Spatiotemporal Downscaling Method of Land Surface Temperature Based on Daily Change Model of Temperature

Land surface temperature (LST) is one of the most crucial variables of surface energy processes. However, the trade-off between spatial and temporal resolutions of remote sensing data has greatly limited the availability of concurrently high-spatiotemporal resolution LST data for wide applications. Existing downscaling methods are easily affected by null values of LST data and effective time distribution of high-resolution LST data, resulting in large downscaling errors at sometimes. Within this context, this study proposes a novel spatiotemporal fusion model of LST based on diurnal variation information (BDSTFM) to predict LST data with a high temporal resolution and spatiotemporal continuity based on FY-4A and MODIS. Results indicated that the accuracy of the downscaling results was comparable to that of MODIS LST products. The BDSTFM model exhibited the following characteristics: use low-spatial resolution data to establish a diurnal temperature cycle (DTC) model for scale deduction, and retention of the temporal distribution characteristics of LST data; extend the observation time of high-spatial resolution data to improve the accuracy and stability of the model; add an invalid pixel reconstruction step that considers the LST spatiotemporal continuity, and can obtain a realistic and reliable 1-km seamless LST datasets at hourly intervals under clear skies. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), 4-parameter DTC model, and Random Forest model, the BDSTFM model attained a higher downscaling accuracy.