STCCN: A spatiotemporal consistency constraint network for all-weather MODIS LST reconstruction by fusing reanalysis and thermal infrared data

Land surface temperature (LST) is a critical parameter for understanding the physical properties of the boundary between the earth's surface and the atmosphere, and it has a significant impact on various research areas, including agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensing is often hindered by clouds and aerosols, resulting in gaps in LST data products, which hinders the practical application of these products. Therefore, reconstruction of cloud-covered thermal infrared LST is vital for the measurement of physical properties in land surface at regional and global scales. In this paper, a novel reconstruction method for Moderate Resolution Imaging Spectroradiometer (MODIS) LST data with a 1-km spatial resolution is proposed by a spatiotemporal consistency constraint network (STCCN) model fusing reanalysis and thermal infrared data. Firstly, a new spatio-temporal consistency loss function was developed to minimize the discrepancies between the reconstructed LST and the actual LST, by using a non-local reinforced convolutional neural network. Secondly, ERA5 surface net solar radiation (SSR) data was applied as one of the important factors for network inputs, it can characterize the influence of the Sun on surface warming and correct the LST reconstruction results. The experimental results show that (1) the STCCN model can precisely reconstruct cloud-covered LST, the coefficient of determination (R) is 0.8973 and the mean absolute error (MAE) is 0.8070 K; (2) with the introduction of ERA5 SSR data, the MAE of reconstructed LST decreases by 17.15% while the R is kept close, indicating that it is necessary and beneficial to consider the effects of radiation data on LST; (3) the analysis of spatial and temporal adaptability indicates that the proposed method exhibits strong resilience and flexibility in accommodating variations across different spatial and temporal scales, suggesting its potential for effective and reliable application in different scenarios; (4) referring to the SURFRAD station observations, the reconstructed R ranges from 0.8 to 0.9, and MAE ranges from 1 to 3 K, demonstrating the high effectiveness and validity of the proposed model for reconstructing regional cloud-covered LST.