AlphaEarth Foundations Provide Superior Accuracy in Vegetation Height Mapping Across Severely Disturbed Forest Areas

Abstract

Large-scale vegetation height mapping remains a key challenge in remote sensing, with implications for harvest planning, carbon accounting, and habitat assessment. Despite recent methodological advances, limitations persist, including signal saturation in high biomass stands and high preprocessing demands for multisensor harmonization. Advances in artificial intelligence, particularly geospatial foundation models, offer new opportunities to address these limitations. AlphaEarth Foundations (AEF; Google DeepMind, 2025) integrates multisource Earth observation data into compact representations that may alleviate some of these constraints. However, its performance relative to traditional approaches remains unclear. Here, we confronted models trained on AEF embeddings with models trained on conventional optical and SAR features derived from Sentinel-1 and Sentinel-2 data. We tested two modeling approaches–random forest (RF) and a deep learning U-Net architecture–resulting in four data-model configurations: AEF+RF, AEF+U-Net, Sentinel+RF, and Sentinel+U-Net. Vegetation height reference data were derived from uncrewed aerial vehicle-based canopy height models (40 sites; 478 ha). AEF+U-Net significantly outperformed all other configurations with R2 = 0.83, root-mean-square error (RMSE) = 1.30 m, and relative RMSE (rRMSE) = 54.7% (p<0.001, Diebold–Mariano test, K = 40 spatial blocks). Importantly, AEF+U-Net showed greater performance stability across the vegetation height gradient than all other configurations. Beyond predictive accuracy, AEF+U-Net required 29%–40% fewer manual preprocessing and feature-engineering tasks relative to conventional configurations, further supporting the operational viability of foundation model embedding-based approaches for vegetation structure mapping.