A comprehensive framework for large-scale all-weather land surface temperature generation by fusing satellite and simulated data

Thermal infrared remote sensing retrieval is a unique way to acquire large-scale and high-precision land surface temperature (LST), but is often affected by clouds and fog, leading to noticeable spatial gaps. In this article, to address this limitation, we propose a comprehensive framework that integrates satellite and model-simulated data to generate 1 km all-weather LST four times per day across China. Specifically, the framework comprises three key modules. First, we present a data quality optimization preprocessing approach that combines quality flags with morphological processing to balance the quantity and accuracy of satellite LST observations. To address the missing data, a two-stage spatiotemporal fusion method is developed that leverages the complementarity of time-series satellite and model-simulated LST. Additionally, a local-global cascade correction postprocessing strategy is designed to progressively refine the reconstructed results, ultimately achieving stable gapless LST. Upon validation and analysis, the proposed all-weather LST demonstrates a better performance compared to the existing large-scale LST datasets, exhibiting an advantage in mean absolute error ranging from 0.15 K to 0.83 K, verified by in-situ LST. Furthermore, the proposed LST product is consistent with objective geophysical principles and historical meteorological records, which can be anticipated to support research areas such as agriculture and climate change analysis.