Leveraging AlphaEarth Foundations Embeddings for High-Accuracy County-Scale Corn and Soybean Yield Estimation

Accurate, timely crop yield estimates are critical for food security and agricultural planning, yet most operational pipelines still rely on handcrafted indices or weather-driven regressors. We assess whether Google DeepMind's AlphaEarth Foundations (AEF), a multimodal Geo-Foundation Model that provides annual 64-D embeddings at 10 m resolution, are sufficient for county-level U.S. corn yield estimation. Using one embedding per county-year and a light RBF-SVR learner, we evaluate leave-one-year-out generalization. Despite the absence of time-resolved inputs, the embeddings yield strong predictive accuracy across years. For corn, mean test performance is R2 =0.825 with RMSE 14.8 bu/ac, reaching R 2 =0.861 in the best year. The same workflow transfers to soybean with R2 =0.814. Spatial diagnostics show strong positive autocorrelation of residuals every year, indicating missing slow-varying drivers rather than stochastic noise. Test-fold permutation importance shows feature importance, with several directions shared across crops, while a few flip sign, suggesting crop-specific semantics. These results demonstrate that annual AEF embeddings serve as analysis-ready features for county-scale yield estimation, enabling a lightweight, reproducible pipeline (mask, mean, regress) that is competitive with more complex alternatives.