Knowledge Management System Of Guangzhou Institute of Geochemistry，CAS
| Sun, Yishan1,2,3; Chen, Shuisen1,2,3,4; Dai, Xuemei1,2,3; Li, Dan2; Jiang, Hao2; Jia, Kai2 | |
| Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery | |
| Source Publication | JOURNAL OF HAZARDOUS MATERIALS
 |
| ISSN | 0304-3894 |
| 2023-03-15 | |
| Volume | 446Pages:15 |
| DOI | 10.1016/j.jhazmat.2023.130722 |
| Language | 英语 |
| WOS Research Area | Engineering ; Environmental Sciences & Ecology |
| Abstract | Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide. |
| Keyword | Hyperspectral satellite Soil Ni Spectral feature exaction Machine learning |
| WOS ID | WOS:000922838400001 |
| Indexed By | SCI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.gig.ac.cn/handle/344008/72609 |
| Collection | 中国科学院广州地球化学研究所 |
| Corresponding Author | Chen, Shuisen |
| Affiliation | 1.Chinese Acad Sci, Guangzhou Inst Geochem, Guangzhou 510640, Peoples R China 2.Guangdong Open Lab Geospatial Informat Technol & A, Key Lab Guangdong Utilizat Remote Sensing & Geog I, Guangzhou 510070, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Shaoguan Shenwan Low Carbon Digital Technol Co Ltd, Shaoguan 512026, Peoples R China |
| Recommended Citation GB/T 7714 | Sun, Yishan,Chen, Shuisen,Dai, Xuemei,et al. Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery[J]. JOURNAL OF HAZARDOUS MATERIALS,2023,446:15. |
| APA | Sun, Yishan,Chen, Shuisen,Dai, Xuemei,Li, Dan,Jiang, Hao,&Jia, Kai.(2023).Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery.JOURNAL OF HAZARDOUS MATERIALS,446,15. |
| MLA | Sun, Yishan,et al."Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery".JOURNAL OF HAZARDOUS MATERIALS 446(2023):15. |
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