Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model

Land surface temperature (LST) is a key parameter in numerous thermal environmental studies. Due to technical constraints, satellite thermal sensors are unable to supply thermal infrared images with simultaneous high spatial and temporal resolution. LST downscaling algorithms can alleviate this problem and improve the spatiotemporal resolution of LST data. Spatial nonstationary and spatial autocorrelation coexist in most spatial variables. The spatial characteristics of the LST should be fully considered as a spatial variable in the downscaling process. However, previous studies on LST downscaling considered only spatial nonstationary, and spatial autocorrelation was neglected. In this article, we propose a new algorithm based on the geographically weighted autoregressive (GWAR) model for LST spatial downscaling. The digital elevation model and normalized difference build-up index were chosen as explanatory variables to downscale the spatial resolution of the moderate resolution imaging spectroradiometer LST data from 1000 to 100 m, and Lanzhou and Beijing were taken as the study areas. The performance of the GWAR model was compared with that of the thermal data sharpening (TsHARP) model and the geographically weighted regression (GWR) model. The Landsat 8 LST was used to verify the downscaled LST. The results indicate that the GWAR-based algorithm outperforms the TsHARP- and GWR-based algorithms with lower root mean square error (Beijing: 1.37 °C, Lanzhou: 1.76 °C) and mean absolute error (Beijing: 0.86 °C, Lanzhou: 1.33 °C).