Digital mapping of soil organic carbon across Europe using multi-source optical and radar observations

Accurate spatial prediction of soil organic carbon (SOC) is a key component of digital soil mapping and is essential for environmental modelling and land management. The increasing availability of multi-source satellite observations offers new opportunities to improve SOC mapping. However, the relative contribution of different optical and radar data sources remains still not fully clear. In this study, we systematically evaluate the influence of multi-temporal optical and synthetic aperture radar (SAR) observations on SOC prediction at the continental scale. A total of 18,984 topsoil samples (0–20 cm) from the LUCAS soil survey were combined with historical satellite-derived predictors. SOC prediction models were developed using geostatistical and machine-learning approaches and evaluated using cross-validation. Different combinations of optical and SAR predictors were tested to assess their impact on model performance and spatial patterns of SOC maps. Results showed that SOC prediction accuracy was strongly dependent on the type of satellite information used. Sentinel-3 long-term optical observations provided the highest predictive performance, explaining up to 70% of SOC spatial variability. SAR co- and cross-polarization contributed similarly to SOC prediction, while their combination further improved model performance. Predicted SOC maps exhibited pronounced spatial heterogeneity, with consistent large-scale patterns but varying local details across data sources. These findings highlight the complementary value of optical and radar observations and provide practical guidance for data selection in large-scale digital soil mapping of SOC.