Conference Agenda

The rapid expansion of medium- and high-resolution Earth Observation (EO) datasets has created new opportunities for generating products with enhanced spatial and temporal resolution through multi-source data fusion. Existing approaches have primarily focused on integrating datasets such as Landsat 8, Sentinel-2, and MODIS, supported by a range of established fusion algorithms. More recently, harmonized products such as the Harmonized Landsat and Sentinel-2 (HLS) dataset have become operationally accessible via platforms such as NASA Earthdata.

However, the integration of Chinese high-resolution satellite data (e.g., GF-1 and GF-6 WFV) with European and international missions (e.g., Sentinel-2 and Landsat) remains relatively underexplored, particularly in fusion frameworks that avoid reliance on coarse-resolution inputs. Addressing this gap offers significant potential for advancing EO-based monitoring capabilities.

Over the past year, the collaboration between the Chinese and European teams has been further strengthened and expanded. On the European side, three early-career researchers from the University of Oxford, Newcastle University, and King’s College London have joined the project, contributing to methodological development and application-oriented studies. In parallel, the joint research scope has evolved beyond land surface monitoring to include emerging applications in renewable energy, leveraging EO data synergy for tasks ranging from infrastructure detection (e.g., solar facilities) to energy-related parameter retrieval and forecasting.

Sandy land, as a key manifestation of land desertification, remains a core application focus. Accurate characterization of its spatial distribution and temporal dynamics is essential for understanding environmental change and supporting land management. Thermal infrared (TIR) observations are particularly valuable in this context due to the distinctive emissivity properties of sandy surfaces. At the same time, increasing emphasis on sustainable development and the green transition has intensified the demand for improved thermal observations, including applications in land surface temperature retrieval and energy efficiency assessment.

A range of current and upcoming EO missions provide relevant TIR data, including Landsat TIRS, ECOSTRESS, Sentinel-3, and the Copernicus Land Surface Temperature Monitoring (LSTM) mission. In parallel, the Chinese SDG-Sat mission offers 30 m TIR observations over targeted regions, creating opportunities for complementary use. Nevertheless, systematic exploration of synergies between these missions, particularly across Chinese and European systems, remains limited.

Building on these developments, the project focuses on the following objectives:

• Assess the complementarity of Chinese and European EO datasets for integrated and fusion-based applications, with emphasis on Sentinel-2 and GF-6, as well as TIR data from SDG-Sat and other land surface temperature (LST) products.
• Investigate and compare harmonization and fusion approaches, including STARFM, sen2like, and related methods.
• Develop and implement a prototype fusion processing framework within a cloud-based environment.
• Evaluate fused data streams across multiple application domains, including desertification monitoring and renewable energy assessment (e.g., facility detection and energy-related analysis).

The project will deliver:

1. A prototype multi-source data fusion processor.
2. Fused China-EU EO datasets for selected study areas, including products such as spatial distributions of sandy land and thermal emission characteristics (e.g., in Xilin Gol League, China), as well as exploratory datasets supporting renewable energy applications.
3. Comprehensive reports detailing methodologies, processing workflows, and implementation strategies.
4. Publication of 3-5 peer-reviewed articles presenting methodological advances and application results.
5. Training and exchange of early-career researchers, including the active involvement of newly recruited European team members.

The wave data retrieved by Artificial intelligence algorithms significantly impact operational wave forecasting and consequently improve wave-submersion vigilance during storm events. In this work, we present the updated processing for wave retrieval across the swath of scatterometers from satellite missions HY-2B & 2C and CFOSAT. Significant wave heights are provided with a spatial resolution of 25 km over a 200 km swath. An evaluation of this new processing version was carried out through data assimilation experiments into the operational wave model MFWAM. Long term period simulations have been performed with and without assimilation of these new wave data in global configuration of the wave model. The results show a significant improvement in Significant Wave Height estimation, with a reduction in the scatter index of wave heights compared to independent wave observations from altimeters and drifting buoys. We examined the results for extreme storm cases, particularly in the North-East Atlantic and during typhoon season in the west Pacific Ocean. Also, the analysis of the results highlights the influence of enhanced wave parameters on ocean-wave coupling, especially in key ocean regions such as the Southern Ocean and marginal ice zones.

The second part of this study focuses on the analysis of maximum wave height derived from AI algorithm applied to CFOSAT missions, as well as the analysis of wave forecasts for dangerous seas indicators on a global scale.

In this presentation we will discuss the next developments in the frame of DRAGON-6 program.