High-Resolution Downscaling of Urban Land Surface Temperature via Machine Learning
TL;DR
It is demonstrated that enhancing the spatial resolution of urban LST is essential for urban applications, including modeling surface energy fluxes, pedestrian-level heat exposure, and energy consumption, all of which benefit from higher spatial resolution.
Abstract
Abstract. Land surface temperature (LST) obtained from satellite observations is a key parameter for understanding Earth surface-atmosphere energy exchange and urban thermal environments. However, the use of existing satellite-derived LST datasets for urban applications is limited by the coarse spatial resolution and the mixed-pixel problem. By integrating both two-dimensional (2D) surface properties and three-dimensional (3D) urban morphological characteristics, this study proposes a machine learning-based framework for high-resolution downscaling of satellite-based urban land surface temperature (SULST). A Random Forest model was developed to generate a 1-m downscaled SULST (DSULST) map. The model demonstrates strong performance, with a Pearson correlation coefficient of 0.89, RMSE of 1.15 K, NRMSE of 0.095, MAE of 0.56 K, and an index of agreement of 0.95. The 1-m DSULST maps reveal substantial sub-pixel thermal heterogeneity that is not captured by conventional 30-m LST data. Fine-scale spatial patterns associated with vegetation, building structures, and roads are clearly resolved in the downscaled 1-m temperature maps. These results highlight a critical limitation of satellite-derived LST in representing intra-urban thermal variability. The findings demonstrate that enhancing the spatial resolution of urban LST is essential for urban applications, including modeling surface energy fluxes, pedestrian-level heat exposure, and energy consumption, all of which benefit from higher spatial resolution.