Estimating Hourly Land Surface Temperature From FY-4A AGRI Using an Explicitly Emissivity-Dependent Split-Window Algorithm

Land surface emissivity (LSE) has been roughly treated in the current split-window (SW) land surface temperature (LST) retrieval algorithms. This article extended the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm to Feng Yun-4A/Advanced Geostationary Radiation Imager (AGRI) thermal infrared data by incorporating a daily LSE database for high-temporal-resolution LST retrieval. To improve the retrieval accuracy, day/night SW algorithm coefficients were calculated for different total water vapor content and view zenith angle conditions using a simulation database constructed by moderate spectral resolution atmospheric transmittance model version 5.2 and SeeBor V5.0 atmospheric profiles. The validation results show that the daily AGRI LSE has better accuracy than the LSE retrieved from the vegetation cover method (VCM), with average biases of −1.1×10⁻³ and −6×10⁻³ for channels 12 and 13. The accuracy of the AGRI LST retrieved using the daily AGRI LSE is slightly better than that retrieved using the VCM-retrieved LSE. The overall bias, MAE, and root mean square error of the AGRI LST retrieved using the daily AGRI LSE at 14 in situ sites are 0.11, 2.55, and 2.55 K, respectively, whereas these values are −0.11, 2.70, and 2.70 K, respectively, for the LST using the VCM-retrieved LSE. This study demonstrates that the daily LSE constructed from physically retrieved LSE can improve the accuracy of LST retrieved with the SW algorithm. The constructed daily LSE has high spatial coverage and dynamic emissivity information and can provide nearly complete spatial coverage if supplemented by the constructed eight-day or monthly AGRI LSE. It can also be applied to other LST retrieval algorithms that need LSE a priori.