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Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data

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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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26 pages, 17855 KB

Open AccessArticle

Deep Learning Retrieval and Prediction of Summer Average Near-Surface Air Temperature in China with Vegetation Regionalization

by Wenting Lu, Zhefan Li, Ya Wen, Shujuan Xie, Jiaming Ou, Jianfang Wang, Zhenhua Liu, Jiahe Si, Zheyu Gan, Yue Lyu, Zitong Ji, Qianyi Fang and Mingzhe Jin

Remote Sens. 2025, 17(18), 3209; https://doi.org/10.3390/rs17183209 - 17 Sep 2025

Viewed by 170

Abstract

Retrieving and predicting summer average near-surface air temperature (SANSAT) across China remain challenging due to the country’s complex topography and heterogeneous vegetation cover. This study proposes an innovative deep learning framework that incorporates vegetation regionalization to achieve high-precision spatiotemporal temperature retrieval and prediction. Using MODIS land surface temperature, vegetation indices, weather station data (2000–2019) and other relevant datasets, we first apply GeoDetector to identify key influencing factors (e.g., nighttime surface temperature, elevation, vegetation index, and population density) within each vegetation region. Based on these findings, we develop a deep neural network (DNN) model, which achieves high accuracy in SANSAT retrieval (with validation R² ranging from 0.90 to 0.97 and RMSE from 0.46 to 0.64 °C). Results indicate that temperature variations in the eastern monsoon region are primarily influenced by human activity and topography, whereas natural factors dominate in the western regions. Subsequently, using a Long Short-Term Memory (LSTM) network with an optimal seven-year time step, we predict SANSAT for 2020–2023, achieving R² values of 0.71 in training and 0.69 in testing, which confirms the model’s high reliability in SANSAT prediction. The core innovation of this work lies in its vegetation-regionalized deep learning approach, which explicitly addresses landscape heterogeneity by customizing models to specific eco-climatic zones, thereby quantifying human-nature interactions more effectively than traditional, spatially uniform methods. This framework enhances the understanding of summer temperature dynamics and provides valuable spatial data to support applications in agricultural disaster prevention, ecological conservation, and carbon neutrality. Future research will incorporate multi-seasonal data and enhance the spatiotemporal resolution to further improve NSAT modeling. Full article

(This article belongs to the Special Issue Algorithms Exploration of Land Surface Temperature Retrieval from Satellites Data)

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