Publications

Luo, XB; Chen, Y; Wang, Z; Li, H; Peng, YD (2021). Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 14, 7637-7653.

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

DOI:
10.1109/JSTARS.2021.3094184

ISSN:
1939-1404