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Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)

Author

Listed:
  • Liangwei Cheng

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Mingzhi Yan

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Wenhui Zhang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Weiyan Guan

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Lang Zhong

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Jianbo Xu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

Abstract

Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted R 2 ) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg −1 , respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making.

Suggested Citation

  • Liangwei Cheng & Mingzhi Yan & Wenhui Zhang & Weiyan Guan & Lang Zhong & Jianbo Xu, 2024. "Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1578-:d:1475935
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    References listed on IDEAS

    as
    1. Wenhui Zhang & Liangwei Cheng & Ruitao Xu & Xiaohua He & Weihan Mo & Jianbo Xu, 2023. "Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping," Agriculture, MDPI, vol. 13(8), pages 1-18, August.
    2. Azamat Suleymanov & Ilyusya Gabbasova & Mikhail Komissarov & Ruslan Suleymanov & Timur Garipov & Iren Tuktarova & Larisa Belan, 2023. "Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas," Agriculture, MDPI, vol. 13(5), pages 1-11, April.
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