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The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China

Author

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  • Huihui Zhao

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Peijia Liu

    (School of Politics and Public Administration, Zhengzhou University, Zhengzhou 450001, China
    Henan Geological Survey Institute, Zhengzhou 450001, China
    Contemporary Capitalism Research Center, Zhengzhou University, Zhengzhou 450001, China)

  • Baojin Qiao

    (School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Kening Wu

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)

Abstract

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 ( p < 0.01), and of Hg was 0.318 ( p < 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1227-:d:676792
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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. Shuaiwei Shi & Meiyi Hou & Zifan Gu & Ce Jiang & Weiqiang Zhang & Mengyang Hou & Chenxi Li & Zenglei Xi, 2022. "Estimation of Heavy Metal Content in Soil Based on Machine Learning Models," Land, MDPI, vol. 11(7), pages 1-19, July.
    2. Yu Zhang & Meiling Liu & Li Kong & Tao Peng & Dong Xie & Li Zhang & Lingwen Tian & Xinyu Zou, 2022. "Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images," IJERPH, MDPI, vol. 19(5), pages 1-14, February.
    3. Ahmed Saleh & Yehia H. Dawood & Ahmed Gad, 2022. "Assessment of Potentially Toxic Elements’ Contamination in the Soil of Greater Cairo, Egypt Using Geochemical and Magnetic Attributes," Land, MDPI, vol. 11(3), pages 1-19, February.
    4. Siyu Tang & Chong Du & Tangzhe Nie, 2022. "Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis," Land, MDPI, vol. 11(5), pages 1-18, April.

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