Combining specialized Sentinel-2 time series features with AlphaEarth Foundations for forest type mapping

Accurate mapping of forest types and vegetation characteristics is essential for monitoring biodiversity and forest dynamics. Traditional Deep Learning (DL) models trained on Sentinel-2 time series achieve high performance, but require extensive preprocessing and sensor-related fine-tuning. In this study, we evaluate the recently introduced AlphaEarth Foundations (AEF) embeddings, which is a global, multi-modal feature representation of the earths surface, for forest mapping in Italy. We compare a) a Random Forest model trained on Sentinel-2 and climate time series features, b) a Multi-Layer Perceptron trained on AEF, c) a Time-Series Transformer trained on Sentinel-2 and climate annual time series, and d) a Cross-Attention fusion model combining both feature sets. Using 5-fold cross-validation in a regression and a classification task on two datasets (evergreen broad-leaved tree cover ETC, forest vegetation type FVT) we find that the combined model consistently outperforms the single-source approaches (RMSE = 0.161, Acc = 0.757). AEF-based models achieve comparable accuracy to the Sentinel-2-based models, while reducing extensive time series preprocessing and training time by an order of magnitude. Feature attribution using integrated gradients reveals that AEF provides stable baseline representations, while Sentinel-2 inputs add phenology-related detail. The results show, that integrating generalized embeddings with specialized spectral-temporal features improves predictive performance for forest mapping.