Estimation and Validation of Land Surface Temperature Using Chinese Geostationary FengYun Meteorological Satellite (FY-2D) Data in an Arid Region

This study calibrated a refined split-window algorithm for land surface temperature (LST) retrieval based on Fengyun-2D (FY-2D) meteorological satellite. First, FY-2D land surface emissivity (LSE) was predicted from Moderate-resolution Imaging Spectroradiometer (MODIS) LSE based on sensors spectral similarities. The retrieved FY-2D LST data were validated in an arid region where the traditional split-window algorithm generally performed unsatisfactorily. Validation results show R-2 (coefficient of determination) and RMSE (root mean square error) values range 0.53-0.67 and 2.86-6.21 K, respectively, against ground observed LST. Better LST retrievals were observed over vegetated regions with an RMSE value of similar to 2.8 K. Spatially, the FY-2D LST was highly correlated (R-2 = 0.83) with and showed marginal differences (+/- 2 K) from MODIS LST for similar to 40% of the whole area.