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The occurrence of cloud directly affects the spatial and temporal continuity of surface temperature inverted by satellite remote sensing. In this study, a framework for reconstructing surface temperature with high spatial and temporal resolution based on data assimilation is constructed on the basis of multiple subsurface validation. (1) The accuracy of MYD11A1 LST data varies with terrain and land cover characteristics. High-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong A-E Sites) show high precision and less error, while agricultural fields (Daman) and desert transitional zones (Bajitan) exhibit more variability and larger errors. This suggests that the uniformity and stability of certain terrains, coupled with minimal atmospheric interference, enhance the accuracy of remote sensing observations. (2) A systematic bias, indicating a consistent underestimation of LST by the MYD11A1 product compared to ground-based observations, is observed across all sites. This bias is particularly pronounced in the presence of a sanding phenomenon, which results in a mixture of sand and air near the surface, leading to a lower station observation and a significant bias. (3) The Land Surface Temperature (LST) simulated by noah-MP exhibits a high degree of consistency with the LST observed through remote sensing. The significant correlation between the simulated LST and MODIS observations at the Kalsai and Arou stations indicates that noah-MP is highly applicable to mountain grassland surfaces. (4) A framework has been developed for reconstructing surface temperature with high temporal and spatial resolution, based on data assimilation. This method can generate all-weather, hourly surface temperature data.
Li et al. (Mon,) studied this question.