Generating Annual 10 m Land Cover Maps for 37 Chinese Metropolises Using Google Satellite Embeddings

Accurate urban land cover information is essential for monitoring urbanization and environmental change, yet the complexity and heterogeneity of urban landscapes remain challenging for high-resolution remote sensing classification. This study used Google Satellite Embeddings as the core feature input, combined with ensemble learning and spatiotemporal post-processing, to generate annual 10 m land cover maps for 37 Chinese metropolises from 2017 to 2024. Results showed that the weighted ensemble model achieved an overall accuracy (OA) of 81.99%, 2.13% higher than the best individual model, while spatiotemporal post-processing further increased OA to 82.59%. Compared with existing land cover products such as FROM-GLC10, the proposed product achieved 4.20% higher OA. The ablation experiment showed that the embedding representation was the primary source of performance improvement. Under the same ensemble learning framework, Satellite Embedding features outperformed conventional remote sensing features by 6.54% in OA. SHapley Additive exPlanations (SHAP) analysis identified A16, A36, A51, A61, and A22 as the key embedding dimensions, while Pearson correlation analysis further revealed associations between the key dimensions and spectral, radar, texture, and topographic information. Change analysis indicated concurrent urban expansion, ecological recovery, and agricultural land contraction from 2017 to 2024, with cropland-to-forest and cropland-to-impervious-surface conversions being the most prominent transition pathways. Overall, Satellite Embeddings provide effective feature representations for annual land cover mapping and long-term change analysis in complex metropolitan environments.