Improving soil organic carbon estimation and uncertainty assessment using multi-temporal optical and SAR data

Accurate estimation of soil organic carbon (SOC) is crucial for soil health, agricultural productivity, and climate change mitigation. Digital SOC mapping often lacks multi-sensor integration, improved bare soil compositing, and robust uncertainty assessment. We used 6-year multi-temporal Synthetic Aperture Radar (SAR) from Sentinel-1 and optical data from Sentinel-2. This study also utilized confidence interval (CI)–based bare soil compositing for SOC prediction in an agricultural landscape in northeast Germany to enhance SOC estimation.

Four Random Forest models were developed to isolate and compare the independent and combined contributions of optical and SAR data. Local soil samples collected between 2013 and 2022 were divided into training and testing datasets. Independent validation was conducted using samples collected in 2024. Pixel-wise uncertainty was quantified through 100 repeated model runs with different training and testing splits, resulting in a spatially explicit SOC uncertainty map.

Combining SAR and optical data improved in model calibration, while CI-based compositing further enhanced prediction accuracy. Using important features, the model achieved a coefficient of determination (R²) of 0.79 and a ratio of performance to deviation (RPD) of 2.23 in independent validation. The models incorporating SAR data showed higher uncertainty due to its sensitivity to soil conditions; however, standalone SAR data still yielded acceptable SOC mapping performance (R² = 0.57, RPD = 1.54).  These results show that combining multi-temporal optical and SAR data with explicit uncertainty assessment enhances the robustness and reliability of SOC mapping across agricultural landscapes.