Quantifying the uncertainty of remote sensing-based soil carbon monitoring

Agricultural soils are increasingly under pressure due to land use intensification and the ongoing effects of climate change. Current EU policies, such as the "Carbon Removals and Carbon Farming Regulation" (CRCF), aim to improve the resilience of cropland soils by carbon sequestration through climate-smart management. However, such regulations significantly increase the demand for spatiotemporal soil data to monitor and verify the effectiveness of carbon farming measures.

In recent years, the analysis of remote sensing-based bare soil observations has been increasingly used to generate accurate, high-resolution maps of cropland properties, such as soil organic carbon (SOC). However, due to the lack of robust reference data and the slow-changing nature of SOC, validating temporal model performance remains challenging. In this study, we tested the extent to which spatiotemporal models based on satellite data can support wall-to-wall soil monitoring and provide information on the temporal variability of SOC in cropland soils.

To achieve this, bare soil composites were derived from Landsat and Sentinel-2 data using a moving window approach and compared to Bavarian long-term soil monitoring data from 1986 to 2022. The results showed that while overall model performance was high, the validation of measured SOC trends yielded significantly lower accuracy, underlining the high uncertainty in predicting temporal soil carbon dynamics. While long-term analyses of 25+ years were necessary to detect significant SOC changes in most cases, the classification of the results revealed a low confusion rate between sites with increasing or decreasing SOC trends across the observation period.

These findings are supported by recent results based on the repetition of the German agricultural soil inventory, currently being conducted at the Thünen Institute of Climate-Smart Agriculture. Although significant uncertainties remain in quantifying SOC dynamics within 10-year intervals, results can be improved by taking the plot-scale SOC variability into account. This preprocessing step not only improves the significance of spatiotemporal SOC models ("model-then-derive") but also allows for the direct prediction of SOC changes based on a "derive-then-model" approach.

In summary, these results provide a first step toward an integrated soil monitoring system based on remote sensing and repeated soil sampling. While the findings demonstrate that it is possible to validate spatiotemporal SOC models using long-term sampling data, they also highlight the necessity of further improving the accuracy and applicability of the models. Based on our studies, we will further discuss the opportunities and challenges to independently validate SOC trends claimed by carbon farming schemes using remote sensing data.