The authors evaluate whether geospatial foundation model embeddings (Prithvi-EO and ViT-Base) improve crop yield prediction across countries in sub-Saharan Africa compared to traditional spectral features from Sentinel-2 satellite imagery. Using a leave-one-country-out cross-validation scheme on 6,404 maize fields across five countries, they find a large generalization gap: all methods achieve moderate R² within-country but universally negative R² cross-country. Frozen foundation model embeddings provide no meaningful advantage over hand-engineered spectral features, and the main bottleneck is distributional shift in yields across countries, not representation quality.
Main takeaways:
- Within-country cross-validation overstates model performance; leave-one-country-out reveals near-total failure to generalize.
- Geospatial foundation model embeddings (Prithvi-EO, ViT-Base) perform no better than traditional spectral features for cross-country yield prediction.
- All feature sets achieve universally negative R² under cross-country testing, indicating the models are worse than a constant mean predictor.
- The failure is attributed to yield distribution shift between countries, not poor feature quality.
- The paper releases a reproducible negative benchmark to guide future work on cross-country agricultural prediction.