A Two-Stage Surface Energy Balance-Guided Graph Neural Network for Reconstruction of Missing Land Surface Temperature

Land surface temperature (LST) is a key parameter in the land surface energy cycle. However, due to cloud contamination, LST products derived from thermal infrared remote sensing exhibit significant missing areas. Although many studies have attempted to reconstruct missing areas using reanalysis data, such as the land component of the fifth generation of European ReAnalysis (ERA5 Land), existing methods neglect the temporal differences between MODIS and reanalysis data, suffering from several limitations in applicability. Therefore, a two-stage surface energy balance-guided graph neural network (TSSEB-GNN) was developed, with the innovation of coupling the network with physical mechanisms. First, the physical factors influencing LST were determined based on surface energy balance theory and the Bowen ratio. Second, the stage one model based on GNN module was designed to generate an initial gap-free LST. Third, the second stage model fuses the initial LST by adding sparse and dense self-attention mechanisms and incorporating twelve physical factors to capture spatial and temporal differences. The results show that the TSSEB-GNN model achieves a root mean square error (RMSE) of 2.56–3.45 K under clear-sky conditions and 3.39–3.63 K under cloudy-sky conditions. Compared with existing methods, TSSEB-GNN not only provides higher accuracy but is also less affected by long-term gaps in MODIS LST. Ablation experiments demonstrate that the TSSEB-GNN model achieves the best performance by accounting for both temporal and spatial differences. The results highlight the effectiveness of the TSSEB-GNN, its interpretability, improved applicability, and greater potential for large-scale applications.