Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices

Cheng Han, Jilong Lu, Shengbo Chen, Xitong Xu, Zibo Wang, Zheng Pei, Yu Zhang and Fengxuan Li
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Cheng Han: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Jilong Lu: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Shengbo Chen: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Xitong Xu: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Zibo Wang: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Zheng Pei: The 10th Geological Brigade Co., Ltd., of Liaoning Province, Fushun 113004, China
Yu Zhang: The 10th Geological Brigade Co., Ltd., of Liaoning Province, Fushun 113004, China
Fengxuan Li: The 10th Geological Brigade Co., Ltd., of Liaoning Province, Fushun 113004, China

Sustainability, 2021, vol. 13, issue 21, 1-21

Abstract: For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents of Ni, Hg, Cr, Cu, and As in the agricultural soil of the Suzi River Basin in Liaoning Province were taken as an example. The spectral data, with Savitzky–Golay smoothing, were taken as the original spectra (OR), and the spectral transformation was achieved by continuum removal (CR), reciprocal (1/R), root means square ( R ), first-order differential (FDR), and second-order differential (SDR) methods. Then the spectral indices were calculated by the optimal band combination algorithm. The correlation between Ni, Hg, Cr, Cu, and As contents and spectral indices was analyzed, and the optimal spectral indices were selected. Then, multiple linear regression (MLR), partial least squares regression (PLSR), random forest regression (RFR), and adaptive neuro-fuzzy reasoning system (ANFIS) were used to establish the estimation model based on the combined optimal spectral indices method. The results show that the combined optimal spectral indices method improves the correlation between spectra and heavy metal(loid), the MLR model produces the best estimation effect for Ni and Cu ( R 2 = 0.713 and 0.855 , RMSE = 5.053 and 8.113, RPD = 1.908 and 2.688, respectively), and the PLSR model produces the best effect for Hg, Cr, and As ( R 2 = 0.653, 0.603, and 0.775, RMSE = 0.074, 23.777, and 1.923, RPD = 1.733, 1.621, and 2.154, respectively). Therefore, the combined optimal spectral indices method is feasible for heavy metal(loid) estimation in soils and could provide technical support for large-scale soil heavy metal(loid) content estimation and pollution assessment.

Keywords: optimal spectral indices; spectral transformation; partial least-squares regression (PLSR); multiple linear regression (MLR); random forest regression (RFR); adaptive neuro-fuzzy inference system (ANFIS) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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