Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Satellite-observed pantropical carbon dynamics

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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 http://www.catds.fr/Products/Available-products-from-CEC-SM/SMOS-IC. SMOS-IC L-VOD and AGC products, the Saatchi, Bouvet and Mermoz biomass maps are available from J.-P.W., S.S.S. (sasan.s.saatchi@jpl.nasa.gov), A. Bouvet (alexandre.bouvet@cesbio.cnes.fr) and S. Mermoz (stephane.mermoz@cesbio.cnes.fr) both at CESBIO, Toulouse, France), respectively, on request.

References

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    CAS  PubMed  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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. 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).

    Article  Google Scholar 

  72. Huete, A. R., Justice, C. O. & Van Leeuwen, W. MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document (NASA, 1999); https://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

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

Authors and Affiliations

Authors

Contributions

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.

Corresponding authors

Correspondence to Jean-Pierre Wigneron or Philippe Ciais.

Additional information

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.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, L., Wigneron, JP., Ciais, P. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019). https://doi.org/10.1038/s41477-019-0478-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-019-0478-9

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing