Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data

Dear Colleagues,

Satellite-derived land surface temperature (LST) data are critical for climate studies, urban heat island analysis, and agricultural monitoring. This process relies on algorithms that convert thermal infrared (TIR) radiation measured by satellites into accurate land surface temperature (LST) estimates, addressing challenges such as atmospheric interference and surface emissivity. In recent decades, significant advancements have been made in the theoretical understanding and methodological approaches for satellite data. Various LST retrieval algorithms have been developed from thermal infrared data, such as the single-channel and split-window/dual-window algorithms, which require known LSEs. Others, like the temperature and emissivity separation algorithm and the physics-based day/night algorithm, necessitate up-front atmospheric correction. Despite the capabilities of physics-based TIR models in describing electromagnetic wave interactions with complex surfaces, accurately and stably retrieving surface parameters from limited satellite observations remains a challenging task. In recent years, with the advancement of artificial intelligence (AI), the integration of AI with physical models, particularly through the incorporation of deep learning technologies, has the potential to significantly enhance the interpretation of remote sensing images and information extraction capabilities, marking a pivotal direction for future research. However, those issues, including spatial discontinuity caused by cloud cover, spatiotemporal incomparability due to wide-field scanners and anisotropy, as well as instantaneous characteristics, have limited the broader application of thermal infrared remote sensing.

This Special Issue focuses on “Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data”. Potential topics include but are not limited to the following:

• Approaches to dealing with thermal infrared remote sensing data, such as atmospheric effect correction, land surface temperature, and emissivity separation;
• Downscaling techniques to improve the spatial resolution of LST products;
• Land surface temperature reconstruction under cloud-covered areas;
• Studies on the validation of land surface temperature products.

Prof. Dr. Caixia Gao
Guest Editor

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