This article proposes a combination method of nonlinear split-window (NSW) algorithm and temperature and emissivity separation (TES) algorithm to estimate land surface temperature (LST) from the remotely sensed data observed by the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4B (FY-4B), China’s second-generation meteorological geostationary satellite. The atmospheric radiation transfer model MODTRAN5.2 is used to simulate the AGRI thermal infrared (TIR) channel satellite observations in ten different viewing zenith angles (VZAs) from 0° to 70°. The optimal thermal channel combination and coefficients of the NSW algorithm are determined using a statistical regression method according to the grouping of the mean emissivity, the atmospheric water vapor content (WVC), and the LST. ERA5 reanalysis data provide atmospheric profiles for atmospheric correction, and then, the land surface emissivity (LSE) could be estimated according to the TES algorithm. The combination of Channel-12 (centered at

$8.55~\mu \text{m}$

) and Channel-14 (centered at

$12.00~\mu \text{m}$

) or the combination of Channel-13 (centered at

$10.80~\mu \text{m}$

) and Channel-14 (centered at

$12.00~\mu \text{m}$

) depends on different groups and VZAs. The statistical regression analysis showed that the root-mean-square error (RMSE) between the simulated and estimated LST is less than 0.7 and 1.8 K with the determined emissivity under VZA = 0° and VZA = 60°, respectively. Compared with the MODIS LST products (MYD11A1), the retrieved LST image has a similar spatial distribution, with the RMSE of 1.71 K.

J. Zhao

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