Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

• URL: http://arxiv.org/abs/2607.27217v1
• Date: Thu, 25 Jun 2026 15:07:38 GMT
• Title: Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States
• Abstract summary: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage.<n>Recent Earth observation foundation models provide globally consistent geospatial representations derived from diverse datasets.<n>We evaluate Google Satellite Embeddings (GSE), generated by the AplphaEarth Foundation Model, for regional-scale AGB estimation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements. Recent Earth observation foundation models provide globally consistent geospatial representations derived from diverse multimodal datasets, offering a potential pathway toward scalable biomass monitoring. Here, we evaluate Google Satellite Embeddings (GSE), generated by the AplphaEarth Foundation Model, for regional-scale AGB estimation across diverse temperate forest ecosystems in the northeastern United States. We integrated annual GSE observations, airborne LiDAR, and continuous forest inventory measurements from the Northeastern Forest Inventory Network (NEFIN) within a machine-learning framework. Combined LiDAR-GSE models achieved an R^2 of 0.79 for AGB estimation. Capitalizing on annual GSE observations expanded the training dataset by more than tenfold through temporal growth adjustment, increasing predictive performance to R^2 = 0.82 while reducing model bias by over 70%. Spatial autocorrelation analyses showed that integrating foundation-model representations and structural predictors substantially reduced residual spatial dependence. Monte Carlo simulations demonstrated that hyperparameter optimization reduced model-performance variability by 27.9%. Our findings demonstrate that foundation-model Earth representations capture ecologically meaningful information relevant to forest biomass and provide a scalable framework for annual carbon monitoring in regions with incomplete airborne LiDAR coverage. Our fundings establish a pathway toward next-generation forest carbon assessment based on globally available foundation-model Earth observations.

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