Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China
文献类型：期刊论文
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| 作者 | Zhang, Bo; Guo, Bin; Zou, Bin; Wei, Wei ; Lei, Yongzhi; Li, Tianqi
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| 刊名 | ENVIRONMENTAL POLLUTION
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| 出版日期 | 2022-05-01 |
| 卷号 | 300期号:0页码:118981 |
| 关键词 | REFLECTANCE SPECTROSCOPY ZINC CONCENTRATIONS ENHANCED ASSESSMENT AGRICULTURAL SOILS FIELD SPECTROSCOPY ORGANIC-MATTER CONTAMINATION SPECTRA LEAD POLLUTION |
| ISSN号 | 0269-7491 |
| 英文摘要 | Soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480-677, 827-1029, 1241-1334, 1435-1797, and 1949-2500 nm), Ni (514-630, 835-985, 1258-1325, 1460-1578, and 1949-2319 nm), and Cu (822-831; 1029-1300, 1486-1595, and 1730-2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (R-v(2)) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/47327]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 通讯作者 | Guo, Bin |
| 作者单位 | 1.Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Peoples R China 2.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China 3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Northwest Survey Design & Res Inst Co Ltd, China Power Construct Grp, Xian 710065, Peoples R Ch |
| 推荐引用方式 GB/T 7714 | Zhang, Bo,Guo, Bin,Zou, Bin,et al. Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China, Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China.pdf[J]. ENVIRONMENTAL POLLUTION,2022,300(0):118981. |
| APA | Zhang, Bo,Guo, Bin,Zou, Bin,Wei, Wei,Lei, Yongzhi,&Li, Tianqi.(2022).Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China.ENVIRONMENTAL POLLUTION,300(0),118981. |
| MLA | Zhang, Bo,et al."Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China".ENVIRONMENTAL POLLUTION 300.0(2022):118981. |
入库方式： OAI收割
来源：生态环境研究中心
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