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Preprints
https://doi.org/10.5194/essd-2020-148
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-148
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Jul 2020

21 Jul 2020

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This preprint is currently under review for the journal ESSD.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Maurizio Santoro1, Oliver Cartus1, Nuno Carvalhais2,3, Danaë Rozendaal4,5,6, Valerio Avitabilie7, Arnan Araza4, Sytze de Bruin4, Martin Herold4, Shaun Quegan8, Pedro Rodríguez Veiga9,10, Heiko Balzter9,10, João Carreiras8, Dmitry Schepaschenko11,12,13, Mikhail Korets14, Masanobu Shimada15, Takuya Itoh16, Álvaro Moreno Martínez17,18, Jura Cavlovic19, Roberto Cazzolla Gatti20,21, Polyanna da Conceição Bispo9,22, Nasheta Dewnath23, Nicolas Labrière24, Jingjing Liang25, Jeremy Lindsell26,27, Edward T. A. Mitchard28, Alexandra Morel29, Ana Maria Pacheco Pascagaza9, Casey M. Ryan28, Ferry Slik30, Gaia Vaglio Laurin31, Hans Verbeeck32, Arief Wijaya33, and Simon Willcock34 Maurizio Santoro et al.
  • 1Gamma Remote Sensing, 3073 Gümligen, Switzerland
  • 2Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
  • 3Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
  • 4Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
  • 5Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
  • 6Centre for Crop Systems Analysis, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
  • 7European Commission, Joint Research Centre, Ispra, Italy
  • 8National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, United Kingdom
  • 9Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, United Kingdom
  • 10National Centre for Earth Observation, University of Leicester, United Kingdom
  • 11International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
  • 12Center of Forest Ecology and Productivity of the Russian Academy of Sciences, Profsoyuznaya 84/32/14, Moscow, 117997, Russia
  • 13Institute of Ecology and Geography, Siberian Federal University, 660041, Krasnoyarsk, 79 Svobodny Prospect, Russia
  • 14Laboratory of Ecophysiology of Permafrost Systems, V.N. Sukachev Institute of Forest of the Siberian Branch of Russian Academy of Sciences – separated department of the KSC SB RAS, Krasnoyarsk, 660036, Russia
  • 15Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering
  • 16Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
  • 17Image Processing Laboratory (IPL), Universitat de València, València, Spain
  • 18Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, USA
  • 19University of Zagreb, Faculty of Forestry, Department of Forest Inventory and Management, Svetosimunska cesta 25, 10000, Zagreb, Croatia
  • 20Biological Institute, Tomsk State University, 634050 Tomsk, Russia
  • 21Konrad Lorenz Institute for Evolution and Cognition, 3400 Klosterneuburg, Austria
  • 22School of Environment, Education and Development/Department of Geography, University of Manchester, Oxford Road, M13 9PL Manchester, United Kingdom
  • 23Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown, Guyana
  • 24Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse Cedex 9, France
  • 25Department of Forestry and Natural Resources, Purdue University
  • 26A Rocha International, Cambridge, United Kingdom
  • 27The RSPB Centre for Conservation Science, Bedfordshire, United Kingdom
  • 28University of Edinburgh, School of GeoSciences, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, United Kingdom
  • 29Department of Geography and Environmental Sciences, University of Dundee, United Kingdom
  • 30Faculty of Science,University Brunei Darussalam, Jln Tungku Link, Gadong, BE1410, Brunei Darussalam
  • 31Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
  • 32CAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
  • 33World Resources Institute, Indonesia
  • 34School of Natural Sciences, Bangor University, United Kingdom

Abstract. 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).

Maurizio Santoro et al.

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GlobBiomass - global datasets of forest biomass Santoro, M. https://doi.org/10.1594/PANGAEA.894711

Maurizio Santoro et al.

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Short summary
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest aboveground biomass, i.e., carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests although for dense forests in the tropics values are somewhat underestimated. This dataset will have considerable impact on climate, carbon and socio-economic modelling schemes.
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we...
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