ESTIMATION OF LAND SURFACE TEMPERATURE FROM CHINESE GAOFEN-5 SATELLITE DATA

This work addressed the estimation of Land Surface Temperature (LST) from Chinese Gaofen-5 (GF-5) satellite Thermal Infrared (TIR) data, using a Generalized Split-Window (GSW) algorithm. The numerical values of the GSW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model MODTRAN 5 over a wide range of atmospheric and surface conditions. The LST, mean emissivity, and atmospheric Water Vapor Content (WVC) were divided into several tractable sub-ranges to improve the fitting accuracy. The experimental results showed that the combination of two adjacent channels CH8.20 (centered at 8.20 mu m) and CH8.63 (centered at 8.63 mu m) was comparable with the combination of two adjacent channels CH10.80 (centered at 10.80 mu m) and CH11.95 (centered at 11.92 mu m) for estimating LST using the GSW algorithm, with Root Mean Square Errors (RMSEs) below 0.8 K, provided that the Land Surface Emissivities (LSEs) are known. Particularly, for the high emissivity surfaces under wet and hot atmospheric conditions (WVC>3.0g/cm(2)), two not adjacent channels combination of CH8.63 and CH11.95 could also be used to estimate LST with RMSEs within 0.5 K.