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Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach
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Abstract
FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles ( R 2 >0.94 at 1 ∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr −1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R 2 ∼0.75) than with GPP from DGVMs (mean global pixel-wise R 2 ∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions ( R 2 >0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions ( R 2 =0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO 2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.
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“Datadriven approaches using machine learning to integrate satellite data with site-level information (e.g., from eddy covariance measurement networks [42]) provide diagnostic constraints on global GPP, complementing process-based models [43].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [42]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
A large and persistent carbon sink in the world’s forests
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., et al. (2011) · Science · doi:10.1126/science.1201609 · Cited by 8,458
Self-citationDeep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673
Self-citationReichstein, M., Falge, E. M., Baldocchi, D., Papale, D., Aubinet, M., Paul Berbigier, E., Bernhofer, C., Buchmann, N., Gilmanov, T. G., Granier, A., et al. (2005) · Global Change Biology · doi:10.1111/j.1365-2486.2005.001002.x · Cited by 3,829
Self-citationMODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, M. A., Sulla‐Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. M., & Huang, X. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.08.016 · Cited by 3,501
Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., et al. (2010) · Science · doi:10.1126/science.1184984 · Cited by 2,841
Self-citationImprovements of the MODIS terrestrial gross and net primary production global data set
Zhao, M., Heinsch, F. A., Nemani, R., & Running, S. W. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2004.12.011 · Cited by 1,693
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell, J. G., et al. (2018) · Earth System Science Data · doi:10.5194/essd-10-2141-2018 · Cited by 1,645
Self-citationPapale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W. L., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., et al. (2006) · Biogeosciences · doi:10.5194/bg-3-571-2006 · Cited by 1,597
Self-citationJung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., et al. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2010jg001566 · Cited by 1,393
Self-citationPorcar‐Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C., Flexas, J., Pfündel, E. E., Moreno, J., Frankenberg, C., & Berry, J. A. (2014) · Journal of Experimental Botany · doi:10.1093/jxb/eru191 · Cited by 1,248
Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth, A., Barr, A., Stoy, P. C., & Wohlfahrt, G. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2009.02041.x · Cited by 1,166
Self-citationGlobal and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A., Frankenberg, C., HUETE, A. R., Zarco‐Tejada, P. J., Lee, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320008111 · Cited by 1,122
Self-citationFrankenberg, C., Fisher, J. B., Worden, J. R., Badgley, G., Saatchi, S. S., Lee, J., Toon, G. C., Butz, A., Jung, M., Kuze, A., et al. (2011) · Geophysical Research Letters · doi:10.1029/2011gl048738 · Cited by 1,095
Self-citationWeedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., & Viterbo, P. (2014) · Water Resources Research · doi:10.1002/2014wr015638 · Cited by 1,072
Recent trends and drivers of regional sources and sinks of carbon dioxide
Sitch, S. A., Friedlingstein, P., Gruber, N., Jones, S. D., Tortarolo, G. N. M., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford, C., et al. (2015) · Biogeosciences · doi:10.5194/bg-12-653-2015 · Cited by 991
Self-citationPredicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps‐Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., et al. (2016) · Biogeosciences · doi:10.5194/bg-13-4291-2016 · Cited by 883
Self-citationEcosystem carbon dioxide fluxes after disturbance in forests of North America
Amiro, B. D., Barr, A., Barr, J. G., Black, T. A., Bracho, R., Brown, M., Chen, J., Clark, K. L., Davis, K. J., Desai, A. R., et al. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2010jg001390 · Cited by 829
The FLUXCOM ensemble of global land-atmosphere energy fluxes
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps‐Valls, G., Papale, D., Schwalm, C. R., Tramontana, G., & Reichstein, M. (2019) · Scientific Data · doi:10.1038/s41597-019-0076-8 · Cited by 811
Self-citationSpatiotemporal patterns of terrestrial gross primary production: A review
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., JONES, C. D., Tortarolo, G. N. M., Papale, D., Parazoo, N. C., Peylin, P., et al. (2015) · Reviews of Geophysics · doi:10.1002/2015rg000483 · Cited by 774
Self-citationCompensatory water effects link yearly global land CO2 sink changes to temperature
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S. A., Ahlström, A., Arneth, A., Camps‐Valls, G., Ciais, P., Friedlingstein, P., et al. (2017) · Nature · doi:10.1038/nature20780 · Cited by 764
Self-citationCarbon in Amazon Forests: Unexpected Seasonal Fluxes and Disturbance-Induced Losses
SALESKA, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C., da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H. C., et al. (2003) · Science · doi:10.1126/science.1091165 · Cited by 739
Jung, M., Reichstein, M., & Bondeau, A. (2009) · Biogeosciences · doi:10.5194/bg-6-2001-2009 · Cited by 730
Self-citationOCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence
Sun, Y., Frankenberg, C., Wood, J. D., SCHIMEL, D. S., Jung, M., Guanter, L., Drewry, D. T., Verma, M., Porcar‐Castell, A., Griffis, T. J., et al. (2017) · Science · doi:10.1126/science.aam5747 · Cited by 724
Self-citationWenping, Y., Liu, S., Yu, G., Bonnefond, J.-M., Chen, J., Davis, K. J., Desai, A. R., Goldstein, A. H., Gianelle, D., Rossi, F., et al. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.01.022 · Cited by 700
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
You, J., Li, X., Low, M., Lobell, D. B., & Ermon, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11172 · Cited by 652
Stoy, P. C., Mauder, M. R., Foken, T., Marcolla, B., Boegh, E., Ibrom, A., Arain, M. A., Arneth, A., Aurela, M., Bernhofer, C., et al. (2013) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2012.11.004 · Cited by 638
Self-citationObserving terrestrial ecosystems and the carbon cycle from space
SCHIMEL, D. S., Pavlick, R., Fisher, J. B., Asner, G. P., Saatchi, S. S., Townsend, P. A., Miller, C. E., Frankenberg, C., Hibbard, K. A., & Cox, P. M. (2014) · Global Change Biology · doi:10.1111/gcb.12822 · Cited by 615
Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage
Humphrey, V., Jakob Zscheischler, A., Ciais, P., Gudmundsson, L., Sitch, S. A., & Seneviratne, S. I. (2018) · Nature · doi:10.1038/s41586-018-0424-4 · Cited by 539
Self-citationHistorical carbon dioxide emissions caused by land-use changes are possibly larger than assumed
Arneth, A., Sitch, S. A., Pongratz, J., Stocker, B. D., Ciais, P., Poulter, B., Bayer, A. D., Bondeau, A., Calle, L., Chini, L. P., et al. (2017) · Nature Geoscience · doi:10.1038/ngeo2882 · Cited by 526
Self-citationReflections on the surface energy imbalance problem
Leuning, R., van Gorsel, E., Massman, W. J., & Isaac, P. R. (2012) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2011.12.002 · Cited by 509
Geostationary Enhanced Temporal Interpolation for CERES Flux Products
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D. L., Nguyen, C., Wielicki, B. A., Young, D. F., & Sun, M. (2013) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-12-00136.1 · Cited by 498
Global atmospheric carbon budget: results from an ensemble of atmospheric CO 2 inversions
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Mäki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., et al. (2013) · Biogeosciences · doi:10.5194/bg-10-6699-2013 · Cited by 489
Self-citationInferring CO 2 sources and sinks from satellite observations: Method and application to TOVS data
Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F., Chédin, A., & Ciais, P. (2005) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jd006390 · Cited by 410
Self-citationEffects of foliage clumping on the estimation of global terrestrial gross primary productivity
Chen, J. M., Mo, G., Písek, J., Liu, J. J., Deng, F., Ishizawa, M., & Chan, D. S. T. (2012) · Global Biogeochemical Cycles · doi:10.1029/2010gb003996 · Cited by 399
Self-citationTerrestrial gross primary production: Using NIR V to scale from site to globe
Badgley, G., Anderegg, L. D. L., Berry, J. A., & Field, C. B. (2019) · Global Change Biology · doi:10.1111/gcb.14729 · Cited by 398
Jiang, C., & Ryu, Y. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.08.030 · Cited by 391
Walther, S., Voigt, M., Thum, T., Gonsamo, A., Zhang, Y., Köhler, P., Jung, M., Varlagin, A., & Guanter, L. (2015) · Global Change Biology · doi:10.1111/gcb.13200 · Cited by 311
Self-citationKöhler, P., Guanter, L., & Joiner, J. (2015) · Atmospheric measurement techniques · doi:10.5194/amt-8-2589-2015 · Cited by 306
Self-citationMorales, P., Sykes, M. T., Prentice, I. C., Smith, P., Smith, B., Bugmann, H. K. M., Zierl, B., Friedlingstein, P., Viovy, N., Sabaté, S., et al. (2005) · Global Change Biology · doi:10.1111/j.1365-2486.2005.01036.x · Cited by 287
Self-citationThe Community Climate System Model
Blackmon, M. L., Boville, B. A., Bryan, F. O., Dickinson, R. E., Gent, P. R., Kiehl, J. T., Moritz, R. E., Randall, D. A., Shukla, J., Solomon, S., et al. (2001) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(2001)082 · Cited by 281
Self-citationZhang, Y., Guanter, L., Berry, J. A., van der Tol, C., Yang, X., Tang, J., & Zhang, F. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.10.016 · Cited by 266
A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation
Camps‐Valls, G., Verrelst, J., Muñoz-Marı́, J., Laparra, V., Mateo, F., & Gomez-Dans, J. L. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2015.2510084 · Cited by 262
Self-citationJoiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A., Yujie Wang, A., & Tucker, C. J. (2018) · Remote Sensing · doi:10.3390/rs10091346 · Cited by 260
Self-citationSeasonality of temperate forest photosynthesis and daytime respiration
Wehr, R. A., Munger, J. W., McManus, J. B., Nelson, D. D., Zahniser, M. S., Davidson, E. A., Wofsy, S. C., & SALESKA, S. R. (2016) · Nature · doi:10.1038/nature17966 · Cited by 255
Interannual variability in the oxygen isotopes of atmospheric CO2 driven by El Niño
Welp, L. R., Keeling, R. F., Meijer, H. A. J., Bollenbacher, A. F., Piper, S. C., Yoshimura, K., Francey, R. J., Allison, C. E., & Wahlen, M. (2011) · Nature · doi:10.1038/nature10421 · Cited by 248
Important role of forest disturbances in the global biomass turnover and carbon sinks
Pugh, T. A. M., Arneth, A., Kautz, M., Poulter, B., & Smith, B. (2019) · Nature Geoscience · doi:10.1038/s41561-019-0427-2 · Cited by 233
Peters, W., Krol, M., van der Werf, G. R., Houweling, S., JONES, C. D., HUGHES, J., Schaefer, K. M., Masarie, K. A., Jacobson, A. R., Miller, J. B., et al. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2009.02078.x · Cited by 220
Self-citationMigliavacca, M., Pérez‐Priego, Ó., Rossini, M., El-Madany, T. S., Moreno, G., van der Tol, C., Rascher, U., Berninger, A., Bessenbacher, V., Burkart, A., et al. (2017) · New Phytologist · doi:10.1111/nph.14437 · Cited by 211
Self-citationLuijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanisławska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W., Chen, H., et al. (2017) · Geoscientific model development · doi:10.5194/gmd-10-2785-2017 · Cited by 205
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Aubinet, M., Paul Berbigier, E., Bernhofer, C., Cescatti, A., Feigenwinter, C., Granier, A., Grünwald, T., Havránková, K., Heinesch, B., Longdoz, B., et al. (2005) · Boundary-Layer Meteorology · doi:10.1007/s10546-004-7091-8 · Cited by 182
Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product
Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D., & Reichstein, M. (2018) · Earth system science data · doi:10.5194/essd-10-1327-2018 · Cited by 175
Self-citationWidespread inhibition of daytime ecosystem respiration
Keenan, T. F., Migliavacca, M., Papale, D., Baldocchi, D., Reichstein, M., Torn, M. S., & Wutzler, T. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0809-2 · Cited by 174
Self-citationObjective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions
Chevallier, F., Remaud, M., O’Dell, C., Baker, D. F., Peylin, P., & Cozic, A. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-14233-2019 · Cited by 174
Self-citationGaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E. A., Patra, P. K., Peters, W., Rödenbeck, C., Saeki, T., et al. (2019) · Biogeosciences · doi:10.5194/bg-16-117-2019 · Cited by 168
Self-citationRödenbeck, C., Zaehle, S., Keeling, R. F., & Heimann, M. (2018) · Biogeosciences · doi:10.5194/bg-15-2481-2018 · Cited by 157
Self-citationCampioli, M., Malhi, Y. S., Vicca, S., Luyssaert, S., Papale, D., Peñuelas, J., Reichstein, M., Migliavacca, M., Arain, M. A., & Janssens, I. A. (2016) · Nature Communications · doi:10.1038/ncomms13717 · Cited by 143
Self-citationEstimation of Rainfall Interstation Correlation
Habib, E., Krajewski, W. F., & Ciach, G. J. (2001) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2001)002 · Cited by 141
Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1)
Kim, H. (2017) · Repository for Data Integration and Analysis System · doi:10.20783/dias.501 · Cited by 138
Musavi, T., Migliavacca, M., Reichstein, M., Kattge, J., Wirth, C., Black, T. A., Janssens, I. A., Knohl, A., Loustau, D., Roupsard, O., et al. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-016-0048 · Cited by 137
Self-citationWalther, S., Duveiller, G., Jung, M., Guanter, L., Cescatti, A., & Camps‐Valls, G. (2019) · Geophysical Research Letters · doi:10.1029/2018gl080535 · Cited by 125
Self-citationPapale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M., Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H. A., et al. (2015) · Journal of Geophysical Research Biogeosciences · doi:10.1002/2015jg002997 · Cited by 122
Self-citationStrong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
MacBean, N., Maignan, F., Bacour, C., Lewis, P., Peylin, P., Guanter, L., Köhler, P., Gomez-Dans, J. L., & Disney, M. I. (2018) · Scientific Reports · doi:10.1038/s41598-018-20024-w · Cited by 117
Self-citationMemory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Besnard, S., Carvalhais, N., Arain, M. A., Black, T. A., Brede, B., Buchmann, N., Chen, J., Clevers, J. G. P. W., Dutrieux, L. P., Gans, F., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211510 · Cited by 117
Self-citationQuantifying the effect of forest age in annual net forest carbon balance
Besnard, S., Carvalhais, N., Arain, M. A., Black, T. A., de Bruin, S., Buchmann, N., Cescatti, A., Chen, J., Clevers, J. G. P. W., Desai, A. R., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaeaeb · Cited by 114
Self-citationvan Gorsel, E., Delpierre, N., Leuning, R., Black, A. T., Munger, J. W., Wofsy, S. C., Aubinet, M., Feigenwinter, C., Beringer, J., Bonal, D., et al. (2009) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2009.06.020 · Cited by 112
Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
Yu, T., Sun, R., Xiao, Z., Zhang, Q., Liu, G., Cui, T., & Wang, J. (2018) · Remote Sensing · doi:10.3390/rs10020327 · Cited by 109
Flach, M., Sippel, S., Gans, F., Bastos, A., Brenning, A., Reichstein, M., & Mahecha, M. D. (2018) · Biogeosciences · doi:10.5194/bg-15-6067-2018 · Cited by 108
Self-citationNorton, A., Rayner, P. J., Koffi, E. N., Scholze, M., Silver, J. D., & Wang, Y. (2019) · Biogeosciences · doi:10.5194/bg-16-3069-2019 · Cited by 108
Jakob Zscheischler, A., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., et al. (2017) · Biogeosciences · doi:10.5194/bg-14-3685-2017 · Cited by 104
Self-citationExact Gaussian Processes on a Million Data PointsPreprint
Wang, K. A., Pleiss, G., Gardner, J. R., Tyree, S., Weinberger, K. Q., & Wilson, A. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08114 · Cited by 98
Zhang, Y., Joiner, J., Gentine, P., & Zhou, S. (2018) · Global Change Biology · doi:10.1111/gcb.14134 · Cited by 91
Aubinet, M., Feigenwinter, C., Heinesch, B., Laffineur, Q., Papale, D., Reichstein, M., Rinne, J., & van Gorsel, E. (2011) · Springer eBooks · doi:10.1007/978-94-007-2351-1_5 · Cited by 78
Self-citationKoffi, E. N., Rayner, P. J., Scholze, M., & Beer, C. (2012) · Global Biogeochemical Cycles · doi:10.1029/2010gb003900 · Cited by 73
Large-scale biospheric drought response intensifies linearly with drought duration in arid regions
Orth, R., Destouni, G., Jung, M., & Reichstein, M. (2020) · Biogeosciences · doi:10.5194/bg-17-2647-2020 · Cited by 65
Self-citationFu, Z., Gerken, T., Bromley, G. T., de Araújo, A. C., Bonal, D., Burban, B., Ficklin, D. L., Fuentes, J. D., Goulden, M. L., Hirano, T., et al. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.09.001 · Cited by 65
Spielmann, F. M., Wohlfahrt, G., Hammerle, A., Kitz, F., Migliavacca, M., Alberti, G., Ibrom, A., El-Madany, T. S., Gerdel, K., Moreno, G., et al. (2019) · Geophysical Research Letters · doi:10.1029/2019gl082006 · Cited by 62
Self-citationvan Gorsel, E., Leuning, R., Cleugh, H., Keith, H., Kirschbaum, M. U. F., & Suni, T. (2008) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2008.01.015 · Cited by 62
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Liang, M., Mahata, S., Laskar, A. H., Thiemens, M. H., & Newman, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-12774-w · Cited by 51
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Hellevang, H., & Aagaard, P. (2015) · Scientific Reports · doi:10.1038/srep17352 · Cited by 45
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Orth, R., Destouni, G., Jung, M., & Reichstein, M. (2019) · doi:10.5194/bg-2019-442-supplement · Cited by 1
Self-citation
Research works printed but not linked: 1
- Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F. M., Chédin, A., and Ciais, P.: Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res.-Atmos., 110, 10.1029/2005JD006390, 2005. · DOI
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Self-citation
Metadata
- Authors
- Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., & Reichstein, M.
- Affiliations
- Max Planck Institute for Biogeochemistry (DE)
- Woodwell Climate Research Center (US)
- Karlsruhe Institute of Technology (DE)
- Université Paris-Saclay (FR)
- University of Augsburg (DE)
- CSIRO Oceans and Atmosphere (AU)
- California Institute of Technology (US)
- Chiba University (JP)
- University of Illinois Urbana-Champaign (US)
- Nanjing Normal University (CN)
- NSF National Center for Atmospheric Research (US)
- Max Planck Institute for Meteorology (DE)
- Università degli Studi della Tuscia (IT)
- Wageningen University & Research (NL)
- University of Exeter (GB)
- Oak Ridge National Laboratory (US)
- Corresponding author
- Jung, Martin
- ORCID
- Jung, Martin
- Schwalm, Christopher
- Migliavacca, Mirco
- Walther, Sophia
- Camps-Valls, Gustau
- Koirala, Sujan
- Anthoni, Peter
- Besnard, Simon
- Bodesheim, Paul
- Carvalhais, Nuno
- Chevallier, Frédéric
- Gans, Fabian
- Goll, Daniel S.
- Haverd, Vanessa
- Köhler, Philipp
- Ichii, Kazuhito
- Jain, Atul K.
- Liu, Junzhi
- Lombardozzi, Danica
- Nabel, Julia E. M. S.
- Nelson, Jacob A.
- O'Sullivan, Michael
- Pallandt, Martijn
- Papale, Dario
- Peters, Wouter
- Pongratz, Julia
- Rödenbeck, Christian
- Sitch, Stephen
- Tramontana, Gianluca
- Walker, Anthony
- Weber, Ulrich
- Reichstein, Markus
- Venue
- Biogeosciences
- Publisher
- Copernicus GmbH
- Published
- 2020-03-16
- Volume
- 17
- Issue
- 5
- Pages
- 1343-1365
- ISSN
- 1726-4170 (ISSN-L)
- 1726-4189
- OpenAlex
- W2978485176
- Cited by
- 866
- References
- 91
- Citations by year
- 2026: 116 · 2025: 137 · 2024: 162 · 2023: 127 · 2022: 139 · 2021: 144 · 2020: 40
- FWCI
- 41.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biogeosciences (publishedVersion, cc-by)
- CaltechAUTHORS (California Institute of Technology) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UnitusOpen (Tuscia University) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- European Space Agency (ESRIN/4000123002/18/I-NB)
- H2020 European Institute of Innovation and Technology
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- FLUXCOM, gross primary productivity, net ecosystem exchange, eddy covariance flux towers, FLUXNET, solar-induced chlorophyll fluorescence, dynamic global vegetation models, temperature inversion, interannual variability, tropical forests, biosphere-atmosphere exchange, global carbon budget
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