An Operational Split-Window Algorithm for Land Surface Temperature Estimation From Chinese FY-3C VIRR Data

文献类型： 外文期刊

第一作者： Li, Jia-Hao

作者： Li, Jia-Hao;Li, Zhao-Liang;Liu, Niantang;Fan, Jinlong

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关键词： Land surface temperature; Land surface; Satellite broadcasting; MODIS; Atmospheric measurements; Spatial resolution; Radiometry; Evaluation; Fengyun-3C (FY-3C) Visible and Infrared Radiometer (VIRR); land surface emissivity (LSE); land surface temperature (LST); retrieval; split window (SW) algorithm

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：7.5； 五年影响因子：7.6 ）

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is a critical parameter in global long-term meteorological and climatological studies. The Visible and Infrared Radiometer (VIRR) sensor aboard the Chinese Fengyun-3 (FY-3) series satellites provides a continuous collection of thermal infrared (TIR) data, facilitating the generation of global long-term LST products. Notably, the FY-3C VIRR has served as a key instrument in collecting global TIR data since 2013. In this study, we proposed an operational split-window (SW) algorithm for retrieving LST from FY-3C VIRR TIR data. Initially, the Thermodynamic Initial Guess Retrieval 2000 atmospheric profile library, the atmospheric transfer model MODTRAN, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library were employed to construct a simulation database for fitting the algorithm coefficients. To enhance the accuracy of the SW algorithm, LST, atmospheric water vapor content (WVC), and average emissivity were segmented into various subranges. Subsequently, land surface emissivity (LSE) was dynamically estimated by combining the ASTER Global Emissivity Database (GED) with the normalized difference vegetation index threshold approach. Finally, in situ measurements from the Surface Radiation Budget (SURFRAD) network, along with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (MOD11A1 and MOD21A1), were utilized to evaluate the accuracy of the retrieved LSTs. The results indicate: 1) the retrieved LSTs showed a high correlation with the in situ LSTs, with a coefficient of determination of 0.94, a root-mean-square error (RMSE) of 2.6 K, and a bias of 0.3 K; 2) the retrieved LSTs were consistent with MODIS LST products, showing a root-mean-square difference (RMSD) of approximately 2.4 K; and 3) compared to the result of MOD11A1, MOD21A1 exhibited a significantly smaller bias. These results indicated that the proposed algorithm is effectively capable of estimating global LST from FY-3C VIRR TIR data with reasonable accuracy.

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