Conference Agenda

Abstract: The Northern China was hit by a severe dust storm on March 15, 2021, involving a large range and affecting a deep degree, which was unprecedented in more than a decade. In the study, we carried out a day and night continuous monitoring of the dust storm path, using multi spectral data from the Chinese FY-4A satellite combined with the Japanese Himawary-8 from the visible to near-infrared, mid-infrared and far-infrared band. We monitored .the whole process of the dust storm from the occurrence, developed, transportation and extinction. The HYSPLIT backward tracking results show two main dust sources: one is the west of Mongolian Republic, and the other is western Inner Mongolia Gobi and Ordos and other areas. Analysis shows that the preconditioning and favourable weather conditions were the main reason for the severe dust storm. Continuous high temperature without precipitation in early period led to abundant sources of sand and dust over the west of Mongolia and Inner Mongolia. On March 14-15, strong winds behind the Mongolian cyclone blew a great deal of dust into the air and they were transported over a long distance by the cyclone, resulting in the strong dust storm, which severely affected the vast area of northern China. The strong dust storm occurred under the background of significantly increase frequency and intensity of extreme weather over northern China in recent years, which may be related to the development of global climate change.

Figure 1 shows the process of dust blowing into the air, transportation and developed into dust storm in Mongolia on March 14, 2021.

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Fig1. Monitoring of the process of sand blowing into the air, transportation and developed into dust storm in Mongolia on March 14, 2021.

Figure 2 show the process of dust storm moved towards China and arrived in Beijing at 21:00 March 14, 2021.

Fig2. Monitoring of the process of dust storm moved towards China and arrived in Beijing at 21:00 March 14, 2021.

At the same time of about 22:00 March 14,2021, events of sand and storm blowing into the air occurred from the Hexi corridor, to the western inner Mongolia Gobi and Ordos, and the great deal of dust storm was transported east ward by strong wind behind the Mongolian cyclone, arrived in Beijing at 2:00 March 15 and affected the atmosphere combined with these from Mongolia.

Fig 3. Monitoring of dust storm from the Hexi corridor and western inner Mongolia Gobi and Ordos arrived in Beijing, and affect the atmosphere combined with these from Mongolia.

From 4:00 to 6:00 on March 15, 2021, dust from Mongolia and west China jointly formed the strong dust storm over northern China.

Fig 4. Monitoring the forming of strong dust storm over northern China during 4:00 to 6:00 March 15, 2021.

Figure 5 show the whole process and path Mongolia and combined with the other source of west China, and formed the severe dust storm over northern China and finally disappeared over the Korean Peninsula and Japanese Sea. The whole process lasted more than 40 hours, and transported a distance of about 3900km with a mean speed of 95km per hour.

Fig 5. The process and path of the severe dust storm over northern China

Keywords: the FY-4A Satellite, the Himawary-8 satellite, dust storm, 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 from one region to another needs to be well-understood. Natural climate variability in the Pacific Ocean can, for instance, impact the Arctic Amplification and thus the sea ice conditions (Li et al., 2015; Svendsen et al., 2018; Yang et al., 2020). The way in which this translates into sea level change, on the other hand, 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.

We highlight results of three studies: The first study investigates the benefits of the reprocessed altimetry dataset at 5 Hz with augmented signal resolution to study the sea level variability of the Arctic and Nordic Seas (Bonaduce et al., 2021, in prep). In particular, we compare the ability of this dataset to improve the mesoscale details in the forecasts in comparison to the conventional altimetry sampling (1 Hz) dataset and to the altimetry-blind experiments (e.g. without assimilation of altimeter data), in order to assess the added value of the enhanced altimetry reprocessing as well as the assimilation of the high-resolution altimetry data in ocean re-analysis for the Arctic. The second study highlights the non-stationary nature of the Barents Sea SST response to ENSO (Chatterjee et al., 2021, to be submitted). The causal link is established via the ENSO-related changes in north Atlantic atmospheric circulation which modulate the East Atlantic Pattern (EAP) and thus the Atlantic Water intrusion and SST in the Barents Sea. The third study investigates the impact of Pacific Decadal Oscillation-like (hereafter PDO-like) SST anomaly on surface air temperature over the mid-to-high latitude Northern Hemisphere. After removing the ENSO’s signal, the PDO-like SST anomaly is related to the surface air temperature over northern Europe and the Chukchi Peninsula and Kamchatka Peninsula through changing the atmospheric circulations.

The Third Pole Environment centred on the Tibetan plateau and the Himalayas feeds Asia’s 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.

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.

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 project “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME) addresses research and development activities that 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 is organized into 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).

Intertidal regions are particularly sensitive to natural and anthropogenic hazards. Hence, RP ‘Intertidal regions’ focuses 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 actual and future developments of offshore wind energy. As such, 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’ addresses 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’ addresses both aspects and will help in both optimizing the detection and quantification of marine litter, and understanding the dynamics of green tides.

Finally, 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.

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 were 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 are being 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.

This study provides the first-year progress advancements achieved within the framework of the ESA-MOST Dragon-5 project ID 57979. The latter aims at demonstrating the benefits of radar products for coastal area monitoring. Within this context, improving the understanding of the marine environment, advancing the analysis of sea surface properties and boosting the exploitation of EO satellites for the management of marine disasters is addressed. In detail, the study provides innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions, by means of multi-polarization and multi-frequency Synthetic Aperture Radar (SAR) satellite imagery together with complementary microwave satellite instruments as scatterometers and radiometers on-board of operational and planned missions operated by ESA, ESA TPM and Chinese EO.

The main phenomena to be investigated include coastal water pollution, coastal erosion, in-land water body observation, metallic target detection and typhoon/cyclone monitoring. The proposed piece of research is focused on the development of tailored models combined with AI methodologies that allow the interpretation and the processing of polarimetric SAR measurements,collected under different imaging modes. As a result, user-friendly outputs are generated that include detection maps of metallic targets as aquacultures, ships and wind farms, wetland coastal erosion/accretion trends due to both anthropogenic and natural phenomena, mapping marine pollutants, modelling, tracking and forecasting extreme weather events as cyclones/typhoons.

As a first-year progress, the following articles were published on peer-reviewed international journals under the framework of the ESA-MOST Dragon-5 project ID 57979:

• T. Meng, X. Yang, K.-S. Chen, F. Nunziata, D. Xie and A. Buono, “Radar Backscattering Over Sea Surface Oil Emulsions: Simulation and Observation," IEEE Transactions on Geoscience and Remote Sensing, in print., 2021.
• V. Corcione, A. Buono, F. Nunziata and M. Migliaccio, “A Sensitivity Analysis on the Spectral Signatures of Low-backscattering Sea Areas in Sentinel-1 SAR Images," MDPI Remote Sensing, no. 13, pp. 1183, 2021.
• E. Ferrentino, A. Buono, F. Nunziata, A. Marino and M. Migliaccio, “On the use of multi-polarization satellite SAR data for coastline extraction in harsh coastal environments: the case of Solway Firth," IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 14, pp. 249-257, 2021.

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Our aquatic environments are critical for our wellbeing as well as providing vital resources for established maritime industries (fisheries, coastal tourism, energy and mineral production, boat building, shipping and ports activity) and emerging new industries of the Blue Economy (aquaculture, marine renewable energy, bio-products (pharmaceutical and agrichemicals), sea-bed mineral exploration and extraction, Blue Carbon (carbon storage in mangroves, seagrass and saltmarsh) and desalination. The mounting and conflicting pressures from the number of users and uses, coupled with population growth, industrialisation, 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 Copernicus Sentinels (Sentinel-3) and Chinese Earth observation missions (e.g. from HY-1) has radically transformed the approaches to monitor and sustainably manage coastal, inland and transitional systems and has stimulated the development of innovative products. This project aims to develop and validate innovative products for transitional and coastal waters to support and improve the water ecosystem services, sustainable management and security.

The optical diversity that characterise many of these aquatic systems often challenges the applicability of EO approaches. As a first step, we investigated the optical diversity in coastal and transitional systems. We used in-situ bio-optical data collected between 2015 and 2019 in coastal waters around Europe. We compared the hyperspectral data with optical water types previously suggested for inland and coastal waters and proposed new frameworks for applications in coastal waters. This information is important for the applicability of retrieval algorithms in coastal and transitional waters. We also investigated the performance of different atmospheric correction models (e.g. Polymer, C2RCC, iCOR for Sentinel-3 OLCI and Acolite, C2RCC, Polymer, Sen2Cor) over coastal waters.

During the reporting period we worked on the development of Harmful Algal Blooms detection algorithms from Sentinel-3 OLCI data. This included a preliminary study of the detection of Pseudo-nitzschia blooms in Galician coast (NW Spain). We employed support vector machines (SVM) and in-situ data of Pseudo-nitzschia abundances to develop a set of novel algorithms for the detection of this diatom in the area. The models showed a robust performance in the independent test data set.

Ongoing work is focused on the testing of algorithms for the retrieval of phytoplankton size classes (PSC) in coastal and transitional systems. A dataset of in-situ fractionated chlorophyll-a data from water bodies around Europe was put together to test algorithms based on pigment analysis, chlorophyll a concentrations and phytoplankton absorption. We will present some initial findings from this benchmarking and will show ways of calibrating existing PSC global algorithms for operational use in local circumstances for Sentinel-3 data. Future work will include testing the detection capabilities of the PSC product using coincident datasets of in-situ Apparent optical properties (AOPs), Inherent Optical Properties (IOPs) and phytoplankton characteristics.

Water in oil (WO) and oil in water (OW) emulsions from marine oil spills have different physical properties, volume concentrations, and spectral characteristics. Identification and quantification of these different types of oil emulsions are important for oil spill response and post-spill assessment. Based on image statistics, we proposed here a decision tree method to classify oil type, and oil quantification is further attempted, with results partially validated through spectral analysis and spatial coherence test. The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse-resolution oiled pixels are first classified to WO and OW types, and 1295 nm is the optimal wavelength to perform spectral unmixing of mixed coarse-resolution pixels.

We applied MODIS surface reflectance data to analyse the temporal and spatial distribution characteristics of water clarity (Zsd) in the Jiaozhou Bay, Yellow Sea from 2000 to 2018. Zsd retrieval models were regionally optimized using in-situ data with coincident MODIS images, and then were used to retrieve the Zsd products in Jiaozhou Bay from 2000–2018. The analysis of the Zsd results suggests that the spatial distribution of relative Zsd spatial characteristics in Jiaozhou Bay was stable, being higher Zsd in the southeast and a lower Zsd in the northwest. The annual mean Zsd in Jiaozhou Bay showed a significant upward trend, with an annual increase of approximately 0.02 m. Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Zsd in Jiaozhou Bay, respectively.

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 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 world’s 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. Some selected case-study areas have been identified, including 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 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 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.

Use of Earth Observation data represents the key asset of the project and expected results will contribute to further developments and analyses in the years to come, from both the theoretical and experiemtal point of view.

ID.59257: Mapping forest parameters and forest damage for sustainable forest management from data fusion of satellite data

PI Europe: Dr. Johan Fransson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SWEDEN

PI China: Prof. Xiaoli Zhang, Beijing Forestry University, CHINA

Forests play a critical role in the Earth’s ecosystem and have a strong impact on the environment. Under the threat of global climate change, remote sensing techniques provide information for a better understanding of the forest ecosystems, early detection of forest diseases, and both rapid and continuous monitoring of forest disasters. This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring. The aim is to study and explore remote sensing techniques in forest applications, especially on data fusion of satellite images, laser scanning, and hyperspectral drone images. The research contents are mainly tree species classification, forest parameter estimation and forest insect damage detection.

1. Work carried out during the current year:

(1) Satellite images:

We applied for and acquired ESA, Copernicus Sentinel and Chinese EO data, such as RADARSAT-2, SPOT, WorldView, Sentinel and Gaofen series, combined with hyperspectral data of UAV and LiDAR data to carry out relevant research. The satellite images acquired for the study area in China are:

RADARSAT-2, two scenes altogether. One scene data covers Fushun County, one scene covers Qingyuan County.

• SPOT, two images altogether. One scene data covers Wangyedian, one scene covers Gaofeng.
• Sentinel-1, time-series images from 2019 to 2020, covering Wangyedian.
• Sentinel-2, time-series cloud-free images from 2019 to 2020, covering Wangyedian.
• Gaofen-2, two images acquired 2020, that cover Gaofeng.

The satellite images acquired for the study area in Sweden are:

• Sentinel-2, time-series cloud-free images from 2018 to 2021, covering Remningstorp in Sweden.
• RADARSAT-2, one image acquired 2020 and a second to be acquired during the summer 2021, that cover Remningstorp in Sweden.
• Pleiades, one image (per 29 Apr 2021), of Remningstorp, Sweden.

(2) Survey data:

• One field investigation of forest parameters was conducted in Nanning, Guangxi, China, and the parameters including diameter at breast height (>5cm), tree height, under branch height, environmental parameters and the coordinates of four corners were obtained.
• For forest pests and diseases, a field survey of forests damaged by pine wood nematodes was carried out in Fushun, Liaoning, China. The spectral information of the damaged trees and healthy trees in different susceptible stages were collected.
• The forest information of the field sample plots in the study area Remningstorp were updated and stand-level information about forest managements were noticed. We have previously used the same field sample plots for analysis of tree species from Sentinel-1 and Sentinel-2 data.
• A controlled experiment is conducting at Remningstorp in 2021. Pheromone dispensers was set in 24 plots, expecting the bark beetles attacking around 180 trees. The process of infestations will are recorded during April 2021 to June 2021.

(3) Technical progress:

• In the section on tree species classification, we proposed two deep learning models, an improved prototypical networks (IPrNET) and a new prototypical networks combined with attention mechanism CPAM-P-NET model, using UAVs hyperspectral data, and obtained good classification results for eight major tree species in southern China.
• For forest parameters such as height, biomass, we combined the ZY-3 stereo image and DEM to automatically extract a high resolution spatially continuous tree height product. In addition, the acquired tree height product combined with Sentinel-2 data were used to achieve the forest above-ground biomass distribution map.
• In the section on forest biotic disturbance detection, pine wood nematode (Bursaphelenchus xylophilus) and Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) and the European spruce bark beetle (Ips typographus [L.]) three kinds of forest insect damage were studied.

For D. tabulaeformis, we proposed a spectral-spatial classification framework combining UAV-based hyperspectral images and digital images to achieve more detailed classification and automatically extract damaged tree crowns. For Bursaphelenchus xylophilus, we analyzed the spectral characteristics of three tree species (Pinus tabulaeformis, Pinus koraiensis and Pinus tabulaeformis) in Weihai, Shandong Province and Fushun, Liaoning Province during different susceptible stages, and explored the spectral response characteristics of plants under stress. For spruce bark beetles, we have been developing algorithms to detect infestations using Sentinel-2 images in 2018 and 2019.

2.Plan for next year

(1) Data acquisition:

We would like to apply for TanDEM data covering Wangyedian and Weihai, as well as WorldView-3 data covering Wangyedian, Anhui and Fushun.

We have ordered one WorldView-2 image and one WorldView-3 image covering Remningstorp in May 2021. We will also acquire one more RADARSAT-2 image in September, and one or two RADARSAT-2 images in the winter 2021-2022.

(2) The research content:

• For tree species classification, we will acquire WorldView-3 and Sentinel-2 images for repeated experiments in southern China, and further analyze tree species classification based on deep learning model. And in the study area Remningstorp, the planned analysis from WorldView-3 images will be done for individual trees thanks to the higher resolution of the images.
• In the section on forest parameters, we will combine the Sentinel-1 SAR data for biomass estimation, and combine the satellite images and LiDAR data for tree crown extraction.
• We will use the RADARSAT-2 images for developing change detection methods of forest biomass.
• In the section on forest insect damage detection, early detection of Bursaphelenchus xylophilus based on spectral characteristics of different stages combined with remote sensing data will be studied. And for spruce bark beetles, we will acquire WorldView-3 images and field data from a controlled experiment in 2021. Early detection using WorldView-3 images will be studied.

(3) Cooperation plan:

Our two teams share similar interests in forest remote sensing, conduct similar projects to communicate and collaborate more in scientific research. Annual seminars are planned in 2021 to 2024. The two team will collaborate in joint studies and publications. Niwen Li, a PhD student in BFU, will have a research exchange to SLU from August 2021 to July 2022. And more exchange of PhD students is under consideration. The scientific communication will be conducted every month by the researchers.

The European Space Agency (ESA) will launch in 2023 the BIOMASS spaceborne mission, based on a SAR device operating over several polarizations at P band, and whose main objective is the characterization of dense forests. One of the original features of this mission concerns its ability to provide 3-D SAR information through polarimetric and tomographic SAR (PolTomoSAR) data processing. The whole mission lifetime will be split into two phases: during the first phase (15 months) stacks of 7 coherent images will be acquired over all forested areas, whereas during the second phase (4 years), data will be measured in dual-baseline Polarimetric SAR Interferometry (PolInSAR) configuration, with 3 images every 7 months. This contribution proposes to compare the performance of the different observations modes of the future BIOMASS mission for the characterization of tropical forests, and to evaluate the gain in performance conferred by a synergistic combination of the two phases of the sensor’s life and by the use external information, such Lidar acquisitions etc. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and tests several scenari for the fusion of multi-source information using SAR images acquired at P band in the frame of the TropiSAR campaign and auxiliary data.

This joint project combines the use of field and airborne remote sensing data to validate and calibrate innovative new satellite sensors from CNAS, ESA and NASA for forest inventory, assessment and monitoring applications in China and the UK.

The Pu’er airborne remote sensing experiment was conducted in December 2020, which covered a subtropical forest area near the city of Pu’er in the Yunnan province of China. The multi-sensor airborne remote sensing data covered 900 km² area were acquired using two Chinese Academy of Forestry’s Airborne Observation Systems (CAF-AOS). The LiDAR point cloud has an average footprint density of 2.3 pts/m². The hyperspectral image has a spatial resolution of 2 m and 125 bands with the spectral range spanning from 400 to 990 nm. The spatial resolution of CCD image is 0.5 m. The spatial resolution of the middle-wave infrared image and long-wave infrared image is 1.87 m and 0.85 m, respectively. Moreover, 100 forest plots, and other ground measurements such as land-use/land-cover (LULC) ground truth points, Leaf Area Index (LAI), soil moisture, and chlorophyll content were conducted during that time. These high quality airborne and ground observed data provide a great support for the validation of high resolution satellite remote sensing products.

Field data, ground based structural surveys and UAV remote sensing campaigns took place at the Aberfoyle Research Forest, Loch Lomond and Trossachs National Park, Scotland, during October – November 2019. More field data collection is planned in this area for the summer of 2021. The majority of this site comprises conifer plantations with a predominance of Sitka spruce (60%), Scots pine, Japanese larch and Norway spruce. Additionally, there are some stands of native broadleaf species. Monitoring plots for yield assessment and windthrow risk modelling were surveyed using a handheld laser scanner, enabling the beneath canopy structure and density to be recorded. Sites were overflown with UAV RGB sensors, enabling a 3D reconstruction of the canopy using structure from motion (SfM) analysis. The Research Forest was additionally flown using airborne LiDAR during April 2021, updating a sequence of previous data captured during 2002, 2006, and 2012. All the areas covered in the field and by airborne methods constitute a set of fiducial sites for the constant calibration and validation of satellite imagery. The main goals of the Scottish experiments are the creation of superior and more intensive forest inventory products, improved growth models and a more accurate production forecast system.

The airborne hyperspectral images at meter-scale spatial resolution, and centimetre resolution from UAV multispectral data, provide the capability to describe the canopy structure and monitor the physiological conditions through some narrow bands spectrum, which has great potential for forest health monitoring, forest type/tree species classification. The reflectance from airborne hyperspectral images was firstly compared with the satellite remote sensing products from Gaofen-1/2/6 and sentinel-2. Then, the forest type/tree species was classified based on the composite Sentinel-2 image with 10/20 m spatial resolution on Google Earth Engine (GEE). An automated training dataset was built based on several published land cover products. Finally, forest type/tree species classification results are generated using random forest classifier. The final forest type/tree species classification results will be evaluated using the LULC ground truth data and plots information for further analysis.

The airborne LiDAR data and UAV SfM data were used to evaluate the performances of forest vertical parameters estimation algorithms using Gaofen-7, GEDI and ICESat-2 data. Both terrain and forest parameters will be analyzed.

As grassland is the largest terrestrial ecosystem in China, as well as the sources of many major rivers and key areas of water and soil conservation, it plays an irreplaceable role in ensuring national scale ecological security and promoting ecological civilization construction. However, as an important ecological resource, grassland ecosystem in China has been greatly degrading caused by climate change, overgrazing and other human activities. Therefore, grassland degradation monitoring and assessment have become an extremely urgent work for grassland ecological conservation and restoration of degraded grassland. Some remote sensing mapping methods of grassland types and remote sensing inversion of grassland biomass at regional scale have been put forward in previous studies, but most of them have poor universality and may not meet the needs of grassland dynamic and accurate monitoring.

In Dragon 5 project 59313, we did some scientific studies based on the geomatics methods on remotely sensed data from both European and Chinese side and other geospatial databases. In the first year of Dragon 5, joint research results have been achieved in the following two aspects:

(1) Fully employing the potential of time series sentinel-1 and sentinel-2 of ESA and the observation data of grassland shrubbery sample were used as the data sources. The shrub coverage of Xilingol grassland was estimated by correlation analysis and random forest model methods. It is of great significance for the sustainable utilization and management of grassland and the response analysis of climate change to grasp the information of large-scale grassland shrub accurately.

(2) Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil quality. On the Google Earth Engine (GEE) cloud computing platform, we choose three machine learning methods: random forest (RF), support vector machine (SVM) and Multi-layer perceptron neural networks (MLP Neural Nets) models. Using Sentinel-2 of ESA, topographic factors and climatic factors carry out 30-meter resolution high-precision mapping of the 0-20cm SOC and STN content of the soil surface of grassland. High-resolution SOC and STN digital maps are of great significance for soil quality assessment and land degradation monitoring.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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