首页|Land Surface Temperature Downscaling Using Random Forest Regression: Primary Result and Sensitivity Analysis

Land Surface Temperature Downscaling Using Random Forest Regression: Primary Result and Sensitivity Analysis

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  • NETL
  • Spie-Int Soc Optical Engineering
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。

Land surface temperaturedownscalingrandom forest regressionsensitivity analysis

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

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School of Earth Science and Engineering, Hohai University, 8 Buddha City West Road, Nanjing 210098, China

International conference on graphic and image processing

Qingdao(CN)

Ninth international conference on graphic and image processing

106154C.1-106154C.8

2017

10.1117/12.2302495