A Two-Step Downscaling Model for MODIS Land Surface Temperature Based on Random Forests

High-spatiotemporal-resolution surface temperature data play a crucial role in monitoring urban heat island effects. Compared with Landsat 8, MODIS surface temperature products offer high temporal resolution but suffer from low spatial resolution. To address this limitation, a two-step downscaling model (TSDM) was developed in this study for MODIS surface temperature by leveraging random forest (RF) algorithms. The model integrates remote sensing data, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI), alongside the land cover type, digital elevation model (DEM), slope, and aspect. Additionally, a water surface temperature fitting model (RF-WST) was established to mitigate the issue of missing data over water bodies. Validation using Landsat 8 data reveals that the average out-of-bag (OOB) error for the RF-250 m model is 0.81, that for the RF-WST model is 0.73, and that for the RF-30 m model is 0.76. The root mean square error (RMSE) for all three models is below 1.3 K. The construction of the RF-WST model successfully supplements missing water body data in MODIS outputs, enhancing spatial detail. The downscaling model demonstrates strong performance in grassland areas and shows robust applicability during winter, spring, and autumn. However, due to a half-hour temporal discrepancy in the validation data during the summer, the model exhibits reduced accuracy in that season.