences between modern technology and ancient practices. Improving water use efficiency and water productivity is an immediate requirement of society for sustaining global food security, to preserve quality and quantity of water and to reduce causes of poverties, migrations and conflicts among states. Climate changes and increasing human pressure together with traditional wasteful irrigation practices are enhancing the conflictual problems in water use also in countries traditionally rich in water.The assessment of the main objective will imply the achievement of the following sub-objectives:i) Retrieval of Earth Observation (EO) products at different temporal and spatial scales combining ESA, Chinese, Copernicus and NASA information: land use and land cover, or crop classification map (sentinel 2, GF), soil moisture (SM) by SMOS or FY3 and high resolution SM by downscaling method, land surface temperature byFY3, Sentinel 3, MODIS, Landsat; ii) Calibration/validation/assimilation of hydrological models (the Italian model FEST-EWB and the Chinese model ETMonitor) using EO data of land surface temperature (LST) and soil moisture (SM); iii) Assessing high resolution soil hydraulic parameters using EO data and hydrological models; iv) Irrigation water needs and crop water productivity maps through the combined use of EO data and hydrological models; v) Product Comparison with Copernicus services, vi) Exportability to un-gauged sites simplified approaches based mainly or totally on satellite information.This will be achieved in the Work Packages (WP): WP1: Land surface variables from satellite observations; WP2: Development and improvement of hydrological models to estimate crop water and irrigation needs; WP3: Assessment/prediction of Irrigation water needs; WP4: Crop water productivity.The project partnership between Chinese partner the Aerospace Information Research Institute of Chinese Academy of Sciences (AIR-CAS) and the italian group of Politecnico di Milano is based on a consolidated collaboration experience since 2000 that was also reinforced thanks to the previous Dragon projects.The project activities will be based mainly on partnersÔÇÖ case studies in Italy (Chiese and Capitanata irrigation consortia) and China (agricultural areas in Shiyang River basin and Shandian River basin in Inner-Mongolia), and will be also supported by two other case studies in Spain (Barrax) and in Morocco (Sidi Bennour irrigation district) where common previous activities were developed. These intense cultivated and irrigated areas have been chosen for differences in climatic conditions, water volume availability, crop types, irrigation schemes and water distribution rules.The project deliverables will provide pixel wise irrigation water volumes and a series of ancillary products that will match the project objectives as described in the follows .The present project will be supported by a significant partners heritage projects as : EU PRIMA SMARTIES, EU ERANETMED RET- SIF, China DBAR program (Digital Belt and Road), China NBS-SY project (Nature Based Solution for Desertification Risk by MOST), led by the two present project partners in Europe and In China.
SEISMIC DEFORMATION MONITORING AND ELECTROMAGNETISM ANOMALY DETECTION BY BIG SATELLITE DATA ANALYTICS WITH PARALLEL COMPUTING (SMEAC) 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 [...] Dr. Yaxin Bi, University of Ulster, UK Prof.. Jianbao Sun, China Earthquake Administration, CHINA Solid Earth 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 plan to 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. In Dragon 5, we 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 invert 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. Moreover, 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 project. Since 2012, China Earthquake Administration has constructed a Control Source Extremely Low Frequency (CSELF) observatory network that is composed of more than 30 stations, covering the main seismic zones across the mainland of China. The network observes electromagnetic fields and ground resistivity from natural and artificial sources. By joint analysis of deformation data and any abnormal changes captured in electromagnetic field through CSELF, we expect to detect possible pre-slip events along particular faults. During Dragon 5, seismic events will certainly occur irregularly. We will investigate both the deformation produced by major earthquakes and their CSELF anomaly signals, then try to find the correlations between these two geophysical quantities. In the past 16 years, we had different projects, funded by the National Nature Science Foundation of China and China Earthquake Administration, focusing on geodetic strain measurements of specific faults or areas by InSAR and GPS data, monitoring earthquakes through analyzing the changes of electromagnetic field etc.
SYNERGISTIC MONITORING OF ARCTIC SEA ICE FROM MULTI-SATELLITE-SENSORS Areal shrinkage of Arctic sea ice has been observed over the last 40 years, and its decline is proceeding faster than forecasted. These observed changes in the ice cover have impacts on the regional Arctic and sub-Arctic climate, environment, [...] Dr. Wolfgang Dierking, Alfred Wegener Institute for Polar and Marine Research, GERMANY Dr. Xi Zhang, The First Institute of Oceanography, Ministry of Natural Resources of China, CHINA Cryosphere and Hydrology Areal shrinkage of Arctic sea ice has been observed over the last 40 years, and its decline is proceeding faster than forecasted. These observed changes in the ice cover have impacts on the regional Arctic and sub-Arctic climate, environment, and ecosystems, and directly affect natural resource exploitation, marine transport and offshore operations, commercial fisheries, and indigenous lifestyles. Satellite monitoring offers continuous, near-total coverage of the Arctic ice pack. To enhance the retrieval of parameters describing ice conditions and to alleviate ambiguities in the interpretation of single satellite instruments, the demand for getting comprehensive sea ice information from multi-source satellite data obtained over the Arctic is growing as a result of climate change and its impact on environment and human activities. In this project, the overall objectives of our Sino-European sea ice research group are to upgrade and develop methodologies to retrieve quantitative sea ice information including measurements of ice area, thickness, drift velocity, and concentration using multiple satellite data provided by the EC Copernicus Earth Observation Program, ESA, ESA TPM, and Chinese satellites. The main multi-source satellite data will be combinations of Synthetic Aperture Radar (SAR), optical and infrared images, radar altimeter and passive microwave data. We also plan to assess the possibilities that new satellite missions offer for monitoring sea ice parameters, e.g. CFOSAT or future missions planned by ESA. In the project we will focus on five major research topics. 1. Sea ice type classification: The goal is to develop automated methods for operational ice charting and for achieving results with improved reliability and accuracy. 2. Sea ice thickness retrieval: We will port our algorithms developed for CryoSat-2/Sentinel-3 to HY-2A/B, and complement thickness estimation for thin ice using passive microwave data from SMOS, AMSR2, HY-2 and FY-3. 3. Sea ice drift and deformation retrieval: to this end we will use multi-frequency SAR image sequences and assess the usefulness of our results for regional studies of sea ice dynamics and its possible changes due to increasing temperatures in the Arctic. 4. Sea ice concentration estimation: We pursue to develop sea ice concentration retrieval algorithms for the Chinese passive microwave radiometers HY-2 and FY-3. 5. We will evaluate CFOSAT’s capability for sea ice detection. In our interest are also the Copernicus High Priority Candidate Missions (HPCMs) under discussion at ESA. The results expected from our proposed work will contribute to an improved understanding of impacts of climate change on sea ice dynamics, thus providing useful data for scientists, policy-makers and the general public. The project will demonstrate the benefits of combining Earth Observation data from European and Chinese satellites for operational mapping and interpretation of sea ice cover variations in the Arctic. As the only one sea ice remote sensing group under Dragon phases 2-4, our Sino-European team has cultivated an excellent and productive partnership and developed algorithms for sea ice types classification, retrieval of sea ice thickness, and drift in the Bohai Sea (China), supplemented by complementary studies for the Baltic Sea and the Arctic. This research is supported by the National Key Research and Development Program of China under grant 2016YFA0600102 and 2018YFC1407203, and the National Nature Science Foundation of China under grant 41976173 and 61971455. The European team members are on permanent positions and will support the Dragon Program by linking their national and international sea ice studies to the research topics listed above.
SYNERGISTIC MONITORING OF OCEAN DYNAMIC ENVIRONMENT FROM MULTI-SENSORS Observing, understanding and predicting ocean swell has been a focus on study in both China and Europe for climate, meteorology, environment and economy. To monitor at global scale ocean swell will improve the wind and wave forecast for marine [...] Dr. Bertrand Chapron, Institut Francais de Recherche et Exploitation de la MER, FRANCE Prof.. Jingsong Yang, The Second Inst. of Oceanography, MNR, CHINA Oceans and coastal zones Observing, understanding and predicting ocean swell has been a focus on study in both China and Europe for climate, meteorology, environment and economy. To monitor at global scale ocean swell will improve the wind and wave forecast for marine meteorology (including extreme events), the ocean dynamics modeling and prediction, our knowledge of climate variability, and fundamental understandings on air-sea surface processes. And to monitor and map extreme events during typhoons (hurricanes) or storm surges we have to develop the use of multiple satellite wind, wave and sea level data for forecast. The Dragon projects have been providing an excellent opportunity for Chinese and European ocean research communities to utilize the spaceborne satellite remote sensing data from China, ESA and Third Party Missions (TPM) to actively monitoring ocean swell, wind and other relevant parameters. It is the purpose of this project to continue cutting edge research in synergistic exploitation ocean swell study at global scale and extreme events in coastal region and gain insight of the physical nature of these phenomena, which will lay a solid foundation as we move to operational oceanography. The purpose of the project includes: (1) investigate algorithms for advanced ocean products from multiple microwave satellite sensors together describing wind and wave at the storm event scale ; (2) develop high wind retrieval algorithm from cross-polarization SAR; (3) synergy with existing satellite missions monitoring ocean waves; (4) investigation on global swell climate based on long term series space-borne data. (5) assimilation studies of wind, waves and sea level in the context of hurricanes forecasts; (6) the influence of swell on the studies of coastal extremes; and (7) consistent analysis on winds, waves and storm surges in the context of hurricanes. The Chinese and European parts are both funded by National Programme on Global Change and Air-Sea Interaction and other relevant programme to run this project.
THE CROSS-CALIBRATION AND VALIDATION OF CSES/SWARM MAGNETIC FIELD AND PLASMA DATA China Seismo-Electromagnetic Satellite (CSES) has been launched successfully on Feb. 2, 2018 in a sunsynchronous polar orbit at an altitude around 507 km, measuring the electromagnetic field, the energetic particles and the ionospheric plasma [...] Dr. Claudia Stolle, Deutsche GeoForschungsZentrum - GFZ Potsdam, GERMANY Prof.. Xuhui Shen, Institute of Crustal Dynamics, China Earthquake Administration, CHINA Calibration and Validation China Seismo-Electromagnetic Satellite (CSES) has been launched successfully on Feb. 2, 2018 in a sunsynchronous polar orbit at an altitude around 507 km, measuring the electromagnetic field, the energetic particles and the ionospheric plasma parameters. At present ESA’s Swarm mission is the only in-orbit satellite which has payloads comparable with CSES, allowing a direct cross-validation between the two platforms. The Swarm mission was launched on 22 Nov 2013, with three spacecraft at altitudes from 460 to 530 km. For CSES and Swarm, the comparable payloads include: CSES high precision magnetometer (HPM, for the total magnetic field observations), search-coil magnetometer (for the magnetic field variations) and the Langmuir probe (LAP, for in-situ plasma parameters); Swarm VFM and ASM the magnetic field), EFI (in-situ plasma). The two missions will operate in parallel in the next years, providing a good opportunity for cross-calibration and validation on similar types of payloads at same time intervals. The cooperation is aimed to take full advantages of the simultaneous observations of CSES and Swarm satellite in order to calibrate and validate the geomagnetic field and plasma parameters, to improve electromagnetism satellite data processing methods. Besides data validation, both sides share and exchange data and other related resources in order to achieve high-level scientific applications. Additionally, the cooperation will build a long-term stable international team able to drive and train young scientists for Low Earth Orbit (LEO) satellites data processing and analysis in the frame of geophysical observations, such as ionospheric electromagnetic field and gravity field. Such activity is expected to extend to the Chinese future missions, e.g., CSES-2, Zhangheng 02 gravity satellites. The validation of the magnetic field will be done through the direct comparison between the residual fields of CSES and Swarm during similar geomagnetic conditions. This technique will be done for each orbit and, from a statistical point of view, for the entire CSES/Swarm dataset. Concerning the plasma data, the validation will be realized via the direct comparison of the different plasma parameters (i.e. density, temperature, floating potential, and so on), after the calibration of the I-V curve fitting algorithms. From the scientific point of view, the use of both CSES and Swarm data will allow to study the possible Lithosphere-Atmosphere-Ionosphere (LAIC) effects at the satellite orbits on the occasion of significant earthquakes, the FACs dynamics, the ULF wave property and generation mechanisms, the solar activity and seasonal dependences of plasma density and temperature, and the magnetic perturbations in ionosphere. The deliverables include the comprehensive validation results of the magnetic field and plasma data of CSES and Swarm; a set of well-calibrated and high quality magnetic field and plasma density data; the joint academic activities and scientific publications. Related Funding supports: Besides the limited funding provided by Dragon 5 project, both sides will fund the research activities by themselves, to well organize their own team, to execute the joint cooperation tasks with support of other related funding. For the Chinese side, the activities involved in this proposal will be supported by CSES 01 mission operation budgets, the National Key R&D Program of China (No.2018YFC1503500) and other national science foundation of China. For the German side, these are the DFG SPP 1788 “Dynamic Earth”, ESA’s Swarm ESL/DISC under grant no 4000109587/13/I-NB, and Helmholtz institutional support. For the Italian side, the activities will be supported by Italian Space Agency under the contract ASI ”LIMADOU scienza” n° 2016-16-H0 and INGV national funds.
THREE DIMENSIONAL CLOUD EFFECTS ON ATMOSPHERIC COMPOSITION AND AEROSOLS FROM NEW GENERATION SATELLITE OBSERVATIONS About 70% of the Earth is covered by clouds, therefore clouds are often present in satellite observations. Cloud properties can be retrieved from satellite observation. Cloudy pixels are often screened before deriving atmospheric and surface [...] Dr. Ping Wang, The Royal Netherlands Meteorological Institute (KNMI), NETHERLANDS Prof.. Minzheng Duan, Institute of Atmospheric Physics, Chinese Academy of Science, CHINA Atmosphere About 70% of the Earth is covered by clouds, therefore clouds are often present in satellite observations. Cloud properties can be retrieved from satellite observation. Cloudy pixels are often screened before deriving atmospheric and surface properties. In the satellite products, cloud is typically assumed as a horizontal homogeneous layer. However, in reality cloud is a three dimensional (3D) object: clouds cast shadows on the ground surface or on lower clouds; clouds look brighter on the sun illuminated side and darker on the shadow side. The impacts of 3D cloud features on aerosol retrievals have been studied using high resolution satellite imagery data and lidar measurements. Clouds are also important in the trace gas retrievals. The research on the 3D cloud effects on trace gas retrievals is a new topic because the pixel size of the satellite spectrometers like GOME-2 is too big (40 km x 80 km) to see the 3D cloud effects. Since the launch of Sentinel-5p (S5p) in 2017, the trace gases are retrieved at a pixel size of 3.6 km x 5.6 km. We have seen the cloud shadows on the S5P images, which indicates the present of 3D cloud features in the S5p products. The objectives of the project are to analyze the impacts of the 3D clouds on trace gas retrievals, detect the cloud shadows, and derive aerosol and surface albedo products. Aerosol properties and surface albedo are important input parameters in the trace gas retrievals. Aerosol optical thickness (AOT) and surface albedo will be retrieved for selected scenes using the cloud shadow pixels. This is a complimentary method for the general used method nowadays. The algorithm will be demonstrated using Sentinel-2, Sentinel-5P, GF-1/6. The retrieved AOT will be validated from ground-based measurements and compared with Sentinel-3 aerosol products.We will detect cloud shadows from S5p and compared with collocated VIIRS data. The high resolution imagery of VIIRS will provide more accurate detection of cloud shadows and cloud edges on the S5p data. From selected scenes we will study the variation of trace gas column densities with the distance to the clouds. We will use the 3D radiative transfer components of the Earth Clouds and Aerosol Radiation Explorer (EarthCARE) simulator (ECSIM) together with 3D high resolution cloud fields generated using Large-Eddy Simulation (LES) model to simulate S5p/TROPOMI measurements. The simulations will help us to understand the shadow and the 3D cloud effects on the TROPOMI cloud, Absorbing Aerosol Index (AAI), AOT, and nitrogen dioxide (NO2) products. Ultimately, we plan to correct the impact of 3D clouds (shadows) on the Sentinel-5p/4/5 products. The project will use Sentinel-2/3/5p, GF-1/6 products and can be applied to S4/S5 after they are in orbit.The deliverable are reports, publications, and demonstration products and data analysis results. The KNMI team is partly supported by the User Support Programme Space Research of Dutch Research Council and KNMI internal funding. The IAP/CAS team is supported by IAP internal funding.The topic is Atmosphere. Subtopic is related to air quality but also related to greenhouse gases because the greenhouse gas products from satellite observations will also be impacted by 3D clouds and shadows.
TOWARD A MULTI-SENSOR ANALYSIS OF TROPICAL CYCLONE Oceans, especially the upper oceans , play key roles on the earth climate regulation (e.g., climate change) as well as for human societies. Despite the ever-increasing development of simulation and observation capabilities leading to earth [...] Dr. Alexis Mouche, Institut Francais de Recherche et Exploitation de la MER, FRANCE Prof.. BIAO ZHANG, Nanjing University of Information Science & Technology, CHINA Oceans and coastal zones Oceans, especially the upper oceans , play key roles on the earth climate regulation (e.g., climate change) as well as for human societies. Despite the ever-increasing development of simulation and observation capabilities leading to earth observation big data, our ability to understand, reconstruct and forecast upper ocean and marine atmospheric boundary layer dynamics remains fairly limited for numerous processes.Surprisingly, data characterizing the air-sea interactions at the ocean interface are not used for TC intensity estimates and forecasts. However, a new generation of space-borne sensors able to probe the ocean surface through clouds has emerged ((e.g. Reul et al. 2012, Zhang et al., 2012). In addition, there is also a wealth of non-local information related to TC that can be analyzed to fully characterize the TC intensity and its coupling with the oceans. Indeed, associated with TC extreme wind forcing conditions, quite systematically persistent signatures in TC wake can also be observed. A TC induces vigorous mixing and intense upwelling that generally result in a cooling of the upper ocean mixed layer [Ginis, 2002] and an important displacement of isopycnals [Geissler, 1970], characterized by prominent sea-surface height anomalies in their wake. Swell systems are also fingerprints of extreme ocean storms, and can propagate all the way across ocean basins from the area of high winds that generated them. Very long-period swells have been observed to propagate up to halfway around the globe [e.g. Munk et al., 1963]. Data acquired at different times and locations can thus be gathered to document a given extreme event. This proposal focuses on the interactions between ocean and atmosphere in the case of TC and Extra-Tropical Cyclones (ETC). Main scientific objectives are to develop data-model-driven techniques dedicated to extreme marine-atmosphere events, to provide new insights for air-sea exchanges processes parameterization under extreme conditions, and to drive the specifications of new generation of observation networks for TC monitoring. The project also aims at training young scientists. It includes three PhD students and will elaborate new material for a new tutorial on the benefit of adopting a multi-modal approach to characterize Tropical and Extra-Tropical Cyclones. The tutorial will rely on case studies extracted from the CyclObs database, a specific database for cyclones developed during the project.European team is funded through CNES IWWOC and COWS projects aiming at promoting the use of CFOSAT and ESA Sentinel-1 MPC and SMOS projects focusing on SMOS and Sentinel-1 missions. Chinese team is funded by National Key R&D program of China under Grant 2016YFC1401001.
UTILIZING SINO-EUROPEAN EARTH OBSERVATION DATA TOWARDS AGRO-ECOSYSTEM HEALTH DIAGNOSIS AND SUSTAINABLE AGRICULTURE Agriculture production systems are facing unprecedented challenges from increasing demand for food for a growing population, but an intensified agriculture bares also risks for environmental pollution and unsustainable use of water resources. In [...] Dr. Carsten Montzka, Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), GERMANY Dr. Liang Liang, Jiangsu Normal University, CHINA Sustainable Agriculture and Water Resources Agriculture production systems are facing unprecedented challenges from increasing demand for food for a growing population, but an intensified agriculture bares also risks for environmental pollution and unsustainable use of water resources. In addition, climatic change and soil degradation affect food production in the long term. The ÔÇÿZero HungerÔÇÖ goal was highly stressed as one of the major Sustainable Development Goals by the United Nations, which obviously calls for sustainable agricultural practices and wise management.Therefore, monitoring and prediction of agricultural systems has been a strong motivation for scientists and remains challenging where gaps in our understanding and capability of monitoring systems are limited. Earth Observation (EO) is already used to estimate land surface variables, but the step to a full process understanding of agricultural systems has not yet been taken. Adequate mitigation strategies during droughts, nitrogen pollution events, and pests cannot be adequately employed without this understanding. Integrated agricultural system studies at local, regional and national levels are needed to achieve synergies and adequately address trade-offs among the food-water-energy nexus during land and climate change.Objective The aim of this project is to monitor essential variables in agriculture based on various in situ and remote observations to investigate agricultural processes and to carry out a full agro-ecosystem health diagnosis by data assimilation. This synoptical perspective allows us to conserve, protect, and improve the efficiency of the use of natural resources to facilitate sustainable agricultural development. A near-real-time prototype will enable informing stakeholders about timely management actions. With this proposal, we plan to prepare for agricultural applications of future missions, such as BIOMASS, EnMAP.Research contents and methodsDuring Dragon 5, we will develop a multi-variable EO strategy for agricultural areas. Essential variables such as crop type, LAI, biomass, soil moisture, evapotranspiration, and soil carbon content are monitored by remote sensing methods to characterize the current state of agro-ecosystems. Machine learning, process-based radiative transfer, hybrid inversion, and Bayesian scaling approaches will be applied to timely retrieve the variables in focus. To bring together the different variable types, multi-source ensemble Kalman filter methods will be applied to improve the performance in crop growth and hydrological simulation, where the links between these two aspects will be investigated especially for extreme weather conditions. This will pave the way for simulating near-future states for the implementation of a sound and sustainable soil, land, water, nutrient and pest management, and appropriate use of fertilizers.DeliverablesWe will provide essential agro-ecosystem variable products in the hydrological (soil moisture, evapotranspiration) and plant physical (crop type, LAI, biomass, chlorophyll, and nitrogen content, fire occurrence) domain. It will make use of European, Chinese and TPM EO data in different wavelengths, if it is in visual, infrared, thermal or microwave region of the electromagnetic spectrum. We deliver a strategy how an early warning system can be implemented for crop health diagnosis and investigate the effects of different environmental threads. The transferability of the approaches will be tested at European and Asian sites to assure global applicability. A vital deliverable is also the education of young scientists in the field of remote sensing of agriculture to multiply their EO expertise in their further career at different stakeholders.FundingThe Dragon 5 project will be supported by the National Natural Science Foundation of China (NSFC) (Grant No. 41971305, 41701371, 41807001, 41701236), and the European Commission Horizon 2020 Program ERA-PLANET/GEOEssential (Grant Agreement no. 689443).
VALIDATION AND CALIBRATION OF REMOTE SENSING PRODUCTS OF CRYOSPHERE AND HYDROLOGY Objectives: The main objective is to assess the remotely sensed products of key cryospheric and hydrological elements (snow, evapotranspiration, soil moisture and precipitation) in representative regions, which will be achieved through the [...] Prof.. Jouni Pulliainen, Arctic Research Centre of the Finnish Meteorological Institute, FINLAND Prof.. Tao Che, Northwest Institute of Eco-Environment and Resources, CHINA Cryosphere and Hydrology Objectives: The main objective is to assess the remotely sensed products of key cryospheric and hydrological elements (snow, evapotranspiration, soil moisture and precipitation) in representative regions, which will be achieved through the following sub-objectives: • Establishing an observation network for snow, evapotranspiration, soil moisture and precipitation in representative regions selected in Europe and China (WP1); • Validation of snow products (WP2); • Validation of evapotranspiration products (WP3); • Validation of soil moisture products (WP4); • Validation of precipitation products (WP5). Methods: Remotely sensed products will be evaluated by referencing ground-based observations. The ground-based measurements will be upscaled to match the remote sensing pixel for validating products within the observation network. The validated products will be inter-compared with other gridded products, and the spatiotemporal trends are diagnosed by statistical indexes, e.g., RMSE and correlation coefficient. Finally, the regional and temporal feasibility of each product will be further evaluated based on the datasets in different landscapes, topographic conditions in the representative regions selected in China and Europe. Deliverables: •Datasets collected from the observation network, mainly including the snow, evapotranspiration, soil moisture and precipitation (WP1); • Evaluation of remotely-sensed snow products, including GlobSnow SWE and snow cover extension, MODIS/AVHRR snow cover area/fraction, NASA AMSR-E SWE, IMS snow cover and FY SWE products (WP2); • Evaluation of remotely-sensed evapotranspiration products in typical ecosystems, such as GLEAM, ETMonitor and MODIS-ET products (WP3); • Evaluation of remotely-sensed soil moisture products, such as SMOS, ESA-CCI, ASCAT, FY-3C, SMAP, LPRM and AMSR-E/2 (WP4); • Evaluation of remotely-sensed precipitation products, mainly including GPM_IMERG, GSMaP, TRMM3B42, CMPA and PERSIANN-CCS (WP5). Source of Funding: • The Strategic Priority Research Program of Chinese Academy of Sciences (Grant no: XDA19070101) • The Science and Technology Basic Resources Investigation Program of China (Grant no. 2017FY100501)
VALIDATION OF CHINESE CO2-MEASURING SENSORS AND EUROPEAN TROPOMI/SENTINEL-5 PRECURSOR USING FTIR AND MAX-DOAS DATA AT XIANGHE (VCEX) The project aims at applying FTIR and MAX-DOAS measurements for the validation of air quality and greenhouse gas measurements from the European Copernicus Sentinel-5 Precursor (S5P) and Chinese TanSat satellites. The focus will be on monitoring [...] Dr. Bart Dils, Royal Belgian Institute for Space Aeronomy, BELGIUM Prof.. Pucai Wang, Institute of Atmospheric Physics, Chinese Academy of Sciences, CHINA Calibration and Validation The project aims at applying FTIR and MAX-DOAS measurements for the validation of air quality and greenhouse gas measurements from the European Copernicus Sentinel-5 Precursor (S5P) and Chinese TanSat satellites. The focus will be on monitoring NO2, O3, HCHO, SO2, CHOCHO, CO, CH4 and CO2 columns and Prof.iles using standardised operation protocols and retrieval methods at the Xianghe site, in Northern China. The FTIR instrument allows measurements of O3, HCHO, CO, CH4 and stratospheric NO2 columns, while the MAX-DOAS instruments can measure NO2, O3, HCHO, SO2 and CHOCHO columns as well as aerosol extinction. The differences between the FTIR and MAX-DOAS common targets, in particular regarding their vertical sensitivity, need to be well understood before combining them together to validate satellite measurements. As FTIR and MAX-DOAS instruments are operated from the same building in Xianghe, we have a good opportunity to compare the FTIR and MAX-DOAS NO2, O3 and HCHO measurements and to combine them together for satellite validation. A strong focus of this project will be the implementation of standardized operation and retrieval protocols for the Xianghe FTIR and MAX-DOAS measurements in order to obtain reliable ground-based measurements that are harmonized with the wider scientific community. To reach this target, we will take full advantage of the ongoing activities in projects such as the European ACTRIS-IMP and ECMWF Copernicus Atmospheric Monitoring Service-related CAMS-27 and the Copernicus Climate Change Service-related C3S_311a_Lot3 (C3S-Baron), which concentrate on the establishment of standards for FTIR and MAX-DOAS operation and data processing. Exchange of knowledge, tools and retrieval software will take place, and access to facilities such as the VCEX processing system will be offered.Validation methodologies will be developed and applied, making use of all available sources of information such as the averaging kernels of the satellite products, cloud information, and the vertical Prof.iles of the various trace gases complemented by aerosol data (which will be derived from MAX-DOAS measurements and AERONET measurements). Comprehensive uncertainty budgets will be derived, addressing accuracy, precision and long-term stability. Based on the obtained validation results, recommendations for satellite product quality improvement can eventually be formulated. The project will contribute to validate the S5P and Chinese CO2 sensors (FY-3H/GAS and TanSat) and its successor measurements during the full duration of the project (2020-2023). In this period, the progressive accumulation of data will allow for improved statistics and refinement of the validation results. This will include analysis of, e.g., seasonal cycle effects and longer-term stability. As Xianghe is located in a sub-urban polluted region with a high and variable aerosol concentration, the ground-based MAX-DOAS and FTIR measurements, together with AERONET aerosol optical depth measurements, are valuable to understand the performance of the satellite measurements under different aerosol conditions. In addition, as Xianghe is about 50 km away from the capital Beijing, representativeness effects will also be investigated especially.The main outcomes of the project will be (1) the collection of the ground-based measurements of standardised FTIR and MAX-DOAS column and Prof.ile measurements of NO2, O3, HCHO, SO2, CHOCHO, CO, CH4 and CO2 columns at Xianghe, Northern China, and (2) an assessment of the corresponding quality of the S5P, FY-3H/GAS and TanSat sensors.
VALIDATION OF OLCI AND COCTS/CZI PRODUCTS AND THEIR POTENTIAL UTILIZATION IN MONITORING OF THE DYNAMIC AND QUALITY OF THE CHINESE AND EUROPEAN COASTAL WATERS Objectives: (1) Characterization of the error budgets of officially distributed products of OLCI onboard Sentinel 3 satellites and COCTS/CZI onboard HY-1 satellites in coastal waters around China and Europe, e.g., Yellow Sea in China, English [...] Dr. Cédric Jamet , Laboratoire d'Océanologie et de Géosciences UMR 8187 CNRS/ULCO, FRANCE Dr.Bing HAN, National Ocean Technology Center, CHINA Calibration and Validation Objectives: (1) Characterization of the error budgets of officially distributed products of OLCI onboard Sentinel 3 satellites and COCTS/CZI onboard HY-1 satellites in coastal waters around China and Europe, e.g., Yellow Sea in China, English Channel in Europe, French Guiana in South America. (2) Examination of the consistency between OLCI and COCTS/CZI, and among other ocean color sensors in these waters. (3) Development and refinement regional algorithms to accurately retrieve marine environment parameters (optical and biogeochemical) in these regions of interest. (4) Utilization of OLCI and COCTS/CZI products to monitor the dynamic and quality of the Chinese and European coastal waters. Methods: In-situ data acquisition Bio-optical, biogeochemical and atmospheric data will be collected and processed following community widely accepted protocols in the areas of interest. Spatial-temporal match-up methodology The in-situ measurements are considered as the reference or ‘true’ values and will be compared with satellite data (pixels) both temporally and spatially over a given pixel-box. Both spatial box and temporal differences during match-up procedure should be selected with care in coastal waters. Moreover, product flags should also be selected appropriately to avoid suspicious or bad retrievals. (3) Satellite-satellite consistency examination Differences in wavelength/spectral response, spatial resolution as well as temporal difference should be thoroughly considered and corrected properly. Effect of adopting various atmospheric correction and retrieval algorithm may also impact product inconsistency. (4) Relationship analysis between reflectance and water constituents Refinement of existing or development of novel algorithms will be investigated, to improve the accuracy of the products (e.g., Chlorophyll a, suspended particulate matter) in local ecosystems. Deliverables: Annual Summary Report Report includes datasets, use of EO data from multiple sources, and outcomes. (2) Peer viewed publications 4~5 peer viewed papers will be planned under this proposal’s framework. (3) Novel EO dataset Novel EO products processed with refined and/or newly developed algorithms. Funding: Chinese partner: (1) National Key Research and Development Program of China – Real-time Validation and Correction of Ocean Color Products (2016YFC1400906) (2) National Satellite Engineering program of China – Field campaign and product evaluation of COCTS/CZI onboard HY-1C/1D(since 2018). French partner: (1) Centre National d’Etudes Spatiales (CNES) – Evaluation and improvement of OLCI atmospheric correction and bio-optical products over French coastal waters (Funding for sea campaigns) (2) Partenariat Hubert Curien (French ministry of Research, French ministry of Foreign Affairs) Monitoring the quality of French and Chinese coastal waters using OLCI and COCTS/CZI satellite images (Funding for travel in 2020)