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Abstract The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO2and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite‐based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite‐based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite‐based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high‐quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite‐ and model‐based AGB estimates. We suggest the carbon‐modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.
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This content will become publicly available on June 13, 2026
Individual Tree and Sapling Aboveground Biomass (AGB) Estimates for 35 NEON Terrestrial Observation Sites for 2014–2023
Abstract Here we present aboveground biomass (AGB) estimates from individual tree diameters scaled to whole‐tree biomass estimates using generalized allometric equations for 35 National Ecological Observatory Network (NEON) sites within the United States and Puerto Rico. These data are in both a standalone data file made publicly available via Figshare and as an R data package (NEONForestAGB) that allows for direct import of data into the R statistical computing environment. AGB is an Essential Climate Variable (ECV), yet biomass estimation from large forest inventory data can be cumbersome. Here we seek to provide a useful data set for community use from NEON data. The data set includes 92,281 unique individuals of 478 different species from 1,216 terrestrial observation plots for 360,570 biomass estimates between the years 2014 and 2023.
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- Award ID(s):
- 2217817
- PAR ID:
- 10600473
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 130
- Issue:
- 6
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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