Soil organic carbon (SOC) plays a critical role in the global carbon cycle and is a key indicator of soil health, with significant implications for global carbon cycling and long-term agricultural productivity. Timely acquisition of farmland SOC spatial distribution maps can facilitate carbon cycle research and the formulation of optimal fertilization strategies. This study takes the southern bank of the midstream for the Huangshui River Basin, Qinghai Province, which has representative farmland soils, as the study area. Surface (0?20 cm) SOC content is analyzed using both field and laboratory-measured spectral data, along with Sentinel-2 satellite imagery and 111 field soil samples. Four spectral transformation methods are employed to process the original full-spectrum bands. Three SOC estimation models—extreme gradient boosting (XGBoost), random forest (RF), and partial least squares regression (PLSR)—are constructed to identify the optimal model combination for estimating SOC content based on Sentinel-2 satellite imagery in the study area. Subsequently, correlation analysis (P<0.01) is employed to select characteristic bands for modeling, comparing the effect of these bands on the accuracy of field and laboratory spectral data-based models. The results indicate that the following: 1) Models built using characteristic bands selected from both field and laboratory spectra exhibit higher accuracy than those based on full-spectrum bands. 2) Sentinel-2 imagery combined with the optimal model [XGBoost+first derivative (FD)] proves effective in estimating and mapping SOC content in the study area, achieving a coefficient of determination (R²) for the training set of 0.707, an R² for the validation set of 0.658, and a residual predictive deviation for the validation set of 2.069. 3) The mapping results reveal that SOC mass fraction in the study area ranges from 0.69 g/kg to 15.93 g/kg, with spatial distribution showing an increasing trend from northwest to southeast, northeast to southwest, and north to south, corresponding to increasing elevation. These findings provide data support for SOC content estimation and digital soil mapping, offering a basis for decision-making in carbon cycle research, precision agriculture, and farmland ecosystem conservation.