Satellite-observed pantropical carbon dynamics


Changes in terrestrial tropical carbon stocks have an important role in the global carbon budget. However, current observational tools do not allow accurate and large-scale monitoring of the spatial distribution and dynamics of carbon stocks1. Here, we used low-frequency L-band passive microwave observations to compute a direct and spatially explicit quantification of annual aboveground carbon (AGC) fluxes and show that the tropical net AGC budget was approximately in balance during 2010 to 2017, the net budget being composed of gross losses of −2.86 PgC yr−1 offset by gross gains of −2.97 PgC yr−1 between continents. Large interannual and spatial fluctuations of tropical AGC were quantified during the wet 2011 La Niña year and throughout the extreme dry and warm 2015–2016 El Niño episode. These interannual fluctuations, controlled predominantly by semiarid biomes, were shown to be closely related to independent global atmospheric CO2 growth-rate anomalies (Pearson’s r = 0.86), highlighting the pivotal role of tropical AGC in the global carbon budget.

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Fig. 1: Temporal variations in annual AGC in the tropics (continents and biomes), expressed as the difference from 2010 values.
Fig. 2: Spatial patterns and trends in tropical carbon changes.
Fig. 3: Interannual variability of global atmospheric CGR and tropical AGC fluxes.

Data availability

The IGBP land-cover classification map, EVI, forest area loss map, GRACE data for terrestrial groundwater storage, precipitation data, skin temperature product, global CO2 growth-rate data, MEI and the Baccini and Avitabile biomass maps are publicly available. The SMOS-IC soil moisture dataset is available via Centre Aval de Traitement des Données SMOS at SMOS-IC L-VOD and AGC products, the Saatchi, Bouvet and Mermoz biomass maps are available from J.-P.W., S.S.S. (, A. Bouvet ( and S. Mermoz ( both at CESBIO, Toulouse, France), respectively, on request.


  1. 1.

    Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).

  2. 2.

    Hansen, M. C., Potapov, P. & Tyukavina, A. Comment on “Tropical forests are a net carbon source based on aboveground measurements of gain and loss”. Science 363, eaar3629 (2019).

  3. 3.

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

  4. 4.

    Gaubert, B. et al. Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate. Biogeosciences 16, 117–134 (2019).

  5. 5.

    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182 (2012).

  6. 6.

    Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).

  7. 7.

    Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554 (2014).

  8. 8.

    Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).

  9. 9.

    Yang, Y. et al. Post-drought decline of the Amazon carbon sink. Nat. Commun. 9, 3172 (2018).

  10. 10.

    Brienen, R. J. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344 (2015).

  11. 11.

    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011). 1201609.

  12. 12.

    Malhi, Y. The productivity, metabolism and carbon cycle of tropical forest vegetation. J. Ecol. 100, 65–75 (2012).

  13. 13.

    Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science 358, eaam5690 (2017).

  14. 14.

    Yue, C. et al. Vegetation greenness and land carbon-flux anomalies associated with climate variations: a focus on the year 2015. Atmos. Chem. Phys. 17, 13903–13919 (2017).

  15. 15.

    Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).

  16. 16.

    Bastos, A. et al. Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches. Phil. Trans. R. Soc. B 373, 20170304 (2018).

  17. 17.

    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

  18. 18.

    Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

  19. 19.

    Bouvet, A. et al. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sens. Environ. 206, 156–173 (2018).

  20. 20.

    Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).

  21. 21.

    Konings, A., Williams, A. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).

  22. 22.

    Wigneron, J.-P., Kerr, Y., Chanzy, A. & Jin, Y.-Q. Inversion of surface parameters from passive microwave measurements over a soybean field. Remote Sens. Environ. 46, 61–72 (1993).

  23. 23.

    Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).

  24. 24.

    Liu, Y. Y., van Dijk, A. I., McCabe, M. F., Evans, J. P. & de Jeu, R. A. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Glob. Ecol. Biogeogr. 22, 692–705 (2013).

  25. 25.

    Wigneron, J.-P. et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sens. Environ. 192, 238–262 (2017).

  26. 26.

    Fernandez-Moran, R. et al. SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).

  27. 27.

    Rodríguez-Fernández, N. J. et al. 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 15, 4627–4645 (2018).

  28. 28.

    Tian, F. et al. Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. Nat. Ecol. Evol. 2, 1428–1435 (2018).

  29. 29.

    Liu, Y. Y., de Jeu, R. A., McGabe, M. F., Evans, J. P. & van Dijk, A. I. Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).

  30. 30.

    Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

  31. 31.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

  32. 32.

    Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

  33. 33.

    Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).

  34. 34.

    Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).

  35. 35.

    Masarie, K. A. & Tans, P. P. Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record. J. Geophys. Res. 100, 11593–11610 (1995).

  36. 36.

    Wang, J., Zeng, N. & Wang, M. Interannual variability of the atmospheric CO2 growth rate: roles of precipitation and temperature. Biogeosciences 13, 2339–2352 (2016).

  37. 37.

    Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeochem. Cycles 19, GB1016 (2005).

  38. 38.

    Anderegg, W. R. et al. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl Acad. Sci. USA 112, 15591–15596 (2015).

  39. 39.

    Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

  40. 40.

    Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).

  41. 41.

    Lohberger, S., Stängel, M., Atwood, E. C. & Siegert, F. Spatial evaluation of Indonesia’s 2015 fire-affected area and estimated carbon emissions using Sentinel-1. Glob. Change Biol. 24, 644–654 (2018).

  42. 42.

    Huijnen, V. et al. Fire carbon emissions over maritime Southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).

  43. 43.

    Yin, Y. et al. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño. Geophys. Res. Lett. 43, 10472–10479 (2016).

  44. 44.

    Tyukavina, A. et al. Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett. 10, 074002 (2015).

  45. 45.

    Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).

  46. 46.

    Ryan, C. M., Berry, N. J. & Joshi, N. Quantifying the causes of deforestation and degradation and creating transparent REDD+ baselines: a method and case study from central Mozambique. Appl. Geogr. 53, 45–54 (2014).

  47. 47.

    Ponce-Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).

  48. 48.

    Poorter, L. et al. Biomass resilience of neotropical secondary forests. Nature 530, 211–214 (2016).

  49. 49.

    Lewis, S. L. et al. Increasing carbon storage in intact African tropical forests. Nature 457, 1003–1006 (2009).

  50. 50.

    Phillips, O. L. et al. Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282, 439–442 (1998).

  51. 51.

    Gloor, M. et al. Does the disturbance hypothesis explain the biomass increase in basin-wide Amazon forest plot data? Glob. Change Biol. 15, 2418–2430 (2009).

  52. 52.

    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. 6, 791–795 (2016).

  53. 53.

    Brandt, M. et al. Changes in rainfall distribution promote woody foliage production in the Sahel. Commun. Biol. 2, 133 (2019).

  54. 54.

    Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).

  55. 55.

    Rutishauser, E. et al. Rapid tree carbon stock recovery in managed Amazonian forests. Curr. Biol. 25, R787–R788 (2015).

  56. 56.

    Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 0081 (2017).

  57. 57.

    Jackson, T. & Schmugge, T. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).

  58. 58.

    Wigneron, J.-P., Waldteufel, P., Chanzy, A., Calvet, J.-C. & Kerr, Y. Two-dimensional microwave interferometer retrieval capabilities over land surfaces (SMOS mission). Remote Sens. Environ. 73, 270–282 (2000).

  59. 59.

    Al-Yaari, A. et al. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sens. Environ. 224, 289–303 (2019).

  60. 60.

    Carreiras, J. M. et al. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions. Remote Sens. Environ. 196, 154–162 (2017).

  61. 61.

    Mermoz, S., Le Toan, T., Villard, L., Réjou-Méchain, M. & Seifert-Granzin, J. Biomass assessment in the Cameroon savanna using ALOS PALSAR data. Remote Sens. Environ. 155, 109–119 (2014).

  62. 62.

    Wigneron, J.-P. et al. L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 107, 639–655 (2007).

  63. 63.

    Kerr, Y. H. et al. The SMOS soil moisture retrieval algorithm. IEEE Trans. Geosci. Remote Sens. 50, 1384–1403 (2012).

  64. 64.

    Oliva, R. et al. SMOS radio frequency interference scenario: status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band. IEEE Trans. Geosci. Remote Sens. 50, 1427–1439 (2012).

  65. 65.

    Kerr, Y. H. et al. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sens. Environ. 180, 40–63 (2016).

  66. 66.

    Fan, L. et al. Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region. Remote Sens. Environ. 205, 210–223 (2018).

  67. 67.

    Broxton, P. D., Zeng, X., Sulla-Menashe, D. & Troch, P. A. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).

  68. 68.

    Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).

  69. 69.

    Qin, Y. et al. Annual dynamics of forest areas in South America during 2007–2010 at 50-m spatial resolution. Remote Sens. Environ. 201, 73–87 (2017).

  70. 70.

    Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. (in the press).

  71. 71.

    Wolter, K. & Timlin, M. S. El Niño/Southern oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol. 31, 1074–1087 (2011).

  72. 72.

    Huete, A. R., Justice, C. O. & Van Leeuwen, W. MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document (NASA, 1999);

  73. 73.

    Dee, D. P. et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

  74. 74.

    Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

  75. 75.

    Wahr, J., Molenaar, M. & Bryan, F. Time variability of the Earth’s gravity field: hydrological and oceanic effects and their possible detection using GRACE. J. Geophys. Res. 103, 30205–30229 (1998).

  76. 76.

    Swenson, S., Chambers, D. & Wahr, J. Estimating geocenter variations from a combination of GRACE and ocean model output. J. Geophys. Res. 113, B08410 (2008).

  77. 77.

    Liu, Y. Y. et al. Enhanced canopy growth precedes senescence in 2005 and 2010 Amazonian droughts. Remote Sens. Environ. 211, 26–37 (2018).

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This work was jointly supported by the TOSCA (Terre Océan Surfaces Continentales et Atmosphère) CNES (Centre National d’Etudes Spatiales) programme, the European Space Agency Support to Science Element programme and SMOS Expert Support Laboratory contract, and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. P.C. acknowledges additional support from the ANR ICONV CLAND grant. J.C. has benefited from ‘Investissement d’Avenir’ grants managed by Agence Nationale de la Recherche (CEBA: ANR-10-LABX- 25-01; TULIP: ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001). M.B. was funded by an AXA postdoctoral fellowship. F.T. is supported by a Marie Skłodowska-Curie grant (project number 746347). R.F. acknowledges funding from the Danish Council for Independent Research (DFF) grant no. DFF–6111-00258. K.H. acknowledges support by the Belgian Science Policy Office-sponsored COBECORE project (contract BR/175/A3/COBECORE). L.F. acknowledges additional support from the National Natural Science Foundation of China (grant no. 41801247) and Natural Science Foundation of Jiangsu Province (grant no. BK20180806). Y.Q. and X.X. are supported in part by NASA Land Use and Land Cover Change programme (NNX14AD78G) and NASA Geostationary Carbon Cycle Observatory (GeoCarb) Mission (GeoCarb contract no. 80LARC17C0001).

Author information

J.-P.W., L.F. and P.C. conceived and designed the study. L.F. carried out all calculations with support from J.-P.W. and P.C. L.F. prepared the SMOS-IC data; S.S.S. prepared the Saatchi biomass map; Y.Q. and X.X. prepared annual forest area maps; C.C. and R.B.M. prepared the MODIS LAI dataset. J.-P.W., L.F. and P.C. conducted the analysis with support from J.C., M.B., R.F., S.S.S., J.P. and A.B. The manuscript was drafted by L.F., J.-P.W., P.C., J.C., R.F., M.B., J.P., K.H. with contributions by all co-authors.

Correspondence to Jean-Pierre Wigneron or Philippe Ciais.

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Peer review information: Nature Plants thanks Edward Mitchard, Kolby Smith and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Supplementary methods, Supplementary Figs. 1–16, Supplementary Tables 1–7 and Supplementary References.

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