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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-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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Zou, Y., & Wang, X. (2025) · Sustainability · doi:10.3390/su17114836 · Cited by 6
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Schumacher, F. L., Knoth, C., Ludwig, M., & Meyer, H. (2025) · Geoscientific model development · doi:10.5194/gmd-18-10185-2025 · Cited by 6
Qu, B., Roy, A., Melton, J. R., Black, T. A., Amiro, B. D., Euskirchen, E. S., Ueyama, M., Kobayashi, H., Schulze, C., Gosselin, G. H., et al. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/ace376 · Cited by 6
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Self-citationBaumgartner, S., Bauters, M., Drake, T. W., Barthel, M., Alebadwa, S., Bahizire, N., Bazirake, B. M., Six, J., Boeckx, P., & Oost, K. V. (2022) · Ecosystems · doi:10.1007/s10021-022-00773-6 · Cited by 6
Huang, L., Liu, M., & Yao, N. (2023) · Remote Sensing · doi:10.3390/rs15204922 · Cited by 6
Kou‐Giesbrecht, S., Arora, V. K., Seiler, C., & Wang, L. (2024) · Biogeosciences · doi:10.5194/bg-21-3339-2024 · Cited by 6
Gallup, S. M., Baker, I., Gallup, J. L., Restrepo‐Coupé, N., Haynes, K., Geyer, N. M., & Denning, S. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002555 · Cited by 5
A dynamic disequilibrium hypothesis for terrestrial carbon cycle
Luo, Y., & Xia, J. (2020) · Biodiversity Science · doi:10.17520/biods.2020262 · Cited by 6
Wang, Y., Zhou, L., Zhuang, J., Sun, L., & Chi, Y. (2021) · Global Ecology and Conservation · doi:10.1016/j.gecco.2021.e01721 · Cited by 6
Wang, H., Yakir, D., & Rotenberg, E. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109415 · Cited by 6
Marino, B. D. V., Bautista, N. E., & Rousseaux, B. (2021) · Land · doi:10.3390/land10040436 · Cited by 6
Bocanegra, S. B. (2022) · doi:10.18174/573967 · Cited by 9
Lei, R., Poe, J., Huntzinger, D. N., Liu, J., Stich, S., Baker, D. F., Feng, L., Gaeta, D. C., Huang, Z., & Miller, S. M. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114326 · Cited by 5
Self-citationObserved increasing light-use efficiency of terrestrial gross primary productivity
Liu, Z., He, C., Xu, J., Sun, H., Dai, X., Cui, E., Qiu, C., Xia, J., & Huang, K. (2024) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2024.110269 · Cited by 5
Wu, Q., Yang, J., Song, J., Sun, H., Wang, H., & Xing, L. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133752 · Cited by 5
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Xu, J., Chen, T., Chen, X., Zhou, S., Gu, Z., Li, W., Cui, Y., Wang, S., & Liu, S. (2025) · Ecological Processes · doi:10.1186/s13717-025-00617-w · Cited by 5
van der Poel, L., Bataille, L., Kruijt, B., Franssen, W., Jans, W. W. P., Biermann, J., Rietman, A., Buzacott, A., van der Velde, Y., Boelens, R., et al. (2025) · Biogeosciences · doi:10.5194/bg-22-3867-2025 · Cited by 5
Fan, X., Zhu, Q., Wei, Y., Yao, N., Zhao, G., Yu, Q., & Wu, G. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110963 · Cited by 5
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Luo, H., Bastos, A., Reichstein, M., Duveiller, G., Kretzschmar, J. G., & Quaas, J. (2026) · Nature Communications · doi:10.1038/s41467-026-69480-3 · Cited by 5
Self-citationKunik, L., Bowling, D. R., Raczka, B., Frankenberg, C., Köhler, P., Cheng, R., Smith, K. R., Goulden, M. L., Jung, M., & Lin, J. C. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/ad07b4 · Cited by 5
Self-citationWu, W., Xu, L., Chen, L., Zheng, H., Zhang, X., & Zhu, M. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.143119 · Cited by 5
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Koirala, S., JONES, C. D., Ahrens, B., Fan, N., Brovkin, V. A., Delire, C., Fan, Y., Gayler, V., Joetzjer, É., Lee, H., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2013805/v1 · Cited by 5
Self-citationChapter 3 : Earth Systems Processes. Fifth National Climate Assessment
Leung, L.-Y. R., Terando, A. J., Joseph, R., Tselioudis, G., Bruhwiler, L., Cook, B. I., Deser, C., Hall, A. D., Hamlington, B. D., Hoell, A., et al. (2023) · doi:10.7930/nca5.2023.ch3 · Cited by 5
The size of tropical vegetation gross primary production
Tian, J., Huang, X., Chen, H. T., Chen, J. M., Ciais, P., Krol, M., Liang, S., Piao, S. L., Wu, J., Xiao, J., et al. (2026) · Nature · doi:10.1038/s41586-026-10562-z · Cited by 5
Wesloh, D., Keller, K. M., Feng, S., Lauvaux, T., & Davis, K. J. (2024) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2023jg007526 · Cited by 5
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Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., & Reichstein, M. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2024ms004655 · Cited by 5
Self-citationLee, H., Jung, M., Carvalhais, N., Reichstein, M., Forkel, M., Bloom, A. A., Pacheco‐Labrador, J., & Koirala, S. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2024ms004479 · Cited by 4
Self-citationKhan, A. S., Ali, M., Mandadi, A., Anjum, A., & Balzter, H. (2025) · Remote Sensing · doi:10.3390/rs17213615 · Cited by 4
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Zhao, R., Luo, X., Walker, A. P., Hoffman, F. M., & Koh, L. P. (2025) · Nature Communications · doi:10.1038/s41467-025-67636-1 · Cited by 6
Self-citationZhao, M., Yang, Y., Weng, G., He, W., Yang, H., Nguyen, N. T., Wang, J., Liu, S., Chen, J., Lei, X., et al. (2025) · Remote Sensing · doi:10.3390/rs18010130 · Cited by 4
Zhang, R., Su, C., Wang, Y., Wang, S., tian, D., Wang, J., Jiang, L., Chen, X., Zhu, J., Pan, J., et al. (2026) · Nature Plants · doi:10.1038/s41477-025-02189-1 · Cited by 4
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Zhang, W., Fensholt, R., & Brandt, M. (2023) · Geophysical Research Letters · doi:10.1029/2023gl103932 · Cited by 4
Socioeconomic and Environmental Changes in Global Drylands
Piao, S. L., Zhang, Y., Zhu, Z., Lian, X., Huang, K., He, M., Zhao, C., & Liu, D. (2024) · Springer eBooks · doi:10.1007/978-981-99-9375-8_6 · Cited by 4
Shao, W., Wu, C., Yue, T., Zhao, N., & Hu, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3556678 · Cited by 4
Pan, B., Xiao, X., Pan, L., Cheng, M., Blanken, P. D., Burns, S. P., Celis, J. A., Zhang, C., & Qin, Y. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110526 · Cited by 4
Zhu, L., Ciais, P., Yao, Y., Goll, D. S., Luyssaert, S., Martínez, I., Fendrich, A. N., Li, L., Yang, H., Saatchi, S. S., et al. (2025) · Geoscientific model development · doi:10.5194/gmd-18-4915-2025 · Cited by 3
Self-citationMastropierro, M., Peano, D., & Zanchettin, D. (2025) · Biogeosciences · doi:10.5194/bg-22-5231-2025 · Cited by 4
Marino, B. D. V., Truong, V., Munger, J. W., & Gyimah, R. (2020) · PeerJ · doi:10.7717/peerj.8891 · Cited by 4
Marconato, U., Fernández, R. J., & Posse, G. (2022) · Frontiers in Soil Science · doi:10.3389/fsoil.2022.903544 · Cited by 4
Shi, H., Luo, G., Hellwich, O., Kurban, A., Maeyer, P. D., & de Voorde, T. V. (2023) · Biogeosciences · doi:10.5194/bg-20-2727-2023 · Cited by 4
Vernay, A., Hasselquist, N. J., Leppä, K., Klosterhalfen, A., López, J. G., Stangl, Z. R., Chi, J., Kozii, N., & Marshall, J. D. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109857 · Cited by 4
Rigatti, E., McDermid, S., Cook, B. I., & Kauwe, M. G. D. (2024) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2024jg008143 · Cited by 4
Carbon sequestration and CO2 flux in six plant species in vertical greenery systems
Pan, L., Luo, S., Hu, W., Lai, P. Y., Wang, H., Liu, M., & Chu, L. M. (2024) · Urban Ecosystems · doi:10.1007/s11252-024-01620-3 · Cited by 4
Terán, C. P., Naz, B. S., Vereecken, H., Baatz, R., Fisher, R. A., & Franssen, H. H. (2025) · Geoscientific model development · doi:10.5194/gmd-18-287-2025 · Cited by 4
Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes
Sharma, B., Jitendra Kumar, A., Ganguly, A. R., & Hoffman, F. M. (2022) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw58026.2022.00148 · Cited by 4
Verma, A., & Ghosh, S. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131890 · Cited by 4
Kim, D., Bond‐Lamberty, B., Ryu, Y., Seo, B., & Papale, D. (2021) · doi:10.5194/bg-2021-85 · Cited by 4
Self-citationBartolomeis, P. D., Meterez, A., Shu, Z., & Stocker, B. D. (2023) · doi:10.5194/egusphere-2023-1826 · Cited by 4
MCI GPP: ensembling a global model- and climate-independent gross primary productivity for 2001–2023
Pu, J., Chang, Y., Gao, S., Bao, S., Yan, K., Sun, X., Carvalhais, N., & Myneni, R. B. (2025) · Scientific Data · doi:10.1038/s41597-025-06218-8 · Cited by 4
Self-citationThe UFLUX ensemble of multiple-scale carbon, water, and energy fluxes
Zhu, S., Xu, J., Zeng, J., Bao, S., Chen, Y., Shi, S., Zheng, Z., Dong, W., Wang, Y., & Shi, J. (2025) · Scientific Data · doi:10.1038/s41597-025-06401-x · Cited by 5
Chebbo, S., Violle, C., Mahaut, L., Kattge, J., Peaucelle, M., Choler, P., & Viovy, N. (2025) · Journal of Biogeography · doi:10.1111/jbi.15079 · Cited by 3
Liu, J., Zhou, T., Zeng, J., Zhang, J., Wu, X., Zhang, Y., Zhang, Q., Qu, Y., Liu, P., Zhang, W., et al. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110985 · Cited by 3
Self-citationZhou, K., Li, J., Zhang, T., Shi, X., & Feng, P. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135078 · Cited by 3
Liu, J., Zhang, M., Xia, Y., Wu, L., & Chen, C. (2026) · Land Use Policy · doi:10.1016/j.landusepol.2026.107974 · Cited by 4
Self-citationLiu, Y., Li, Z., Chen, Y., Gao, L., Zhang, L., Turnadge, C., He, B., Zhou, X., Duan, W., Li, B., et al. (2026) · One Earth · doi:10.1016/j.oneear.2026.101612 · Cited by 3
Volk, J., Dunkerly, C., Majumdar, S., Huntington, J., Minor, B., Kim, Y., Morton, C., ReVelle, P. M., Kiliç, A., Melton, F., et al. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110647 · Cited by 3
Li, H., Zhang, D., Bai, J., Lü, W., Yu, X., & Jia, G. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108947 · Cited by 3
Implications of the steady-state assumption for the global vegetation carbon turnover
Fan, N., Santoro, M., Besnard, S., Cartus, O., Koirala, S., & Carvalhais, N. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/acfb22 · Cited by 3
Self-citationBaşakın, E. E., Stoy, P. C., Demirel, M. C., & Pham, Q. B. (2024) · Remote Sensing · doi:10.3390/rs16111994 · Cited by 3
Yang, L., Li, J., Zhang, Y., Zhao, P., Li, W., Wu, T., & Zhang, G. (2025) · Climate Dynamics · doi:10.1007/s00382-025-07615-w · Cited by 3
Song, B., Hu, J., Wang, Y., Li, D., Zhang, P., Wang, Y., Zhong, L., & Li, R. S. (2025) · Journal of Geophysical Research Atmospheres · doi:10.1029/2024jd041425 · Cited by 3
Liu, W., Feng, L., Niu, Z., Lv, Y., & Zhang, M. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113558 · Cited by 3
Ullah, A., Crétat, J., Michel, G., Mathieu, O., Thévenot, M., Dara, A., Granat, R. A., Wu, Z., Bonnefoy‐Claudet, C., Capelle, J., et al. (2025) · Biogeosciences · doi:10.5194/bg-22-4135-2025 · Cited by 5
Wang, S., Ryu, Y., Dechant, B., Zhang, H., Feng, H., Lee, J., & Choi, C. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.115198 · Cited by 4
Bastos, A., Naipal, V., Ahlström, A., MacBean, N., Smith, W. K., & Poulter, B. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-814952-2.00012-5 · Cited by 3
Autumn cooling paused increased CO2 release in central Eurasia
Kondo, M., Sasakawa, M., Machida, T., Arshinov, M., & Hiyama, T. (2023) · Nature Climate Change · doi:10.1038/s41558-023-01625-4 · Cited by 3
Zhang, Q., Zhao, H., Cheng, W., Cong, N., Wang, X., Liang, H., & Li, X. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.166676 · Cited by 3
Advancing the understanding of coastal disturbances with a network‐of‐networks approach
Myers‐Pigg, A., Moanga, D. A., Bond‐Lamberty, B., Ward, N. D., Megonigal, J. P., White, E., Bailey, V., & Kirwan, M. L. (2025) · Ecosphere · doi:10.1002/ecs2.70156 · Cited by 4
Wang, X., Guo, Z., Zhang, K., Fu, Z., Lee, C. K. F., Yang, D., Detto, M., Zhang, Y., & Wu, J. (2025) · Geophysical Research Letters · doi:10.1029/2024gl110785 · Cited by 3
Network of networks: Time series clustering of AmeriFlux sites
Reed, D. E., Chu, H.-S., Peter, B. G., Chen, J.-Q., Abraha, M., Amiro, B., Anderson, R. G., Arain, M., Arruda, P., Barron-Gafford, G., et al. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110686 · Cited by 3
Athira, K. V., Rajasekaran, E., Boulet, G., Nigam, R., & Bhattacharya, B. K. (2025) · Hydrological Sciences Journal · doi:10.1080/02626667.2025.2541749 · Cited by 3
Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme
Cao, C., Ma, X., Yang, W., Yan, K., Liu, F., Wang, Y., & HUETE, A. R. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3618410 · Cited by 3
A review of in-situsampling protocols to support the remote sensing of vegetation
Burchard‐Levine, V., Riaño, D., & Vilar, L. (2022) · GeoFocus Revista Internacional de Ciencia y Tecnología de la Información Geográfica · doi:10.21138/gf.780 · Cited by 3
A novel data-driven global model of photosynthesis using solar-induced chlorophyll fluorescencePreprint
Doughty, R., Wang, Y., Johnson, J. E., Parazoo, N. C., Magney, T. S., Pierrat, Z., Xiao, X., Guanter, L., Köhler, P., Frankenberg, C., et al. (2023) · doi:10.22541/essoar.168167172.20799710/v1 · Cited by 3
Self-citationRanjbar, S., Losos, D., Hoffman, S., Cuntz, M., & Stoy, P. C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4671918 · Cited by 3
Eldhose, E., & Ghosh, S. (2025) · Environmental Research Letters · doi:10.1088/1748-9326/add8a7 · Cited by 2
Large and Increasing Biospheric Productivity of Northern Ecosystems
Cuntz, M., Smith, B., Canadell, J. G., Knauer, J., & Haverd, V. (2025) · Geophysical Research Letters · doi:10.1029/2025gl115983 · Cited by 3
Self-citationThe Response of Vegetation Productivity to the El Niño‐Southern Oscillation in Southeast Asia
Qian, Z., Chen, S., Li, X., Wang, S., Liu, Z., Chen, X., & Darjee, A. (2025) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2024jg008606 · Cited by 2
Wang, X., Qiao, X., Lin, S., Yue, Q., Hao, M., He, J., Da, R., Zhang, C., & Zhao, X. (2025) · Forest Ecosystems · doi:10.1016/j.fecs.2025.100366 · Cited by 2
Wu, R., Cui, Z., Jia, B., Sun, H., Wang, L., Yu, Y., & Jiang, L. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111292 · Cited by 2
Wang, N., Yue, Z., Liu, Y., Tong, Z., Liu, Y., Lu, Y., & Shi, Y. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110784 · Cited by 2
Qiao, C., Ma, L., Du, L., Wu, H., Wang, Y., Shi, G., Xue, B., Zhang, X., Bao, G., Gu, B., et al. (2025) · CATENA · doi:10.1016/j.catena.2025.109515 · Cited by 2
He, L., Peñuelas, J., Xiao, J., Tang, X., He, Y. L., Si, M., & Li, Z.-L. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.114311 · Cited by 2
Bracho, R., Silveira, M. L., Vendramini, J. M. B., Kohmann, M. M., Cardoso, A. D. S., da Cruz, P. J. R., Celis, G., & Ghimire, N. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.180924 · Cited by 2
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
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.
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