arXiv:2603. 09868v2 Announce Type: replace Abstract: Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy covariance observations.
arXiv:2607. 27217v1 Announce Type: cross 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.
The study evaluates 13 Geospatial Foundation Models (GFMs) for estimating above‑ground biomass (AGB) from satellite imagery, comparing them to a fully supervised state‑of‑the‑art baseline trained on the AGBD dataset. Most GFMs perform worse than the baseline, but TESSERA and AlphaEarth Foundations (AEF) features combined with specific prediction heads achieve superior results, with the best model attaining an RMSE of 50.79 Mg/ha. This AEF‑based model also generalizes better to unseen regions, reduces RMSE by up to 25% in excluded areas, and matches the ESA CCI biomass product when trained on limited regional labels.
arXiv:2608. 04792v1 Announce Type: new Abstract: 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.
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.
arXiv:2608. 06406v1 Announce Type: cross Abstract: Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management.
arXiv:2610.27793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly due to imperfect land surface representations i...
arXiv:2606. 11793v2 Announce Type: replace-cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.
arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.
arXiv:2601.16834v4 Announce Type: replace Abstract: Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes....
arXiv:2606. 11793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.