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  • Maurizio Santoro
    Gamma Remote Sensing, Switzerland
  • Oliver Cartus
    Gamma Remote Sensing, Switzerland
  • Nuno Carvalhais
    Max Planck Institute for Biogeochemistry, Jena
  • Danae Rozendaal
    Wageningen University & Research
  • Valerio Avitabilie
    European Commission, Joint Research Centre (JRC), Ispra, Italy
  • Arnan Araza
    Wageningen University & Research
  • Styze de Bruin
    Wageningen University & Research
  • Martin Herold
    Wageningen University & Research
  • Shaun Quegan
    University of Sheffield
  • Pedro Rodriguez Veiga
    University of Leicester
  • Heiko Baltzer
    University of Leicester
  • Joao Carreiras
    University of Sheffield
  • Dimitry Schepaschenko
    International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • Mikhail Korets
    Russian Academy of Sciences
  • Masanobu Shimada
    Tokyo Denki University
  • Takuya Itoh
    Remote Sensing Technology Center of Japan
  • Alvaro Moreno Martinez
    Universitat de València
  • Jura Cavlovic
    University of Zagreb
  • Roberto Cazzolla Gatti
    Tomsk State University, Russia
  • Polyanna da Concecao Bispo
    University of Leicester
  • Nasheta Dewnath
    Guyana Forestry Commission
  • Nicolas Labriere
    Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS)
  • Jingjing Liang
    Purdue University
  • Jeremy Lindsell
    A Rocha International, Cambridge
  • Edward T.A. Mitchard
    University of Edinburgh
  • Alexandra Morel
    University of Dundee
  • Ana Maria Pacheco Pascagaza
    University of Leicester
  • Casey M. Ryan
    University of Edinburgh
  • Ferry Slik
    University of Brunei Darussalam
  • Gaia Vaglio Laurin
    University of Tuscia
  • Hans Verbeeck
    Ghent University
  • Arief Wijaya
    World Resources Institute, Indonesia
  • Simon Willcock
The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha-1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426 - 571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country’s national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120% of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018).
Original languageEnglish
Pages (from-to)3927-3950
Number of pages39
JournalEarth System Science Data
Volume13
Issue number8
DOIs
Publication statusPublished - 11 Aug 2021

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