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Land Surface Temperature Retrieval From Channel Resolution Enhanced FY-3D/MWRI Observations

文献类型： 外文期刊

作者： Wang, Binqian 1 ; Zhou, Fang-Cheng 3 ; Leng, Pei 1 ; Bai, Yihong 3 ; Zhang, Xia 2 ; Shang, Guo-Fei 2 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing 100081, Peoples R China

2.Hebei GEO Univ, Sch Land Resources & Urban Rural Planning, Shijiazhuang 050030, Peoples R China

3.Natl Satellite Meteorol Ctr, Beijing 100082, Peoples R China

关键词： Channel resolution enhanced (CRE) FengYun-3D (FY-3D)/microwave radiation imager (MWRI); land surface temperature (LST); passive microwave (PMW); precipitable water vapor (PWV)-cloud liquid water (CLW) method; thermal infrared (TIR); three-channel method; Channel resolution enhanced (CRE) FengYun-3D (FY-3D)/microwave radiation imager (MWRI); land surface temperature (LST); passive microwave (PMW); precipitable water vapor (PWV)-cloud liquid water (CLW) method; thermal infrared (TIR); three-channel method

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is a critical parameter in meteorology, hydrology, and environmental science. Compared to thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing for LST retrieval offers advantage under cloudy conditions. In this study, we utilized the channel resolution enhanced (CRE) microwave radiation imager (MWRI) brightness temperature data from Chinese FengYun-3D (FY-3D) polar-orbiting meteorological satellite as the primary input to obtain global LST. Two physics-based PMW retrieval methods were introduced: the three-channel method (18.7, 36.5, and 89.0 GHz) and the precipitable water vapor (PWV)-cloud liquid water (CLW) method, which integrates the 18.7- and 23.8-GHz channels with PWV and CLW. The results indicate that both methods have generally achieved good accuracy. The three-channel method performs well in grasslands and barren lands during the daytime, with a root-mean-square error (RMSE) ranging from 4 to 5 K. At night, it excels in grasslands, croplands, and barren lands, with an RMSE from 2 to 3 K. The PWV-CLW method demonstrates superior accuracy for forests and croplands during the daytime, with RMSE values from 3.6 to 5.3 K. At night, this method excels in accuracy for forests, with an RMSE of 2.9 K. Additionally, a fusion method was proposed to improve the overall accuracy of LST estimation across different land cover (LC) types. The RMSE values for ascending and descending overpasses are 4.22 and 2.76 K, with biases of -0.29 and -0.6 K, respectively. This approach effectively mitigates spatial heterogeneity and atmospheric effects, enabling all-weather LST retrieval and showcasing the potential of CRE FY-3D/MWRI data for LST monitoring.

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