A Solar Radiation-Based Method for Generating Spatially Seamless and Temporally Consistent Land Surface Temperature

Because of the primary role of land surface temperature (LST) in the physical processes of surface energy balance at local through global scales, dynamic, continuous, and seamless LST monitoring is constantly in urgent need. Thermal infrared (TIR) remote sensing serves as the most commonly used sources for LST retrieval owing to its relatively fine spatial-temporal resolution and presentable accuracy. However, limited by the inability to penetrate clouds, original TIR LST data suffers significantly from data missing problems. Furthermore, the view time of pixels along the scan line differs significantly for polar-orbiting satellites, exerting appreciable influence on the subsequent data applications. To cope with the above setbacks simultaneously, we proposed a practical reconstruction framework based on the inner physical connection between LST and solar radiation, which was accurately expressed by random forest regression model, with the consideration of various auxiliary environmental factors (i.e., elevation, slope, longitude, latitude, and surface reflectance). Taking the Tibetan Plateau (TP) as the study area, the proposed method was applied to generate spatially seamless and time-consistent LST products with the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime LST product. From visual assessment, the reconstructed product exhibits ideal spatial-temporal continuity within the TP. Through the validation with in-situ observations from five different stations, the results show a higher consistency with ground measurements than the LST product from the Global Land Data Assimilation System (GLDAS) and other all-weather LST product, with an average improvement on RMSE of 1.06 K and 1.59 K under clear conditions, and 1.86 K and 2.72 K under cloudy conditions. The validation demonstrates that the proposed method is well applicable for all-weather LST reconstruction over a large-scale area with significant surface heterogeneity, which also shows good ability to remove the temporal inconsistency induced by satellite observations. Additionally, it can be reliably generalized to different areas with similar data requirements for its sufficient effectiveness and flexibility.