# Multi-Modal, Multi-Temporal Geospatial Foundation Models Improve Global Above-Ground Biomass Estimation

arXiv: https://arxiv.org/abs/2608.04792 · HTML: https://mlsanity.com/paper/2608.04792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 74 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Foundation Models for Generalist Geospatial Artificial Intelligence](https://mlsanity.com/paper/2310.18660/) (2023)
- [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://mlsanity.com/paper/2412.02732/) (2024)
- [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://mlsanity.com/paper/2507.22291/) (2025)
- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Towards a Unified Copernicus Foundation Model for Earth Vision](https://mlsanity.com/paper/2503.11849/) (2025)
- [FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring](https://mlsanity.com/paper/2312.10114/) (2023)
- [OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation](https://mlsanity.com/paper/2511.13655/) (2025)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications](https://mlsanity.com/paper/2601.16011/) (2026)
- [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://mlsanity.com/paper/2602.06285/) (2026)
- [UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation](https://mlsanity.com/paper/2606.23503/) (2026)
- [Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping](https://mlsanity.com/paper/2603.01576/) (2026)

## Built on by (most cited first, 1 of 1)

- [Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference](https://mlsanity.com/paper/2608.15282/) (2026)

## Abstract (arXiv)

Accurate estimation of above-ground biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale. Geospatial Foundation Models (GFMs) have recently emerged as a promising machine learning paradigm to derive general-purpose representations from Earth observation (EO) data, but their utility for quantitative regression tasks like biomass estimation remains largely unexplored, as most benchmarks focus on classification and segmentation. Here, we benchmark 13 GFMs for above-ground biomass estimation, specifically GEDI L4A biomass values. We compare them against a fully supervised state-of-the-art (SOTA) baseline trained on A Global-scale Biomass Dataset (AGBD), a machine learning-ready benchmark spanning diverse biomes and geographies. Most of the GFMs evaluated substantially underperform with respect to the supervised baseline, whether frozen or fine-tuned; only TESSERA and AlphaEarth Foundations (AEF) features with certain prediction heads yield a better performance. Our SOTA model trained on AEF embeddings and selected raw EO features achieves the best overall result (RMSE of 50.79 Mg/ha vs. 53.73 Mg/ha on AGBD features). This feature combination also generalizes better than AGBD features: it reduces RMSE by up to 25% in selected regions excluded from training, and, trained on single-year data alone, it outperforms the model trained on multi-year AGBD features. With labels from only 11 regions around the world, the AEF-based model also performs on par with the globally calibrated ESA CCI biomass product on independent reference plots. These findings indicate that GFM features can be highly informative for biomass estimation, when the underlying model is trained on rich multi-modal data.