Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery

Urban surface temperatures are increasing because of climate change and rapid urbanisation, contributing to the urban heat island (UHI) effect and significantly influencing local climates. Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. This limitation affects the assessments of UHI effects and hinders effective mitigation strategies. We proposed a hybrid model named "geospatial machine learning" (GeoML) to address these challenges, combining random forest and kriging downscaling techniques. This method utilises high spatial resolution data from Sentinel-2 to enhance the LST derived from Landsat 8/9 data. Tested in Perth, Australia, GeoML generated an enhanced LST with good agreement with ground-based measurements, with a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) of 2.7 °C, and a mean absolute error (MAE) of less than 2.2 °C. Validation with LST derived from another TIR sensor also provided promising outputs. The results were compared with the high-resolution urban thermal sharpener (HUTS) downscaling methods, which GeoML outperformed, demonstrating its effectiveness as a valuable tool for urban thermal studies involving high-resolution LST data.