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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cheng Han & Jilong Lu & Shengbo Chen & Xitong Xu & Zibo Wang & Zheng Pei & Yu Zhang & Fengxuan Li, 2021. "Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices," Sustainability, MDPI, vol. 13(21), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12088-:d:670398
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    References listed on IDEAS

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    1. United Nations, 2016. "The Sustainable Development Goals 2016," Working Papers id:11456, eSocialSciences.
    2. Farah Tariq & Xiukang Wang & Muhammad Hamzah Saleem & Zafar Iqbal Khan & Kafeel Ahmad & Ifra Saleem Malik & Mudasra Munir & Shehzadi Mahpara & Naunain Mehmood & Tasneem Ahmad & Hafsa Memona & Ilker Ug, 2021. "Risk Assessment of Heavy Metals in Basmati Rice: Implications for Public Health," Sustainability, MDPI, vol. 13(15), pages 1-13, July.
    3. Guangcai Yin & Hanghai Zhu & Zhiliang Chen & Chuanghong Su & Zechen He & Xinglin Chen & Jinrong Qiu & Tieyu Wang, 2021. "Spatial Distribution and Source Apportionment of Soil Heavy Metals in Pearl River Delta, China," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
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    Cited by:

    1. Chang Meng & Mei Hong & Yuncai Hu & Fei Li, 2024. "Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas," Sustainability, MDPI, vol. 16(10), pages 1-23, May.

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