Conference Agenda

Objective and methods:

With the increasing availability of different medium and high-resolution remote sensing data, the fusion of multiple data sources to generate high-temporal-spatial- resolution data for grassland monitoring has been widely used, among which the fusion of Landsat 8 with Sentinel-2 and MODIS is the most common. Data fusion processors have been developed in the EO community for a longer time already. Now, Harmonized Landsat Sentinel-2(HLS) could be download from the Earthdata Search and Land Processes Distributed Active Archive Center. However, there is currently limited research that considers fusing Chinese high-resolution data(such as GF-1 and GF-6 WFV) with foreign medium and high-resolution data(Such as Sentinel-2 and Landsat) and does not introduce low spatial resolution data information.

Sandy land is one of the results of land desertification, which refers to land mostly covered by sand or sandy soil (sand-covered land), including desert. Knowledge of the spatial distribution and variation in sandy land is important to better understand desertification processes, land resource management, and environmental research. The emissivity of sandy soil in the TIR bands has remarkable characteristics. In addition, the green transition increasing attention is paid to the thermal insulation efficiency of building. Several thermal mission can support related information needs such as Landsat TIRS, Ecostress and at coarser resolution also Sentinel-3. The upcoming Copernicus LSTM mission will provide systematic global TIR observations at high spatial resolution. Certainly, synergies between such mission need to be explored. Here the Chinese SDG-Sat mission is highly relevant. It provides 30m TIR observation yet over a small spatial footprint. Therefore, complementary use need to be explored and potential for fusion these sources need be assessed.

Based on the background above, the objectives of this proposal are to focus on the following aspects:

• Evaluate complementary EO data sources from Chinese and European missions in terms of their characteristics for integrated or fused applications. Special focus will be paid to Sentinel2 and Geofen6. Also thermal complementarities between SDG Sat and other LST data sources will be explored.
• Explore the value of different harmonization and fusion processors such as STARFM, sen2like and others.
• Perform a prototype implementation of a fusion processor in a cloud environment
• Evaluate fused data stream in different application contexts

The deliverables include：

(1) Image Processing Algorithms: A prototype of a fusion processor will be developed and transformed into an cloud based on demand service prototype.

(2) Geospatial Products: Fused China-EU EO data of study area. Spatial distribution of heat emission of study area. Spatial distribution of sandy land in Xilin Gol League, China

(3) Technical Reports: Comprehensive technical reports detailing the methods, processes, and techniques used in the project.

(4) Scientific Publications: 3-5 research papers or scientific publications presenting the project's findings, methodologies, and significant results.

(5) Young scientist training: 3-5 young scientist exchange and training

Three-dimensional ocean dynamic prediction holds significant implications for maritime activities, marine economy, and meteorological-climatic systems. Traditional numerical forecast models for ocean dynamics require substantial computational resources and time consumption. With advancements in artificial intelligence (AI) methodologies, data-driven AI-based ocean models can now achieve superior prediction accuracy compared to numerical models while demanding quite limited computational time and resources. However, current AI ocean models remain constrained by numerical forecasting systems as they continue to rely on numerical analysis fields for forecast initials. This study proposes an end-to-end AI-based ocean forecasting framework that directly utilizes satellite remote sensing observations for three-dimensional ocean dynamic prediction. The model architecture connects satellite observations (input) with three-dimensional oceanic parameter forecasts (output). Input parameters include sea surface temperature(SST), sea level anomalies (SLA), and sea surface current derived from satellite remote sensing. The model generates 10-day forecasts of three-dimensional parameters across 0-200 meters depth with 20 vertical layers. By eliminating the dependence on numerical model outputs, this AI framework enables near real-time prediction updates through instantaneous computation (seconds-level processing) upon acquisition of new satellite observations. This innovation facilitates rolling updates of three-dimensional forecasts, thereby fundamentally transforming the conventional role of satellite remote sensing from merely providing observational references to enabling predictive capabilities for future oceanic states. This advancement establishes a new operational framework that satellite remote sensing can transcend its traditional function as a diagnostic tool, emerging as a proactive predictive foundation for marine environmental forecasting.