Abstract
Soil organic carbon (SOC) is widely recognized as a fundamental indicator of soil fertility, ecosystem functioning, and overall soil health. Effective land management requires continuous monitoring of SOC variations through modern technological approaches. In this study, 477 soil samples were meticulously collected and analyzed for SOC content in the laboratory. Terrain attributes and spectral indices were then derived from satellite data. Machine learning models, including support vector machine (SVM), artificial neural network (ANN), and random forest (RF), were employed to predict SOC content. To improve computational efficiency and model accuracy, the variance inflation factor (VIF) and Boruta’s variable selection methods were applied, identifying the most relevant environmental covariates. Results demonstrated that only 5 out of 40 environmental covariates were optimal for SOC modeling. Using these selected covariates, the RF model achieved the highest prediction accuracy (R² = 0.84, RMSE = 0.069%, and PRD = 3.6%). The RF model effectively captured the inherent variability and complexity of soil properties, yielding precise and reliable SOC predictions. The results emphasize the capability of machine learning in predicting SOC levels, aiding in the enhancement of soil management strategies and agricultural planning. Ultimately, this study provides a foundation for integrating advanced predictive techniques to enhance SOC assessment in future study.
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Acknowledgements
This study was supported by the Forestry Science and Technology Research and Innovation Project of Hunan Province, China [NO. XLK202435] and Hunan Province Post-Graduate Research Innovation Project [NO. CX20230776]. The authors of this article would like to thank from them for accepting all expenses of this study.
Funding
(1) 2024 Forestry Science and Technology Research and Innovation Project of Hunan Province, China [NO. XLK202435]. (2) 2023 Hunan Province Post-Graduate Research Innovation Project [NO. CX20230776].
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Cai, X., Liu, F. & Cai, Z. Optimizing environmental covariates for digital mapping of soil organic carbon in Hunan Province, China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-56073-9
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DOI: https://doi.org/10.1038/s41598-026-56073-9
Keywords
- Boruta’s variable selection
- Environmental covariates
- Machine learning models
- Variance inflation factor
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