Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

Xin Pan; Chen Cao; Yingbao Yang; Xiaolong Li; Liangliang Shan; Xi Zhu

doi:10.1117/12.2302495

10 April 2018 Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

Xin Pan, Chen Cao, Yingbao Yang, Xiaolong Li, Liangliang Shan, Xi Zhu

Author Affiliations +

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106154C (2018) https://doi.org/10.1117/12.2302495
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of ±0.02 predictor perturbances, respectively.

Citation Download Citation

Xin Pan, Chen Cao, Yingbao Yang, Xiaolong Li, Liangliang Shan, and Xi Zhu "Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106154C (10 April 2018); https://doi.org/10.1117/12.2302495

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