Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Mar 14;13(6):831.
doi: 10.3390/plants13060831.

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Liang Zhong  1   2 , Shengjie Yang  1   2 , Yicheng Rong  1   2 , Jiawei Qian  1   2 , Lei Zhou  3 , Jianlong Li  1   2 , Zhengguo Sun  4
Affiliations

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Liang Zhong et al. Plants (Basel). .

Abstract

The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.

Keywords: genetic algorithm; heavy metal contamination; indirect estimation; rice; soil-crop system; spectral technique.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of Cd and As content at sampling sites in the study area.
Figure 2
Figure 2
Characteristics of the spectral curves of rice leaves with different pre-processing.
Figure 3
Figure 3
The feature bands of rice leaves spectral screened by GA.
Figure 4
Figure 4
Location of the study area and distribution of sampling sites.
See this image and copyright information in PMC

References

    1. Sanchez P., Ahamed S., Carre F., Hartemink A., Hempel J., Huising J., Lagacherie P., McBratney A., McKenzie N., Mendonca-Santos M., et al. Digital Soil Map of the World. Science. 2009;325:680–681. doi: 10.1126/science.1175084. - DOI - PubMed
    1. Jiang Y., Huang M., Chen X., Wang Z., Xiao L., Xu K., Zhang S., Wang M., Xu Z., Shi Z. Identification and risk prediction of potentially contaminated sites in the Yangtze River Delta. Sci. Total Environ. 2022;815:151982. doi: 10.1016/j.scitotenv.2021.151982. - DOI - PubMed
    1. Xia F., Zhao Z., Niu X., Wang Z. Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil. J. Hazard. Mater. 2024;465:133215. doi: 10.1016/j.jhazmat.2023.133215. - DOI - PubMed
    1. Jiang Y., Chen S., Hu B., Zhou Y., Liang Z., Jia X., Huang M., Wei J., Shi Z. A comprehensive framework for assessing the impact of potential agricultural pollution on grain security and human health in economically developed areas. Environ. Pollut. 2020;263:114653. doi: 10.1016/j.envpol.2020.114653. - DOI
    1. Yuan X., Xue N., Han Z. A meta-analysis of heavy metals pollution in farmland and urban soils in China over the past 20 years. J. Environ. Sci. 2021;101:217–226. doi: 10.1016/j.jes.2020.08.013. - DOI - PubMed