Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model
文献类型：期刊论文
| 作者 | Zhou, Wei1,2,3; Yang, Han3; Xie, Lijuan3; Li, Haoran3; Huang, Lu3 ; Zhao, Yapeng2; Yue, Tianxiang2
|
| 刊名 | CATENA
 |
| 出版日期 | 2021-07-01 |
| 卷号 | 202页码:10 |
| 关键词 | Soil heavy metal Hyperspectral Random forest model (RF) Support vector machine (SVM) Three-River Source Region |
| ISSN号 | 0341-8162 |
| DOI | 10.1016/j.catena.2021.105222 |
| 通讯作者 | Zhou, Wei(zhouw866@163.com) ; Yue, Tianxiang(yue@lreis.ac.cn) |
| 英文摘要 | Hyperspectral remote sensing technology has considerable research value in monitoring and evaluating soil heavy metal pollution. In this study, the Three-River Source Region was taken as the study area. The occurrence relationship of six heavy metals in soil, such as Mn, Cu, Zn, Pb, Cr, Ni, with soil organic matter, clay minerals, and iron-manganese oxides, was studied through the determination and analysis of soil samples and the collection of soil reflectance spectrum. Spectral transformation was carried out by first derivative, second derivative, inverse-log, continuum removal and multiple scattering correction of the spectrum. The correlation between soil heavy metal content and soil spectrum was analyzed to select the characteristic band, and partial least squares (PLS) method, support vector machine (SVM) method and random forest (RF) model were used to build inversion model based on characteristic band. Then the best combination of spectral transformation and inversion model were explored. The results showed that Pb contents were the twice of the background in Qinghai province. The combination spectrum processing method can improve the correlation between spectrum and heavy metals. The location and quantity of characteristic bands of six heavy metals are different. The accuracy of RF was significantly better than that of SVM and PLS for all six heavy metal (i.e. pb: R-RF(2) = 0.83, R-SVM(2) = 0.62, R-PLS(2) = 0.18), and the model effective of soil properties in non-polluted sites were reliable (i.e. clay: R-RF(2) = 0.93, R-SVM(2) = 0.87, R-PLS(2) = 0.74). This study can provide technical support for the larger-scale monitoring of soil heavy metal content and heavy metal pollution assessment. |
| WOS关键词 | REFLECTANCE SPECTROSCOPY ; CLASSIFICATION ; CONTAMINATION |
| 资助项目 | National Natural Science Foundation of China[41501575] ; National Natural Science Foundation of China[41977337] ; National Natural Science Foundation of China[41590844] ; National Natural Science Foundation of China[41930647] ; China Post-doctoral Science Foundation[2019M650821] ; Scientific and Technological Research Program of Chongqing Municipal Education Commission[KJQN201800702] ; Project of Chongqing Science and Technology Bureau[cstc2019jscx-fxydX0036] |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000643594100004 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; China Post-doctoral Science Foundation ; Scientific and Technological Research Program of Chongqing Municipal Education Commission ; Project of Chongqing Science and Technology Bureau |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/162860]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhou, Wei; Yue, Tianxiang |
| 作者单位 | 1.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Chongqing Jiaotong Univ, Chongqing 400074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Wei,Yang, Han,Xie, Lijuan,et al. Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model[J]. CATENA,2021,202:10. |
| APA | Zhou, Wei.,Yang, Han.,Xie, Lijuan.,Li, Haoran.,Huang, Lu.,...&Yue, Tianxiang.(2021).Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model.CATENA,202,10. |
| MLA | Zhou, Wei,et al."Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model".CATENA 202(2021):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。