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Estimation of soil lead content based on GF-5 hyperspectral images, considering the influence of soil environmental factors

  • Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Heavy metal pollution is an increasingly serious problem. The accumulation of heavy metals in soil is a threat to ecological balance and human health, and therefore, it is very important to monitor heavy metal concentrations efficiently and accurately over large areas. Hyperspectral remote sensing (HRS) images are beneficial for such large-scale monitoring, but the process of image acquisition is inevitably affected by soil environmental factors. In this study, we proposed a method to construct a mechanistic model by considering the influence of soil environmental factors.

Materials and methods

Fifty-one soil samples were collected in Xiong and Anxin counties of Xiong’an New Area in Hebei Province. First, a direct standardization (DS) algorithm was applied to eliminate the influence of soil environmental factors on Gaofen-5 (GF-5) satellite image spectra. Then, the DS-corrected image spectra were jointly modeled with the corresponding laboratory spectra to increase the sample size. Due to the strong sorption and retention of lead (Pb) on iron oxide particles in soil, the characteristic bands associated with iron oxide were extracted for model calibration. Then, the trained model was applied to GF-5 image spectra to obtain the Pb distribution in the arable area.

Results

The model validation accuracy (R2) without DS correction was 0.30, while the R2 value without extracting the characteristic iron oxide band was 0.58. The R2 value of the method in this paper could reach 0.77. The Pb distribution map made according to this study was consistent with the actual distribution pattern and the results of previous studies, which confirms that the method used in this study is reliable.

Conclusion

The DS algorithm eliminated the influence of soil environmental factors and greatly improved the estimation ability of the model. And the use of the iron oxide characteristic band improved the accuracy of the estimation of Pb concentration.

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Funding

This work was supported by Special Project of High-Resolution Earth Observation System (30-H30C01-9004–19/21) and National Natural Science Foundation of China (42001282).

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing, 100101, China

    Songtao Ding, Xia Zhang, Weichao Sun & Yibo Wang

  2. University of Chinese Academy of Sciences, Yuquan Street, Shijingshan District, Beijing, 100049, China

    Songtao Ding & Yibo Wang

  3. Land Satellite Remote Sensing Applications Center, MNR, Haidian District, No.1 Baisheng Village, Beijing, 100048, China

    Kun Shang

Authors
  1. Songtao Ding
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  2. Xia Zhang
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  3. Weichao Sun
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  4. Kun Shang
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  5. Yibo Wang
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Correspondence to Xia Zhang.

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Responsible editor: Xiuping Jia

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Ding, S., Zhang, X., Sun, W. et al. Estimation of soil lead content based on GF-5 hyperspectral images, considering the influence of soil environmental factors. J Soils Sediments 22, 1431–1445 (2022). https://doi.org/10.1007/s11368-022-03169-0

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  • DOI: https://doi.org/10.1007/s11368-022-03169-0

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