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Surveys in Geophysics

, Volume 40, Issue 4, pp 735–755 | Cite as

Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects

  • Jean-François ExbrayatEmail author
  • A. Anthony Bloom
  • Nuno Carvalhais
  • Rico Fischer
  • Andreas Huth
  • Natasha MacBean
  • Mathew Williams
Article
First Online:

Abstract

Our understanding of the terrestrial carbon cycle has been greatly enhanced since satellite observations of the land surface started. The advantage of remote sensing is that it provides wall-to-wall observations including in regions where in situ monitoring is challenging. This paper reviews how satellite observations of the biosphere have helped improve our understanding of the terrestrial carbon cycle. First, it details how remotely sensed information of the land surface has provided new means to monitor vegetation dynamics and estimate carbon fluxes and stocks. Second, we present examples of studies which have used satellite products to evaluate and improve simulations from global vegetation models. Third, we focus on model data integration approaches ranging from bottom-up extrapolation of single variables to carbon cycle data assimilation system able to ingest multiple types of observations. Finally, we present an overview of upcoming satellite missions which are likely to further improve our understanding of the terrestrial carbon cycle and its response to climate change and extremes.

Keywords

Terrestrial carbon cycle Earth observation Satellite Ecosystem modelling Model data integration 
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Notes

Acknowledgements

This review stemmed from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” at the International Space Science Institute in Bern during November 2017. Contribution from AAB was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The first and last authors were supported by the Natural Environment Research Council through the National Centre for Earth Observation, contract number PR140015, and the Newton Fund, through CSSP Brazil.

References

  1. Akagi SK, Yokelson RJ, Wiedinmyer C et al (2011) Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos Chem Phys 11:4039–4072.  https://doi.org/10.5194/acp-11-4039-2011 Google Scholar
  2. Anav A, Friedlingstein P, Kidston M et al (2013) Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. J Clim 26:6801–6843.  https://doi.org/10.1175/JCLI-D-12-00417.1 Google Scholar
  3. Andela N, Morton DC, Giglio L et al (2017) A human-driven decline in global burned area. Science 356:1356–1362.  https://doi.org/10.1126/science.aal4108 Google Scholar
  4. Andreae MO, Merlet P (2001) Emission of trace gases and aerosols from biomass burning. Glob Biogeochem Cycles 15:955–966.  https://doi.org/10.1029/2000GB001382 Google Scholar
  5. Archibald S, Lehmann CER, Gómez-Dans JL, Bradstock RA (2013) Defining pyromes and global syndromes of fire regimes. Proc Natl Acad Sci USA 110:6442–7.  https://doi.org/10.1073/pnas.1211466110 Google Scholar
  6. Arneth A, Sitch S, Pongratz J et al (2017) Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat Geosci 10:79–84.  https://doi.org/10.1038/ngeo2882 Google Scholar
  7. Arora VK, Melton JR (2018) Reduction in global area burned and wildfire emissions since 1930s enhances carbon uptake by land. Nat Commun 9:1326.  https://doi.org/10.1038/s41467-018-03838-0 Google Scholar
  8. Arora VK, Boer GJ, Friedlingstein P et al (2013) Carbon-concentration and carbon-climate feedbacks in CMIP5 earth system models. J Clim 26:5289–5314.  https://doi.org/10.1175/JCLI-D-12-00494.1 Google Scholar
  9. Avitabile V, Herold M, Heuvelink GBM et al (2016) An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol 22:1406–1420.  https://doi.org/10.1111/gcb.13139 Google Scholar
  10. Baccini A, Goetz SJ, Walker WS et al (2012) Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat Clim Change 2:182–185.  https://doi.org/10.1038/nclimate1354 Google Scholar
  11. Baccini A, Walker W, Carvalho L et al (2017) Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science (80) 358:230–234.  https://doi.org/10.1126/science.aam5962 Google Scholar
  12. Baldocchi D, Falge E, Gu LH et al (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434.  https://doi.org/10.1175/1520-0477(2001)082%3c2415:FANTTS%3e2.3.CO;2 Google Scholar
  13. Baret F, Weiss M, Lacaze R et al (2013) GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: principles of development and production. Remote Sens Environ 137:299–309.  https://doi.org/10.1016/J.RSE.2012.12.027 Google Scholar
  14. Bastos A, Running SW, Gouveia C, Trigo RM (2013) The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J Geophys Res Biogeosci 118:1247–1255.  https://doi.org/10.1002/jgrg.20100 Google Scholar
  15. Bastos A, Gouveia CM, Trigo RM, Running SW (2014) Analysing the spatio-temporal impacts of the 2003 and 2010 extreme heatwaves on plant productivity in Europe. Biogeosciences 11:3421–3435.  https://doi.org/10.5194/bg-11-3421-2014 Google Scholar
  16. Bastos A, Ciais P, Park T et al (2017) Was the extreme Northern Hemisphere greening in 2015 predictable? Environ Res Lett 12:44016.  https://doi.org/10.1088/1748-9326/aa67b5 Google Scholar
  17. Basu S, Baker DF, Chevallier F et al (2018) The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2. Atmos Chem Phys 18:7189–7215.  https://doi.org/10.5194/acp-18-7189-2018 Google Scholar
  18. Beer C, Reichstein M, Tomelleri E et al (2010) Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329:834–838.  https://doi.org/10.1126/science.1184984 Google Scholar
  19. Bloom AA, Worden J, Jiang Z et al (2015) Remote-sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data. Geophys Res Lett 42:1268–1274.  https://doi.org/10.1002/2014GL062584 Google Scholar
  20. Bloom AA, Exbrayat J-F, van der Velde IR, Feng L, Williams M (2016) The decadal state of the terrestrial carbon cycle: global retrievals of terrestrial carbon allocation, pools, and residence times. Proc Natl Acad Sci 113(5):1285–1290.  https://doi.org/10.1073/pnas.1515160113 Google Scholar
  21. Borsdorff T, Aan de Brugh J, Hu H et al (2018) Measuring carbon monoxide with TROPOMI: first results and a comparison with ECMWF-IFS analysis data. Geophys Res Lett 45:2826–2832.  https://doi.org/10.1002/2018GL077045 Google Scholar
  22. Bowman KW, Liu J, Bloom AA et al (2017) Global and Brazilian carbon response to El Niño Modoki 2011–2010. Earth Space Sci 4:637–660.  https://doi.org/10.1002/2016EA000204 Google Scholar
  23. Brandt M, Wigneron J-P, Chave J et al (2018) Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat Ecol Evol 2:827–835.  https://doi.org/10.1038/s41559-018-0530-6 Google Scholar
  24. Broich M, Huete A, Tulbure MG et al (2014) Land surface phenological response to decadal climate variability across Australia using satellite remote sensing. Biogeosciences 11:5181–5198.  https://doi.org/10.5194/bg-11-5181-2014 Google Scholar
  25. Buchwitz M, Burrows JP (2004) Retrieval of CH4, CO, and CO2 total column amounts from SCIAMACHY near-infrared nadir spectra: retrieval algorithm and first results. In: Schaefer K, Comeron A, Carleer MR, Picard RH (eds) Proceedings of SPIE, p 375Google Scholar
  26. Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global leaf phenology. Nat Clim Change 5:364–368.  https://doi.org/10.1038/nclimate2533 Google Scholar
  27. Caldararu S, Purves DW, Palmer PI (2014) Phenology as a strategy for carbon optimality: a global model. Biogeosciences 11:763–778.  https://doi.org/10.5194/bg-11-763-2014 Google Scholar
  28. Canadell JG, Le Quéré C, Raupach MR et al (2007) Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc Natl Acad Sci USA 104:18866–18870.  https://doi.org/10.1073/pnas.0702737104 Google Scholar
  29. Carvalhais N, Forkel M, Khomik M et al (2014) Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514:213–217.  https://doi.org/10.1038/nature13731 Google Scholar
  30. Chave J, Coomes D, Jansen S et al (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366.  https://doi.org/10.1111/j.1461-0248.2009.01285.x Google Scholar
  31. Chave J, Réjou-Méchain M, Búrquez A et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol 20:3177–3190.  https://doi.org/10.1111/gcb.12629 Google Scholar
  32. Clark DB, Mercado LM, Sitch S et al (2011) The joint UK land environment simulator (JULES), model description—part 2: carbon fluxes and vegetation dynamics. Geosci Model Dev 4:701–722.  https://doi.org/10.5194/gmd-4-701-2011 Google Scholar
  33. Dannenberg MP, Wise EK, Janko M et al (2018) Atmospheric teleconnection influence on North American land surface phenology. Environ Res Lett 13:34029.  https://doi.org/10.1088/1748-9326/aaa85a Google Scholar
  34. Deng F, Jones DBA, O’Dell CW et al (2016) Combining GOSAT × CO2 observations over land and ocean to improve regional CO2 flux estimates. J Geophys Res Atmos 121:1896–1913.  https://doi.org/10.1002/2015JD024157 Google Scholar
  35. Detmers RG, Hasekamp O, Aben I et al (2015) Anomalous carbon uptake in Australia as seen by GOSAT. Geophys Res Lett 42:8177–8184.  https://doi.org/10.1002/2015GL065161 Google Scholar
  36. Eldering A, Wennberg PO, Crisp D et al (2017) The orbiting carbon observatory-2 early science investigations of regional carbon dioxide fluxes. Science 358:eaam5745.  https://doi.org/10.1126/science.aam5745 Google Scholar
  37. Erb K-H, Fetzel T, Plutzar C et al (2016) Biomass turnover time in terrestrial ecosystems halved by land use. Nat Geosci 9:674–678.  https://doi.org/10.1038/ngeo2782 Google Scholar
  38. Exbrayat J-F, Williams M (2015) Quantifying the net contribution of the historical Amazonian deforestation to climate change. Geophys Res Lett 42:2968–2976.  https://doi.org/10.1002/2015GL063497 Google Scholar
  39. Exbrayat J-F, Pitman AJ, Abramowitz G (2014) Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100. Geosci Model Dev 7:2683–2692.  https://doi.org/10.5194/gmd-7-2683-2014 Google Scholar
  40. Exbrayat J-F, Liu YY, Williams M (2017) Impact of deforestation and climate on the Amazon Basin’s above-ground biomass during 1993–2012. Sci Rep 7:15615.  https://doi.org/10.1038/s41598-017-15788-6 Google Scholar
  41. Exbrayat J-F, Anthony Bloom A, Falloon P et al (2018a) Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties. Earth Syst Dyn 9:1.  https://doi.org/10.5194/esd-9-153-2018 Google Scholar
  42. Exbrayat J-F, Smallman TL, Anthony Bloom A, Hutley LB, Williams M (2018b) Inverse determination of the influence of fire on vegetation carbon turnover in the pantropics. Glob Biogeochem Cycles 32(12):1776–1789.  https://doi.org/10.1029/2018GB005925 Google Scholar
  43. Fasullo JT, Boening C, Landerer FW, Nerem RS (2013) Australia’s unique influence on global sea level in 2010–2011. Geophys Res Lett 40:4368–4373.  https://doi.org/10.1002/grl.50834 Google Scholar
  44. Feng L, Palmer PI, Parker RJ et al (2016) Estimates of European uptake of CO2 inferred from GOSAT × CO2 retrievals: sensitivity to measurement bias inside and outside Europe. Atmos Chem Phys 16:1289–1302.  https://doi.org/10.5194/acp-16-1289-2016 Google Scholar
  45. Field CB, Randerson JT, Malmström CM (1995) Global net primary production: combining ecology and remote sensing. Remote Sens Environ 51:74–88.  https://doi.org/10.1016/0034-4257(94)00066-V Google Scholar
  46. Fischer R, Bohn F, Dantas de Paula M et al (2016) Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests. Ecol Modell 326:124–133.  https://doi.org/10.1016/j.ecolmodel.2015.11.018 Google Scholar
  47. Flannigan MD, Haar THV (1986) Forest fire monitoring using NOAA satellite AVHRR. Can J For Res 16:975–982.  https://doi.org/10.1139/x86-171 Google Scholar
  48. Forkel M, Carvalhais N, Schaphoff S et al (2014) Identifying environmental controls on vegetation greenness phenology through model-data integration. Biogeosciences 11:7025–7050.  https://doi.org/10.5194/bg-11-7025-2014 Google Scholar
  49. Forkel M, Migliavacca M, Thonicke K et al (2015) Codominant water control on global interannual variability and trends in land surface phenology and greenness. Glob Change Biol 21:3414–3435.  https://doi.org/10.1111/gcb.12950 Google Scholar
  50. Forkel M, Carvalhais N, Rodenbeck C et al (2016) Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science 351:696–699.  https://doi.org/10.1126/science.aac4971 Google Scholar
  51. Frankenberg C, Fisher JB, Worden J et al (2011) New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys Res Lett 1:1.  https://doi.org/10.1029/2011gl048738 Google Scholar
  52. Freeborn PH, Wooster MJ, Roy DP, Cochrane MA (2014) Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation. Geophys Res Lett 41:1988–1994.  https://doi.org/10.1002/2013GL059086 Google Scholar
  53. Friedlingstein P, Cox P, Betts R et al (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. J Clim 19:3337–3353.  https://doi.org/10.1175/JCLI3800.1 Google Scholar
  54. Friend AD, Lucht W, Rademacher TT et al (2014) Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci USA 111:3280–3285.  https://doi.org/10.1073/pnas.1222477110 Google Scholar
  55. Gatti LV, Gloor M, Miller JB et al (2014) Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506:76–80.  https://doi.org/10.1038/nature12957 Google Scholar
  56. Giglio L, Randerson JT, van der Werf GR (2013) Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J Geophys Res Biogeosci 118:317–328.  https://doi.org/10.1002/jgrg.20042 Google Scholar
  57. Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. J Clim 15:1141–1158.  https://doi.org/10.1175/1520-0442(2002)015%3c1141:COAURA%3e2.0.CO;2 Google Scholar
  58. Gonzi S, Feng L, Palmer PI (2011) Seasonal cycle of emissions of CO inferred from MOPITT profiles of CO: sensitivity to pyroconvection and profile retrieval assumptions. Geophys Res Lett 38:1–11.  https://doi.org/10.1029/2011gl046789 Google Scholar
  59. Guanter L, Frankenberg C, Dudhia A et al (2012) Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens Environ 121:236–251.  https://doi.org/10.1016/j.rse.2012.02.006 Google Scholar
  60. Haarsma RJ, Roberts MJ, Vidale PL et al (2016) High resolution model intercomparison project (HighResMIP v1.0) for CMIP6. Geosci Model Dev 9:4185–4208.  https://doi.org/10.5194/gmd-9-4185-2016 Google Scholar
  61. Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853.  https://doi.org/10.1126/science.1244693 Google Scholar
  62. Harris NL, Brown S, Hagen SC et al (2012) Baseline map of carbon emissions from deforestation in tropical regions. Science 336:1573–1576.  https://doi.org/10.1126/science.1217962 Google Scholar
  63. Hartmann H, Adams HD, Anderegg WRL et al (2015) Research frontiers in drought-induced tree mortality: crossing scales and disciplines. New Phytol 205:965–969.  https://doi.org/10.1111/nph.13246 Google Scholar
  64. Heymann J, Reuter M, Buchwitz M et al (2017) CO2 emission of Indonesian fires in 2015 estimated from satellite-derived atmospheric CO2 concentrations. Geophys Res Lett 45:1621.  https://doi.org/10.1002/2016gl072042 CrossRefGoogle Scholar
  65. Houweling S, Baker D, Basu S et al (2015) An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements. J Geophys Res Atmos 120:5253–5266.  https://doi.org/10.1002/2014JD022962 Google Scholar
  66. Huete A, Didan K, Miura T et al (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213.  https://doi.org/10.1016/S0034-4257(02)00096-2 Google Scholar
  67. Huete AR, Didan K, Shimabukuro YE et al (2006) Amazon rainforests green-up with sunlight in dry season. Geophys Res Lett 33:L06405.  https://doi.org/10.1029/2005GL025583 Google Scholar
  68. Huntzinger DN, Schwalm C, Michalak AM et al (2013) The North American carbon program multi-scale synthesis and terrestrial model intercomparison project ? Part 1: overview and experimental design. Geosci Model Dev 6:2121–2133.  https://doi.org/10.5194/gmd-6-2121-2013 Google Scholar
  69. Ito A, Nishina K, Reyer CPO et al (2017) Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies. Environ Res Lett 12:085001.  https://doi.org/10.1088/1748-9326/aa7a19 Google Scholar
  70. Jiang Z, Jones DBA, Worden HM, Henze DK (2015) Sensitivity of top-down CO source estimates to the modeled vertical structure in atmospheric CO. Atmos Chem Phys 15:1521–1537.  https://doi.org/10.5194/acp-15-1521-2015 Google Scholar
  71. Joiner J, Guanter L, Lindstrot R et al (2013) Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos Meas Tech 6:2803–2823.  https://doi.org/10.5194/amt-6-2803-2013 Google Scholar
  72. Joiner J, Yoshida Y, Vasilkov AP et al (2014) The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens Environ 152:375–391.  https://doi.org/10.1016/j.rse.2014.06.022 Google Scholar
  73. Joiner J, Yoshida Y, Guanter L, Middleton EM (2016) New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY. Atmos Meas Tech 9:3939–3967.  https://doi.org/10.5194/amt-9-3939-2016 Google Scholar
  74. Jolly WM, Nemani R, Running SW (2005) A generalized, bioclimatic index to predict foliar phenology in response to climate. Glob Change Biol 11:619–632.  https://doi.org/10.1111/j.1365-2486.2005.00930.x Google Scholar
  75. Jung M, Reichstein M, Bondeau A (2009) Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 6:2001–2013.  https://doi.org/10.5194/bg-6-2001-2009 CrossRefGoogle Scholar
  76. Jung M, Reichstein M, Margolis HA et al (2011) Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J Geophys Res 116:G00J07.  https://doi.org/10.1029/2010jg001566 Google Scholar
  77. Kala J, Decker M, Exbrayat J-F et al (2014) Influence of leaf area index prescriptions on simulations of heat, moisture, and carbon fluxes. J Hydrometeorol 15:489–503.  https://doi.org/10.1175/JHM-D-13-063.1 Google Scholar
  78. Kaminski T, Knorr W, Schürmann G et al (2013) The BETHY/JSBACH carbon cycle data assimilation system: experiences and challenges. J Geophys Res Biogeosci 118:1414.  https://doi.org/10.1002/jgrg.20118 Google Scholar
  79. Knapp N, Fischer R, Huth A (2018a) Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sens Environ 205:199–209.  https://doi.org/10.1016/j.rse.2017.11.018 Google Scholar
  80. Knapp N, Huth A, Kugler F et al (2018b) Model-assisted estimation of tropical forest biomass change: a comparison of approaches. Remote Sens 10:731.  https://doi.org/10.3390/rs10050731 Google Scholar
  81. Knorr W (2000) Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties. Glob Ecol Biogeogr 9:225–252.  https://doi.org/10.1046/j.1365-2699.2000.00159.x Google Scholar
  82. Knorr W, Kaminski T, Scholze M et al (2010) Carbon cycle data assimilation with a generic phenology model. J Geophys Res 115:G04017.  https://doi.org/10.1029/2009JG001119 Google Scholar
  83. Knyazikhin Y, Martonchik JV, Myneni RB et al (1998) Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res Atmos 103:32257–32275.  https://doi.org/10.1029/98JD02462 Google Scholar
  84. Köhler P, Joiner J (2015) A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos Meas Tech 8:2589–2608.  https://doi.org/10.5194/amt-8-2589-2015 Google Scholar
  85. Kolby Smith W, Reed SC, Cleveland CC et al (2016) Large divergence of satellite and earth system model estimates of global terrestrial CO2 fertilization. Nat Clim Change 6:306–310.  https://doi.org/10.1038/nclimate2879 Google Scholar
  86. Kopacz M, Jacob DJ, Fisher JA et al (2010) Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES). Atmos Chem Phys 10:855–876.  https://doi.org/10.5194/acp-10-855-2010 Google Scholar
  87. Krishnamurti TN, Kishtawal CM, LaRow TE et al (1999) Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285:1548–1550.  https://doi.org/10.1126/science.285.5433.1548 Google Scholar
  88. Krol M, Peters W, Hooghiemstra P et al (2013) How much CO was emitted by the 2010 fires around Moscow? Atmos Chem Phys 13:4737–4747.  https://doi.org/10.5194/acp-13-4737-2013 Google Scholar
  89. Kuppel S, Peylin P, Maignan F et al (2014) Model-data fusion across ecosystems: from multisite optimizations to global simulations. Geosci Model Dev 7:2581–2597.  https://doi.org/10.5194/gmd-7-2581-2014 Google Scholar
  90. Le Quéré C, Andrew RM, Friedlingstein P et al (2018) Global carbon budget 2017. Earth Syst Sci Data 10:405–448.  https://doi.org/10.5194/essd-10-405-2018 Google Scholar
  91. Le Toan T, Quegan S, Davidson MWJWJ et al (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860.  https://doi.org/10.1016/j.rse.2011.03.020 Google Scholar
  92. Lee J-E, Berry JA, van der Tol C et al (2015) Simulations of chlorophyll fluorescence incorporated into the community land model version 4. Glob Change Biol 21:3469–3477.  https://doi.org/10.1111/gcb.12948 Google Scholar
  93. Lewis P, Gómez-Dans J, Kaminski T et al (2012) An earth observation land data assimilation system (EO-LDAS). Remote Sens Environ 120:219–235.  https://doi.org/10.1016/j.rse.2011.12.027 Google Scholar
  94. Li W, Ciais P, Peng S et al (2017) Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14:5053–5067.  https://doi.org/10.5194/bg-14-5053-2017 Google Scholar
  95. Lienert S, Joos F (2018) A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions. Biogeosciences 15:2909–2930.  https://doi.org/10.5194/bg-15-2909-2018 Google Scholar
  96. Liu YY, van Dijk AIJM, de Jeu RAM et al (2015) Recent reversal in loss of global terrestrial biomass. Nat Clim Change 5:470–474.  https://doi.org/10.1038/nclimate2581 Google Scholar
  97. Liu J, Bowman KW, Schimel DS et al (2017) Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science eaam358:5690.  https://doi.org/10.1126/science.aam5690 Google Scholar
  98. MacBean N, Maignan F, Peylin P et al (2015) Using satellite data to improve the leaf phenology of a global terrestrial biosphere model. Biogeosciences 12:7185–7208.  https://doi.org/10.5194/bg-12-7185-2015 Google Scholar
  99. MacBean N, Maignan F, Bacour C et al (2018) Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. Sci Rep.  https://doi.org/10.1038/s41598-018-20024-w CrossRefGoogle Scholar
  100. Migliavacca M, Sonnentag O, Keenan TF et al (2012) On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model. Biogeosciences 9:2063–2083.  https://doi.org/10.5194/bg-9-2063-2012 Google Scholar
  101. Miller SM, Michalak AM, Yadav V, Tadić JM (2018) Characterizing biospheric carbon balance using CO2 observations from the OCO-2 satellite. Atmos Chem Phys 18:6785–6799.  https://doi.org/10.5194/acp-18-6785-2018 Google Scholar
  102. Morton DC, Le Page Y, DeFries R et al (2013) Understorey fire frequency and the fate of burned forests in southern Amazonia. Philos Trans R Soc B Biol Sci 368:20120163.  https://doi.org/10.1098/rstb.2012.0163 Google Scholar
  103. Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995) The interpretation of spectral vegetation indexes. IEEE Trans Geosci Remote Sens 33:481–486.  https://doi.org/10.1109/36.377948 Google Scholar
  104. Myneni RB, Keeling CD, Tucker CJ et al (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702.  https://doi.org/10.1038/386698a0 Google Scholar
  105. Myneni RB, Hoffman S, Knyazikhin Y et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231.  https://doi.org/10.1016/S0034-4257(02)00074-3 Google Scholar
  106. Myneni RB, Yang W, Nemani RR et al (2007) Large seasonal swings in leaf area of Amazon rainforests. Proc Natl Acad Sci 104:4820–4823.  https://doi.org/10.1073/pnas.0611338104 Google Scholar
  107. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Clim Res 21:1–25.  https://doi.org/10.3354/cr021001 Google Scholar
  108. Norton AJ, Rayner PJ, Koffi EN, Scholze M (2018a) Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content. Geosci Model Dev 11:1517–1536.  https://doi.org/10.5194/gmd-11-1517-2018 Google Scholar
  109. Norton AJ, Rayner PJ, Koffi EN et al (2018b) Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model. Biogeosciences 12:835.  https://doi.org/10.5194/bg-2018-270 CrossRefGoogle Scholar
  110. Papale D, Valentini R (2003) A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Glob Change Biol 9:525–535.  https://doi.org/10.1046/j.1365-2486.2003.00609.x Google Scholar
  111. Parazoo NC, Bowman K, Frankenberg C et al (2013) Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT. Geophys Res Lett 40:2829–2833.  https://doi.org/10.1002/grl.50452 Google Scholar
  112. Parazoo NC, Bowman K, Fisher JB et al (2014) Terrestrial gross primary production inferred from satellite fluorescence and vegetation models. Glob Change Biol 20:3103–3121.  https://doi.org/10.1111/gcb.12652 Google Scholar
  113. Peylin P, Bacour C, MacBean N et al (2016) A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle. Geosci Model Dev 9:3321–3346.  https://doi.org/10.5194/gmd-9-3321-2016 Google Scholar
  114. Pinzon JE, Tucker CJ (2014) A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens 6:6929–6960.  https://doi.org/10.3390/rs6086929 Google Scholar
  115. Pitman AJ (2003) The evolution of, and revolution in, land surface schemes designed for climate models. Int J Climatol 23:479–510.  https://doi.org/10.1002/joc.893 Google Scholar
  116. Porcar-Castell A, Tyystjärvi E, Atherton J et al (2014) Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J Exp Bot 65:4065–4095.  https://doi.org/10.1093/jxb/eru191 Google Scholar
  117. Potapov P, Yaroshenko A, Turubanova S et al (2008) Mapping the world’s intact forest landscapes by remote sensing. Ecol Soc 13:51.  https://doi.org/10.5751/es-02670-130251 Google Scholar
  118. Potter CS, Randerson JT, Field CB et al (1993) Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob Biogeochem Cycles 7:811–841.  https://doi.org/10.1029/93GB02725 Google Scholar
  119. Poulter B, Frank D, Ciais P et al (2014) Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509:600–603.  https://doi.org/10.1038/nature13376 Google Scholar
  120. Prince SD, Goward SN (1995) Global primary production: a remote sensing approach. J Biogeogr 22:815.  https://doi.org/10.2307/2845983 Google Scholar
  121. Quaife T, Lewis P, De Kauwe M et al (2008) Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sens Environ 112:1347.  https://doi.org/10.1016/j.rse.2007.05.020 Google Scholar
  122. Richardson AD, Anderson RS, Arain MA et al (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob Change Biol 18:566–584.  https://doi.org/10.1111/j.1365-2486.2011.02562.x Google Scholar
  123. Rödig E, Cuntz M, Heinke J et al (2017) Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: linking remote sensing, forest modelling and field inventory. Glob Ecol Biogeogr 26:1292–1302.  https://doi.org/10.1111/geb.12639 Google Scholar
  124. Rödig E, Cuntz M, Rammig A et al (2018) The importance of forest structure for carbon fluxes of the Amazon rainforest. Environ Res Lett 13:054013.  https://doi.org/10.1088/1748-9326/aabc61 Google Scholar
  125. Rodríguez-Veiga P, Saatchi S, Tansey K, Balzter H (2016) Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico. Remote Sens Environ 183:265–281Google Scholar
  126. Rogers BM, Soja AJ, Goulden ML, Randerson JT (2015) Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat Geosci 8:228–234.  https://doi.org/10.1038/ngeo2352 Google Scholar
  127. Running SW, Nemani RR, Heinsch FA et al (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54:547.  https://doi.org/10.1641/0006-3568(2004)054%5b0547:ACSMOG%5d2.0.CO;2 Google Scholar
  128. Saatchi SS, Harris NL, Brown S et al (2011) Benchmark map of forest carbon stocks in tropical regions across three continents. Proc Natl Acad Sci USA 108:9899–9904.  https://doi.org/10.1073/pnas.1019576108 Google Scholar
  129. Santoro M, Beer C, Cartus O et al (2011) Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements. Remote Sens Environ 115:490–507.  https://doi.org/10.1016/j.rse.2010.09.018 Google Scholar
  130. Santoro M, Beaudoin A, Beer C et al (2015) Forest growing stock volume of the northern hemisphere: spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sens Environ 168:316–334.  https://doi.org/10.1016/j.rse.2015.07.005 Google Scholar
  131. Scholze M, Buchwitz M, Dorigo W et al (2017) Reviews and syntheses: systematic earth observations for use in terrestrial carbon cycle data assimilation systems. Biogeosciences 14:3401–3429Google Scholar
  132. Schwalm CR, Huntzinger DN, Fisher JB et al (2015) Toward “optimal” integration of terrestrial biosphere models. Geophys Res Lett 42:4418–4428.  https://doi.org/10.1002/2015GL064002 Google Scholar
  133. Shugart HH, Asner GP, Fischer R et al (2015) Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models. Front Ecol Environ 13:503–511.  https://doi.org/10.1890/140327 Google Scholar
  134. Shugart HH, Wang B, Fischer R et al (2018) Gap models and their individual-based relatives in the assessment of the consequences of global change. Environ Res Lett 13:033001.  https://doi.org/10.1088/1748-9326/aaaacc Google Scholar
  135. Sierra CA, Müller M, Metzler H et al (2017) The muddle of ages, turnover, transit, and residence times in the carbon cycle. Glob Change Biol 23:1763–1773.  https://doi.org/10.1111/gcb.13556 Google Scholar
  136. Sitch S, Friedlingstein P, Gruber N et al (2015) Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12:653–679.  https://doi.org/10.5194/bg-12-653-2015 Google Scholar
  137. Slevin D, Tett SFB, Exbrayat J-F et al (2017) Global evaluation of gross primary productivity in the JULES land surface model v3.4.1. Geosci Model Dev 10:2651–2670.  https://doi.org/10.5194/gmd-10-2651-2017 Google Scholar
  138. Smallman TL, Exbrayat J-F, Mencuccini M et al (2017) Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests. J Geophys Res Biogeosci 122:528–545.  https://doi.org/10.1002/2016JG003520 Google Scholar
  139. Stavros EN, Schimel D, Pavlick R et al (2017) ISS observations offer insights into plant function. Nat Ecol Evol 1:0194.  https://doi.org/10.1038/s41559-017-0194 Google Scholar
  140. Stöckli R, Rutishauser T, Baker I et al (2011) A global reanalysis of vegetation phenology. J Geophys Res 116:G03020.  https://doi.org/10.1029/2010JG001545 Google Scholar
  141. Sun Y, Frankenberg C, Jung M et al (2018) Overview of solar-induced chlorophyll fluorescence (SIF) from the orbiting carbon observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens Environ 209:808–823.  https://doi.org/10.1016/j.rse.2018.02.016 Google Scholar
  142. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498.  https://doi.org/10.1175/BAMS-D-11-00094.1 Google Scholar
  143. Thum T, Zaehle S, Köhler P et al (2017) Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe. Biogeosciences 14:1969–1987.  https://doi.org/10.5194/bg-14-1969-2017 Google Scholar
  144. Thurner M, Beer C, Santoro M et al (2014) Carbon stock and density of northern boreal and temperate forests. Glob Ecol Biogeogr 23:297–310.  https://doi.org/10.1111/geb.12125 Google Scholar
  145. Thurner M, Beer C, Carvalhais N et al (2016) Large-scale variation in boreal and temperate forest carbon turnover rate related to climate. Geophys Res Lett 43:4576–4585.  https://doi.org/10.1002/2016GL068794 Google Scholar
  146. Thurner M, Beer C, Ciais P et al (2017) Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests. Glob Change Biol 23:3076–3091.  https://doi.org/10.1111/gcb.13660 Google Scholar
  147. Tramontana G, Jung M, Schwalm CR et al (2016) Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13:4291–4313.  https://doi.org/10.5194/bg-13-4291-2016 Google Scholar
  148. van der Tol C, Verhoef W, Rosema A (2009) A model for chlorophyll fluorescence and photosynthesis at leaf scale. Agric For Meteorol 149:96.  https://doi.org/10.1016/j.agrformet.2008.07.007 Google Scholar
  149. van der Werf GR, Morton DC, DeFries RS et al (2009) CO2 emissions from forest loss. Nat Geosci 2:737–738.  https://doi.org/10.1038/ngeo671 Google Scholar
  150. van der Werf GR, Randerson JT, Giglio L et al (2010) Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys 10:11707–11735.  https://doi.org/10.5194/acp-10-11707-2010 Google Scholar
  151. Waigl CF, Stuefer M, Prakash A, Ichoku C (2017) Detecting high and low-intensity fires in Alaska using VIIRS I-band data: an improved operational approach for high latitudes. Remote Sens Environ 199:389–400.  https://doi.org/10.1016/j.rse.2017.07.003 Google Scholar
  152. Warszawski L, Frieler K, Huber V et al (2014) The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc Natl Acad Sci USA 111:3228–3232.  https://doi.org/10.1073/pnas.1312330110 Google Scholar
  153. Williams M, Schwarz PA, Law BE et al (2005) An improved analysis of forest carbon dynamics using data assimilation. Glob Change Biol 11:89–105.  https://doi.org/10.1111/j.1365-2486.2004.00891.x Google Scholar
  154. Williams M, Richardson AD, Reichstein M et al (2009) Improving land surface models with FLUXNET data. Biogeosciences 6:1341–1359.  https://doi.org/10.5194/bg-6-1341-2009 Google Scholar
  155. Wood JD, Griffis TJ, Baker JM et al (2017) Multiscale analyses of solar-induced florescence and gross primary production. Geophys Res Lett 53:785–818.  https://doi.org/10.1002/2016gl070775 CrossRefGoogle Scholar
  156. Worden JR, Bloom AA, Pandey S et al (2017a) Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget. Nat Commun 8:2227.  https://doi.org/10.1038/s41467-017-02246-0 Google Scholar
  157. Worden JR, Doran G, Kulawik S et al (2017b) Evaluation and attribution of OCO-2 XCO2 uncertainties. Atmos Meas Tech 10:2759–2771.  https://doi.org/10.5194/amt-10-2759-2017 Google Scholar
  158. Xu L, Saatchi SS, Shapiro A et al (2017) Spatial distribution of carbon stored in forests of the Democratic Republic of Congo. Sci Rep 7:15030.  https://doi.org/10.1038/s41598-017-15050-z Google Scholar
  159. Yang X, Tang J, Mustard JF et al (2015) Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys Res Lett 42:2977–2987.  https://doi.org/10.1002/2015GL063201 Google Scholar
  160. Yang H, Yang X, Zhang Y et al (2017) Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest. Glob Change Biol 23:2874–2886.  https://doi.org/10.1111/gcb.13590 Google Scholar
  161. Yokota T, Yoshida Y, Eguchi N et al (2009) Global concentrations of CO2 and CH4 retrieved from GOSAT: first preliminary results. SOLA 5:160–163.  https://doi.org/10.2151/sola.2009-041 Google Scholar
  162. Zhang Y, Guanter L, Berry JA et al (2014) Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Glob Change Biol 20:3727–3742.  https://doi.org/10.1111/gcb.12664 Google Scholar
  163. Zhang Y, Xiao X, Jin C et al (2016) Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America. Remote Sens Environ 183:154–169.  https://doi.org/10.1016/j.rse.2016.05.015 Google Scholar
  164. Zhao M, Running SW (2010) Drought-induced reduction in global. Science (80) 329:940–943.  https://doi.org/10.1126/science.1192666 Google Scholar
  165. Zhu Z, Bi J, Pan Y et al (2013) Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3G) for the period 1981 to 2011. Remote Sens 5:927–948.  https://doi.org/10.3390/rs5020927 Google Scholar
  166. Zhu Z, Piao S, Myneni RB et al (2016) Greening of the earth and its drivers. Nat Clim Change 6:791–795.  https://doi.org/10.1038/nclimate3004 Google Scholar
  167. Zwally HJ, Schutz B, Abdalati W et al (2002) ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J Geodyn 34:405–445.  https://doi.org/10.1016/S0264-3707(02)00042-X Google Scholar
  168. Ziehn T, Scholze M, Knorr W (2012) On the capability of Monte Carlo and adjoint inversion techniques to derive posterior parameter uncertainties in terrestrial ecosystem models. Glob Biogeochem Cycles  https://doi.org/10.1029/2011GB004185 CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.National Centre for Earth Observation and School of GeoSciencesUniversity of EdinburghEdinburghUK
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Max Planck Institute for BiogeochemistryDepartment Biogeochemical IntegrationJenaGermany
  4. 4.Helmholtz Centre for Environmental Research – UFZDepartment of Ecological ModellingLeipzigGermany
  5. 5.Department of GeographyIndiana UniversityBloomingtonUSA
  6. 6.CENSE, Departamento de Ciências e Engenharia do AmbienteFaculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Campus de CaparicaCaparicaPortugal
  7. 7.Institute of Environmental Systems ResearchUniversity of OsnabrückOsnabrückGermany
  8. 8.German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzigGermany

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