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

journal contribution
posted on 2024-03-27, 11:25 authored by Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, Danaë Rozendaal, Valerio Avitabilie, Arnan Araza, Sytze de Bruin, Martin Herold, Shaun Quegan, Pedro Rodríguez Veiga, Heiko Balzter, João Carreiras, Dmitry Schepaschenko, Mikhail Korets, Masanobu Shimada, Takuya Itoh, Álvaro Moreno Martínez, Jura Cavlovic, Roberto Cazzolla Gatti, Polyanna da Conceição Bispo, Nasheta Dewnath, Nicolas Labrière, Jingjing Liang, Jeremy Lindsell, Edward TA Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, Simon Willcock
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).

Funding

European Space Agency (ESRIN contract no. 4000113100/14/I-NB)

Russian Science Foundation (grant no. 19-77-30015)

History

Citation

Earth Syst. Sci. Data, 13, 3927–3950

Author affiliation

National Centre for Earth Observation (NCEO)

Version

  • VoR (Version of Record)

Published in

Earth System Science Data

Volume

13

Issue

8

Publisher

Copernicus GmbH

issn

1866-3508

eissn

1866-3516

Acceptance date

2021-06-26

Copyright date

2021

Available date

2024-03-27

Notes

Supplement to Data description paper attached together with DOI.

Language

en

Rights Retention Statement

  • No

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