Conference Agenda

In recent years, 10 to 30 meter resolution optical multispectral and hyperspectral satellites data and SAR data from Europe and China became available and is encouraging the Remote sensing community to explore the new technology to harness all advantages of all these satellite to achieve best crop monitoring at various scale. These satellites data provide the opportunity to fill the gaps existing in each other. The capability of agricultural monitoring in general is being enhanced and improved with these diverse 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. It has to take this situation into account when the crop mentoring is being conducted. To what extent can these diverse satellite data together improve crop monitoring in various scenarios in China and what is the difference in crop monitoring between the typic cultivations in China and Europe? In this proposal, 5 study sites, 4 from China and 1 from Belgium, are selected representing the major cropping systems, including winter wheat, maize, rice, sugarcane and vegetables. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the North and the South. The field campaigns will be organized and the data collection will be following the JECAM guideline. The Sentinal1/2, EnMAP, PRISMA from Europe and GF1/3/6, CBERS, Obita from China as well as other satellite data will be mainly investigated to support this study. The remote sensing parameters, like LAI/FPAR/FCOVER/NDVI will be retrieved with the adapted algorithm either from one satellite data or from combination of some. The crop classification algorithm will be evaluated with various satellite data either alone or combination to make best crop type maps. Crop specific N retrieval algorithm will be developed. Finally, the retrieved information in the field level will be communicated with the farmers and jointly come up with the management suggestions for the nutritional application, irrigation and other practices. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm will be exchanged and the objective of this project will be fulfilled as the task team brings a step forward to support agricultural monitoring at fine scale. The collaboration will also make great technological contributions to GEO Global Agricultural Monitoring Imitative, GEOGLAM.

The objectives of this proposal are to explore the data processing and application of satellite radar altimeters by combining the traditional radar altimeters (HY-2 series) and new-generation altimeters (SAR altimeters (Sentinel-3A/B, Cryosat-2 and Sentinel-6) and wide-swath altimeter (SWOT)) for the marine dynamic environment monitoring over specific areas such as coastal, polar and upper oceans. With the long-term operations of European SAR altimeters including Sentinel-3A/B, Cryosat-2 and Sentinel-6, the networked observations of traditional altimeters HY-2B/C/D in China and the emergence of the wide-swath altimeter-SWOT (Surface Water Ocean Topography), the spatiotemporal resolution of sea surface height (SSH) observation data by different kinds of altimeters continues to improve, and the ability of SSH observations in specific areas such as the coastal and polar regions is improved. In addition, how to use a large amount of satellite remote sensing observation data of ocean surface to obtain subsurface three-dimensional temperature and salinity in the upper ocean has become a hotspot. In this project, European SAR altimeters with the high spatial resolution in the coastal areas, combined with HY-2 series altimeters with high spatiotemporal coverage and the wide-swath altimeter SWOT are utilized to conduct researches on ocean waves, sub-mesoscale eddies/currents, mean sea surface, and marine gravity in coastal and polar regions. Based on remote sensing data of SSH, sea surface temperature (SST) and SSS (sea surface salinity), subsurface three-dimensional temperature and salinity in upper ocean are reconstructed. By combining traditional, SAR and wide swath altimeter data, this study will enhance the application potential of different type altimeter SSH observation data and promote the exploitation of Chinese HY-2 series satellite altimeters and European SAR altimeters data. The proposed project contains 4 main scientific topics as follows. 1) data processing and application on ocean wave and mean sea surface of multi-type altimeter data in the coastal and polar regions. 2) extraction of the characteristic of submesoscale eddies/currents by combining multi-type altimeter data. 3) Finer marine gravity recovery over coastal zones by combining multi-type altimeter data. 4) reconstruction of subsurface 3-D temperature and salinity in upper ocean by combining multi-type altimeter data and in situ T-S profile data. The work of this project will improve the marine dynamic environment monitoring in specific areas by multi-type altimeters. The funding to support this project includes the National Natural Science Foundation of China (No.62231028) and National key research and development program of China (2022YFC3104902).

Ocean waves plays a major role in the exchange of heat and momentum between the ocean and the atmosphere. In the dragon-5 program, we have developed a database of key wave parameters describing waves, such as Significant Wave Heights (SWH) over wide swath of scatterometters, based on the synergy between HY2 series and CFOSAT missions. We have also implemented the retrieval of maximum wave heights using an AI technique. For the first time, we are able to characterize the occurence of rogue waves derived from satellite wave observations provided by HY2 and CFOSAT. The results showed a good consistency with buoys data and open the perspective of using such retrieved parameters in operational applications. In the dragon 6 proposal, we propose to improve the AI-based retrieval models and also to implement AI-based SWH prediction models. The objective of the work proposed in dragon6 is one the hand to improve the AI retrieval models for wide swath SWH and maximum wave height. On the other hand we will extend the use of such AI technique to past period for wave climate analysis. In this presentation we will highlight the upgraded algorithms for the AI model and the first results dedicated to improve the assimilation of these data in operational wave models. Further discussions will be focused on applications related to prediction of extreme events and better use of AI-based data for the understanding of coupled ocean-wave-atmosphere systems.

The UK has set a Net Zero target by 2050, which means no longer adding to the total amount of greenhouse gases in the atmosphere. The two main greenhouse gases (GHGs) are carbon dioxide (CO2) and methane (CH4). CO2 is released when oil, gas, and coal are burned in homes, factories, and for transportation, while CH4 is produced through farming and landfill. Integrating renewable energy for energy supply will be a key solution, especially to find an optimized solution with renewable energy (RE) implementation for power generation to replace fossil fuels.

Regarding CH4, agriculture is a major contributor to methane emissions. The effects of reducing greenhouse gases through the implementation of renewable energy sources are complicated and influenced by multiple factors, including geographical area, availability and intermittency of renewable energy sources, peatland CO2 release, local economy, and policies, etc. It is essential to study the reduction of CO2 & CH4 with different renewable energy installations as a comprehensive task and also worth exploring the effects of individual factors.

Since 1983, the World Meteorological Organization (WMO) has established various Global Atmosphere Watch stations worldwide to continuously monitor changes in atmospheric CO2 and CH4 concentrations at near-surface levels. To understand the transport mechanisms of global greenhouse gases (GHGs), JAXA launched the Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2 in 2009 and 2018 to clarify the sources and sinks of CO2. NASA put the OCO-2 and OCO-3 satellites into operation in 2014 and 2019 to quantify variations in the column-averaged atmospheric CO2 dry air mole fraction, namely XCO2. The Chinese carbon dioxide observation satellite (TanSat) was launched on December 22, 2016. In addition, China has launched other satellites to monitor greenhouse gases, such as Gaofen5-01/02. In particular, China successfully launched the DQ-1 (the world's first spaceborne Lidar to detect CO2).

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 & CH₄ 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 & CH₄ for the whole area with the prevision of integration of REs. The project aims at exploiting the synergic measurements together with RE technology implementation and advanced artificial intelligence to quantify the effect of REs in the terrestrial carbon cycle. Specifically the key objectives include:

• Develop fusion algorithms for combining measurements from different satellites on required trend and temporal scales for the regions.
• Develop retrieve algorithms of CO₂ & CH₄ from satellite and combined measurements with In-situ.
• Validate and apply GHG products of satellites to estimate CO₂ & CH₄ concentration and distribution.
• Investigate the energy demanding, the greenhouse emission and installation of RE in the region of Ireland, UK and China.
• Study the effect of implementing the REs for the power generation with wind, solar and ocean sink to reduce greenhouse gas emission.
• Provide policy makers with the evidence of CO₂ & CH₄ reduction over regions that have integrated REs as energy suppliers.

The proposed project involves a collaboration between the Sustainable Technology Centre (CST) at Ulster University, UK and the National Satellite Meteorological Centre (NSMC) at China Meteorological Administration (CMA). Work conducted by both teams are part of their respective research commitments, hence limited funding will be used to support this cooperation research. It is also expected the Dragon 6 program would provide certain amount of funding to support EU partners for attending symposia and for young scientists to carry out the project research.

Lately, we witnessed a growing concern over the profound and far-reaching effects of climate change. Among the various environmental challenges posed by a warming Earth, two phenomena stand out as particularly devastating and widespread: droughts and floods. Seriously impacted by extreme weather events, they have the potential to reshape landscapes, disrupt ecosystems, and profoundly affect human societies. Climate change exacerbates the frequency and severity of these phenomena. Fine-scale investigation of droughts and floods highly impacts water resource management, rising of infrastructure resilience in the face of climate-induced hazards, urban planning, and land use, assisting health authorities to prevent waterborne diseases or deployment of resources for disaster relief, insurance, and risk assessment.

The sequence of remotely sensed Earth observations (EO) data at regular intervals over a specific geographic area – Satellite Image Time Series (SITS) offer a dynamic perspective of the Earth's surface, enabling the understanding of various environmental parameters and phenomena over time. Moreover, the available various data like weather and climate data, hydrological data, data measures by ground sensors, soil moisture or groundwater level, topographic and water quality data, that has never been used jointly with the EO data, enhances our ability to detect, predict, and respond to extreme events with precision and accuracy.

This project targets to develop an AI-based holistic approach to process multi-source data that enables fine-scale detection of both environmental and societal effects of floods and droughts, ultimately supporting more effective disaster management and climate adaptation efforts. Specifically, we defined 4 objectives, aiming to:

1) Perform a joint China-ESA EO missions synergy analysis for climate events across spectral bands, temporal and spatial dimensions; We respond to the first objective of the Dragon 6 Call, by promoting the joint exploitation of data focused on algorithms development that addresses the understanding of a key societal issue.

2) Create benchmarking data sets for floods and drought monitoring using a time series of satellite images from multisource China-ESA Earth Observation (EO) data and climate time series data;

3) Build multimodal EO foundation models focused on climate-related information extraction for multisource and multitemporal EO data that will enable the quantification of climate change effects, hence supporting adaptation and mitigation.

4) Implement two use cases focused on floods and droughts. The selected areas of interest are:

• Danube Delta, a unique and expansive natural area located in south-eastern Europe, where the Danube River meets the Black Sea. It is one of the largest and best-preserved deltas in Europe, known for its remarkable biodiversity and ecological significance, and
• Poyang Lake, China, located in the Jiangxi Province of China, the largest freshwater lake in the country and one of the largest in the world. This lake has a dynamic and fluctuating water level, with its size varying significantly between the wet and dry seasons.

The insights acquired during the implementation of Dragon 5 by the same Sino-European team made up of CEOSpaceTech and Tongji University researchers provide a solid foundation to complete the settled objectives, while advancing innovative approaches developed to assess the effects of climate change induced floods and droughts.