Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence

Dear Colleagues,

Land Surface Temperature (LST) is a key physical variable in understanding land–atmosphere interactions, surface energy balance, and climate dynamics. Accurate retrieval of spatio-temporal LST plays a vital role in monitoring global climate change, water cycle processes, ecosystem health, and human–environment interactions. Despite advances in satellite, reanalysis, and ground-based observations, challenges remain in producing long time-series, high-resolution, space-time continuous, and accurate LST data, particularly in heterogeneous or mountainous regions. Recent developments in artificial intelligence (AI) provide promising avenues for addressing these challenges through enhanced modeling, data fusion, and gap-filling techniques. The integration of ground-based measurement, satellite observation, and artificial intelligence to achieve accurate LST retrieval in time and space is an important development direction for the current surface temperature extension and application.

This Special Issue aims to bring together cutting-edge research focused on innovative methods for spatio-temporal LST retrieval by integrating ground-based measurements, satellite remote sensing, and AI technologies. It aligns closely with the journal’s scope by advancing remote sensing applications, algorithm development, and Earth system monitoring. Contributions that enhance the accuracy, resolution, and applicability of LST products in environmental and climate-related fields are especially welcome.

Articles may address, but are not limited to, the following topics:

• LST retrieval algorithms based on multi-source data
• The coupling of artificial intelligence and physical models
• AI and machine learning approaches for LST reconstruction
• LST retrieval in complex terrains or under extreme conditions
• Validation and uncertainty analysis of LST products
• Applications of LST in hydrology, ecology, and climate studies

Article types include original research, reviews, methodological papers, and case studies.

Dr. Bo-Hui Tang
Dr. Xiangyang Liu
Dr. Tian Hu
Guest Editors

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