J. Parde, . Forest, and . Biomass, Forestry Abstracts, vol.41, pp.343-362, 1980.

E. T. Mitchard, The tropical forest carbon cycle and climate change, Nature, vol.559, pp.527-534, 2018.

M. Herold, The role and need for space-based forest biomass-related measurements in environmental management and policy, Surv. Geophys, vol.40, pp.757-778, 2019.

Y. Pan, A large and persistent carbon sink in the world's forests, Science, vol.333, pp.988-993, 2011.

E. T. Mitchard, Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps, Carbon Balance Manag, vol.8, p.10, 2013.

R. A. Houghton, Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon, Nature, vol.403, pp.301-304, 2000.

A. Baccini, Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, vol.2, pp.182-185, 2012.

X. Song, Global land change from 1982 to 2016, Nature, vol.560, pp.639-643, 2018.

S. L. Maxwell, Degradation and forgone removals increase the carbon impact of intact forest loss by 626%, Sci. Adv, vol.5, p.2546, 2019.

R. Andrade, Alarming surge in Amazon fires prompts global outcry, Nature, 2019.

L. E. Aragão, 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions, Nat. Commun, vol.9, p.536, 2018.

R. E. Mcroberts, E. O. Tomppo, and E. Naesset, Advances and emerging issues in national forest inventories, Scand. J. For. Res, vol.25, pp.368-381, 2010.

V. Avitabile and A. Camia, An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots, For. Ecol. Manag, vol.409, pp.489-498, 2018.

C. Vidal, T. Bélouard, J. Hervé, N. Robert, and J. Wolsack, A new flexible forest inventory in France, Proceedings of the seventh annual forest inventory and analysis symposium, vol.77, pp.67-73, 2005.

G. Kandler, The design of the second German national forest inventory, Proceedings of the eighth annual forest inventory and analysis symposium, 2006.

C. A. Monterey and . Gen, , vol.79, pp.19-24, 2009.

L. Duncanson, The importance of consistent global forest aboveground biomass product validation, Surv. Geophys, vol.40, pp.979-999, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02403066

J. Chave, Ground data are essential for biomass remote sensing missions, Surv. Geophys, vol.40, pp.863-880, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02403077

C. Wasseige and . De, Les Forêts du Bassin du Congo: etat des Forêts, 2008.

, Rapport stratégique régional. Développement intègre et durable de la filière bois dans le bassin du Congo: opportunités, défis et recommandations opérationnelles. (Banque Africaine de Développement, 2019.

E. Cci, New release of 300 m global land cover and 150 m water products (v.1.6.1) and new version of the User Tool (3.10) for download, 2016.

M. Réjou-méchain, Detecting large-scale diversity patterns in tropical trees: Can we trust commercial forest inventories? For, Ecol. Manag, vol.261, pp.187-194, 2011.

P. Ploton, . Africadiv, and . Extract, , 2020.

K. J. Anderson-teixeira, CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change, Glob. Change Biol, vol.21, pp.528-549, 2015.

J. Bastin, Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach, Ecol. Appl, vol.24, 1984.
URL : https://hal.archives-ouvertes.fr/hal-02065085

O. L. Phillips, T. Baker, T. Feldspauch, and R. J. Brienen, Field manual for plot establishment and remeasurement (RAINFOR), Amaz. For. Inventory Netw. Sixth Framew. Programme, 2002.

M. Réjou-méchain, A. Tanguy, C. Piponiot, J. Chave, and B. Hérault, biomass: an r package for estimating above-ground biomass and its uncertainty in tropical forests, Methods Ecol. Evol, vol.8, pp.1163-1167, 2017.

J. Chave, Improved allometric models to estimate the aboveground biomass of tropical trees, Glob. Change Biol, vol.20, pp.3177-3190, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02063299

A. E. Zanne, Data from: Towards a worldwide wood economics spectrum, 2009.

T. R. Feldpausch, Tree height integrated into pan-tropical forest biomass estimates, Biogeosciences, vol.9, pp.3381-3403, 2012.

D. Schepaschenko, The Forest Observation System, building a global reference dataset for remote sensing of forest biomass, Sci. Data, vol.6, pp.1-11, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02316194

J. Vanderwal, Package 'SDMTools' . R Package, 2014.

L. Devroye, Sample-based non-uniform random variate generation, Proceedings of the 18th conference on Winter simulation, pp.260-265, 1986.

P. Ploton, A 1-km resolution dataset of African humid tropical forests aboveground biomass derived from management inventories, 2020.

P. Ploton, Aggregated CoFor forest management inventories, 2020.

N. J. Rodríguez-fernández, An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa, Biogeosciences, vol.15, pp.4627-4645, 2018.

R. Dubayah, The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography, Sci. Remote Sens, vol.1, p.100002, 2020.

E. Naesset, Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania, Int. J. Appl. Earth Obs. Geoinformation, vol.89, p.102109, 2020.

C. Beirne, Landscape-level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type, Ecol. Appl, vol.29, p.1987, 2019.

E. Kearsley, Conventional tree height-diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin, Nat. Commun, vol.4, pp.1-8, 2013.

,

P. P. , F. M. , R. P. , S. G. , N. Bay et al., conceived the study, analyzed the data and led the writing of the paper