Impact of Atmospheric Correction on Spatial Heterogeneity Relations Between Land Surface Temperature and Biophysical Compositions

文献类型： 外文期刊

第一作者： Zhu, Xin-Ming

作者： Zhu, Xin-Ming;Song, Xiao-Ning;Guo, Da;Cai, Shuo-Hao;Leng, Pei

作者机构：

关键词： Atmospheric effect; driving mechanism; geodetector; land surface temperature (LST); spatial variability

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：5.6； 五年影响因子：6.086 ）

ISSN： 0196-2892

年卷期： 2021 年 59 卷 3 期

页码：

收录情况： SCI

摘要： Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi'an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.

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