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

Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there no satellite sensor that can completely provide LST data with both high spatial and temporal resolutions simultaneously. downscaling regarded as an effective remedy for improving the of data. In this article, geographically temporally weighted autoregressive (GTWAR) model comprehensively considers heterogeneity, autoregression, temporality newly proposed. The normalized difference water index, built-up vegetation index were selected explanatory variables to downscale moderate resolution imaging spectroradiometer (MODIS) from 1000 100 m, while Landsat 8 was reference Compared thermal sharpening (TsHARP), regression (GWR), (GWAR) (GTWR) methods, proposed method superior based on quantitative indices, lowest root mean square error (Zhangye: 1.57 °C, Beijing: 1.22 °C) absolute 1.06 0.85 °C). GTWAR will facilitate improvements accuracy series