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

S. S. Saatchi, Benchmark map of forest carbon stocks in tropical regions across three continents, Proc. Natl Acad. Sci. USA, vol.108, pp.9899-9904, 2011.

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

Y. Y. Liu, Recent reversal in loss of global terrestrial biomass, Nat. Clim. Change, vol.5, pp.470-474, 2015.

N. L. Harris, Baseline map of carbon emissions from deforestation in tropical regions, Science, vol.336, pp.1573-1576, 2012.

M. D. Marco, J. E. Watson, D. J. Currie, H. P. Possingham, and O. Venter, The extent and predictability of the biodiversity-carbon correlation, Ecol. Lett, vol.21, pp.365-375, 2018.

F. Giardina, Tall Amazonian forests are less sensitive to precipitation variability, Nat. Geosci, vol.11, pp.405-409, 2018.

K. Erb, Unexpectedly large impact of forest management and grazing on global vegetation biomass, Nature, vol.553, pp.73-76, 2018.

D. J. Zarin, Can carbon emissions from tropical deforestation drop by 50% in 5 years?, Glob. Change Biol, vol.22, pp.1336-1347, 2016.

R. Chaplin-kramer, Degradation in carbon stocks near tropical forest edges, Nat. Commun, vol.6, p.10158, 2015.

M. Brandt, Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands, Nat. Ecol. Evol, vol.2, p.827, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01806711

L. Fan, Satellite-observed pantropical carbon dynamics, Nat. Plants, vol.5, pp.944-951, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02403129

E. T. Mitchard, Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites, Glob. Ecol. Biogeogr, vol.23, pp.935-946, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01555979

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.

V. Avitabile, An integrated pan-tropical biomass map using multiple reference datasets, Glob. Change Biol, vol.22, pp.1406-1420, 2016.

M. Réjou-méchain, Upscaling forest biomass from field to satellite measurements: Sources of errors and ways to reduce them, Surv. Geophys, vol.40, pp.881-911, 2019.

S. Saatchi, Mapping tropical forest biomass: synthesis of ground and remote sensing inventory, Consult. Rep. 2 High Carbon Stock Sci. Study, 2015.

P. Ploton, A map of African humid tropical forest aboveground biomass derived from management inventories, Sci. Data, vol.7, p.221, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02929027

N. Philippon, The light-deficient climates of Western Central African evergreen forests, Environ. Res. Lett, vol.14, p.34007, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02080499

S. Saatchi, Seeing the forest beyond the trees, Glob. Ecol. Biogeogr, vol.24, pp.606-610, 2015.

S. Mermoz, T. Le-toan, L. Villard, M. Réjou-méchain, and J. Seifert-granzin, Biomass assessment in the Cameroon savanna using ALOS PALSAR data, Remote Sens. Environ, vol.155, pp.109-119, 2014.

S. L. Lewis, Above-ground biomass and structure of 260 African tropical forests, Philos. Trans. R. Soc. B Biol. Sci, vol.368, p.20120295, 2013.

M. C. Hansen, P. Potapov, and A. Tyukavina, Tropical forests are a net carbon source based on aboveground measurements of gain and loss, Science, vol.363, p.3629, 2019.

A. Baccini, Tropical forests are a net carbon source based on aboveground measurements of gain and loss, Science, vol.358, pp.230-234, 2017.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

A. Lyapustin, Y. Wang, S. Korkin, and D. Huang, MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech, vol.11, pp.5741-5765, 2018.

S. E. Fick and R. J. Hijmans, WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol, vol.37, pp.4302-4315, 2017.

I. Kühn, Incorporating spatial autocorrelation may invert observed patterns, Divers. Distrib, vol.13, pp.66-69, 2007.

C. F. Dormann, Effects of incorporating spatial autocorrelation into the analysis of species distribution data, Glob. Ecol. Biogeogr, vol.16, pp.129-138, 2007.

D. R. Roberts, Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, vol.40, pp.913-929, 2017.

R. Valavi, J. Elith, J. J. Lahoz-monfort, and G. Guillera-arroita, blockCV: an r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models, Methods Ecol. Evol, vol.10, pp.225-232, 2019.

I. Parmentier, Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model, J. Biogeogr, vol.38, pp.1164-1176, 2011.

A. Baccini, W. Walker, L. Carvalho, M. Farina, and R. A. Houghton, Response to Comment on "Tropical forests are a net carbon source based on aboveground measurements of gain and loss, Science, vol.363, p.1205, 2019.

L. Xu, S. S. Saatchi, Y. Yang, Y. Yu, and L. White, Performance of nonparametric algorithms for spatial mapping of tropical forest structure, Carbon Balance Manag, vol.11, p.18, 2016.

C. F. Dormann, Methods to account for spatial autocorrelation in the analysis of species distributional data: a review, Ecography, vol.30, pp.609-628, 2007.

A. Irwin, The ecologist who wants to map everything, Nature, vol.573, pp.478-481, 2019.

P. Legendre, Spatial autocorrelation: trouble or new paradigm?, Ecology, vol.74, pp.1659-1673, 1993.

H. Meyer, C. Reudenbach, S. Wöllauer, and T. Nauss, Importance of spatial predictor variable selection in machine learning applications-Moving from data reproduction to spatial prediction, Ecol. Model, vol.411, p.108815, 2019.

C. Strobl, A. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, Conditional variable importance for random forests, BMC Bioinformatics, vol.9, p.307, 2008.

M. A. Lefsky, Estimates of forest canopy height and aboveground biomass using ICESat, Geophys. Res. Lett, vol.32, pp.22-24, 2005.

M. Réjou-méchain, Upscaling Forest biomass from field to satellite measurements: sources of errors and ways to reduce them, Surv. Geophys, vol.40, pp.881-911, 2019.

E. T. Mitchard, Comment on 'A first map of tropical Africa's aboveground biomass derived from satellite imagery', Environ. Res. Lett, vol.6, p.49001, 2011.

G. P. Asner, High-resolution carbon mapping on the million-hectare Island of Hawaii, Front. Ecol. Environ, vol.9, pp.434-439, 2011.

G. P. Asner, Human and environmental controls over aboveground carbon storage in Madagascar, Carbon Balance Manag, vol.7, issue.2, 2012.

G. P. Asner, High-resolution mapping of forest carbon stocks in the Colombian Amazon, Biogeosciences, vol.9, p.2683, 2012.

G. P. Asner, Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo, Biol. Conserv, vol.217, pp.289-310, 2018.

L. Xu, Spatial distribution of carbon stored in forests of the Democratic Republic of Congo, Sci. Rep, vol.7, p.15030, 2017.

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. 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

J. Van-den-hoogen, Soil nematode abundance and functional group composition at a global scale, Nature, vol.572, pp.194-198, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02871790

B. S. Steidinger, Climatic controls of decomposition drive the global biogeography of forest-tree symbioses, Nature, vol.569, pp.404-408, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02147493

J. Bastin, The global tree restoration potential, Science, vol.365, pp.76-79, 2019.

A. Trabucco and R. J. Zomer, Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database, CGIAR Consort Spat Information, 2009.

A. M. Wilson and W. Jetz, Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions, PLoS Biol, vol.14, p.1002415, 2016.

D. M. Bowman, G. J. Williamson, R. J. Keenan, and L. D. Prior, A warmer world will reduce tree growth in evergreen broadleaf forests: evidence from A ustralian temperate and subtropical eucalypt forests, Glob. Ecol. Biogeogr, vol.23, pp.925-934, 2014.

Y. Malhi, Exploring the likelihood and mechanism of a climate-changeinduced dieback of the Amazon rainforest, Proc. Natl Acad. Sci. USA, vol.106, pp.20610-20615, 2009.

C. D. Rennó, HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia, Remote Sens. Environ, vol.112, pp.3469-3481, 2008.

F. Nachtergaele, H. V. Velthuizen, L. Verelst, and D. Wiberg, Harmonized World Soil Database (HWSD) (Food and Agriculture Organization, 2009.

P. Defourny, Algorithm Theoretical Basis Document for Land Cover Climate Change Initiative, 2014.

M. Segal and Y. Xiao, Multivariate random forests, WIREs Data Min. Knowl. Discov, vol.1, pp.80-87, 2011.

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.