Simple yet efficient downscaling of land surface temperatures by suitably integrating kernel- and fusion-based methods

•SED that suitably combines the kernel- and fusion-based methods was proposed for LST downscaling.•SED uses a Landsat image to downscale MODIS LST by statistical regression.•SED has an average RMSE of 1.29 K over 50 diverse study areas worldwide.•SED is superior to kernel-based and fusion-based methods and their combinations. Kernel-based and fusion-based methods have been widely used to downscale satellite-derived land surface temperatures (LSTs) for obtaining LSTs with high spatiotemporal resolution. The integration of these two methods can potentially provide a more effective method for LST downscaling. However, a downscaling algorithm that appropriately integrates the advantages of these two methods in accuracy and stability, yet is characterized by adequate simplicity, effectiveness, and global applicability, is still lacking. To address this issue, here we proposed the Simple and Effective Downscaling (SED) algorithm, which integrates kernel- and fusion-based methods suitably and uses a single adjacent Landsat image to downscale MODIS LSTs by statistical regression. The SED was tested in 50 different study areas worldwide, and its performance was compared with four benchmark downscaling algorithms, including a kernel-based algorithm (the RFT21), a fusion-based algorithm (the FSDAF), and the simple average (the Hybrid 1) and weighted average (the Hybrid 2) of the RFT21 and the FSDAF. Our evaluations show that the SED performs better in almost all study areas and has finer global applicability when compared with these four benchmark algorithms. The mean RMSE of the SED is 1.29 K over these 50 study regions, with an accuracy improvement of over 13 % relative to the best benchmark algorithm. Particularly, the SED exhibits higher accuracies in tropical and temperate regions, over urban areas and savannas, and when using Landsat images closer to MODIS LSTs. We believe that the SED can contribute to the generation of high-quality and high spatiotemporal resolution LSTs over global lands.