Soil organic carbon (SOC) plays a crucial role in soil health and global carbon cycling, therefore accurate estimates of its spatial distribution are important for managing soil health and mitigating global climate change. Digital soil mapping shows its potential to provide accurate and high-resolution spatial distribution of SOC across scales. To convert SOC content to SOC density (SOCD), two inference trajectories exist for predicting SOCD in digital soil mapping: the direct approach (calculate-then-model) and indirect approach (model-then-calculate). However, there is a lack of comprehensive exploration regarding the differences in their performance in SOCD estimates, particularly in regions characterized by diverse pedoclimatic conditions. To bridge this knowledge gap, we evaluated the two approaches based on model performance of SOCD in France. Using 916 topsoils (0−20 ​cm) from the LUCAS Soil 2018 and 24 environmental covariates, random forest model and forward recursive feature selection were used to build the spatial predictive models of SOCD using direct and indirect approaches. The results show that, using random forest model and full covariates, both approaches show moderate performance (R2 ​= ​0.28−0.32). By utilizing forward recursive feature selection model, the number of predictors was reduced from 24 to 9, enhancing model performance for direct approach (R2 of 0.35), with no improvement for indirect approach (R2 of 0.28). The mean SOCD of the French topsoil was 5.29 and 6.14 ​kg ​m−2 by direct and indirect approaches, resulting in SOC stock of 2.8 and 3.3 ​Pg, respectively. We found that the direct approach clearly underestimated the high SOCD (>9 ​kg ​m−2), while the indirect approach performed much better for high SOCD. Our findings serve as a valuable reference for SOCD mapping, thereby providing a scientific basis for maintaining soil health.