数字农科院2.0

Improving Land Surface Temperature Retrieval From MODIS Data: Explicit Correction for Aerosol Optical Depth Variability

文献类型： 外文期刊

作者： 段四波;;凌凯;;闵肖肖;;魏冉;;管永娟

关键词： Aerosol effect; generalized split-window (GSW) algorithm; land surface temperature (LST); validation

期刊名称： IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN： 0196-2892

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Land surface temperature (LST) is a critical param-eter at the land–atmosphere interface, playing a key role in hydrology, climate, and ecological studies. The widely used gen-eralized split-window (GSW) algorithm assumes constant aerosol influence, yet aerosol variability introduces significant errors in LST retrieval. To address this limitation, we propose an improved algorithm (GSW AOD) that explicitly incorporates an aerosol optical depth (AOD) correction term into the GSW framework. Evaluation using an independent simulation dataset demon-strated substantial improvement. Under low-aerosol conditions (AOD <0.3), the root mean squared error (RMSE) decreased from approximately 1.7 K for GSW to 0.8 K for GSW AOD. Under high-aerosol conditions (AOD ≥ 0.3), RMSE decreased more dramatically from approximately 4.0 K for GSW to 1.7 K for GSW AOD. Validation with in situ measurements from eight ground sites showed that while both algorithms performed similarly at low AOD (AOD <0.3; RMSEs between 2.0 and 3.3 K, differences <0.3 K), GSW AOD significantly outperformed GSW under high aerosol loading (AOD ≥ 0.3), where GSW RMSEs reached 2.84.0 K. Compared to the standard moderate resolution imaging spectroradiometer (MODIS) LST product, GSW AOD achieved lower RMSEs at most sites for AOD <0.3 and reduced RMSEs at all sites for AOD ≥ 0.3. These results confirm that explicitly correcting for variable AOD significantly enhances the accuracy and robustness of thermal infrared LST retrievals, particularly under moderate-to-high aerosol conditions.

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