Optimizing environmental covariates for digital mapping of soil organic carbon in Hunan Province, China.

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.