Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Data-driven approaches using machine learning to integrate satellite data with site-level information provide diagnostic constraints on global GPP, complementing process-based models.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,208 · see citing works
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 · see citing works
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 · see citing works
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 1,094 · see citing works
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 · see citing works
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 · see citing works
Self-citationChanges in the Onset of Spring in the Western United States
Cayan, D. R., Dettinger, M. D., Kammerdiener, S. A., Caprio, J. M., & Peterson, D. H. (2001) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(2001)082 · Cited by 906 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 748 · see citing works
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 · see citing works
Jung, M., Reichstein, M., & Bondeau, A. (2009) · Biogeosciences · doi:10.5194/bg-6-2001-2009 · Cited by 720 · see citing works
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 706 · see citing works
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 · see citing works
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 619 · see citing works
Self-citationDeep 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 603 · see citing works
Observing 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 598 · see citing works
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 · see citing works
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 510 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 396 · see citing works
Effects 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 394 · see citing works
Self-citationJiang, C., & Ryu, Y. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.08.030 · Cited by 383 · see citing works
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 · see citing works
Self-citationKöhler, P., Guanter, L., & Joiner, J. (2015) · Atmospheric measurement techniques · doi:10.5194/amt-8-2589-2015 · Cited by 306 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 194 · see citing works
Self-citationComparing CO2 Storage and Advection Conditions at Night at Different Carboeuroflux Sites
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 · see citing works
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 · see citing works
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 · see citing works
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 164 · see citing works
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 161 · see citing works
Self-citationRödenbeck, C., Zaehle, S., Keeling, R. F., & Heimann, M. (2018) · Biogeosciences · doi:10.5194/bg-15-2481-2018 · Cited by 157 · see citing works
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 · see citing works
Self-citationGlobal 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 · see citing works
Multiscale Statistical Properties of a High-Resolution Precipitation Forecast
Harris, D., Foufoula‐Georgiou, E., Droegemeier, K. K., & Levit, J. J. (2001) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2001)002 · Cited by 137 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Norton, 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 · see citing works
Quantifying 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 108 · see citing works
Self-citationFlach, 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 105 · see citing works
Self-citationJakob 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 · see citing works
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 · see citing works
Zhang, Y., Joiner, J., Gentine, P., & Zhou, S. (2018) · Global Change Biology · doi:10.1111/gcb.14134 · Cited by 91 · see citing works
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 · see citing works
Self-citationKoffi, E. N., Rayner, P. J., Scholze, M., & Beer, C. (2012) · Global Biogeochemical Cycles · doi:10.1029/2010gb003900 · Cited by 73 · see citing works
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 · see citing works
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 63 · see citing works
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 · see citing works
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 · see citing works
Hutyra, L. R., Munger, J. W., Pyle, E. H., SALESKA, S. R., Restrepo‐Coupé, N., Daube, B. C., de Camargo, P. B., & Wofsy, S. C. (2008) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2008.03.007 · Cited by 54 · see citing works
Patterns and controls of inter-annual variability in the terrestrial carbon budget
Marcolla, B., Rödenbeck, C., & Cescatti, A. (2017) · Biogeosciences · doi:10.5194/bg-14-3815-2017 · Cited by 50 · see citing works
Self-citationLiang, M., Mahata, S., Laskar, A. H., Thiemens, M. H., & Newman, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-12774-w · Cited by 49 · see citing works
Towards a more complete quantification of the global carbon cycle
Kirschbaum, M. U. F., Zeng, G., Ximenes, F. A., Giltrap, D. L., & Zeldis, J. (2019) · Biogeosciences · doi:10.5194/bg-16-831-2019 · Cited by 47 · see citing works
Hellevang, H., & Aagaard, P. (2015) · Scientific Reports · doi:10.1038/srep17352 · Cited by 45 · see citing works
McHugh, I., Beringer, J., Cunningham, S. C., Baker, P. J., Cavagnaro, T. R., Nally, R. M., & Thompson, R. M. (2017) · Biogeosciences · doi:10.5194/bg-14-3027-2017 · Cited by 38 · see citing works
A novel correction for biases in forest eddy covariance carbon balance
Hayek, M., Wehr, R. A., Longo, M., Hutyra, L. R., Wiedemann, K. T., Munger, J. W., Bonal, D., SALESKA, S. R., Fitzjarrald, D. R., & Wofsy, S. C. (2017) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2017.12.186 · Cited by 37 · see citing works
Tamrakar, R., Rayment, M. B., Moyano, F. E., Mund, M., & Knohl, A. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.08.027 · Cited by 34 · see citing works
On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?
Byrne, B., Jones, D. B. A., Strong, K., Polavarapu, S. M., Harper, A., Baker, D. F., & Maksyutov, S. S. (2019) · Atmospheric chemistry and physics · doi:10.5194/acp-19-13017-2019 · Cited by 28 · see citing works
Orth, R., Destouni, G., Jung, M., & Reichstein, M. (2019) · doi:10.5194/bg-2019-442-supplement · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
2 of the 91 references couldn’t be matched to a known paper.
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 CO 2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2005JD006390, 2005. · DOI
Cited by
Sorted by citations
ERA5-Land: a state-of-the-art global reanalysis dataset for land applications
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., et al. (2021) · Earth System Science Data · doi:10.5194/essd-13-4349-2021 · Cited by 5,540 · see citing works
Emerging signals of declining forest resilience under climate change
Forzieri, G., Dakos, V., McDowell, N. G., Alkama, R., & Cescatti, A. (2022) · Nature · doi:10.1038/s41586-022-04959-9 · Cited by 815 · see citing works
Soil moisture–atmosphere feedback dominates land carbon uptake variability
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M., Seneviratne, S. I., & Frankenberg, C. (2021) · Nature · doi:10.1038/s41586-021-03325-5 · Cited by 682 · see citing works
Self-citationBastos, A., Ciais, P., Friedlingstein, P., Sitch, S. A., Pongratz, J., Fan, L., Wigneron, J., Weber, U., Reichstein, M., Fu, Z., et al. (2020) · Science Advances · doi:10.1126/sciadv.aba2724 · Cited by 555 · see citing works
Self-citationInterannual variation of terrestrial carbon cycle: Issues and perspectives
Piao, S. L., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J. G., Ciais, P., Friedlingstein, P., & Sitch, S. A. (2019) · Global Change Biology · doi:10.1111/gcb.14884 · Cited by 495 · see citing works
Self-citationTerrestrial carbon sinks in China and around the world and their contribution to carbon neutrality
Yang, Y., Shi, Y., Sun, W., Chang, J., Zhu, J., Chen, L., Wang, X., Guo, Y., Zhang, H., Yu, L., et al. (2022) · Science China Life Sciences · doi:10.1007/s11427-021-2045-5 · Cited by 483 · see citing works
Artificial intelligence for geoscience: Progress, challenges, and perspectives
Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., Long, Y., Wang, F., Xie, Y., Li, J., et al. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100691 · Cited by 394 · see citing works
Wang, S., Zhang, Y., Ju, W., Qiu, B., & Zhang, Z. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.142569 · Cited by 321 · see citing works
Machine learning-based global maps of ecological variables and the challenge of assessing them
Meyer, H., & Pebesma, E. (2022) · Nature Communications · doi:10.1038/s41467-022-29838-9 · Cited by 310 · see citing works
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308 · see citing works
Global water use efficiency saturation due to increased vapor pressure deficit
Li, F., Xiao, J., Chen, J., Ballantyne, A. P., Jin, K., Li, B., Abraha, M., & John, R. (2023) · Science · doi:10.1126/science.adf5041 · Cited by 297 · see citing works
Evidence and attribution of the enhanced land carbon sink
Ruehr, S., Keenan, T. F., Williams, C. A., Zhou, Y., Lu, X., Bastos, A., Canadell, J. G., Prentice, I. C., Sitch, S. A., & Terrer, C. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00456-3 · Cited by 268 · see citing works
Self-citationThe Integrated Carbon Observation System in Europe
Heiskanen, J., Brümmer, C., Buchmann, N., Calfapietra, C., Chen, H., Gielen, B., Gkritzalis, T., Hammer, S., Hartman, S. E., Herbst, M., et al. (2021) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-19-0364.1 · Cited by 249 · see citing works
Self-citationEvolution of light use efficiency models: Improvement, uncertainties, and implications
Pei, Y., Dong, J., Zhang, Y., Yuan, W., Doughty, R., Yang, J., Zhou, D., Zhang, L., & Xiao, X. (2022) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2022.108905 · Cited by 248 · see citing works
Cross-biome synthesis of source versus sink limits to tree growth
Cabon, A., Kannenberg, S. A., Arain, M. A., Babst, F., Baldocchi, D., Belmecheri, S., Delpierre, N., Guerrieri, R., Maxwell, J. T., McKenzie, S. M., et al. (2022) · Science · doi:10.1126/science.abm4875 · Cited by 224 · see citing works
Virkkala, A., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C., Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., Mauritz, M., et al. (2021) · Global Change Biology · doi:10.1111/gcb.15659 · Cited by 238 · see citing works
Dechant, B., Ryu, Y., Badgley, G., Köhler, P., Rascher, U., Migliavacca, M., Zhang, Y., Tagliabue, G., Guan, K., Rossini, M., et al. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112763 · Cited by 209 · see citing works
Self-citationMontero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., & Wieneke, S. (2023) · Scientific Data · doi:10.1038/s41597-023-02096-0 · Cited by 209 · see citing works
Delwiche, K., Knox, S., Malhotra, A., Fluet‐Chouinard, E., McNicol, G., Feron, S. C., Ouyang, Z., Papale, D., Trotta, C., Canfora, E., et al. (2021) · Earth system science data · doi:10.5194/essd-13-3607-2021 · Cited by 205 · see citing works
Self-citationZeng, J., Matsunaga, T., Tan, Z., Saigusa, N., Shirai, T., Tang, Y., Peng, S., & Fukuda, Y. (2020) · Scientific Data · doi:10.1038/s41597-020-00653-5 · Cited by 213 · see citing works
Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
Liu, L., Zhou, W. M., Guan, K., Peng, B., Xu, S., Tang, J., Zhu, Q., Till, J. L., Jia, X., Jiang, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-43860-5 · Cited by 193 · see citing works
Persello, C., Wegner, J. D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps‐Valls, G. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3136100 · Cited by 197 · see citing works
Self-citationCO 2 fertilization of terrestrial photosynthesis inferred from site to global scales
Chen, C., Riley, W. J., Prentice, I. C., & Keenan, T. F. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115627119 · Cited by 189 · see citing works
Plant uptake of CO 2 outpaces losses from permafrost and plant respiration on the Tibetan Plateau
Wei, D., Qi, Y., Ma, Y., Wang, X., Ma, W., Gao, T., Huang, L., Zhao, H., Zhang, J., & Wang, X. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015283118 · Cited by 186 · see citing works
Joiner, J., & Yoshida, Y. (2020) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2020.108092 · Cited by 174 · see citing works
Towards hybrid modeling of the global hydrological cycle
Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M. (2022) · Hydrology and Earth System Sciences · doi:10.5194/hess-26-1579-2022 · Cited by 173 · see citing works
Self-citationResponses of vegetation greenness and carbon cycle to extreme droughts in China
Deng, Y., Wang, X., Wang, K., Ciais, P., Tang, S., Jin, L., Li, L., & Piao, S. L. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2020.108307 · Cited by 170 · see citing works
Persistent and enhanced carbon sequestration capacity of alpine grasslands on Earth’s Third Pole
Wang, Y., Xiao, J., Ma, Y., Ding, J., Chen, X., Ding, Z., & Luo, Y. (2023) · Science Advances · doi:10.1126/sciadv.ade6875 · Cited by 166 · see citing works
Mapping global forest age from forest inventories, biomass and climate data
Besnard, S., Koirala, S., Santoro, M., Weber, U., Nelson, J. A., Gütter, J., Hérault, B., N’Dja, J. K., N’Guessan, A. E., Neigh, C. S. R., et al. (2021) · Earth system science data · doi:10.5194/essd-13-4881-2021 · Cited by 175 · see citing works
Self-citationImpacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018
Bastos, A., Fu, Z., Ciais, P., Friedlingstein, P., Sitch, S. A., Pongratz, J., Weber, U., Reichstein, M., Anthoni, P. M., Arneth, A., et al. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0507 · Cited by 157 · see citing works
Self-citationHow Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?
Crisp, D., Dolman, A. J., Tanhua, T., McKinley, G. A., Hauck, J., Bastos, A., Sitch, S. A., Eggleston, S., & Aich, V. (2022) · Reviews of Geophysics · doi:10.1029/2021rg000736 · Cited by 156 · see citing works
Self-citationCiais, P., Bastos, A., Chevallier, F., Lauerwald, R., Poulter, B., Canadell, J. G., Hugelius, G., Jackson, R. B., Jain, A. K., Jones, M. W., et al. (2022) · Geoscientific model development · doi:10.5194/gmd-15-1289-2022 · Cited by 167 · see citing works
Self-citationAre Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?
Seiler, C., Melton, J. R., Arora, V. K., Sitch, S. A., Friedlingstein, P., Arneth, A., Goll, D. S., Jain, A. K., Joetzjer, É., Lienert, S., et al. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002946 · Cited by 149 · see citing works
Self-citationDecoupling of greenness and gross primary productivity as aridity decreases
Hu, Z., Piao, S. L., Knapp, A. K., Wang, X., Peng, S., Yuan, W., Running, S. W., Mao, J., Shi, X., Ciais, P., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113120 · Cited by 133 · see citing works
Nelson, J. A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L., et al. (2024) · Biogeosciences · doi:10.5194/bg-21-5079-2024 · Cited by 136 · see citing works
Self-citationLi, B., Ryu, Y., Jiang, C., Dechant, B., Liu, J., Yan, Y., & Li, X. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113696 · Cited by 127 · see citing works
Self-citationECLand: The ECMWF Land Surface Modelling System
Boussetta, S., Balsamo, G., Arduini, G., Dutra, E., McNorton, J., Choulga, M., Agustí‐Panareda, A., Beljaars, A. C. M., Wedi, N., Muñoz‐Sabater, J., et al. (2021) · Atmosphere · doi:10.3390/atmos12060723 · Cited by 136 · see citing works
He, Q., Ju, W., Dai, S., He, W., Song, L., Wang, S., Li, X., & Mao, G. (2021) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg005944 · Cited by 123 · see citing works
Zhang, Y., & Ye, A. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146965 · Cited by 120 · see citing works
O’Sullivan, M., Smith, W. K., Sitch, S. A., Friedlingstein, P., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Kautz, M., Lombardozzi, D., et al. (2020) · Global Biogeochemical Cycles · doi:10.1029/2020gb006613 · Cited by 118 · see citing works
Self-citationPartitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
Tramontana, G., Migliavacca, M., Jung, M., Reichstein, M., Keenan, T. F., Camps‐Valls, G., Ogée, J., Verrelst, J., & Papale, D. (2020) · Global Change Biology · doi:10.1111/gcb.15203 · Cited by 111 · see citing works
Self-citationWild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S. A., & Dorigo, W. A. (2022) · Earth system science data · doi:10.5194/essd-14-1063-2022 · Cited by 110 · see citing works
Self-citationDetecting vulnerability of humid tropical forests to multiple stressors
Saatchi, S. S., Longo, M., Xu, L., Yang, Y., Abe, H., André, M., Aukema, J. E., Carvalhais, N., Cadillo‐Quiroz, H., Cerbu, G. A., et al. (2021) · One Earth · doi:10.1016/j.oneear.2021.06.002 · Cited by 107 · see citing works
Self-citationWang, S., Zhang, Y., Ju, W., Porcar‐Castell, A., Ye, S., Zhang, Z., Brümmer, C., Urbaniak, M., Mammarella, I., Juszczak, R., et al. (2020) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2020.108195 · Cited by 100 · see citing works
Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle
Jian, J., Bailey, V., Dorheim, K., Konings, A. G., Hao, D., Shiklomanov, A., Snyder, A., Steele, M. K., Teramoto, M., Vargas, R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29391-5 · Cited by 103 · see citing works
Environment-sensitivity functions for gross primary productivity in light use efficiency models
Bao, S., Wutzler, T., Koirala, S., Cuntz, M., Ibrom, A., Besnard, S., Walther, S., Šigut, L., Moreno‐Martínez, Á., Weber, U., et al. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108708 · Cited by 97 · see citing works
Self-citationPallandt, M. M. T. A., Jitendra Kumar, A., Mauritz, M., Schuur, E. A. G., Virkkala, A., Celis, G., Hoffman, F. M., & Goeckede, M. (2022) · Biogeosciences · doi:10.5194/bg-19-559-2022 · Cited by 89 · see citing works
Self-citationWang, J., Yang, D., Detto, M., Nelson, B., Chen, M., Guan, K., Wu, S., Yan, Z., & Wu, J. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111865 · Cited by 87 · see citing works
Inland Water Greenhouse Gas Budgets for RECCAP2: 2. Regionalization and Homogenization of Estimates
Lauerwald, R., Allen, G. H., Deemer, B. R., Liu, S., Maavara, T., Raymond, P. A., Alcott, L., Bastviken, D., Hastie, A., Holgerson, M. A., et al. (2023) · Global Biogeochemical Cycles · doi:10.1029/2022gb007658 · Cited by 94 · see citing works
Increased photosynthesis during spring drought in energy-limited ecosystems
Miller, D. L., Wolf, S., Fisher, J. B., Zaitchik, B. F., Xiao, J., & Keenan, T. F. (2023) · Nature Communications · doi:10.1038/s41467-023-43430-9 · Cited by 86 · see citing works
The global contribution of roots to total soil respiration
Jian, J., Frissell, M., Hao, D., Tang, X., Berryman, E. M., & Bond‐Lamberty, B. (2022) · Global Ecology and Biogeography · doi:10.1111/geb.13454 · Cited by 85 · see citing works
Wang, K., Bastos, A., Ciais, P., Wang, X., Rödenbeck, C., Gentine, P., Chevallier, F., Humphrey, V., Huntingford, C., O’Sullivan, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31175-w · Cited by 90 · see citing works
Self-citationMcNicol, G., Fluet‐Chouinard, E., Ouyang, Z., Knox, S., Zhang, Z., Aalto, T., Bansal, S., Chang, K., Chen, M., Delwiche, K., et al. (2023) · AGU Advances · doi:10.1029/2023av000956 · Cited by 85 · see citing works
Self-citationPractical Guide to Measuring Wetland Carbon Pools and Fluxes
Bansal, S., Creed, I. F., Tangen, B. A., Bridgham, S. D., Desai, A. R., Krauss, K. W., Neubauer, S. C., Noe, G. B., Rosenberry, D. O., Trettin, C., et al. (2023) · Wetlands · doi:10.1007/s13157-023-01722-2 · Cited by 86 · see citing works
Paddy rice methane emissions across Monsoon Asia
Ouyang, Z., Jackson, R. B., McNicol, G., Fluet‐Chouinard, E., Runkle, B. R. K., Papale, D., Knox, S., Cooley, S., Delwiche, K., Feron, S. C., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113335 · Cited by 79 · see citing works
Self-citationMaksyutov, S. S., Oda, T., Saito, M., Janardanan, R., Belikov, D., Kaiser, J. W., Zhuravlev, R., Ganshin, A., Valsala, V. K., Andrews, A. E., et al. (2021) · Atmospheric chemistry and physics · doi:10.5194/acp-21-1245-2021 · Cited by 84 · see citing works
Soil respiration strongly offsets carbon uptake in Alaska and Northwest Canada
Watts, J. D., Natali, S. M., Minions, C., Risk, D. A., Arndt, K. A., Zona, D., Euskirchen, E. S., Rocha, A. V., Sonnentag, O., Helbig, M., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac1222 · Cited by 81 · see citing works
Serious underestimation of reduced carbon uptake due to vegetation compound droughts
Song, J., Zhou, S., Yu, B., Li, Y., Liu, Y., Yao, Y., Wang, S., & Fu, B. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00571-y · Cited by 76 · see citing works
Lagged effects regulate the inter-annual variability of the tropical carbon balance
Bloom, A. A., Bowman, K. W., Liu, J., Konings, A. G., Worden, J. R., Parazoo, N. C., Meyer, V., Reager, J. T., Worden, H. M., Jiang, Z., et al. (2020) · Biogeosciences · doi:10.5194/bg-17-6393-2020 · Cited by 74 · see citing works
Self-citationRitter, F., Berkelhammer, M., & Garcia‐Eidell, C. (2020) · Communications Earth & Environment · doi:10.1038/s43247-020-00034-1 · Cited by 73 · see citing works
Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget
Watts, J. D., Farina, M. K., Kimball, J. S., Schiferl, L. D., Liu, Z., Arndt, K. A., Zona, D., Ballantyne, A. P., Euskirchen, E. S., Parmentier, F. W., et al. (2023) · Global Change Biology · doi:10.1111/gcb.16553 · Cited by 78 · see citing works
Increasing terrestrial ecosystem carbon release in response to autumn cooling and warming
Tang, R., He, B., Chen, H. W., Chen, D., Chen, Y., Fu, Y. H., Yuan, W., Li, B., Li, Z., Guo, L., et al. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01304-w · Cited by 69 · see citing works
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Kooistra, L., Berger, K., Brede, B., Graf, L. V., Aasen, H., Roujean, J., Machwitz, M., Schlerf, M., Atzberger, C., Prikaziuk, E., et al. (2024) · Biogeosciences · doi:10.5194/bg-21-473-2024 · Cited by 74 · see citing works
Zhang, X., Zhang, Y., Ma, N., Kong, D., Tian, J., Shao, X., & Tang, Q. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac3532 · Cited by 67 · see citing works
Green, J. K., Ballantyne, A. P., Abramoff, R., Gentine, P., Makowski, D., & Ciais, P. (2022) · Global Change Biology · doi:10.1111/gcb.16139 · Cited by 65 · see citing works
Soil respiration–driven CO 2 pulses dominate Australia’s flux variability
Metz, E., Vardag, S. N., Basu, S., Jung, M., Ahrens, B., El-Madany, T. S., Sitch, S. A., Arora, V. K., Briggs, P., Friedlingstein, P., et al. (2023) · Science · doi:10.1126/science.add7833 · Cited by 69 · see citing works
Self-citationPrithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications
Szwarcman, D., Roy, S., Fraccaro, P., Gislason, T. E., Blumenstiel, B., Ghosal, R., de Oliveira, P. H., de Sousa Almeida, J. L., Sedona, R., Kang, Y., et al. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3642610 · Cited by 100 · see citing works
Al‐Yaari, A., Wigneron, J., Ciais, P., Reichstein, M., Ballantyne, A. P., Ogée, J., Ducharne, A., Swenson, J. J., Frappart, F., Fan, L., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15345 · Cited by 62 · see citing works
Self-citationGlobal evaluation of the Ecosystem Demography model (ED v3.0)
Ma, L., HURTT, G. C., Ott, L. E., Sahajpal, R., Fisk, J., Lamb, R., Tang, H., Flanagan, S. A., Chini, L. P., Chatterjee, A., et al. (2022) · Geoscientific model development · doi:10.5194/gmd-15-1971-2022 · Cited by 78 · see citing works
Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set
Walther, S., Besnard, S., Nelson, J. A., El-Madany, T. S., Migliavacca, M., Weber, U., Carvalhais, N., Ermida, S. L., Brümmer, C., Schrader, F., et al. (2022) · Biogeosciences · doi:10.5194/bg-19-2805-2022 · Cited by 64 · see citing works
Self-citationKraft, B., Jung, M., Körner, M., & Reichstein, M. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-1537-2020 · Cited by 63 · see citing works
Self-citationBeringer, J., Moore, C. E., Cleverly, J., Campbell, D. I., Cleugh, H., Kauwe, M. G. D., Kirschbaum, M. U. F., Griebel, A., Grover, S., HUETE, A. R., et al. (2022) · Global Change Biology · doi:10.1111/gcb.16141 · Cited by 63 · see citing works
Regional asymmetry in the response of global vegetation growth to springtime compound climate events
Li, J., Bevacqua, E., Chen, C., Wang, Z., Chen, X., Myneni, R. B., Wu, X., Xu, C., Zhang, Z., & Jakob Zscheischler, A. (2022) · Communications Earth & Environment · doi:10.1038/s43247-022-00455-0 · Cited by 63 · see citing works
The climate benefit of carbon sequestration
Sierra, C. A., Crow, S. E., Heimann, M., Metzler, H., & Schulze, E. (2021) · Biogeosciences · doi:10.5194/bg-18-1029-2021 · Cited by 62 · see citing works
Hu, L., Montzka, S. A., Kaushik, A., Andrews, A. E., Sweeney, C., Miller, J. B., Baker, I., Denning, S., Campbell, E. T., Shiga, Y. P., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2103423118 · Cited by 62 · see citing works
Huang, B., Lu, F., Wang, X., Wu, X., Zheng, H., Su, Y., Yuan, Y., & Ouyang, Z. (2023) · Global Change Biology · doi:10.1111/gcb.16825 · Cited by 64 · see citing works
Towards an increasingly biased view on Arctic change
López‐Blanco, E., Topp-Jorgensen, E. J., Christensen, T. R., Rasch, M., Skov, H., Arndal, M. F., Bret-Harte, M. S., Callaghan, T. V., & Schmidt, N. M. (2024) · Nature Climate Change · doi:10.1038/s41558-023-01903-1 · Cited by 62 · see citing works
Zhu, W., Xie, Z., Zhao, C., Zheng, Z., Kun, Q., Peng, D., & Fu, Y. H. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2318846 · Cited by 62 · see citing works
Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake
Virkkala, A., Rogers, B. M., Watts, J. D., Arndt, K. A., Potter, S., Wargowsky, I. K., Schuur, E. A. G., See, C. R., Mauritz, M., Boike, J., et al. (2025) · Nature Climate Change · doi:10.1038/s41558-024-02234-5 · Cited by 62 · see citing works
Self-citationMa, N., Szilágyi, J., & Józsa, J. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125221 · Cited by 61 · see citing works
Error characterization of global land evapotranspiration products: Collocation-based approach
Li, C., Yang, H., Yang, W., Liu, Z., Yao, J., Li, S., & Yang, D. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128102 · Cited by 61 · see citing works
Kyker‐Snowman, E., Lombardozzi, D., Bonan, G. B., Cheng, S. J., Dukes, J. S., Frey, S. D., Jacobs, E. M., McNellis, R., Rady, J. M., Smith, N. G., et al. (2021) · Global Change Biology · doi:10.1111/gcb.15894 · Cited by 60 · see citing works
Self-citationJiao, K., Liu, Z., Wang, W., Yu, K., McGrath, M. J., & Xu, W. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170053 · Cited by 62 · see citing works
Quantifying the impacts of land cover change on gross primary productivity globally
Krause, A., Papastefanou, P., Gregor, K., Layritz, L. S., Zang, C., Buras, A., Li, X., Xiao, J., & Rammig, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-23120-0 · Cited by 59 · see citing works
Botía, S., Komiya, S., Marshall, J., Koch, T., Gałkowski, M., Lavrič, J. V., Alves, E. G., Walter, D., Fisch, G., Pinho, D. M., et al. (2021) · Global Change Biology · doi:10.1111/gcb.15905 · Cited by 62 · see citing works
Self-citationChang, X., Xing, Y., Gong, W., Yang, C., Guo, Z., Wang, D., Wang, J., Yang, H., Gang, X., & Yang, S. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162601 · Cited by 58 · see citing works
Satellite remote sensing for global stocktaking： Methods， progress and perspectives
Liu, L., Liangfu, C. H. E. N., Liu, Y., Dongxu, Y., ZHANG, X., Lu, N., Ju, W., Jiang, F., Zengshan, Y., Guohua, L., et al. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20221806 · Cited by 59 · see citing works
Liu, Y., Chen, J. M., He, L., Zhang, Z., Wang, R., Rogers, C. A., Fan, W., de Oliveira, G., & Xie, X. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.112896 · Cited by 56 · see citing works
Treat, C. C., Virkkala, A., Burke, E., Bruhwiler, L., Chatterjee, A., Fisher, J. B., Hashemi, J., Parmentier, F. W., Rogers, B. M., Westermann, S., et al. (2024) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2023jg007638 · Cited by 56 · see citing works
Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model
Huang, N., Wang, L., Zhang, Y., Gao, S., & Niu, Z. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3114190 · Cited by 55 · see citing works
Long‐term changes in forest response to extreme atmospheric dryness
Shekhar, A., Hörtnagl, L. J., Buchmann, N., & Gharun, M. (2023) · Global Change Biology · doi:10.1111/gcb.16846 · Cited by 55 · see citing works
Securing the forest carbon sink for the European Union’s climate ambition
Migliavacca, M., Grassi, G., Bastos, A., Ceccherini, G., Ciais, P., Janssens‐Maenhout, G., Lugato, E., Mahecha, M. D., Novick, K. A., Peñuelas, J., et al. (2025) · Nature · doi:10.1038/s41586-025-08967-3 · Cited by 63 · see citing works
Self-citationElevation-dependent pattern of net CO2 uptake across China
Wei, D., Tao, J., Wang, Z., Zhao, H., Zhao, W., & Wang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-46930-4 · Cited by 54 · see citing works
Wang, Y., Köhler, P., He, L., Doughty, R., Braghiere, R. K., Wood, J. D., & Frankenberg, C. (2021) · Geoscientific model development · doi:10.5194/gmd-14-6741-2021 · Cited by 52 · see citing works
Self-citationTropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability
Luo, X., & Keenan, T. F. (2022) · Nature Communications · doi:10.1038/s41467-022-28824-5 · Cited by 52 · see citing works
He, X., Liu, S., Xu, T., Yu, K., Gentine, P., Zhang, Z., Xu, Z., Jiao, D., & Wu, D. (2022) · Agricultural Water Management · doi:10.1016/j.agwat.2022.107827 · Cited by 52 · see citing works
ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., & Winkler, A. J. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/acbbe0 · Cited by 52 · see citing works
Self-citationIdeas and perspectives: enhancing the impact of the FLUXNET network of eddy covariance sites
Papale, D. (2020) · Biogeosciences · doi:10.5194/bg-17-5587-2020 · Cited by 52 · see citing works
Self-citationImproved dryland carbon flux predictions with explicit consideration of water-carbon coupling
Barnes, M. L., Farella, M. M., Scott, R. L., MOORE, D. J. P., Ponce‐Campos, G. E., Biederman, J. A., MacBean, N., Litvak, M. E., & Breshears, D. D. (2021) · Communications Earth & Environment · doi:10.1038/s43247-021-00308-2 · Cited by 56 · see citing works
Modeling Global Carbon Costs of Plant Nitrogen and Phosphorus Acquisition
Braghiere, R. K., Fisher, J. B., Allen, K., Brzostek, E., Shi, M., Yang, X., Ricciuto, D. M., Fisher, R. A., Zhu, Q., & Phillips, R. P. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003204 · Cited by 51 · see citing works
- 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 · 2019: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
