A hybrid ILSTM-DDTC framework for generating all-weather hourly land surface temperature across China

: Land surface temperature (LST) plays a critical role in understanding the interactions between the land and atmosphere. Particularly, hourly LST data holds significant potential for a variety of applications, such as improving the understanding of daily fluctuations in surface heat fluxes, enhancing the accuracy of weather and climate forecasting, and supporting ecosystem management. The reconstruction of all-weather hourly LST is both essential and valuable. Currently, thermal infrared (TIR) LST data is often limited by infrequent observations and adverse weather conditions such as clouds and precipitation. These factors result in the generation of LST data at daily or even coarser temporal resolutions, with hourly LST datasets remaining scarce. Recently, advances in sub-hourly observations from geostationary satellites (e.g., Himawari-8), minute-by-minute ground truth data from automated meteorological stations, and hourly reanalysis datasets have made considerable progress. This raises the question: is it feasible to generate a gapless hourly LST product by integrating high-frequency observational multi-source data? In this study, to generate all-weather hourly LST, we applied an optimized Long Short-Term Memory (LSTM) model (referred to as ILSTM) to fuse multi-source data and produce hourly LST under cloud-free conditions. For cloud-covered conditions, a Dynamic Diurnal Temperature Cycle (DDTC) model was proposed to reconstruct hourly LST across China, which integrates real-time physical covariates (cloud optical depth, downwelling radiation, land cover, etc.) and uses Gradient-Boosted Regression Trees (GBRT) to dynamically parameterize diurnal LST trends. When validated against in-situ radiation flux stations, the results showed an RMSE of 2.876 K for cloud-free LST and 3.314 K for cloud-covered LST. Compared with MODIS LST, the reconstructed all-weather hourly LST data displayed consistent spatial coverage without significant boundary reconstruction anomalies. The findings of this study demonstrate the effectiveness and reliability of the proposed method for generating all-weather hourly LST data.