Estimation of Land Surface Temperature Using FengYun-2E (FY-2E) Data: A Case Study of the Source Area of the Yellow River

文献类型： 外文期刊

第一作者： Song, Xiaoning

作者： Song, Xiaoning;Chuan, Sun;Wang, Yawei;Peng, Jian;Loew, Alexander;Tang, Bohui;Leng, Pei

作者机构：

关键词： FengYun-2E (FY-2E);land surface temperature (LST);moderate-resolution imaging spectroradiometer (MODIS);split-window algorithm

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2017 年 10 卷 8 期

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is a key variable used for studies of water cycles and energy budgets of land-atmosphere interfaces. This study addresses the theory of LST retrieval from data acquired by the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) in two thermal infrared channels (IR1: 10.29-11.45 mu m and IR2: 11.59-12.79 mu m) using a generalized split-window algorithm. Specifically, land surface emissivity (LSE) in the two thermal infrared channels is estimated from the LSE in channels 31 and 32 of the moderate-resolution imaging spectroradiometer (MODIS) product. In addition, an eight-day composition MODIS LSE product (MOD11A2) and the daily MODIS LSE product (MOD11A1) are used in the algorithm to estimate FY-2E emissivities. The results indicate that the LST derived from MOD11A1 is more accurate and, therefore, more appropriate for daily cloud-free LST estimation. Finally, the estimated LST was validated using the MODIS LST product for the heterogeneous source area of the Yellow River. The results show a significant correlation between the two datasets, with a correlation coefficient (R) varying from 0.60 to 0.94 and a root mean square error ranging from 1.89 to 3.71 K. Moreover, the estimated LST agrees well with ground-measured soil temperatures, with an R of 0.98.

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