Spatial downscaling of land surface temperature with the multi-scale geographically weighted regression

Abstract

Due to the limitations of thermal infrared technology, a single sensor cannot provide both high frequency and fine spatial resolution Land Surface Temperature (LST) data. For solving this problem, it becomes an effective way by conducting the spatial downscaling of LST product with low-resolution and high frequency in collaboration with other auxiliary data. However, the existing LST downscaling methods do not fully consider the scale effects of different biophysical parameters on the distribution of LST, which makes the accuracy and spatial distribution of the downscaled LST are inconsistent. In view of this, taking Beijing and Zhangye as two study areas, this paper proposed a kind of LST downscaling algorithm to sharpen the MODIS LST using Multi-scale Geographically Weighted Regression (MGWR) according to the analyse of effects of NDVI, DEM, slope, latitude, and longitude on LST heterogeneity. Furthermore, four kinds of LST downscaling methods (i.e., TsHARP algorithm, ML algorithm, GWR algorithm, and RF algorithm) were introduced in this paper for further comparison and validation. Results show that the constructed LST conversion function based on the MGWR reveals the actual interaction between various scale factors and LST at various spatial scales. NDVI and slope have global impacts on the LST, while DEM and geolocation present local impacts on the LST. Compared with the four referenced methods, the downscaled 100 m resolution LST based on the MGWR has better spatial textures and displays clear landscape features in heterogeneous areas such as deserts and towns. In addition, all images predicted by the MGWR algorithm showed better accuracy, in which the area proportion under the 0-1 K error level were all more than 57%, the root-mean-square error (RMSE) were all less than 2.85 K, and the coefficient of determination (R2) were all more than 0.88.