Land Surface Temperature and Emissivity Estimation from MODIS Observations

Abstract

Land surface temperature (LST) is a key variable for studying global or regional land surface processes, energy and water cycle, and thus, has important applications in various areas. LST retrieval, however, is a difficult subject and a challenge issue due to complex interactions between land surface and atmosphere. Based on the water vapor dependent (WVD) and the extended water vapor dependent (EWVD) algorithms, a new approach for separating and determining LST and land surface emissivity (LSE) using only two MODIS thermal infrared (TIR) channels is proposed. In the algorithm, both WVD and EWVD algorithms are used to get first guess estimates of LST and at-surface brightness temperature, which are used to determine LSE, first. Then, the LST is recalculated from the surface radiance combined with the estimated LSE. Compared with the MODIS LST and LSE products, the root mean square error (RMSE) of LST and LSE retrieved by our algorithm is 0.62K, and 0.008, respectively. The advantages of the proposed algorithm is discussed briefly. The preliminary results show that the new algorithm is able to provide an accurate estimation of LST and emissivity from MODIS data. And compared with the current MODIS algorithm, our algorithm is more easy to be implemented.