Analysis of Sensibility on Split-window Algorithms for Retrieving Land-surface Temperature
陆面温度反演算法——劈窗算法的敏感度分析

Thermal infrared remote sensing technology supplies an attractive way to measure land_suface temperature (LST) on a large scale simultaneously. NOAA_AVHRR data is commonly used in the inversion study of LST. But the inversion is a challenge for the scientists because of the complexity of land surface. So far split_window algorithm is a major solution to retrieve land_surface temperature from thermal infrared remote sensing data. But because of the obstacle to obtain the in_situ validation measurements simultaneously, including land_surface temperature and emissivity especially in pixel scale, it is confined to judge directly which one is more precious and more applicable.In this paper, using radiative transfer code LOWTRAN_7 and six standard atmosphere models supplied by it, we analyzed, by simulation, the sensibility of the six common split_window algorithms to the atmospheric profile error, including atmospheric water vapor profile and temperature profile. The sensibility to spectral emissivity, including the average emissivity and the differential emissivity between AVHRR channels 4 and 5, is also analyzed. It turns out that, (1) algorithms of SOB and UVM have less sensibility to atmospheric profile, and have more satisfactory accuracy of better than 1.5K; (2) most algorithms are more sensible to the mean emissivity than to the emissivity difference. Sobrino (1991) algorithm has the least sensibility to emissivity. As a result, we obtained an indirect criterion priliminarily for the six split_window algorithms.