Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China

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 (R-2 = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R-2 = 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.

Automated summary

Climate change is connected to changes in soil organic carbon (SOC) content, making accurate prediction of SOC content crucial for carbon accounting and sustainable soil management. This study used soil samples, environmental covariates, and optical remote sensing variables to build SOC content prediction models. Three machine learning models were applied across different land use classes in karst areas, and the results showed that RF and XGBoost outperformed SVM in prediction accuracy. The models based on individual land use types achieved higher simulation accuracy than the model based on the entire study area. These findings provide new insights for estimating SOC content with high precision in karst areas.