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.