A Nonlinear Split-Window Algorithm for Retrieving Land Surface Temperatures from Fengyun-4B Thermal Infrared Data

This paper 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 MODTRAN 5.2 is used to simulate the AGRI thermal infrared channel satellite observations in 10 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 provides 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 μm) and Channel-14 (centered at 12.00 μm) or the combination of Channel-13 (centered at 10.80 μm) and Channel-14 (centered at 12.00 μm) 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 K 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.