A soil-type-specific stratified approach to bare soil mosaicking and SOC prediction from Sentinel-2 time series

Accurate, high-resolution mapping of soil organic carbon (SOC) is essential for environmental modelling and sustainable land management, yet its prediction based on satellite imagery is often affected by vegetation and moisture, possibly causing generalised models to fail in landscapes with heterogeneous soils. To address this, we developed a stratified framework that tailors bare soil thresholding and SOC modelling to specific soil types. Using a 7-year Sentinel-2 time series and 414 soil samples over the Centre-Val de Loire region in France, we first identified the Visible and Shortwave Infrared Drought Index (VSDI) as an effective moisture proxy, avoiding the need for availability-limited external moisture data. We then optimised NDVI, NBR2, and VSDI thresholds individually for each major soil type to filter bare soil observations. Finally, soil-type-specific partial least squares regression (PLSR) models were built and compared against a single generalised model. Our results showed that our soil-type-specific strategy substantially outperformed the generalised model (e.g., RPIQ increased from 0.68 to 2.38 for Brunisols eutriques). The primary spectral predictors for SOC were highly variable, varying from visible to SWIR bands according to the soil type. The optimised VSDI filter was also critical for this improvement, reducing prediction RMSE by nearly 50% for loamy-texture soils like Luvisols. This study demonstrates that, for accurate SOC prediction at a very large regional scale, context (soil-type)-specific stratification of bare soil thresholding and SOC modelling is critical, serving as a framework for integrating pedological knowledge into SOC prediction and subsequent digital soil mapping workflow.