A geographically weighted Durbin model for spatial downscaling of land surface temperatures

Xiaoqun Shen; Xiaobo Luo; Xingang Sang

doi:10.1117/12.2682581

1 June 2023 A geographically weighted Durbin model for spatial downscaling of land surface temperatures

Xiaoqun Shen, Xiaobo Luo, Xingang Sang

Proceedings Volume 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023); 127100R (2023) https://doi.org/10.1117/12.2682581
Event: International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 2023, Changsha, China

Abstract

To obtain land surface temperatures data with high spatiotemporal resolution, A Geographically Weighted Durbin Model (GWDM) for Spatial Downscaling of Land Surface Temperatures is newly proposed in this study. The normalized difference water index (NDWI), the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI) were selected as scale factors to conduct downscaling experiments. Beijing and Zhangye were taken as the study area. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR). The results indicate that the GWDM-based algorithm has better spatial texture and is closer to the real image. The determination coefficient (Beijing: 0.88, Zhangye: 0.91), mean absolute error (Beijing: 0.85℃, Zhangye: 1.06℃) and root mean square error (Beijing: 1.22℃, Zhangye: 1.57℃) are better than the other three methods.

Citation Download Citation

Xiaoqun Shen, Xiaobo Luo, and Xingang Sang "A geographically weighted Durbin model for spatial downscaling of land surface temperatures", Proc. SPIE 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 127100R (1 June 2023); https://doi.org/10.1117/12.2682581

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KEYWORDS

Landsat

Earth observing sensors

Spatial resolution

Near infrared

Autocorrelation

MODIS

Data modeling

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