A Spatio-Temporal Consistency-Guided Global-Local Fusion Network for All-Weather LST Reconstruction

Land surface temperature (LST) serves as a key indicator for studying the thermal characteristics of the land-atmosphere interface. Nevertheless, cloud cover and atmospheric particulates frequently obstruct the thermal infrared (TIR) spectral range in satellite observations, leading to significant data missing in derived LST products. Multi-source data fusion methods are the most effective means for all-weather TIR LST reconstruction. However, most of the existing multi-source data fusion reconstruction methods only consider the spatial and temporal differences individually, which makes it challenging to ensure spatio-temporal consistency in LST gap-filling. In this study, a novel framework for LST reconstruction is proposed by constructing a spatio-temporal consistency-guided global-local fusion network (STCGL-Net) to fuse TIR and reanalysis data. Utilizing a deep convolutional network architecture, the STCGL-Net efficiently captures the local spatial features and texture details of multi-source observations. At the same time, spatio-temporal consistency Transformer and coordinate attention mechanisms are cleverly embedded to capture the global spatiotemporal dependencies that drive changes in LST. Using spatio-temporal consistency as the core physical constraint to guide the STCGL-Net's training process ensures that the reconstructed LST is not only numerically accurate, but also evolves in accordance with physical laws. As a key input parameter, ERA5 surface solar radiation data quantitatively characterize solar forcing on land surface thermal dynamics and enhance LST reconstruction accuracy. Evaluation demonstrates STCGL-Net achieves cloud-contaminated LST reconstruction with R²=0.87 and MAE=0.54K. Comparative evaluations with three established classic methods confirm the STCGL-Net’s consistent performance across various spatial and temporal scales. Validation against Surface Radiation Budget station LST, confirms reconstruction robustness (R²:0.8-0.9, MAE:3-5K), establishing reliable all-weather LST generation capability.