Soil organic carbon (SOC) is a critical factor influencing global carbon cycling. Accurate estimates of its spatial distribution are essential for addressing global climate change. Digital soil mapping has demonstrated significant potential in providing precise and high-resolution spatial information about SOC across various scales. We conducted an evaluation of two soil mapping approaches for SOCD estimates in France: the direct approach (calculate-then-model) and the indirect approach (model-then-calculate). Our study utilized 916 topsoil samples (0−20 cm) from the LUCAS Soil 2018 dataset and 24 environmental covariates. We employed a random forest model and forward recursive feature selection to build spatial predictive models of SOCD using both the direct and indirect approaches. The results revealed that, with the random forest model and full covariates, both approaches demonstrated moderate performance (R2 = 0.28−0.32). Through the use of forward recursive feature selection, the number of predictors was reduced from 24 to 9, leading to enhanced model performance for the direct approach (R2 of 0.35), while no improvement was observed for the indirect approach (R2 of 0.28). The mean SOCD of French topsoil was estimated at 5.29 and 6.14 kg m−2 using the direct and indirect approaches, respectively, resulting in SOC stocks of 2.8 and 3.3 Pg, respectively. Notably, the indirect approach exhibited better performance in estimating high SOCD. These findings serve as a valuable reference for SOCD mapping.