Estimation of Soil Organic Carbon and Bulk Density for Carbon Stock Assessment in Croplands Using the Sentinel-1 and 2 Observations

Remote sensing coupled with machine learning is useful for non-invasive monitoring and prediction of Soil Organic Carbon (SOC) and carbon stock. The use of Sentinel-1 and 2 datasets was attempted in this work for SOC, Bulk Density (BD), and carbon stock estimation. The key objective of this study is to estimate Soil Organic Carbon and Bulk Density for carbon stock assessment in croplands using multi-spectral and Synthetic Aperture Radar (SAR) satellite observations. The fields from Colorado and Nebraska states are selected, and assessment is performed after the harvest of the cropping season in 2020. Soil samples were collected during 10-30 Oct 2020. Regression models were developed using bands and indices derived from Sentinel-1 and 2 datasets as independent variables, however SOC or BD were used individually as dependent variables. Models were developed by considering data from individual states and then combining all the data using Random Forest Regression (RFR) and Support Vector Regression (SVR). Results for the SOC estimation showed that RFR performed better than SVR with individual state data. Lowest RMSE achieved using the RFR were 0.114 and 0.159 for fields from Nebraska and Colorado states. However, SVR outperformed over RFR in the case of BD estimation using the combined data from both states (RMSE = 0.24). Further comparison between estimated carbon stock and actual carbon stock at field level shows the good agreement over fields from Colorado compared to Nebraska.