Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it is a crucial parameter in various climate and environmental models. Compared to other multispectral bands, the thermal infrared bands have lower spatial resolution, which limits their practical applications. Taking the Heihe River Basin in China as a case study, this research focuses on LST data retrieved from the SDGSAT-1 using the three-channel split-window algorithm. In this paper, we propose a novel approach, the Information-Guided Diffusion Model (IGDM), and apply it to downscale the SDGSAT-1 LST image. The results indicate that the downscaling accuracy of the SDGSAT-1 LST image using the proposed IGDM model outperforms that of Linear, Enhanced Deep Super-Resolution Network (EDSR), Super-Resolution Convolutional Neural Network (SRCNN), Discrete Cosine Transform and Local Spatial Attention (DCTLSA), and Denoising Diffusion Probabilistic Models (DDPM). Specifically, the RMSE of IGDM is reduced by 55.16%, 51.29%, 48.39%, 52.88%, and 17.18%. By incorporating auxiliary information, particularly when using NDVI and NDWI as auxiliary inputs, the performance of the IGDM model is significantly improved. Compared to DDPM, the RMSE of IGDM decreased from 0.666 to 0.574, MAE dropped from 0.517 to 0.376, and PSNR increased from 38.55 to 40.27. Overall, the results highlight the effectiveness of the auxiliary information-guided SDGSAT-1 LST downscaling diffusion model in generating high-resolution remote sensing LST data. Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges.