FoundationSoil: Enhancing Soil Organic Carbon Mapping Using a Multi-Temporal Geospatial Foundation Model

Soil organic carbon (SOC) is a key indicator of soil health and climate resilience, yet large-scale spatial prediction remains challenging due to sparse field data and the complex dynamics of soil processes. We present FoundationSoil, a digital soil mapping framework that integrates a geospatial foundation model (Prithvi-EO-2.0) with environmental covariates to predict the spatial variability of SOC concentrations. Prithvi-EO-2.0 encodes multi-temporal satellite image patches via a transformer encoder, producing spatiotemporal representations that capture land-surface dynamics across seasons. These features are combined with 109 environmental covariates (e.g., climate, soil moisture) to train Random Forest and neural network regressors. We evaluated FoundationSoil using 12,692 georeferenced topsoil samples from the WoSIS database across the continental United States. Stratified grid-based partitions were applied to mitigate the influence of spatial autocorrelation on model performance evaluation. Results show that FoundationSoil consistently outperformed a pixel-based baseline, achieving best performance at R2 = 0.72 and RMSE = 38.62. Feature attribution shows that Prithvi-derived components capture semantically rich, non-redundant signals relevant to SOC processes. These results demonstrate the promise of pretrained vision foundation models for digital soil mapping and highlight their potential for data-efficient, transferable learning in Earth science applications.