Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of optical remote sensing data and environmental factors to predict SOC content, few studies have explored their applicability in karst areas. Therefore, it remains unclear how SOC content can be accurately simulated in these areas. In this study, 160 soil samples, 8 environmental covariates and 14 optical remote sensing variables were used to build SOC content prediction models. Three machine learning models, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were applied for each of three land use classes, including the entire study area, as well as farmland and forest areas. The variables with the greatest influence were the optical remote sensing bands, derived indices, as well as precipitation and temperature for forest areas, and optical remote sensing band11 and Pop-density for farmland. The results from this study suggest that RF and XGBoost are superior to SVM in prediction accuracy. Additionally, the simulation accuracy of the RF model for the forest areas (R² = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R² = 0.28, RMSE = 4.03, MAE = 3.27) was the greatest. The prediction model based on different land use types could obtain a higher simulation accuracy than that based on the whole study area. These findings provide new insights for the estimation of SOC content with high precision in karst areas.

Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R² = 0.55; RMSE = 2.89) and Habitat Patch 3 (R² = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R² = 0.44; RMSE = 4.35); although the R² increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R² of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.