Guided Super Resolution of Land Surface Temperature Using Multisatellite Imageries

As understanding and monitoring global warming and heatwaves have become increasingly important, the demand for higher spatial and temporal resolution in satellite-observed Land Surface Temperature (LST) data has risen. LST derived from geostationary satellite observations plays a crucial role in temperature monitoring, offering high temporal resolution across wide areas. However, a primary limitation of geostationary satellite-derived LST products is their low spatial resolution. In this study, we aim to overcome this limitation by employing a deep learning-based super-resolution (SR) approach and propose a model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">References and Residual in Residual Blocks to Perform Super Resolution Network (3R-Net)</i>. This model incorporates terrain-guided reference images and residual blocks to enable more accurate super-resolution. By using LST products from GEO-KOMPSAT-2A (GK2A) as low-resolution input, visible channel imagery from GEO-KOMPSAT-2B (GK2B) as terrain-reflective high-resolution reference, and Landsat-8 LST products as high-resolution target images, our model effectively enhances the 2 km resolution of GK2A LST products to the 500 m resolution of Landsat-8 with improved spatial detail and accuracy. Unlike many previous studies relying on single-image super-resolution with synthetically downsampled inputs, our approach uses real-world LR inputs, HR targets, and reference images, making the learning process more realistic and practical. Experimental results show that, when clear guidance is provided through reference images, 3R-Net surpasses existing SR methods, achieving higher PSNR, SSIM, and lower RMSE while capturing critical spatial and temporal features, including surface characteristics and daily heating patterns. By integrating residual-in-residual blocks our model achieves a simple yet powerful enhancement in capturing fine-grained spatial and temporal patterns. These advancements suggest that 3R-Net can provide enhanced LST data crucial for climate research, environmental monitoring, and early warning systems.