Interpreting AlphaEarth Foundation Model Embeddings for Soil Organic Carbon Estimation Across European Agricultural Landscapes

Soil organic carbon (SOC) estimation from Earth Observation is increasingly important for assessing soil health, carbon dynamics and the effects of agricultural management. However, conventional optical approaches are often constrained by the limited availability of bare-soil observations, particularly in cropping systems affected by vegetation cover, residues and conservation practices. AlphaEarth Foundation (AEF) embeddings provide annual, multi-source, 10 m representations of the land surface and may offer an alternative representation for SOC modelling, although their environmental meaning for soil applications remains poorly understood. In this study, we investigated whether AEF embedding dimensions encode SOC-relevant information consistently across contrasting European agricultural soil datasets. We used 1060 georeferenced topsoil samples collected in Italy, France and Slovakia between 2020 and 2025. SOC–embedding relationships were assessed using Pearson and Spearman correlations, while Random Forest models and bootstrap permutation variable importance were used to identify stable predictive dimensions. Model transferability was evaluated through leave-one-dataset-out validation. To support functional and environmental interpretation, selected embeddings were correlated with separately derived EO and terrain covariates derived from Sentinel-2, Landsat 8/9, Sentinel-1 and SRTM, including bare-soil reflectance, vegetation indices, radar backscatter and topographic variables. Results showed that AEF embeddings contained SOC-relevant information, but this information was distributed across multiple latent dimensions and was strongly context dependent. At the pooled level, A55, A30, A51 and A27 were among the most influential embeddings, with A55 and A30 representing opposite orientations of a soil-brightness gradient. A55, A50 and A30 were associated with bare-soil reflectance patterns consistent with the darkening effect of higher SOC, whereas other embeddings were associated with vegetation, radar and terrain-related signals. Dataset-specific models revealed different importance profiles across France, Italy and Slovakia, and leave-one-dataset-out validation produced negative R2 values for all excluded datasets, indicating poor direct transferability. Overall, AEF embeddings can support SOC estimation within individual environmental domains, but their predictive relevance and functional interpretation are strongly context dependent, and no single embedding dimension showed a universal SOC-related meaning. These findings highlight that geospatial foundation-model embeddings should not be used as black-box predictors for soil applications, but require local or regional calibration, geographically independent validation and domain-aware interpretation. These findings support a practical framework for using foundation-model embeddings more transparently in digital soil mapping, by linking latent dimensions to SOC, EO covariates and transferability behaviour.