Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg−1, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R2 of 0.45 and RMSE of 7.78 g kg−1, while the hybrid model further improved prediction accuracy (R2 = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making.
Mao et al. (Thu,) studied this question.