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Estimating the Soil Copper Content of Urban Land in a Megacity Using Piecewise Spectral Pretreatment

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Listed:
  • Yi Liu

    (School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China)

  • Tiezhu Shi

    (State Key Laboratory of Subtropical Building and Urban Science & Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities & MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China)

  • Zeying Lan

    (School of Management, Guangdong University of Technology, Guangzhou 510520, China)

  • Kai Guo

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Dachang Zhuang

    (School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China)

  • Xiangyang Zhang

    (School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China)

  • Xiaojin Liang

    (Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510030, China)

  • Tianqi Qiu

    (Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510030, China)

  • Shengfei Zhang

    (School of Public Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China)

  • Yiyun Chen

    (School of Resource and Environmental Science & Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China)

Abstract

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model.

Suggested Citation

  • Yi Liu & Tiezhu Shi & Zeying Lan & Kai Guo & Dachang Zhuang & Xiangyang Zhang & Xiaojin Liang & Tianqi Qiu & Shengfei Zhang & Yiyun Chen, 2024. "Estimating the Soil Copper Content of Urban Land in a Megacity Using Piecewise Spectral Pretreatment," Land, MDPI, vol. 13(4), pages 1-21, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:4:p:517-:d:1375453
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    References listed on IDEAS

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    1. Asa Gholizadeh & Luboš Borůvka & Mohammad Mehdi Saberioon & Josef Kozák & Radim Vašát & Karel Němeček, 2015. "Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 10(4), pages 218-227.
    2. Raj Echambadi & James D. Hess, 2007. "Mean-Centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models," Marketing Science, INFORMS, vol. 26(3), pages 438-445, 05-06.
    3. Kathryn Elmer & Raymond J. Soffer & J. Pablo Arroyo-Mora & Margaret Kalacska, 2020. "ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data," Data, MDPI, vol. 5(4), pages 1-15, October.
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