A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression

Abstract

Spatial downscaling is an important approach to obtain high-resolution land surface temperature (LST) for thermal environment research. However, existing downscaling methods are unable to sufficiently address both spatial heterogeneity and complex nonlinearity, especially in high-resolution scenes (2 of 0.974 and minimum RMSE of 0.896 °C) compared to widely used methods in four test areas with large differences in topography, landforms, and seasons. We also achieved the best extracted and most detailed spatial textures. Our findings suggest that GNNWR is a practical method for surface temperature downscaling considering its high accuracy and model performance.

Keywords

• land surface temperature
• spatial downscaling
• geographical neural network weighted regression
• spatial heterogeneity
• complex nonlinearity