Conference Agenda

This year’s work has not only established a fine-scale monitoring network covering various ecosystem types at the field observation level but also deepened the understanding of the complex environmental system of the Eurasia in the fields of remote sensing technology and digital twin earth approach. The following research progress have been made in monitoring and modelling climate change in water, energy and carbon cycles in Eurasia:

Initially, systematic observational studies were conducted on the mechanisms of atmospheric-soil water and heat exchange over the Tibetan Plateau. Hourly land-atmosphere interaction datasets were released. High-precision evapotranspiration datasets were constructed by integrating multi-source data. Radiosonde and microwave radiometer data were combined to effectively monitor the evolution of the water and heat structure in the convective boundary layer over the plateau. These datasets provide essential observational data for in-depth research on energy and water cycling in the Tibetan Plateau region.

Additionally, Tibetan Plateau evapotranspiration observational data were integrated to reveal the trends in evapotranspiration over the past 40 years and the next century. The relationships between evapotranspiration and meteorological and remote sensing variables were clarified. The main factors influencing the trends in plateau evapotranspiration were quantified, and the potential evolution patterns of evapotranspiration under different climate scenarios were predicted. These efforts provide a scientific basis for regional water cycle and climate change research.

Concurrently, simulations of the weather and climate effects of the Tibetan Plateau were conducted. On one hand, the gravel parameterization scheme of the CLM4.5 model was optimized to reveal the regulatory mechanisms of gravel on soil water and heat transfer in permafrost regions and quantify its impact on water and freeze-thaw processes. On the other hand, the Noah albedo scheme was improved to enhance the WRF model’s ability to simulate heavy snowfall over the Tibetan Plateau, providing a reference for improving the accuracy of weather and climate simulations in this region.

The STEMMUS-SCOPE Digital Twin Earth system component has been further enhanced that simulates water-energy-carbon fluxes and states in the land-atmosphere system by integrating key terrestrial processes such as radiative transfer, photosynthesis, and soil moisture and soil temperature dynamics, as well as vegetation growth dynamics. The newest development prognostically generates biomass of storage organs (i.e., yield), leaves, stems, and roots that closely matched with the observed data. It offers also a process-based and mechanistic framework to link remote sensing measurements (e.g., solar-induced chlorophyll fluorescence) to land surface fluxes (radiation, heat, water and carbon fluxes), LAI, plant biomass, and root-zone soil water in a physically consistent manner.

An emulator based on the STEMMUS-SCOPE digital twin and machine learning has been developed and a global dataset is generated at 9 km and hourly resolution from 2000 to 2020, including seven variables (net radiation, latent heat flux, sensible heat flux, soil heat flux, gross primary productivity and solar induced fluorescence at 685, and 740 nm). This product and other finer scale data will be used for case studies for detecting and monitoring droughts and floods and other environmental changes in Eurasia.

On the scientific exchange side, the team led by Prof. Ma from ITP engaged in close collaboration and exchange with the team led by Professor Su from UT in the Netherlands. In April, Prof. Ma led a delegation to visit the UT, where a specialized discussion was held with Professor Su’s team on satellite remote sensing observation technology for energy and water cycle over the Tibetan Plateau. Subsequently, Prof. Ma and Prof. Su, as co-conveners, hosted the “TPE” session at the European Geosciences Union (EGU 2024) General Assembly, which attracted 30 oral presentations and 23 poster presentations. International peers systematically presented the latest research findings in the fields of hydro-meteorology, glacier changes, and ecological environment in the Himalayan region. In November, Professor Su paid a return visit to the ITP, and the two sides discussed cooperation mechanisms such as Tibetan Plateau land-atmosphere interaction observation and joint graduate student training.

The Dragon-6 project Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience explores how remote sensing can be applied to assess and enhance the resilience of cultural heritage sites facing increasing threats from climate change. Many UNESCO World Heritage Sites, as well as other significant archaeological locations, are vulnerable to a range of climate-induced hazards—including flooding, permafrost thaw, desertification, and intensified storm activity, as well as active tectonics.

Remote sensing offers essential capabilities for monitoring and evaluating these evolving risks. Our project aims to address methodological gaps and to apply advanced multi-sensor remote sensing techniques to a diverse selection of heritage sites located in complex environmental contexts around the world.

Key study areas include the Wushaoling, Jiayuguan and Hongguozi sections of the Great Wall in Gansu Province and Ningxia Hui Autonomous Region, China; rapidly melting ice patches in the Mongolian Altai; riverine regions in Alaska affected by rising temperatures and increased erosion; and the archaeological landscape of Metaponto, Italy, which faces multiple climate-related threats.

We plan to begin fieldwork prior to the 2025 Dragon-6 Symposium, with an initial campaign focused on collecting ground data from the Great Wall sites in Gansu Province and Ningxia Hui Autonomous Region, China.

Through this project, we demonstrate the critical role of remote sensing technologies in supporting global efforts to monitor, protect, and build climate resilience for vulnerable cultural heritage sites.

This project focuses on artificial intelligence (AI)-driven approaches for monitoring extreme climate events with environmental and societal impact, like floods and droughts, to better support disaster preparedness and inform climate adaptation strategies.

Specifically, four main objectives are defined: a) Perform a joint China-ESA Earth Observations (EO) missions synergy analysis, b) Establish joint EO benchmark datasets for flood and drought monitoring, c) Build multimodal EO models to create climate-related information extraction for multi-source and multi-temporal data, and d) Develop AI-enabled flood and drought prediction algorithms.

Climate change is altering atmospheric dynamics and Earth's water cycle. It leads to the amplification of existing climate patterns and an increase in water-related climate extremes, reinforcing the observed trend of wet regions becoming wetter and dry regions drier. The integration of data across sensor modalities becomes critical. Recognizing the potential of EO Big Data - with its varied sensing platforms and broad spatial-temporal coverage - we seek to harness these resources and integrate them with multi-modal geophysical and environmental measurements.

However, multi-modal remote sensing remains challenging due to significant variations across instruments, including domain gaps and inconsistencies in spatial and temporal resolution. In parallel, many AI and deep learning approaches in remote sensing rely on data-intensive supervised algorithms to achieve high accuracy results, but they often fall short in terms of interpretability and explainability. The cost of obtaining semantically annotated datasets is an expensive task, essentially for large satellite image time series (SITS) and in scenarios where ground truth data is scarce or unavailable. Additionally, while climate change Earth System Models describe phenomena at scales of thousands of kilometers over many decades, climate change adaptation measures need to be applied at the scale of human activities (e.g., temporal: days/months, spatial: 10 m - 1 km, etc.).

We showcase four recent studies that are consistent with our research objectives and in which core members of our team have been involved before the Batch 3 Kickoff. Particularly, they present approaches in multi-temporal analysis, multi-source data integration, and label annotation optimization by active learning, as follows:

• A Memory-based Multimodal Change Detection (MMCD) method was proposed in [1], where distinct images for change detection are treated like video frames. Historical optical images are fused with current synthetic aperture radar (SAR) data to improve detection accuracy, and a difference map enhancement module is introduced to suppress false alarms caused by cross-modality inconsistencies.
• The newly introduced method Diff-MRCD [2] implements a diffusion-based framework which directly generates change maps through reverse denoising. This method is reinforced with a feature alignment module, which ensures robust handling of inputs with differing resolutions by preserving both fine details and semantic meaning.
• The label data scarcity in remote sensing can be addressed through active learning strategies. The comprehensive review from [3] examines the integration of deep learning with active learning in remote sensing and how this paradigm enables optimization of training data under annotation constraints.
• In [4], a framework to analyze and visualize changes in Sentinel-1 SAR image time series with limited ground truth data is proposed. An expert-knowledge-enhanced classifier is paired with an unsupervised Latent Dirichlet Allocation (LDA)-based method to discover and analyze temporal dynamics and change signatures. Visualization is emphasized by obtaining color-coded change maps of temporal evolution at image patch level.

In the project “Remote Sensing of Environmental Effects on Materials - Application to the Degradation of Cultural Heritage Monuments” (ID: 95481), we have outlined three tasks focused on Cultural Heritage Monuments.

Task 1 will develop two indices: the Limestone Deterioration Index (LDI) to assess environmental deterioration potential on limestone, and the Soiling Index (SI) to evaluate soiling potential. These indices will be created for the monuments listed in Table 1.

Table 1. The selected archaeological sites in China and Europe for this project.

Chinese sites

• Fujian Tulou
• Historic Centre of Macao
• Ancient Building Complex in the Wudang Mountains
• Dazu Rock Carvings
• Circular Mound Altar - Temple of Heaven

European sites

• Acropolis
• Colosseum
• Historic Centre of Vienna
• Historic City of Toledo
• Palace of Versailles

For this task, we are gathering essential satellite environmental data from both European and Chinese databases. Next, we will utilize Satellite Sensed Data Dose-Response Functions (SSD-DRFs) to develop deterioration model tools that estimate the annual degradation and soiling characteristics of the atmosphere at various sites.

Task 2 is focused on developing two new SSD-DRFs for copper and stainless steel. These materials are significant because they are linked to cultural heritage artifacts and monuments, and they are also commonly used in modern construction. To support this task, we are collecting both environmental satellite data and deterioration data from relevant exposure specimen campaigns. Both types of data are crucial for developing the new SSD-DRFs.

Task 3 focuses on monitoring seismic activity in archaeologically significant areas using satellite observations. The case studies include the archaeological site of Delphi, identified as a European candidate site, as well as the Temple and Cemetery of Confucius and the Kong Family Mansion in Qufu, recognized as a Chinese candidate location. Both sites are part of the UNESCO World Heritage Convention. This research effort aims to develop tools to protect cultural heritage sites from degradation and natural hazards by utilizing satellite data.

To support this task, we have begun collecting satellite data from the China Seismo-Electromagnetic Satellite (CSES) database. The data, obtained from ZH-1(01), covers the period from February 2018 to the present. Our next steps will involve analyzing this data to study earthquake dynamics at the aforementioned archaeological sites.