Spatial Distribution and Driving Mechanisms of Soil Organic Carbon in the Yellow River Source Region

Soil organic carbon (SOC) plays a vital role in regional carbon cycling and ecosystem services. However, previous studies have primarily focused on spatial patterns and environmental drivers, with limited attention to long-term observations, underlying mechanisms, and large-scale modeling. In this study, we collected surface soil samples (0–20 cm) and integrated topography, soil physicochemical properties, climate, vegetation, and MODIS remote sensing data to develop 16 SOC prediction models using linear regression and machine learning approaches. SOC was significantly correlated with latitude, mean annual temperature, and precipitation and negatively associated with several remote sensing indices. The LASSO-selected variable set combined with a support vector machine (SVM) achieved the highest predictive accuracy (R2 = 0.53, RMSE = 36.19). From 2001 to 2020, the mean SOC stock in the Yellow River source region was estimated at 1683.98 g C/m2, showing higher values in the southeast and lower values in the northwest. Alpine meadow exhibited the highest total stock due to its extensive coverage, whereas the cold temperate wet coniferous forest had higher mean content and unit area value, indicating strong carbon sequestration potential. This study identifies key SOC drivers and mechanisms, provides quantitative estimates of regional SOC content and stock, and offers a scientific basis for grassland carbon management and large-scale digital soil mapping.