A Novel Approach to All-Weather LST Estimation Using XGBoost Model and Multisource Data

文献类型： 外文期刊

第一作者： Duan, Si-Bo

作者： Duan, Si-Bo;Lian, Yihua;Han, Wenjing;Wu, Zihao;Duan, Si-Bo;Lian, Yihua;Han, Wenjing;Wu, Zihao;Zhao, Enyu;Chen, Hong

作者机构：

关键词： All-weather; extreme gradient boosting (XGBoost); land surface temperature (LST); MYD11A1

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.2； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2023 年 61 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) plays a crucial role in the physical and chemical processes of the land-atmosphere system. Remote sensing technology has greatly advanced the measurement of thermal infrared LST (TIR LST), which is the most widely utilized surface temperature product. However, cloud cover and mist often cause significant data loss in TIR LST. To address this issue and reconstruct the MYD11A1 LST under cloudy conditions, this study proposes an all-weather LST generation method based on the extreme gradient boosting (XGBoost) model. This method incorporates spatial-seamless passive microwave LST (PMW LST) to capture the nonlinear relationship between TIR LST and other variables. Compared to the MYD11A1 LST, the generated all-weather LST provides continuous spatial texture information without a significant boundary reconstruction effect, improving the accuracy of spatiotemporal variations in LST in China. In situ validation demonstrated the high accuracy of the generated all-weather LST, with mean R(2 )bias, and unbiased root-mean-square error (ubRMSE) of 0.96 (0.91), 1.08 K (3.61 K), and 2.92 K (4.54 K) under clear (cloudy) daytime conditions, and 0.92 (0.95), -0.93 K (-2.96 K), and 3.09 K (3.04 K) under clear (cloudy) nighttime conditions. These results indicate the feasibility and reasonableness of the all-weather LST generation method developed in this study and affirm its ability to generate highly accurate all-weather LST.

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