Derivation of new split window algorithm for retrieving land surface temperature from FY-3/VIRR data

Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, methods for its estimation from space have continuously been developed, while most studies focus on the Split-Window (SW) algorithms. According to the published works, some approximations and assumptions were used to develop SW algorithms. This paper investigated and revised the error caused by these approximations and assumptions with the help of TIGR 2000 database and MODTRAN 4.0 software. Then a new SW method to estimate LST from FY-3A/VIRR was proposed in this paper. The primarily accuracy evaluation of the proposed method shows that the root mean square error (RMSE) of LST estimation using TIGR atmospheric profiles is 0.768 K, with the bias of -0.122 K.