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Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

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Listed:
  • Shuai Mei

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Tong Tong

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Shoufu Zhang

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Chunyang Ying

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Mengmeng Tang

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Mei Zhang

    (Department of Business Administration, School of Business, Anhui University, Hefei 230036, China)

  • Tianpei Cai

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Youhua Ma

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

  • Qiang Wang

    (Department of Resources, Environment and Information Technology, College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China)

Abstract

Traditional soil organic matter mapping is mostly polygonal drawing, which is even more difficult to accurately depict in complex terrain areas. The spatial distribution of soil organic matter is closely related to agricultural production, natural resources, environmental governance, and socio-economic development. Efficiently, economically, and accurately obtaining information on changes in soil organic matter in areas with diverse topography is an urgent problem to be solved. Mingguang City has a high research value because of its unique topography and natural landscape. To solve the problem of soil organic matter mapping in this area, this study will construct an excellent organic matter prediction model. Using 173 soil survey samples (123 for training and 50 for testing), the optimal feature variable subsets selected from 31 environmental variables through Pearson correlation, stepwise regression-variance inflation factor, and recursive feature elimination models based on different algorithms were employed. Each selected feature subset was then used to construct organic matter prediction models using multiple advanced machine learning algorithms. By comparing accuracy validation and model performance, the organic matter prediction model suitable for Mingguang City (RFE-RF_SVM) was obtained, that is, the prediction model of organic matter based on support vector machines with the feature variables screened by the feature recursive elimination algorithm of random forest with RMSE = 3.504, VSI = 0.036, and R -squared = 0.730. Furthermore, the analysis focused on assessing the significance of the predictive factors. The mapping results of this study show that the soil organic matter content in the central and northwestern parts of the study area is low, and the reasons for this situation are different. The central part is mainly caused by the change of land use and topography, while the northwestern part is caused by the loose soil structure caused by the parent material. The government can take targeted measures to improve the soil in the areas with poor organic matter.

Suggested Citation

  • Shuai Mei & Tong Tong & Shoufu Zhang & Chunyang Ying & Mengmeng Tang & Mei Zhang & Tianpei Cai & Youhua Ma & Qiang Wang, 2024. "Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China," Sustainability, MDPI, vol. 16(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4312-:d:1398162
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    References listed on IDEAS

    as
    1. Johannes Lehmann & Markus Kleber, 2015. "The contentious nature of soil organic matter," Nature, Nature, vol. 528(7580), pages 60-68, December.
    2. Zhi-Dong Zhao & Ming-Song Zhao & Hong-Liang Lu & Shi-Hang Wang & Yuan-Yuan Lu, 2023. "Digital Mapping of Soil pH Based on Machine Learning Combined with Feature Selection Methods in East China," Sustainability, MDPI, vol. 15(17), pages 1-13, August.
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