Journal ArticleComparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation ModelsEl_Masri, Bassil [Department of Earth and Environmental Sciences Murray State University Murray KY USA] (ORCID:0000000210175467); Xiao, Jingfeng [Earth Systems Research Center Institute for the Study of Earth, Oceans, and Space University of New Hampshire Durham NH USA]<title>Abstract</title> <p>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.</p>Wiley2025-01-0110642548Journal of Geophysical Research: Biogeosciences13012169-8953https://doi.org/10.1029/2024JG0083052017870; 2327374National Science Foundation