ArticleHeliyon2023
Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 11 citations in OpenAlex.
- Spatial heterogeneity of heavy metals in contaminated soil using hyperspectral inversion models: a case study of Dongting lake region, south-central China.Scientific reports · 2025Article
- Ecological risk evaluation of heavy metals based on hyperspectral: a case study of rice paddy soil in Xiangtan County, China.Environmental monitoring and assessment · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
The exploitation of mineral resources has seriously polluted the environment around mines, notably in terms of heavy metal contamination of tailings pond soil. Hyperspectral remote sensing, as opposed to conventional on-site sampling and laboratory analysis, offers a potent tool for effective monitoring the content of soil heavy metals. Therefore, we investigated the inversion models of heavy metal content in metal tailings area based on measured hyperspectral and multispectral data. Hyperspectral and its transformation, as well as the simulated Landsat8-OLI multispectral were used for model inversion respectively. Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Back Propagation Neuron Network (BPNN) were established to study the spectral inversion of eight heavy metals (Cu, Cd, Cr, Ni, Pb, Zn, As, and Hg). The direct inversion models were established on the basis of correlation analysis and the adjust coefficient of determination (
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