Hourly All-Weather Land Surface Temperature Estimation Through Data Assimilation of Fengyun-4A Satellite Observations and Model Simulations

Land surface temperature (LST) is a critical parameter for monitoring surface energy balance and evaluating climate and environmental changes. However, LST retrieval from thermal infrared satellite remote sensing often suffers from data gaps in cloud-affected regions. Existing methods for estimating cloud-covered LST do not adequately account for physical mechanisms under complex meteorological and surface conditions, nor do they address dynamic error variations during the fusion process. To address these limitations, this study integrates the numerical weather prediction model [weather research and forecasting (WRF)], the land surface model (Noah-MP), and the satellite observation data. It comprehensively evaluates the accuracy of the LST simulated by the WRF and Noah-MP. Moreover, a data assimilation and fusion method based on the Kalman filter is used to consider the changes of errors, and the dynamic fusion of these LST data is carried out to obtain the hourly LST with a resolution of 1 km. Furthermore, assimilating downward shortwave and longwave radiation into the Noah-MP model improves its simulated LST accuracy to a certain extent. The fused LST is not only spatially continuous but also exhibits improved reliability. Validation with in situ measurements shows that the root mean square error (RMSE) under clear-sky conditions is 2.56 K, and the RMSE of the LST under all-weather conditions is 2.88 K. This method has good potential in generating spatially continuous LST with high temporal and spatial resolution, thus promoting relevant research and applications.