A temperature separation algorithm of soil and vegetation considering hot-spot effect using dual-angle slstr and geostationary-satellite ahi data

Land surface component temperature (LSCT) is a vital parameter in many remote sensing applied fields such as drought monitoring and evapotranspiration. Previous large-scale two-source (vegetation and soil) LSCT retrieval method seldom considered the hot-spot effect of soil, thereby failing to account for the observed higher temperatures when the view direction is closer to the sun. Thus, an algorithm using both Sea and Land Surface Temperature Radiometer (SLSTR) and the Advanced Himawari Imager (AHI) dataset was performed and validated. The validation dataset was collected from Daman, China with underlying of forest and crop. The validation results demonstrated that the proposed method, which considers the hot-spot effect, significantly improves the accuracy of LSCT estimation, as indicated by root mean square error (RMSE) values of 2.58K and 3.34K for crops and trees, respectively. This study paving the way for improved understanding and applications of LSCT estimation account for the hot-spot effect in real-world scenarios.