Downscaling of Urban Land Surface Temperature Based on Multi-Factor Geographically Weighted Regression

Land surface temperature (LST) is an important input parameter to characterize urban environmental heat change. Existing satellite-borne thermal infrared sensor technology cannot completely support the applications using high spatial resolution LST, such as analysis of urban thermal environment and energy consumption assessment. Downscaling LST is an alternative method to retrieve LST of high spatial resolution. In this paper, we propose an improved multi-factor geographically weighted regression (MFGWR) algorithm for LST downscaling. More factors were incorporated into geographically weighted regression method by taking into account different land covers and temporal variation so that the downscaled LST at urban areas with complicated land cover at various seasons was improved. It was applied to four urban areas with large difference on land cover at different seasons. Taking into account different factors, the temperature distribution of MFGWR reproduced additional spatial detail. Compared with the major statistical LST downscaling methods including thermal image sharpening algorithm (TsHarp), multiple scale factors with adaptive thresholds algorithm (MSFAT), support vector machine regression combined with gradient boosting (SVR-GB), and GWR, MFWGR showed a stable performance and higher accuracy.