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Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

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  • Li Zhao

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Yue-Ming Hu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Wu Zhou

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Zhen-Hua Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Yu-Chun Pan

    (Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Zhou Shi

    (Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China)

  • Lu Wang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Guang-Xing Wang

    (Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
    Department of Geography and Environmental Resources, College of Liberal Arts, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA)

Abstract

Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R 2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R 2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

Suggested Citation

  • Li Zhao & Yue-Ming Hu & Wu Zhou & Zhen-Hua Liu & Yu-Chun Pan & Zhou Shi & Lu Wang & Guang-Xing Wang, 2018. "Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing," Sustainability, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2474-:d:158085
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    Citations

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    Cited by:

    1. Lei Han & Rui Chen & Huili Zhu & Yonghua Zhao & Zhao Liu & Hong Huo, 2020. "Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    2. Mykola Dyvak & Artur Rot & Roman Pasichnyk & Vasyl Tymchyshyn & Nazar Huliiev & Yurii Maslyiak, 2021. "Monitoring and Mathematical Modeling of Soil and Groundwater Contamination by Harmful Emissions of Nitrogen Dioxide from Motor Vehicles," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    3. Mingbang Zhu & Shanshan Liu & Ziqing Xia & Guangxing Wang & Yueming Hu & Zhenhua Liu, 2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN," Agriculture, MDPI, vol. 10(8), pages 1-16, August.
    4. Huihui Zhao & Peijia Liu & Baojin Qiao & Kening Wu, 2021. "The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China," Land, MDPI, vol. 10(11), pages 1-13, November.
    5. Na Wang & Jichang Han & Yang Wei & Gang Li & Yingying Sun, 2019. "Potential Ecological Risk and Health Risk Assessment of Heavy Metals and Metalloid in Soil around Xunyang Mining Areas," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    6. Hailong Zhao & Shu Gan & Xiping Yuan & Lin Hu & Junjie Wang & Shuai Liu, 2022. "Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide," Agriculture, MDPI, vol. 12(8), pages 1-20, August.

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