A Geographically and Temporally Weighted Regression Model for Spatial Downscaling of MODIS Land Surface Temperatures Over Urban Heterogeneous Regions

Abstract

The fine spatial resolution (~100 m) land surface temperature (LST) is a key variable of great concern in various environmental studies over urban heterogeneous regions. An improvement in the spatial resolution of the coarse spatial resolution LST is an effective way to extend its potential uses in applications that have strict requests on both the spatial and temporal resolutions. However, previous statistical downscaling algorithms were proposed mainly by addressing the spatial variability in the LST while neglecting the temporal variability. In this paper, we propose a new algorithm based on a geographically and temporally weighted regression (GTWR) model for spatial downscaling of the Moderate Resolution Imaging Spectroradiometer LST data from 1000 to 100 m. The GTWR-based algorithm with temporally and geographically varying regression coefficients can capture both the spatial and temporal variabilities in the LST from the time series data at a coarse spatial resolution for effectively reconstructing the subpixel variability in the LST at fine spatial resolution. In addition, because of a better ability to explain the LST variability over urban heterogeneous regions, a normalized difference built-up index and a digital elevation model were selected as auxiliary variables. Taking Beijing and Lanzhou as examples, the performance of the GTWR-based algorithm was assessed by comparing the results with the TsHARP and GWR-based algorithms and the Landsat-8 LST. The results indicate that the GTWR-based algorithm outperforms the above-mentioned algorithms with lower mean root mean square error (1.62 °C) and mean absolute error (1.28 °C) and better agreement between the GTWR downscaled LST and the Landsat-8 LST.