Tracking land cover changes in typical oasis regions based on the alphaearth foundations embedding dataset: a case study of Korla

Arid oasis landscapes are characterized by strong spatiotemporal heterogeneity and complex planting structures, posing substantial challenges for consistent land cover mapping across years and at a fine spatial scale. Korla City was selected as a representative arid oasis case. A 10 m resolution land cover monitoring framework for 2017–2024 was constructed by integrating 64-dimensional deep semantic embeddings from the geospatial foundation model AlphaEarth Foundations (AEF) with the Extreme Gradient Boosting (XGBoost) algorithm. An automated sample migration strategy was implemented through high-dimensional vector similarity mapping, and the classification decision logic was quantitatively interpreted using the SHapley Additive exPlanations (SHAP) method. The proposed framework exhibited strong robustness, with overall accuracy (OA) ranging from 0.88 to 0.97 and F1-score ranging from 0.87 to 0.97, outperforming public products including CLCD and ESA WorldCover. Principal component analysis (PCA) revealed that AEF embeddings provided a more distributed and lower redundancy representation of land cover changes than traditional optical features, with the first two principal components capturing only 38.18% of the total variance. SHAP attribution further identified a complementary mechanism in which shared embedding dimensions constrained the environmental background, while class specific dimensions enhanced the discrimination of spectrally similar types. These findings demonstrate that geospatial foundation model embeddings provide an effective feature representation for spatiotemporally consistent land cover monitoring in heterogeneous arid environments.