Estimate of Land Surface Temperature from MTSAT-1R Observations

Land surface temperature (LST) is a key parameter in the course of interaction between land and atmosphere, and is particularly significant for the research of surface energy balance and terrestrial water cycle. A practical algorithm is developed to retrieve LST from the Multi-functional Transport Satellite (MTSAT), which was launched in 2005 by Japan Meteorological Agency (JMA) and can provide hourly data including two thermal infrared bands (IR1in 10.3-11.3 mu m and IR2 in 11.5-12.5 mu m). A classified Split-Window algorithm is developed in this paper under various atmospheric and surface conditions through simulations by MODTRAN 4. The coefficients in the algorithm are separated in several groups by a series of different parameters including the atmospheric water vapor content, the emissivity and the satellite zenith angle for their large impact on the LST retrieval accuracy. In this paper, the atmospheric water vapor content is retrieved using the Split-Window Covariance-Variance Ratio (SWCVR) method. And the land surface emissivity of MTSAT thermal infrared channels is estimated from data in channels 31 (10.78-11.28 mu m) and 32 (11.77-12.27 mu m) of Moderate Resolution Imaging Spectroradiometer (MODIS), respectively, provided by the MODIS LST product MOD11B1 using the three spectral datasets, UCSB, JHU and ASTER. Finally, validation for MTSAT LST is done by comparison with LST retrieved from Fengyun Meteorological Satellite (FY), airborne and ground observations collected during the HiWATER (Heihe Watershed Allied Telemetry Experimental Research) in the Heihe river basin of China. The results show small discrepancy with in-situ measurements and consistent pattern in regional scale with FY-2C. The analysis indicates that the classified Split-Window algorithm can be successfully applied to the LST retrievals from MTSAT data.