Conference Agenda

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.

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 new RE integration with existing power generations on the CO2 reduction by using satellite monitoring, RE demand 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 CO₂and CH₄ concentrations at near-surface level. Several satellites have been launched, including Japan Greenhouse gases Observing Satellite (GOSAT), NASA OCO-2 and OCO-3 satellites, and Chinese carbon dioxide observation satellite (TanSat). 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 report will present the project objectives and initial progress of the project, including the preparation of satellite data, the distribution of renewable resources and energy demanding over the urban areas,and the latest provisional estimates of regional greenhouse gas emissions based on provisional inland energy consumption statistics.

The EMPAC project 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. Satellite and ground-based remote sensing together with detailed in situ measurements 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 are studied, i.e. aerosols, in AQ studies often represented as PM_2.5, 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. However, satellite observations provide column-integrated quantities, rather than near-surface concentrations. The relation between column-integrated and near-surface quantities depends on various processes. This relationship and the implications for the application of satellite observations in AQ studies are the focus of the EMPAC project. Detailed process studies are planned to be undertaken, using ground/based in situ measurements, instrumented towers, as well as remote sensing using lidar and Max-DOAS. A unique source of information on the vertical variation of NO₂, O₃, PM_2.5 and BC is obtained from the use of an instrumented drone.
The results of the first year will be presented, including newly derived NO_x emissions over the Yangtze River Delta from the Sentinel 5P satellite.

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:

• the upcoming P-Band mission BIOMASS.
• 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.
• 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.

Sub-topic 2 will consider: Kenya or South-Africa, of interest for retrieval of water-vapor and deformation over large scale, and other suitable test sites,. 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.

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. The lack of available global CO₂measurements makes it difficult to estimate CO₂ emissions accurately. Satellite measurements would be very helpful for understanding the global CO₂ flux distribution if CO₂ column-averaged dry air mole fractions (XCO₂) could be measured with a precision of better than 2 ppm. To this point, the main objectives of this research project in Dragon 5 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 inter-comparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. In this presentation, we will introduce the 1^st global map of carbon flux from the new TanSat XCO2 product by an ETKF data assimilation system coupled with GEOS-Chem CTM. The error reduction compared to a priori indicates the benefit on carbon flux estimation after assimilating global satellite XCO2 measurement, and the carbon flux distribution over global changed through the whole year from May 2017 to April 2018. A new SIF product also produced by IAPCAS and a comparison with OCO-2 measurement shows a comparative result. Ground based long-term measurement by automatic running of EM27 is performed at IAP building in Beijing from 2019 which has been used in satellite measurement validation and city carbon monitoring. We will also show the new results and progress on next generation TanSat mission.

The purpose of project ‘Monitoring of Greenhouse Gases with Advanced Hyper-Spectral and Polarimetric Techniques’ is to improve the accuracy of the greenhouse gas products XCO2 and XCH4, inferred from the combination of hyper-spectral satellite measurement and polarization satellite measurement in close collaboration between the Chinese and European partners. In the first year, we have done the works as following:
1 We have collected three types of data we need, satellite global measurement data such as GOSAT, OCO-2, POLDER, GMI and DPC and so on, auxiliary data such as MODIS products and ECMWF reanalysis data, and validation data such as TCCON data and products of GOSAT and OCO-2.
2 Chinese scientists have developed the atmospheric CO2 retrieval method for clear sky measurements, which contains atmospheric radiative transfer calculate module, environmental data preprocessing module and retrieval module. We tested the method using GOSAT data and the result shows that the method is able to retrieve the hyper-spectral measurements.
3 European scientists have developed the retrieval method for a synergistic retrieval of aerosol properties and XCO2 column mixing ratios. The software is tested extensively on GOSAT data with global coverage and retrieval shows encouraging results.
In the next year, our main works include improving the retrieval method developed by Chinese scientists and studying the joint utilization of polarimeter and spectrometer observations. With the support of European scientists, we will improve the radiative transfer calculation, key parameters setting, iteration updating strategy and results correction, to make the retrieval method fit for GMI instrument characteristics better. In parallel, European scientists will research on the joint utilization of polarimeter and spectrometer observations, develop the retrieval method and analyze the improvement in greenhouse gases retrieval. Through on-line academic exchanges, visiting terms if permitted and annual reporting of Dragon 5 program, young scientists will improve their academic level through the project.

It is presented in this paper the scientific objectives and some progresses of ESA-MOST China Dragon Cooperation Program “Synergistic Monitoring of Ocean Dynamic Environment from Multi-Sensors (ID. 58009)” including: (1) assimilation studies of wind, waves and sea level in the context of hurricanes forecasts; (2) the influence of swell on the studies of coastal extremes such as sea level rise, storm surges and extreme wave events; (3) studies of vortex Rossby waves, asymmetric tropical cyclone structures, rain bands, and sub-scale circulations by using high spatial resolution ocean wind data; (4) analysis of relationship between the above internal dynamical processes and tropical cyclone intensity changes; and (5) consistent monitoring of ocean surface current and internal waves using multi-source satellite data.

Tropical cyclones (TC) transfer kinetic energy to the upper ocean and thus enhance the ocean mixed layer (OML) currents. However, the quantitative link between near-surface currents and high wind speeds, under extreme weather conditions, remains poorly understood. In this study, we use multi-mission satellite and drifting-buoy observations to investigate the connections between TC surface winds and currents, including their spatial distribution characteristics. Observed ageostrophic current speeds in the OML increase linearly with wind speeds. The ratios of the ageostrophic current speeds to the wind speed are found to vary with TC quadrants. In particular, the mean ratio is around 2% in the left-front and left-rear quadrants with relatively small variability, compared to between 2% and 4% in the right-front and right-rear quadrants, with much higher variations. Surface winds and currents both exhibit strong asymmetric features, with the largest wind speeds and currents on the TC right side. In the TC eyewall region, high winds (e.g. 47 m/s) induce strong currents (2 m/s). The directional rotations of surface winds and currents are coupled and dependent of specific locations. Wind directions are approximately aligned with current directions in the right-front quadrant; a difference of about 90^o occurs in the left-front and left-rear quadrants. The directional discrepancy between winds and currents in the right-rear quadrant is relatively smaller. Reliable observations of the wind-current relation, including asymmetric features, will enhance our understanding of TC air-sea interactions.

Satellite altimeter is a fundamental important global ocean remote sensing technique to monitor the marine dynamic environment. Sentinel-3A/3B and Sentinel-6 satellite equipped with altimeters have been launched on 16 Feb. 2016, 25 Apr. 2018 and 21 Nov. 2020 in Europe, and HY-2B/2C satellite equipped Radar Altimeter were launched on 25 Oct. 2018 and 21 Sep. 2020 in China. 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. First we combine Sentinel-3 and HY-2 series and other altimeters data into high spatial resolution grid data in the China seas and western Pacific Ocean. Then we 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. Subsequently we 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, Altimetry data of Sentinel-3A/3B, Cryosat-2, Sentinel-6 HY-2B/2C and CFOSAT SWIM data will be investigated in this study. Field data of tide gauge stations and buoys are used for data validation of SSH and SWH. Two master students and young scientist Dr. Wei Cui from the First Institute of Oceanography, MNR of China are involved in this study. For the first year of Dragon 5, waveform retracking processing of HY-2B altimeter in coastal areas of the China seas are carried out by different methods and the results are analyzed. The accuracy of HY-2B SSH and SWH data in the coastal area are improved by data reprocessing. Based on in situ data from the tide gauge station and buoy, the HY-2B altimeter SSH and SWH are evaluated, and the improvement of the HY-2B in the coastal area by the reprocessing is summarized. The European partners are mainly contributing to the data reprocessing and mean surface model of altimeters, and the Chinese partners are contributing to data reprocessing of altimeters and their applications in Marine dynamic environment monitoring.

The investigations carried out by the European partners have focused in the first year on studying the effect of surface wave breaking on the radar imaging mechanism of internal waves. It is known since long time that the conventional radar imaging theory based on weak hydrodynamic interaction theory and Bragg scattering theory fails to describe the often observed strong co-polarization radar signatures of internal waves at C- and X-band and their weak dependence on look direction of the radar antenna. This calls for an improved radar imaging theory of internal waves, which includes scattering from breaking surface waves. To this end, we have analyzed a TerraSAR-X image of an internal wave packet acquired at HH and VV polarization and C-band Sentinel-1 SAR and L-band ALOS/PALSAR images of internal solitary waves (ISWs) acquired at co- and cross-polarizations. We found that in the case of co-polarized scattering (i.e., at HH and VV polarizations) the measured radar signature of large ISWs can only be explained by including non-polarized scattering from breaking waves into the scattering mechanism. Furthermore, we found that in the case cross-polarized scattering (i.e., at VH polarization), the cross-polarized radar signature of ISWs can be similarly strong as the co-polarization one. Furthermore, we have analyzed Sentinel-3 SAR altimetry data and found clear evidence of significant wave height (SWH) variations along the propagation paths of ISWs.

The investigations carried out by the Chinese partners have focused in the first year on improving models for the description of ISW propagation in the South China Sea.

Jianqiang Liu^1,2，Ying Xu^1,2，Daniele Hauser³,Jing Ding^1,2，Qingjun Song^1,2，Maohua Guo ^1,2，Xiuzhong Li⁴，Wenming Lin⁴， Lingling Xie⁵, François Schmitt⁶

¹(National Satellite Ocean Application Service, MNR, Beijing, China)

²(Key Laboratory of Space Ocean Remote Sensing and Application, MNR)

³(CNRS/LATMOS, Guyancourt, France)

⁴(Nanjing University of Information Science & Technology, Nanjing, China)

⁵(Guangdong Ocean University, Zhanjiang, China)

⁶(CNRS/Laboratory of Oceanology and Geosciences ,Wimereux, France)

Abstract

The China France Oceanography Satellite (CFOSAT) and Haiyang-2B (HY-2B) satellites were successively launched in China in 2018. As missions for measuring the dynamic marine environment, both satellites can measure the nadir significant wave height (SWH). In this project, the HY-2B altimeter and CFOSAT nadir SWHs have been validated against the National Data Buoy Center (NDBC) buoys and the Jason-3 altimeter SWH data, respectively, which resulted in CFOSAT nadir SWH having the best accuracy and HY-2B having the best precision. The SWHs of the two missions are also calibrated by Jason-3 and NDBC buoys. Following calibration, the root mean square error (RMSE) of CFOSAT and HY-2B are 0.21 and 0.27 m, respectively, when compared to Jason-3, and 0.23 and 0.30 m, respectively, compared to the buoys. Our results show that the two missions can provide good-quality SWH and can be relied upon as a new data resource of global SWH.

Using simultaneous observations of wind and wave fields by the CFOSAT, this project reports preliminary investigation results of the typhoon waves during the passage of super typhoon Lingling (2019) over the China offshore waters. The results show that the significant wave heights (SWHs) are over 5 m on the right side of the typhoon track for wind speeds over 14 m s-1, agreeing with the theoretical estimates. The dominant waves have wavelengths of 150 – 180 m, and propagate eastward for northwestward blowing winds. The misalignments of the wind and wave directions increase with the distance from the typhoon center, agreeing with theoretical prediction. We also present the typhoon monitoring results with multiple satellites such as CFOSAT, HY-2B and ASCAT.

HY-1D satellite which is China’s fourth series of ocean color satellites, was successfully launched in 2020. The overall objective of HY-1 serial satellite is to monitor global ocean color and SST (Sea Surface Temperature), as well as the coastal zones’ environment. Using HY-1 C/D data and Sentinel satellite data, this project investigates the sea ice, oil spill and green tide disaster in Bohai Sea and the Yellow Sea, red tide in East China Sea. The results show that combing HY-1 C/D and Sentinel satellite data have played an important role in ocean ecological disaster monitoring.

The project addresses, mainly with the use of Earth Observation data and techniques, two distinct themes : on the one hand climate change as this relates to the thermal resilience of cities and on the other urbanization and environment. In the former theme, the overall aim is to support climate friendly cities through the drafting of climate change adaptation plans as far as urban heat is concerned; in the latter case, the overall aim is to detect and assess urban geological hazards. Areas of application are in principle the overall Beijing and Athens urban areas, although results have strong replication potential for other cities as well.

In terms of climate change, the scientific objectives are: (a) to assess the impact of climate change to the urban thermal environment and work out long term series analysis (also with the use of ERA5 data) to define the response times of extreme air temperatures; (b) to examine the contribution of earth observation for cascade modelling (from RCMs to microscale) at the urban scale; (c) to study the relationship between urban form and the state of urban thermal environment both with respect to air and land surface temperatures; (d) to work out a methdology for delineating cities into climate zones; and (e) to define and map urban heat risk and assess climate resilience.

In terms of urbanization and environment (smart cities), the scientific objectives are: (a) to monitor and model urban geological hazards; (b) to combine remote sensing, geophysical prospecting, and hydrogeological theories methods (by using InSAR, ground penetrating radar, and multi-field numerical analysis) to establish three-dimensional monitoring network of land subsidence in urban area for hydrogeological process and (c) to identify land subsidence mode, establish dynamic models, quantify multi-field contributions, and reveal the mechanisms of land subsidence.

The research objectives of the project as well as the methodologies and a first set of results (estimates for the return period (frequency) of extreme air temperature values for both cities, extreme value analysis in terms of the number of days with daily maximum temperature above the 90th percentile of daily maximum temperature, etc.), will be presented along with a discussion on the potential of the project for climate friendly and smart cities as well as for its replication potential.

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). 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 SARbased 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 project is partially funded by the projects that the team partners have been secured. Specifically, the EOAI4ChangeDetection 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.

Surface waves are one of the most common phenomena in the oceans. The accurate monitoring and forecasting of waves are critical for guaranteeing the safety of all kinds of marine activities, such as sailing and fishing, and are also of great importance to understanding air-sea interactions, which significantly impact weather and climate projections. Remotely sensed ocean waves from European and Chinese space missions have significantly supplemented the insufficient coverage of traditional wave observations such as buoys. The objectives of this program are improving the wave remote sensing and enhancing the positive effect of assimilation. The progresses are listed below:

1). A deep learning technique is novelly applied for the calibration of Chinese HY2B SWH and wind speed. Deep neural network (DNN) is built and trained to correct SWH and wind speed by using input from parameters provided by the altimeter such as sigma0, sigma0 standard deviation (STD). The results based on DNN show a significant reduction of the bias, root mean square error (RMSE), and scatter index (SI) for both SWH and wind speed. Several DNN schemes based on different combination of input parameters have been examined in order to obtain the best model for the calibration. The analysis reveals that sigma0 STD is a key parameter for the calibration of HY2B SWH and wind speed.

2). In addition to the nadir significant wave height (SWH), the Surface Waves Investigation and Monitoring (SWIM) onboard Chinese-French Oceanic SATellite (CFOSAT) provides two additional columns of wave spectra observations within wavelengths from 70 m to 500 m. A model based on a DNN is developed to retrieve the total SWH from the partially wave spectra observed by SWIM. The DNN model uses the parameters from both the SWIM spectra and the nearest nadir as the inputs, and the DNN is trained on the SWH from cross-matched altimeter observations. The DNN-based acquisition of the SWH is verified to achieve a high accuracy. A set of assimilation experiments are performed based on MFWAM and show promising results. Compared to the assimilation of SWIM nadir SWHs only, the addition of the newly obtained SWIM SWH notably enhances the positive impacts of assimilation, not only proving the effectiveness and accuracy of the DNN model but also demonstrating the unique potential of SWIM in wave assimilation.

3). The accuracy of a wave model can be improved by assimilating an adequate number of remotely sensed wave heights. The SWIM and Scatterometer (SCAT) instruments onboard CFOSAT provide simultaneous observations of waves and wide swath wind fields. Based on these synchronous observations, a method for retrieving the SWH over an extended swath is developed using the DNN approach. With the combination of observations from both SWIM and SCAT, the SWH estimates achieve significantly increased spatial coverage and promising accuracy. As evidenced by the assessments of assimilation experiments, the assimilation of this ‘wide swath SWH’ achieves an equivalent or better accuracy than the assimilation of the traditional nadir SWH alone and enhances the positive impact when assimilated with the nadir SWH.

Overall, the deep learning, which is based on artificial neural networks, has proved its efficiency and effectiveness in improving the European and Chinese wave remote sensing missions, and obtaining better assimilation effects in wave numerical model simulations.

We present an ocean visualization framework, which focuses on analyzing multidimensional and spatiotemporal ocean data. GPU-based visualization methods are explored to effectively visualize ocean data. An improved ray casting algorithm for heterogeneous multisection ocean volume data is presented. A two-layer spherical shell is taken as the ocean data proxy geometry, which enables. oceanographers to obtain a real geographic background based on global terrain. An efficient ray sampling technique including an adaptive sampling technique and a preintegrated transfer function is proposed to achieve high-effectiveness and high-efficiency rendering. Moreover, an interactive transfer function is also designed to analyze the 3D structure of ocean temperature and salinity anomaly phenomena. Based on the framework, an integrated visualization system called i4Ocean is created. The visualization of ocean temperature and salinity anomalies extracted interactively by the transfer function is demonstrated.

The Lagrangian eddies in the western Pacific Ocean are identified and analysed based on Maps of Sea Level Anomaly (MSLA) data from 1998 to 2018. By calculating the Lagrangian eddy advected by the AVISO velocity field, we analyse the variations in Lagrangian eddies and the average transport effects on different time scales. By introducing the Niño coefficient, the lag response of the Lagrangian eddy to El Niño is found. These data are helpful to further explore the role of mesoscale eddies in ocean energy transfer. Through normalized chlorophyll data, we observed chlorophyll aggregation and hole effects caused by Lagrangian eddies. These findings demonstrate the important role of Lagrangian eddies in material transport. The transportation volume of the Lagrangian eddy is calculated quantitatively, and several major transport routes have been identified, which helps us to more accurately and objectively estimate the transport capacity of Lagrangian eddies in the western Pacific Ocean.

The overall goal of this project is to provide an effective solution for large-scale dense Satellite Image Time Series analysis, being capable of automatic discovery of regularities, relationships, and dynamic evolution patterns that leads to comprehensive understanding of the underlying processes of specific scenes and targets. Young researchers, postgraduate and PhD students from China and Romania joined the research workplan targeting to access various optical and SAR data from Sentinel, ESA, ESA TMP and Chinese Earth observation data, benefiting from EO data complementarity.

In the frame of the first objective of the project this paper addresses the supervised learning techniques for object extraction and semantic classification of EO-SAR urban scenes. The evaluation and validation process considers one of the two envisaged uses cases: monitoring the urban evolution of Shanghai, China, in support of smart and sustainable urban information services.

The need to exploit spatial and temporal information content of EO data increases with a wide range of applications, including urban development. OpenSARUrban is Sentinel-1 dataset dedicated to the content- related interpretation of urban SAR scene, covering 21 major cities of China. This set includes patches of “Denselow”, “General Residential”, “High buildings” and “Single Building”, all composed of strong scattering points reflected from the building surface, that are hard to be classified even by trained experts. The majority of the methods addressing image classification focus on the algorithm design, neglecting the fact that, the dataset itself is an important factor affecting classification performance, particularly for SAR images.

Open Set Recognition (OSR) describes a scenario where new classes, unseen in training, appear in testing challenging the classifiers to not only accurately classify the know classes- labeled positive training samples, but also effectively deal with completely unknow classes.

The SAR Distinguishability Analysor (SAR-DA) we propose, evaluates the distinguishability of the OpenSARUrban dataset. By modeling the latent multivariate Gaussian distribution of each class, SAR-DA can not only classify the classes seen in the training phase, but also can recognize unknown sample if a test sample is out of any known distribution. Each class in OpenSARUrban is set unknown alternatively, then we apply the SAR-DA on the split dataset in OSR and supervised setting. The distinguishability can be reflected by the unknown classification precision. Most importantly, though the classes are semantically different from each other, some classes are similar and of low distinguishability. In addition, SAR Dataset-wise Separability Index (DSI) and SAR Class-wise Separability Index (CSI) are proposed to quantify the separability in open set condition from the dataset level and class level respectively. Extensive experiments have been performed and the results demonstrated that in open set condition, the data set level separability is nearly half of that in supervised setting, leading to more difficult classification than under supervised conditions. In class level, even though the SAR image classes are semantically different from each other, there exits more or less overlap between the latent distributions of supervised known classes and unknown class, classes with low CSI are harder to be recognized as unknown correctly when it is unknown.

This may be the first work that adopts the OSR method to evaluate to evaluate the distinguishability of SAR classification dataset.

The innovation of the research approach could be highlighted as follows:

(1) By modeling each known class as a multivariate Gaussian distribution, SAR Separability Analysor (SAR-SA) is proposed to for known class classification and unknown class recognition.

(2) Implementing the idea of class scatter matrix, Dataset-wise Separability Index (DSI) is defined to quantify the separability of a dataset from dataset level in open set setting.

(3) Combing precision and recall results, Class-wise Separability Index (CSI) is defined by using 𝐹2 score to quantify the separability of each class from class level in open set setting.

(4) Two SAR image datasets were prepared for relevant experiments. These sets also enabled the detailed analysis of results, highlighting the difficulty of classification tasks in open set condition.

The results mentioned above were accepted for dissemination at IGARSS 2021. A journal paper “Analyzing the Separability of SAR Classification Dataset in Open Set Condition” was submitted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Moreover, Ning Liao won the fourth place at “2020 Gaofen Challenge on Automated

High-Resolution Earth Observation Image Interpretation”.

This research work will continue to focus on the discovery of unknown classes in EO scenes. We are also preparing an abstract for ESA Phi-Week while UPB team focused on a dense satellite image time series preparation for the landcover monitoring of Danube Delta, a UNESCO protected site in Dobrogea- Romania.

This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3, Sentinel-6 and the Chinese HY-2 satellite altimeters using two permanent Cal/Val facilities: (1) the Permanent Facility for Altimetry Calibration established by ESA in Crete, Greece and (2) the National Altimetry Calibration Cooperation Plan of China. Other satellites, such as the Guanlan, CryoSat-2, CFOSAT, CRISTAL, etc., may also be supported by these Cal/Val infrastructures.

Satellites will be calibrated and monitored using uniform, standardized procedures and protocols while exploiting trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. At present, the PFAC, Greece implements the action plan established by ESA for Fiducial Reference Measurements for Altimetry and reports its Cal/Val results along with their FRM uncertainty.

Through the ESA Dragon-5 project, the FRM procedures, protocols and best practices, will be updated, upgraded and followed at both Cal/Val facilities in Europe and China. 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. The final uncertainty (FRM status) 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.

During this first year, the following tasks are been carried out:

• The European CRS1 Cal/Val site in the west coast of Crete, Greece and the Wanshan, Zhuhai, China Cal/Val facility have been employed for Sentinel-3A, Sentinel-3B and HY-2B satellite altimeters calibration and validation;
• The CRS1 Cal/Val site is equipped with two tide gauges (radar and pressure), one GNSS station and one weather stations. A second European Cal/Val site currently operating for Sentinel-3A and Sentinel-6MF mission will be also employed for the calibration of HY-2B/C satellite altimeters;
• The Wanshan Cal/Val facility comprises from four sites at Wailingding, Dangan, Zhiwan and Miaowan islands. These sites are equipped with four GNSS receivers, three acoustic tide gauges, one automatic weather station and one solar photometer. A moored meteorological buoy and a GNSS buoy will be installed in May 2021 25 km south of Zhiwan island;
• Determination of the FRM uncertainty in both (European and Chinese) Cal/Val sites;
• Relative calibration of the HY-2B altimeter with respect to Sentinel-3 via crossover analysis.
• Inter-comparison of the Cal/Val results obtained at the two Cal/Val facilities and investigation of any deviations.

The main findings of the joint work carried out by the European and Chinese teams so far are:

• Both European and Chinese Cal/Val sites are operational and implement independent calibration methodologies for European and Chinese satellite altimetry missions (i.e., Sentinel-3A/B, HY-2B/C, etc.);
• Standardization of operations and data processing followed by both Cal/Val infrastructures in Europe and China is a complex procedure as different instrumentation/models and processing algorithms/strategies are employed whereas deviations also arise due to the geographic location (i.e., bathymetry, tides, etc.) of each Cal/Val facility;
• Identification of several common procedures identified (i.e., tide gauge quality control, GNSS positioning, etc.) which pave the path towards the standardization of operations and data processing.
• Products of HY-2B satellite altimeter differ depending on the repository used. Quality control and standardization of these satellite measurements shall be secured prior to any Cal/Val activity;
• The performance of the HY-2B and HY-2C satellite altimeter is within the mission requirements.

Respect to the objectives and schedule of our project, the first-year report will include on-going activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. The presentation has three parts. Firstly, a short introduction will be given about Bufeng-1 twin satellites that carry the Chinese first generation spaceborne GNSS-R instruments started using reflected GNSS signals to perform earth observation. Secondly, by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements, the preliminary results show that BuFeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. In this presentation, the measurements of Bufeng-1 will be aligned with SFMR collected hurricanes, SMAP derived soil moisture, and DTU10 sea level models. Then, the validations of the accuracy and correlation coefficients will be analyzed to discuss the limitations and issues for the future research. For the last part, we will give the outlook about our future works of the objectives and the future plan of Bufeng missions.

This report provides an overview of the recent progress on the cross-calibration and validation of CSES/Swarm satellite magnetic field and plasma measurements. Preliminary validation of the high precision magnetometer (HPM) measurements from CSES has shown good agreement with magnetic field measurements from Swarm. The HPM Level-2 scientific dataset and data description document, including data format, naming convention, and quality flags, have been released and can now be used as a reference by interested users. This dataset has recently been used to derive the CSES global geomagnetic field model (CGGM), one of the candidate models for the most recent version of the International Geomagnetic Reference Field (IGRF-13). Regarding the electron density and temperature measured by the Langmuir probe onboard CSES, comprehensive comparisons have been performed against measurements from both Swarm and the incoherent scatter radar at Millstone Hill, and predictions from the International Reference Ionosphere (IRI) model. The results showed that the CSES electron density measurements generally agree with the other measurements, though they present a relatively lower absolute value. Two kinds of platform interference on the CSES Langmuir probe have been identified: a sudden drop and another sudden rise of floating potential on the nightside and dayside, respectively, both linked to the adjustment of the current system equilibrium of the CSES platform (i.e., when the satellite flies into/out of the sunlight region).

It is very important to establish a longstanding ground-based FTIR and MAXDOAS measurement dataset at Xianghe that can be applied to evaluate the S5P and FY-3H/GAS and TanSat satellite measurements in northern China region. It is still of few ground measurements of FTIR and MAXDOAS measurement data that can be used as the reference data for validation of satellite observations in China. For the common products (NO₂, O₃ and HCHO) , the simultaneous FTIR and MAXDOAS measurements at Xianghe site allow us to understand their differences before combining them together for satellite validation. For other products (CO, CH₄ and CO₂), more focus will be taken on the ground-based FTIR retrievals. The column-averaged dry-air mole fractions of CO₂ (XCO_­2), CH₄ (XCH₄) and CO (XCO) have been measured with a Bruker IFS 125HR Fourier-transform infrared (FTIR) spectrometer at Xianghe since June 2018. The HCHO data derived from the MAX-DOAS spectrometer and the FTIR instrument operating in parallel at Xianghe station (39.75° N, 116.96° E; ~55 km southeast of Beijing) were used to validate TROPOMI HCHO data products. The comparison results appear consistent with validation results obtained at TCCON sites for XCO₂ and XCH₄.

Sea ice is a highly sensitive indicator of past and present climate change. The demand for getting comprehensive, continuous, and reliable sea ice information from multi-source satellite data is growing as a result of climate change and its impact on environment, regional weather conditions, and on human activities such as operations in ice-covered ocean regions. The major objectives of the project are to upgrade and develop methodologies to retrieve quantitative sea ice and iceberg information using multiple satellite data provided by the EC Copernicus Earth Observation Program, ESA TPM, and Chinese satellites. During the last year, the Sino-European group has been focusing on the application of microwave sensor for detection sea ice thickness, ice concentration, and iceberg detection.

The partners from NSOAS and FMI developed sea ice concentration (SIC) estimation and SIC noise reduction algorithms with the Chinese microwave radiometers e.g. HY-2 Microwave Radiometer and FY-3 Microwave Radiation Imager. We investigated the brightness temperature signatures of different surface types in various sea ice and weather conditions. The uncertainty and error statistics of the retrieved SIC are determined using validation data from in-situ measurements and high-resolution SAR satellite data.

The work of sea ice thickness estimation by altimeter was carried out at FIO and NSOAS. We developed a method for calculating sea ice freeboard from HY-2B data. Two echo waveform retracking techniques were assessed for their performances in measuring lead and sea ice elevations from HY-2B data. These derived HY-2B sea ice freeboard and thickness data were compared with those from CryoSat-2 acquisitions. Recently the effect of uncertainties in the input parameters and related sensitivities of the algorithms when retrieving ice-thickness from HY-2 is being analyzed.

Another joint effort by AWI/UiT, FIO, FMI, and SCMU is in preparation, namely dealing with the detection of icebergs in sea ice and on the open ocean. The plan is to develop robust and automated methods for iceberg detection. The research will analyze and evaluate the capability of the proposed methods using different radar frequencies, and in dependence of spatial resolution, incident angle, and the surface conditions around the icebergs. This study will be presented in more detail at the Symposium.

High Mountain Asia cryosphere and water resources remain still largely unconstrained, despite very substantial advances in modelling and remote sensing. In this project, we leverage the opportunity offered d by ESA and NRSCC to exploit new remotely sensed datasets to advance our understanding of blue and green water interactions in high elevation catchments in the first truly inter-comparative study across HMA. I will provide an update of the main project objectives, methods and modelling approaches, and present preliminary results in both modelling and remote sensing.

Our main overall aim is to understand how green water processes affect the availability of blue water from glaciers, snow and precipitation across High Mountain Asia. To achieve this goal, we are working with our Chinese partners in a very close collaboration and have designed three main objectives. The first objective is to advance our understanding of cryospheric, vegetation and land surface changes from remote sensing observations at benchmark sites; the second objective is to generate glacier-specific altitudinal surface mass balance profiles to investigate patterns of changes and validate the glacier component of the land surface model; and the third one ist o quantify the water cycle of select HMA water towers using a novel hyper-resolution land surface model to examine variability, seasonality and long term changes, with emphasis on the feedback between blue and green water.

Use of a hyper-resolution ecohydrological model, fed by Earth System Observations, is very novel. 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. We will 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. The new model will be applied to 10 benchmark catchments representative of the climatic differences of HMA. Its application and validation will be based on remote sensing data: high-resolution satellite data of land-cover, surface albedo, snow, 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. 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 also available and will complement the remote sensing data.

To present, we have advanced on all three main objectives. We have developed a method to derive snow cover and snow line elevation from satellite images and applied to the entire HMA, providing both a novel understanding of snow cover patterns across this very broad scale and validation data for the modelling at the 10 catchments. We have devised a new method to retrieve glacier albedo from remote sensing that has been applied also to the entire HMA region, and is being refined now to understand catchment-scale patterns and variability. For the second objective, we have developed a new method to retrieve altitudinally resolved surface mass balance from satellite derived elevation differences, have applied it to all glaciers in HMA and are now refining it to provide the validation datasets for the 10 catchments. I will present results from its application to the Langtang benchmark catchment in Nepal. Towards objective 3, we have setup the new land surface model in the same Langtang catchment, for the two data-rich years of 2017-2019, and are analysing the first simulations now. The model setup entailed a number of major challenges, from the spatial redistribution of the meteorological forcing from station data to the characterisation of the parameters controlling the vegetation response. It is the first time that a physically-based model that calculates all energy and mass fluxes in a distributed manner is applied to a HMA catchment: the energy balance calculations require knowledge of wind, radiation fluxes, relative humidity fields in addition to the temperature and precipitation forcing commonly used in more empirical glacio-hydrological models, and parameters need to be defined in space and time. I’ll present the application of the model to the Langtang catchment, its validation with remote sensing data and highlight the advantages that this new modelling perspective offers over more traditional modelling results.

Finally, I’ll present the project next steps and the planning towards the achievement of our goals. At the end of the project, our multidisciplinary team of European and Chinese scientists will: 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 and determine patterns and drivers of surface mass balance; iii) use a novel hyper-resolution earth-surface model to simulate the complexity of the high mountain water budget, understand blue-green water fluxes and quantify changes in past and future streamflow.

Backscatter coefficient (σ₀) of SAR is a key feature to map the freezing and thawing status of Greenland Ice Sheet. However, backscatter coefficient not only depends on the status of the observed object but also on the incidence angle of the electromagnetic wave that illuminate the object. For SAR image with a narrow swath width, incidence angle difference near range and far range does not affect the backscatter coefficient much for ice sheet observation, however Sentinel-1 employs IW (Interferometric Wide) and EW (Extra Wide) mode to observe the Greenland Ice Sheet, which means incidence angle difference roughly at 17° and 27°。The traditional method of normalizing backscatter coefficient to a reference angle is by presuming that the observed object behave like a Lamber-tian surface reflection model, which means uniform scattering in the hemisphere, that is Backscatter coefficient (σ₀) is proportional to the square of cosine value of incidence angle. However, snow and ice do not exactly follow the Lamber-tian surface reflection model, as front scattering dominate dry snow zone when surface scattering dominate the bare ice zone on the ice sheet.

Here we present a method by using linear regression to backscatter coefficient (in dB) difference and incidence angle to normalize backscatter coefficient to a reference incidence angle. We employ the Sentinel-1 images that observed the same area within 24 hours, and presume that the scattering feature does not change during such short period. We take the overlap area of ascending and descending area. Considering that the surface features of Greenland Ice Sheet vary when snow metamorphose to ice, which is highly correlated to season and altitude. Therefore, we calculate the regression coefficient for every season (MAM, JJA, SON, DJF) and every 200 meters. We test both Sentinel-1 IW and EW data and find that our method present a lower RMSE for HH images and similar RMSE for HV images by comparing to the traditional Lamber-tian method. Winter images only show RMSE at 0.7dB indicate that our proposed method is potential in normalize backscatter coefficient of Sentinel-1 IW and EW images to derive the freezing and thawing status of the Greenland Ice Sheet.

By employing our proposed method to Sentinel-1 image that observed Greenland Ice Sheet in ascending track and EW mode, we generate the Sentinel-1 mosaic for Greenland Ice Sheet during 2016 to 2020 at every 12 (without S1B) or 6 (with S1A) days.

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. In this project, we are monitoring contemporary glacier changes in HMA using recently available satellite data with the focus on Sentinel-1 and 2, CryoSat-2 and ICESat-2 data but also high-resolution stereo data. We plan to develop new methods to monitor glaciers focusing on changes in area, thickness, velocity and accumulation area ratio (AAR), and reveal the most recent trends in HMA. The methods will be developed, validated and calibrated by multi-temporal very high-resolution stereo satellite data such as TerraSAR-X, Pleiades, ZY3, GF7 and glaciological field measurements. We focus first on selected benchmark sites located in different climatic regions. In the next step it will then be tested with which accuracy the methods can be applied to whole HMA. The benchmark sites include Ile Alatau (northern Tien Shan), Muztag Ata (eastern Pamir), Poiqu and Langtang basin (central Himalaya), Western Nyaiqentanglha (south-central Tibetan Plateau) and the Bomi region (south-eastern Tibetan Mountains). Preliminary results show that highest mass loss is found in the Tien Shan, central and south-east Himalaya in eastern Pamir. Even in regions where glaciers have been previously in balance with climate mass loss now prevails. Overall, this project will provide comprehensive information about heterogenous glacier characteristics and changes which will be of high value calibrate and validate the glacier component of glacio-hydrological models.

Feeding a growing global population while minimizing the subsequent environment impact are twin challenges faced by the international communities on food production and security. While agriculture is the largest fresh water consuming sector on the globe, climate change has created further uncertainties in water availability by changing climate patterns or reducing glacier size, putting to a greater extent, food security at stake. Although effort for timely monitoring food production has been made by international communities active on food security, for example using earth observation technologies (https://www.earthobservations.org/area.php?a=fssa), the environmental impact of water use needs to be further addressed. There are tremendous differences in the quantum of water used to produce a unit of grains, also called water productivity (WP), between farm fields in various part of the world, because of various cropping conditions and different water and farming management practices (1). It is therefore very opportune and important not only to measure this indicator, but also to dissect potential drivers of this parameter in context of food security, by identifying areas where the variability occurs, and to propose subsequently the strategies of improvement.

Agricultural water productivity (WP) is a measure of water use efficiency expressed as a ratio of crop production or crop yield to the water consumption for this production. The objective of the proposed project is to assess both the agricultural output (enumerator) and the water consumption for crop growth (denominator) using satellite information and compute subsequently the water productivity, this on two study areas in Europe and China. The outcome of the research could be used as a scientific evidence for water use policy making by considering the environmental impact while meeting food security imperatives.

Retrieving the crop growth information from multiple source satellite data in support of the agricultural management

Abstract: 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 China and the rest of 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. Therefore, the compromise of application has to be made between the optimized spatial resolution of satellite data and the field size of the study area. 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. In this study, 8 study sites are selected representing the major cropping systems, including winter wheat, maize, rice, sugarcane and vegetables. These sites also are representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the North and the South. The Sentinal1/2 and GF1/3/5/6, CBERS data are to be mainly data sources to support this study. The remote sensing parameters, like LAI/FPAR/FCOVER/NDVI are being retrieved with the adapted algorithm. The crop classification algorithm is applied to make crop type maps. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm is being exchanged and it is expected to bring a step forwards to support agricultural monitoring at fine scale.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global fresh water withdrawals. Irrigation efficiency and crop water use efficiency are key concerns for agricultural water management. The objective of the project is to assess irrigation water needs and crop water productivity based on the integrated use of satellite data with high resolution, ground hydro-meteorological data and numerical modelling, which is particularly significant for large un gauged agricultural areas. In such studies, satellite observation-based products or information with high accuracy and continuously spatial and temporal coverage are essential to support monitoring and modelling of agricultural water use and efficiency at farm and basin scales. The following progresses have been made in the first year of project implementation:

1) Soil moisture retrieval from SMOS data by a new multi-temporal and multi-angular approach. Improvement of SMOS (Soil Moisture and Ocean Salinity) soil moisture retrieval accuracy was made by a proposed multi-temporal and multi-angular approach. This approach can simultaneously retrieve soil moisture, vegetation optical depth and two soil parameters. Compared with the ground measurements, the results from this new approach in most sites showed advanced accuracy against the existing SMOS soil moisture products from SMOS.

2) Crop mapping from Sentinel-2 MSI data by machine learning method. Timely and accurate crop classification is crucial information for agriculture management. However, such information is often not available during the agricultural practice. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. We have developed a crop mapping algorithm by applying multi-temporal Sentinel-2 data acquired in the growing season to a machine learning algorithm, i.e., the Random Forest algorithm, to generate the crop classification map at 10 m spatial resolution. This algorithm was applied to the Shiyang River Basin, in the northwest of China with arid/semi-arid climate and scarce water resource, proper agricultural planting structure is of importance for efficient use of limited water resource. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for crop classification with higher accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July.

3) Estimate of gross/net crop water requirements, actual crop water use and irrigation efficiency by high resolution satellite Sentinel-2 and ETMonitor model of center pivot irrigation system at farm scale. A case study was conducted for two major crops, i.e. wheat and potato, in Inner Mongolia autonomous region of China, where modern equipment and adequate irrigation methods are deployed for efficient use of water resource. The method estimated and mapped explicitly the net crop water requirements, the water losses (water droplet evaporation directly to the air during irrigation application before droplets fall on the canopy) and canopy interception loss, and the gross irrigation water requirements were mapped finally. Daily estimates of crop water requirement and actual water use were generated using data from Multi Spectral Instrument (MSI) of Sentinel-2 with fine resolution combined with meteorological forcing data and soil moisture retrievals. A good agreement between the estimated values and ground observations for crop actual water use and for water losses were obtained. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient in fulfilling the water requirement in this irrigated area. This suggested that the amount of gross irrigation water was inadequate to meet the crop water requirement and the inherent water losses occurred during water application by center pivot irrigation systems.

4) FEST-EWB energy-water balance model is coupled with derived vegetation and land surface temperature (LST) data over two of the project case studies in Italy: the Chiese and Capitanata irrigation consortia.

Remotely- sensed data at different temporal and spatial resolution of vegetation parameters (leaf area index (LAI), fractional coverage of vegetation, albedo) which are used as inputs to hydrological model are obtained at high spatial and temporal resolution merging Sentinel 2 data with Landsat 7 and 8 for the Capitanata area and also with MODIS data for the Chiese basin.

Satellite LST is further retrieved from Landsat 7 and 8 at 30 m spatial resolution as well as from MODIS and Sentinel 3 data at 1km resolution, to be used for the hydrological model calibration.

Indeed, the energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parameters with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration.

The calibrated and validated hydrological models coupled with satellite data will provide consistent outputs of the different hydrological processes overcoming the limitation of remote sensing data caused by cloud cover, retrieval algorithms, temporal and spatial resolutions, etc.

Preliminary results of the amount of precision irrigation water supply and the Evapotranspiration deficit at pixel scale will also be shown.

The overall objective of project 59197 is to carry out agro-ecosystem health diagnosis and to investigate agricultural processes based on various in situ and EO data, allowing to conserve, protect and improve the efficiency in the use of natural resources to facilitate sustainable agriculture development. Two study areas are identified: the Rur basin observatory in Germany and the Huaihai Economic Zone in China. This selection enables us to investigate the transferability of results between European and Asian agricultural systems and to ensure global applicability.

Five work packages (WP) are proposed to fulfill the project’s research goal, including: 1) Crop classification based on multi-source remote sensing data; 2) Retrieval of soil parameters and plant growth stress factors; 3) Monitoring of crop biophysical variables; 4) EO-based evaluation of cropland carbon budgets; 5) Data assimilation of remote sensing products for synoptic systems analysis.

This paper presents the progress of our research activities since the kick-off of the Dragon 5 programme.

For crop classification, to take full advantage of high spatial resolution of panchromatic images and polarimetric synthetic aperture radar (PolSAR) data with rich scattering information, a novel dual-domain data fusion method is explored by combining spherically invariant random vector (SIRV) model with a novel generalized adaptive linear combination approximation (GALCA) technology. Gaofen (GF)-2, 3 and Radarsat-2 data are used. Experimental results show that this method is able to significantly improve the spatial resolution of PolSAR data without degrading polarimetric information.

Studies in agricultural hydrology are highly stressed, including the estimation of soil moisture, evapotranspiration, and groundwater table depths. Surface soil moisture is estimated by a Copernicus Sentinel-1 time series approach at a sub-field scale. The C-band SAR data is processed and analyzed on the cloud computing platform Google Earth Engine. This allows for high-performance investigations at larger scales and high resolution as well as straightforward transfer to alternative regions. The results are validated against in situ soil moisture networks consisting of state-of-the-art time domain reflectometry and innovative cosmic-ray neutron sensors. Besides, we propose an optimal estimation approach combined using SAR and optical remote sensing imagery, in order to retrieve vegetation water content, roughness and soil moisture simultaneously. Three optical remote sensing indices are investigated. The proposed method is performed by using Sentinel-1and Landsat 8 data. Retrieved results are validated against ground measurements and show a good agreement between remote sensing estimates and ground measurements. Additionally, it is found that the result of estimated vegetation water content and the parameterization scheme of vegetation parameters have pronounced influence on the accuracy of soil moisture estimates. Evapotranspiration is estimated by Spinning Enhanced Visible and Infrared Imager (SEVIRI) as well as Landsat observations, the implementation of Sentinel-2 and Sentinel-3 is foreseen. The Evaporative Drought Index as a relationship between actual and potential evapotranspiration provides levels of water stress and informs about the irrigation demand in agriculture. Extensive validation is performed against in situ data of the Integrated Carbon Observation System (ICOS). To investigate the small-scale heterogeneity of evapotranspiration for the area of Eddy Covariance footprints, unmanned aerial vehicles with multispectral and thermal sensors are employed. Here we discuss the impact of soil texture on plant growth and evapotranspiration. To ensure sustainable groundwater abstraction for irrigation, we predict groundwater table depth anomalies by machine learning approaches. Long-Short-Term Memory networks are trained on integrated hydrologic simulations from groundwater to the upper atmosphere. This enables the utilization of precipitation and soil moisture information to predict groundwater table depth anomalies with high agreement to reference wells. Besides further vegetation and weather indicators, the hydrological conditions are also drivers for fire risks.

We propose a new method of surface soil salinity estimation in coastal areas based on ground-based digital photographs to obtain soil salinity information quickly and conveniently under complicated weather conditions. Color parameters obtained from digital images provides a new approach for soil salinity estimation effectively.

Crop parameters in farmland areas of the Huaihai economic region, such as leaf area index (LAI) and canopy chlorophyll content (CCC) are accurately retrieved by new spectral indices such as OSAVI_{[864, 866]} and SR_{[790, 631]} and a hybrid inversion model, which provides data support for crop growth monitoring and yield estimation. Then, the corresponding net ecosystem productivity (NEP) is estimated based on the improved Carnegie Ames Stanford approach (CASA) model and geostatistical model, which lay a foundation for the assessment of farmland ecosystem carbon budget in this region.

To study the method of cropland carbon budgets evaluation, a projection-based model driven by satellite remote sensing data (GIMMS NDVI) that represent temporal dynamics of climate, vegetation, and land cover is developed to investigate the spatiotemporal changes of soil organic carbon (SOC) during two time periods: 1980s and 2010s. We find that the spatiotemporal patterns in SOC along the gradients of temporal covariates are similar across space and time. Model projections with temporal covariates result in more accurate estimates.

The project #57475 deals with the set up and testing of pre-operative algorithms and processing chain convolving ESA/TPM EO data and including the exploitation of the hyperspectral images provided by the ASI PRISMA mission (that can be considered as the European precursor of the Copernicus candidate CHIME) and Chinese multi-bands EO data. The project aims at developing operational thematic products tuned to match the farmer’s requirements. User requirements that, for EU, regard the EC policies related to “Agriculture & Food Security” application domain. The project intends to identify specific “use case demonstration” related to:

i) the agriculture and topsoil monitoring;

ii) forecast yields, also in terms of proteins content and crop pest and disease.

iii) identification of sustainable agricultural practices;

The project cross cutting methodological approach foresees the exploitation of the DIAS systems (e.g. ONDA) to support a multi-sensor/spatial/temporal approach for the “use case demonstration”.

As regards the agricultural crop and topsoil monitoring the project, within this first year of activity, has started a comparison of the different retrieval algorithms for both domains: vegetation and topsoil. For the crop-vegetation domain the different approaches for the improvement of the accuracy in the estimation of crop biophysical variables such as pigments (including carotenoids and anthocyanins) and variables related to nitrogen and water stress have been evaluated. To this aim, parametric methods like (vegetation indexes) and non-parametric both linear and non-linear regression methods (e,g. Linear Regression (LR), Partial Least Square Regression (PLSR), Random Forest regression (RFR)) and PROSAIL RTM based approaches as hybrid methods will be compared to evaluate their performance in estimating the crop biophysical variables of interest. Optimization of the retrieval process will be tested with a synergistic use of both S2, GF6 data set and PRISMA hyperspectral data when applied to local scale retrieval applications. As regards topsoil properties (i.e. texture and SOC) retrieval algorithms such as chemometrics techniques and multivariate calibrations, like multiple linear regression (MLR), principal components regression (PCR), partial least-squares regression (PLSR) neural networks (ANN), including support vector machines (SVM) have been explored. Moreover, in this first year of activity intensive, according to the limitation due to theCOVID-2019 pandemic, field campaigns in different sites on cultivar and on different bare soil fields have been conduction in order to define a cal/val data set to validate the retrieval algorithms performances and the products accuracies also considering errors and uncertainties in the remote sensing observations. Whenever possible campaigns have been performed contemporary to EO data acquisitions. First year results for vegetation and top soil domains, regard the comparison of the different retrieval procedures and the start of collection of a cav/val data set to be applied in the following years of activity

As for the retrieval for agronomical variables of interest like yields, grain quality and pest and disease the focus is on the development of data assimilation algorithms that specifically address issues of the multi-scale and multivariate nature of multitemporal optical (S-2, S-5 and GF-6) and eventual SAR datasets. At present we are evaluating two variables for the assimilation i.e. LAI and soil moisture. Within this year of activity, the two different assimilation algorithms (deterministic and stochastic) based on the Ensemble Kalman Filter (EnKF) and Particle Swarm Optimization (PSO) methods have been evaluated. These methods will update the state variables and/or parameters of the Aquacrop model, allowing to estimate variables of agronomic interests, such as crop yield and grain protein quality. First year results for the retrieval of agronomical variables of interest (e.g. yield and grain quality), regard the consolidation of the different assimilation procedures, while the collection of the cav/val data set is actually ongoing on the different test cases in Italy and China.

The presentation will provide an overview of all the ongoing activities for project ID#57475 in terms of: EO data collection; data processing: cal/val acquisition strategies’ set up for the different sites.