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Rapid inversion of heavy metal concentration in karst grain producing areas based on hyperspectral bands associated with soil components
Qian Lu; Shijie Wang; Xiaoyong Bai; Fang Liu; Mingming Wang; Jinfeng Wang; Shiqi Tian
2019
Source PublicationMicrochemical Journal
Volume148Pages:404-411
Abstract

Heavy metal pollution in soil has become a prominent problem affecting agricultural security and ecological health. Hyperspectral remote sensing is used as a rapid method to predict soil heavy metal concentrations. The processing of spectral data and the variables of the estimated model has an important impact on the predictive model of soil heavy metal elements. In this paper, smoothed and resampled spectral reflections are preprocessed by using three preprocessing methods, namely, standard normal variate (SNV), multiplication scatter correlation (MSC) and normalization (NOR). Then, first and second order differential (FD and SD, respectively) and absorbance transformation (AT) are performed. Based on the adsorption and retention of heavy metals by various soil components, the relevant spectral bands are extracted as modeling variables. An extreme learning machine algorithm (ELM) is used to establish the model, and the effects of different factors on the model are compared. Results show that the combination of the three preprocessing methods (SNV, MSC and NOR) with spectral transformation can enhance the stability and predictive ability of the resulting model. The combination of SNV and FD can predict the contents of Cr, Ni and Pb. The R2 of the model is 0.85, 0.87 and 0.80 respectively. The optimal model of Cu is derived from the combination of NOR and SD (R2 = 0.84), and the spectral responses of soil Cr, Ni, Cu and Pb, are closely related to clay mineral-related and organic matter-related bands. The model established by the clay-related bands enhances the stability of the prediction of Ni content, and the RPD value was increased from 2.46 to 2.72 compared with the full-band model. The combination of bands associated with organic matter and clay minerals can accurately predict the content of Cr and Cu in soil; indeed, the predict model R2 for these elements reaches 0.88. Accurate prediction of soil Pb by the full-band model indicates that the Pb concentration in the study area is related to a various of soil chemical components. The prediction effects of the four heavy metal elements show the order Cr > Cu > Ni > Pb. The results of the current study complement the theoretical basis for estimating the heavy metal content of soil by hyperspectral spectroscopy, and provide important insights into the application of hyperspectral remote sensing to monitor other heavy metals.

KeywordSoil Spectrum pretreatment extreme Learning Machine heavy Metal organic Matter And Clay Minerals
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.gyig.ac.cn/handle/42920512-1/10195
Collection环境地球化学国家重点实验室
Affiliation1.State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, 99 Lincheng West Road, Guiyang 550081, Guizhou Province, PR China
2.CAS Center for Excellence in Quaternary Science and Global Change, Xi'an 710061, Shanxi Province, China
3.College of Resource and Environment, Guizhou University, Guiyang 550000, China
4.Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China
Recommended Citation
GB/T 7714
Qian Lu,Shijie Wang,Xiaoyong Bai,et al. Rapid inversion of heavy metal concentration in karst grain producing areas based on hyperspectral bands associated with soil components[J]. Microchemical Journal,2019,148:404-411.
APA Qian Lu.,Shijie Wang.,Xiaoyong Bai.,Fang Liu.,Mingming Wang.,...&Shiqi Tian.(2019).Rapid inversion of heavy metal concentration in karst grain producing areas based on hyperspectral bands associated with soil components.Microchemical Journal,148,404-411.
MLA Qian Lu,et al."Rapid inversion of heavy metal concentration in karst grain producing areas based on hyperspectral bands associated with soil components".Microchemical Journal 148(2019):404-411.
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