A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the geographically weighted regression (GWR) and random forest (RF), a new downscaling method [i.e., weighted GWR (WGWR)] considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 thermal infrared sensor (TIRS) and Terra advanced spaceborne thermal emission and reflection radiometer (ASTER) LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the root mean square error (RMSE) can be reduced by 0.34 K (0.26 K) in Zhangye (ZY) and 0.22 K (0.1 K) in Beijing (BJ). Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based on in situ LST indicates that the downscaled LST based on WGWR has better agreement with the in situ LST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.