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Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches

文献类型：期刊论文

作者Shi, Tiezhu1,2,3; Yang, Chao1,2,3; Liu, Huizeng1,2,3; Wu, Chao4,5; Wang, Zhihua6; Li, He6; Zhang, Huifang6; Guo, Long7; Wu, Guofeng1,2,3; Su, Fenzhen6
刊名ENVIRONMENTAL POLLUTION
出版日期2021-03-01
卷号272页码:10
关键词Jenny's state factor model Visible and near-infrared reflectance spectroscopy Landsat image Geographically weighted regression Regression kriging
ISSN号0269-7491
DOI10.1016/j.envpol.2020.116041
通讯作者Wu, Guofeng(guofeng.wu@szu.edu.cn)
英文摘要Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350-2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors. (C) 2020 Elsevier Ltd. All rights reserved.
资助项目National Natural Science Foundation of China[41890854] ; National Natural Science Foundation of China[4170010438] ; Natural Science Funding of Shenzhen University[2019060]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000615555000094
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Natural Science Funding of Shenzhen University
源URL[http://ir.igsnrr.ac.cn/handle/311030/160433]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Guofeng
作者单位1.Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China
2.Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China
3.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China
4.Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China
5.Nanjing Univ Posts & Telecommun, Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji, Nanjing 210023, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
7.Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Tiezhu,Yang, Chao,Liu, Huizeng,et al. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches[J]. ENVIRONMENTAL POLLUTION,2021,272:10.
APA Shi, Tiezhu.,Yang, Chao.,Liu, Huizeng.,Wu, Chao.,Wang, Zhihua.,...&Su, Fenzhen.(2021).Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches.ENVIRONMENTAL POLLUTION,272,10.
MLA Shi, Tiezhu,et al."Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches".ENVIRONMENTAL POLLUTION 272(2021):10.

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来源：地理科学与资源研究所

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