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) LST retrieval algorithms. This paper extended National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm to Feng Yun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) thermal infrared (TIR) data by incorporating a daily LSE database for high-temporal resolution retrieval. To improve accuracy, day/night coefficients were calculated different total water vapor content view zenith angle conditions using simulation constructed MODTRAN 5.2 SeeBor V5.0 atmospheric profiles. The validation results show that AGRI better accuracy than retrieved from vegetation cover method (VCM), with average biases of -1.1×10 -3 -6×10 channels 12 13. is slightly VCM-retrieved LSE. overall bias, MAE, RMSE at fourteen situ sites are 0.11, 2.55, 2.55 K, whereas these values -0.11, 2.70 K study demonstrates physically can SW algorithm. high spatial coverage dynamic information provide nearly complete if supplemented 8-day or monthly It also be applied other algorithms need as priori.