Annual 10-m high-resolution cropland maps for Southeast Asia since 2019 using AlphaEarth embeddings

Abstract. Southeast Asia (SEA) contributes substantially to tropical agriculture, but remains underserved by high-precision cropland data due to persistent cloud cover, fragmented farming, prevalent shifting cultivation, and complex phenology. Here, we developed a 10-m annual cropland dataset for SEA (SEA_Cropland10) covering 2019–2024 using a random forest model on Google Earth Engine. The model integrated 88,088 Sentinel-1 SAR scenes, 599,255 Sentinel-2 optical images, and Google AlphaEarth embeddings, and was trained using 37,192 visually interpreted samples. An independent accuracy assessment was performed using 1,200 samples stratified by land-cover change trajectories to validate both temporal dynamics and spatial extent. SEA_Cropland10 achieved an overall accuracy (OA) of 92.67 % (±1.47 %) for cropland dynamics, with annual static accuracies consistently exceeding 92.42 % (±1.50 %). The incorporation of AlphaEarth embeddings proved critical, improving model performance by 5.81 %. Compared to existing global products (e.g., GLAD, WorldCereal), SEA_Cropland10 improved OA by 14.56 %–19.97 % and substantially enhanced the detection of sloping cropland (>5°) in mountainous regions of SEA, increasing producer’s accuracy in global baselines from below 29.7 %–32.9 % to 89.0 %–99.1 %. Consequently, we identified three- to fourfold more sloping cropland area than GLAD and WorldCereal reported. Based on SEA_Cropland10, the estimated total cropland area in SEA shifted from 68.7 (±2.8) Mha in 2019 to 67.3 (±3.2) Mha in 2024, showing strong consistency with national statistics (r = 0.90–0.95). This dataset provides an important improvement for regional food security monitoring and carbon cycle modeling. The SEA-Cropland10 is publicly available at Zenodo: https://doi.org/10.5281/zenodo.17828801 (Cai and Zeng, 2026) and Google Earth Engine App: https://ee-caiyt33tc.projects.earthengine.app/view/seacrop10.