Evaluation of MODIS LST Products over the Tibetan Plateau and Plain Areas with in Situ Measurements

Land surface temperature (LST) is a crucial physical parameter for hydrological, meteorological, climatological, and climate change studies. To encourage the use of satellite-derived LST products in a wide range of applications, providing feedback on product performance over regional and global scales is an urgent task. However, considering that the uncertainty of newly released LST products is still unclear, it is urgently necessary to perform a comprehensive validation and error analysis, especially in areas with special geographical and weather conditions such as the Tibetan Plateau (TP). In particular, fewer studies have been concerned with the degraded LST retrieval accuracy over the TP because of the sparse ground measurements. In this study, MODIS LST products (C6.1) were comprehensively evaluated based on independent ground observation systems with different atmospheric and LST conditions. The in situ measurements collected from the TORP and SURFRAD systems are located on the American Plain and the TP, respectively, incorporating various land cover types, including barren land, grassland, cropland, shrubland and sparse and dense vegetation, among others. The spatial representativeness evaluation indicated that relatively high-quality in situ LSTs can be obtained during nighttime. Compared with the North American Plain (with a mean RMSE of 1.56 K), MODIS LST retrievals have larger discrepancies (mean RMSE of 2.34 K) over the TP with complex terrain and weather conditions. Emissivity determination is the primary source of the uncertainty in the generalized split-window algorithm (GSW). Moreover, simulation settings of atmospheric and LST conditions in the GSW algorithm cannot cover a wide range of conditions at global scale. It is expected to develop new LST retrieval algorithm to meet the quality specifications of users over the TP. Overall, this study identifies critical further research needs and improves the understanding of LST product performance under complex circumstances.