A Physically Constrained Downscaling Framework for Hourly, All-Sky Land Surface Temperature in Mountainous Regions

High-resolution, all-weather land surface temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained land surface model (LSM) simulated LST downscaling and polar-orbiting satellite LST for reconstructing a time-series framework that is particularly effective in addressing the challenges associated with frequent cloud cover and precipitation in mountainous regions. The framework employs observations from 11 stations and MODIS data, integrating the Noah-MP and the ensemble Kalman filter to construct the data assimilation downscaling framework (DADF). This approach provides a comprehensive solution for acquiring high-resolution, all-weather LST, with both temporal (hourly) and spatial (1 km) precision. To improve the accuracy of LSMs and DADF, we corrected the momentum roughness lengths for each of the six surface categories and soil emissivity based on site observations. Six underlying surface parameters derived from measured data serve as a valuable reference for investigating land surface processes in this region. In a validation of MODIS day and night LST with data from 11 measurements, we observed high precision in high-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong sites A–E). The sand–air admixture in desert terrain, caused by wind, introduces errors in site-based observations. The average RMSE of DADF-LST is 3.85 K (compared to the RMSE of 4.72 K for MODIS LST), and the R² > 0.82 across six categories. The DADF incorporates data assimilation algorithms for LST downscaling, enabling accurate capture of actual LST under cloudy conditions. It provides a physically constrained solution for obtaining LST data with high spatial and temporal resolution globally.