Accurate large-scale monitoring of wheat cultivation and its crop rotation patterns is essential for food security and agricultural management. However, traditional remote sensing classification approaches typically rely on long-term multi-source imagery, complex feature engineering, and extensive labelled samples, limiting their scalability and spatiotemporal generalisation. To address these challenges, this study explores the potential of the AlphaEarth Foundation (AEF) embeddings—a global, annual, analysis-ready satellite embedding dataset—for winter wheat and crop rotation mapping. Firstly, we analyze AEF embeddings for intra-class consistency and inter-class separability, assessing their effectiveness in representing wheat. Subsequently, we compare multiple lightweight classifiers to identify an optimal model and conduct spatiotemporal generalization experiments across Henan Province from 2018 to 2024 using only limited labelled samples from 2020. Based on the resulting wheat maps, crop rotation patterns are further identified. Experimental results demonstrate that AEF embeddings exhibit strong semantic coherence and discriminative capability. Acceptable accuracy (OA=0.85) can already be achieved with simple models like cosine similarity and linear regression. More advanced lightweight classifiers further improve performance (OA=0.86–0.93) while maintaining stable results across different years and regions (spatial consistency=0.82). In addition, the crop rotation maps show high spatial agreement with existing products, while producing more spatially contiguous field patterns. Overall, AEF embeddings can serve as effective, ready-to-use features for large-scale agricultural remote sensing applications. By substantially reducing reliance on complex feature engineering and extensive training samples, they provide a practical and scalable solution for mapping winter wheat and its crop rotation patterns.