Downscaling CLDAS Land Surface Temperature Using MODIS Data and a Multiattention Multiresidual Super-Resolution Network

Land surface temperature (LST) simulated by land surface model (LSM) can maintain spatial integrity and high temporal resolution. However, the relatively low spatial resolution limits the practical applications of LSM-simulated LST. Traditional downscaling methods often require lots of auxiliary data and suffer from significant loss of spatial details under large scale differences. To this end, we propose a super-resolution (SR) downscaling method based on multi-source reference using a multi-attention multi-residual network (MAMRN). The Moderate Resolution Imaging Spectroradiometer (MODIS) LST was used as the reference to improve the spatial resolution of LSM-simulated China Land Data Assimilation System (CLDAS) LST. Six regions with different land cover types covered the Chinese mainland were selected to test the MAMRN’s performance, and a traditional bilinear interpolation method and three deep learning-based SR methods were used for comparison. Comparative experiments demonstrate that MAMRN achieves improved performance, both visually and quantitatively in all six regions. Specifically, the average Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS) are 2.06 K, 16.76, and 0.54, respectively. The quantitative evaluation scores surpass those of the comparison methods. The transfer experiments and ablation studies also indicate MAMRN’s superiority and effectiveness. Validation by in-situ LST shows that MAMRN can accurately retrieve LST under both clear-sky and cloudy-sky conditions, with the overall accuracy of clear-sky slightly superior to that of cloudy-sky. In a word, MAMRN can enhance the spatial resolution of CLDAS LST (about 6.25 km) to MODIS (1 km) scale while preserving clear spatial details. This capability is beneficial for generating spatially integrity LST with high spatiotemporal resolution, and contributes to the study of global climate change. Our code can be available at https://github.com/AHU-RS/MAMRN.