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Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

Author

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  • Lei Han

    (School of Land Engineering, Chang’an University, Xi’an 710054, China
    Shaanxi Key Laboratory of Land consolidation, Xi’an 710054, China
    Degraded and Unused Land Consolidation Engineering, The Ministry of Land and Resources, Xi’an 710054, China)

  • Rui Chen

    (Shaanxi Key Laboratory of Land consolidation, Xi’an 710054, China
    College of Earth Sciences and Resources, Chang’an University, Xi’an 710054, China)

  • Huili Zhu

    (College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China)

  • Yonghua Zhao

    (School of Land Engineering, Chang’an University, Xi’an 710054, China
    Shaanxi Key Laboratory of Land consolidation, Xi’an 710054, China)

  • Zhao Liu

    (School of Land Engineering, Chang’an University, Xi’an 710054, China
    Shaanxi Key Laboratory of Land consolidation, Xi’an 710054, China)

  • Hong Huo

    (Shaanxi Key Laboratory of Land consolidation, Xi’an 710054, China
    College of Earth Sciences and Resources, Chang’an University, Xi’an 710054, China)

Abstract

Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1476-:d:321430
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Qing Zhong & Mamattursun Eziz & Rukeya Sawut & Mireguli Ainiwaer & Haoran Li & Liling Wang, 2023. "Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
    2. 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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