The DOI matches its Crossref record; 89 of the 91 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“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.
Citations
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
Cited by
Sorted by citations
Items 101–200 of 865
You, C., Wang, Y., Tan, X., Cui, E., Zhang, B., Bian, C., Chen, B., Xu, M., Han, X., Xia, J., et al. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109719 · Cited by 49
Oliver, R. J., Mercado, L. M., Clark, D. B., Huntingford, C., Taylor, C. M., Vidale, P. L., McGuire, P. C., Todt, M., Folwell, S. S., Semeena, V. S., et al. (2022) · Geoscientific model development · doi:10.5194/gmd-15-5567-2022 · Cited by 50
Seiler, C., Melton, J. R., Arora, V. K., & Wang, L. (2021) · Geoscientific model development · doi:10.5194/gmd-14-2371-2021 · Cited by 58
Observational Constraints on the Response of High‐Latitude Northern Forests to Warming
Liu, J., Wennberg, P. O., Parazoo, N. C., Yin, Y., & Frankenberg, C. (2020) · AGU Advances · doi:10.1029/2020av000228 · Cited by 52
Self-citationOn the Potential of Sentinel-2 for Estimating Gross Primary Production
Pabon‐Moreno, D. E., Migliavacca, M., Reichstein, M., & Mahecha, M. D. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3152272 · Cited by 48
Self-citationTerrestrial photosynthesis inferred from plant carbonyl sulfide uptake
Lai, J., Kooijmans, L. M. J., Sun, W., Lombardozzi, D., Campbell, J. E., Gu, L., Luo, Y., Kuai, L., & Sun, Y. (2024) · Nature · doi:10.1038/s41586-024-08050-3 · Cited by 48
Self-citationRemaud, M., Chevallier, F., Maignan, F., Belviso, S., Berchet, A., Parouffe, A., Abadie, C., Bacour, C., Lennartz, S. T., & Peylin, P. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-2525-2022 · Cited by 48
Self-citationSoil moisture-atmosphere interactions drive terrestrial carbon-water trade-offs
Sun, W., Zhou, S., Yu, B., Zhang, Y., Keenan, T. F., & Fu, B. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02145-z · Cited by 47
Rafat, A., Rezanezhad, F., Quinton, W. L., Humphreys, E. R., Webster, K. L., & Cappellen, P. V. (2021) · Communications Earth & Environment · doi:10.1038/s43247-021-00184-w · Cited by 53
Liu, J., Zuo, Y., Wang, N., Yuan, F., Zhu, X., Zhang, L., Zhang, J., Sun, Y., Guo, Z., Guo, Y., et al. (2021) · Remote Sensing · doi:10.3390/rs13122242 · Cited by 52
Self-citationRaczka, B., Hoar, T. J., Duarte, H. F., Fox, A. M., Anderson, J. G., Bowling, D. R., & Lin, J. C. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002421 · Cited by 46
Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning
Reitz, O., Graf, A., Schmidt, M., Ketzler, G., Leuchner, M., A. Graf; 2 Agrosphere Institute, F. J. J. G., M. Schmidt; 2 Agrosphere Institute, F. J. J. G., G. Ketzler; 1 Department of Geography, R. A. U. A. G., & M. Leuchner; 1 Department of Geography, R. A. U. A. G. (2021) · Geo-Leo e-docs (Deutsche Initiative für Netzwerkinformation) · doi:10.23689/fidgeo-4388 · Cited by 46
Zhu, X., Yu, G., Chen, Z., Zhang, W., Han, L., Wang, Q., Chen, S., Liu, S., Wang, H., Yan, J., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.159390 · Cited by 46
Global patterns of tree wood density
Yang, H., Wang, S., Son, R., Lee, H., Benson, V., Zhang, W., Zhang, Y., Zhang, Y., Kattge, J., Boenisch, G., et al. (2024) · Global Change Biology · doi:10.1111/gcb.17224 · Cited by 46
Self-citationLeng, J., Chen, J. M., Li, W., Luo, X., Xu, M., Liu, J. J., Wang, R., Rogers, C. A., Li, B., & Yan, Y. (2024) · Earth system science data · doi:10.5194/essd-16-1283-2024 · Cited by 49
Self-citationYang, R., Wang, J., Zeng, N., Sitch, S. A., Tang, W., McGrath, M. J., Cai, Q., Liu, D., Lombardozzi, D., Tian, H., et al. (2022) · Earth System Dynamics · doi:10.5194/esd-13-833-2022 · Cited by 45
Self-citationZhang, X., Zhang, Y., Tian, J., Ma, N., & Wang, Y. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac6c9c · Cited by 50
Kou‐Giesbrecht, S., Arora, V. K., Seiler, C., Arneth, A., Falk, S., Jain, A. K., Joos, F., Kennedy, D., Knauer, J., Sitch, S. A., et al. (2023) · Earth System Dynamics · doi:10.5194/esd-14-767-2023 · Cited by 56
Self-citationApparent ecosystem carbon turnover time: uncertainties and robust features
Fan, N., Koirala, S., Reichstein, M., Thurner, M., Avitabile, V., Santoro, M., Ahrens, B., Weber, U., & Carvalhais, N. (2020) · Earth system science data · doi:10.5194/essd-12-2517-2020 · Cited by 43
Self-citationKhan, A. M., Stoy, P. C., Joiner, J., Baldocchi, D., Verfaillie, J., Chen, M., & Otkin, J. A. (2022) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2021jg006701 · Cited by 43
Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems
Desai, A. R., Murphy, B., Wiesner, S., Thom, J. E., Butterworth, B., Koupaei‐Abyazani, N., Muttaqin, A. S., Paleri, S., Talib, A., Turner, J., et al. (2022) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2022jg007014 · Cited by 43
The potential of urban irrigation for counteracting carbon-climate feedback
Li, P., Wang, Z., & Wang, C. (2024) · Nature Communications · doi:10.1038/s41467-024-46826-3 · Cited by 45
Addressing biases in Arctic–boreal carbon cycling in the Community Land Model Version 5
Birch, L., Schwalm, C. R., Natali, S., Lombardozzi, D., Keppel‐Aleks, G., Watts, J. D., Lin, X., Zona, D., Oechel, W. C., Sachs, T., et al. (2021) · Geoscientific model development · doi:10.5194/gmd-14-3361-2021 · Cited by 46
Self-citationVegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde
Morfopoulos, C., Müller, J., Stavrakou, T., Bauwens, M., Smedt, I. D., Friedlingstein, P., Prentice, I. C., & Regnier, P. A. G. (2021) · Global Change Biology · doi:10.1111/gcb.15880 · Cited by 46
Contrasting Trends in Water Use Efficiency of the Alpine Grassland in Tibetan Plateau
Ma, N., & Zhang, Y. (2022) · Journal of Geophysical Research Atmospheres · doi:10.1029/2022jd036919 · Cited by 42
Sun, Z., An, Y., Kong, J., Zhao, J., Cui, W., Nie, T., Zhang, T., Liu, W., & Wu, L. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.168262 · Cited by 42
Extreme events driving year-to-year differences in gross primary productivity across the US
Turner, A. J., Köhler, P., Magney, T. S., Frankenberg, C., FUNG, I. Y., & Cohen, R. C. (2021) · Biogeosciences · doi:10.5194/bg-18-6579-2021 · Cited by 42
Self-citationWang, Y., Braghiere, R. K., Longo, M., Norton, A., Köhler, P., Doughty, R., Yin, Y., Bloom, A. A., & Frankenberg, C. (2023) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002964 · Cited by 41
Self-citationGlobal prediction of gross primary productivity under future climate change
Lu, Q., Liu, H., Wei, L., Zhong, Y., & Zhou, Z. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169239 · Cited by 43
Principles for satellite monitoring of vegetation carbon uptake
Prentice, I. C., Balzarolo, M., Bloomfield, K. J., Chen, J. M., Dechant, B., Ghent, D. J., Janssens, I. A., Luo, X., Morfopoulos, C., Ryu, Y., et al. (2024) · Nature Reviews Earth & Environment · doi:10.1038/s43017-024-00601-6 · Cited by 47
Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach
Guo, R., Chen, T., Chen, X., Yuan, W., Liu, S., He, B., Li, L., Wang, S., Hu, T., Yan, Q., et al. (2023) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2022jg007100 · Cited by 39
Reyes-Muñoz, P., Kovács, D. D., Berger, K., Pipia, L., Belda, S., Rivera-Caicedo, J. P., & Verrelst, J. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114072 · Cited by 40
Hutley, L. B., Beringer, J., Fatichi, S., Schymanski, S. J., & Northwood, M. (2021) · Global Change Biology · doi:10.1111/gcb.16012 · Cited by 40
Status of crop water use efficiency evaluation methods: A review
Wang, T., Sun, S., Yin, Y., Zhao, J., Tang, Y., Wang, Y., Gao, F., & Luan, X. (2024) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2024.109961 · Cited by 41
Liu, X., Liu, L., Bacour, C., Guanter, L., Chen, J., Ma, Y., Chen, R., & Du, S. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113341 · Cited by 38
Spatial variations and mechanisms for the stability of terrestrial carbon sink in China
Wang, K., Piao, S. L., He, Y. L., Liu, Y., & He, H. (2022) · Science China Earth Sciences · doi:10.1007/s11430-021-1003-5 · Cited by 38
Vegetation growth due to CO2 fertilization is threatened by increasing vapor pressure deficit
Li, S., Wang, G., Zhu, C., Lu, J., Ullah, W., Hagan, D. F. T., Kattel, G., Liu, Y., Zhang, Z., Song, Y., et al. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129292 · Cited by 41
Endsley, K. A., Zhao, M., Kimball, J. S., & Devadiga, S. (2023) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2023jg007457 · Cited by 41
Byrne, B., Liu, J., Bloom, A. A., Bowman, K. W., Butterfield, Z., Joiner, J., Keenan, T. F., Keppel‐Aleks, G., Parazoo, N. C., & Yin, Y. (2020) · Global Biogeochemical Cycles · doi:10.1029/2020gb006598 · Cited by 37
Self-citationLiu, Q., Yang, S., Li, S., Zhang, H., Zhang, J., & Fan, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.175933 · Cited by 38
Impact of temperature and water availability on microwave-derived gross primary production
Teubner, I., Forkel, M., Wild, B., Mösinger, L., & Dorigo, W. A. (2021) · Biogeosciences · doi:10.5194/bg-18-3285-2021 · Cited by 37
Alsafadi, K., Al-Ansari, N. A., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac4765 · Cited by 36
Braghiere, R. K., Fisher, J. B., Miner, K. R., Miller, C. E., Worden, J. R., SCHIMEL, D. S., & Frankenberg, C. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/acb226 · Cited by 42
Li, X., Zhang, W., Vermeulen, A. T., Dong, J., & Duan, Z. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109451 · Cited by 36
Munassar, S., Rödenbeck, C., Koch, F.-T., Totsche, K. U., Gałkowski, M., Walther, S., & Gerbig, C. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-7875-2022 · Cited by 36
Self-citationSolar-induced chlorophyll fluorescence sheds light on global evapotranspiration
Zhang, Q., Liu, X., Zhou, K., Zhou, Y., Gentine, P., Pan, M., & Katul, G. G. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114061 · Cited by 36
Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review
Spafford, L., & MacDougall, A. H. (2021) · Geoscientific model development · doi:10.5194/gmd-14-5863-2021 · Cited by 43
Uncertainty analysis of multiple terrestrial gross primary productivity products
Zhang, Y., & Ye, A. (2022) · Global Ecology and Biogeography · doi:10.1111/geb.13578 · Cited by 35
Dong, S., Du, S., Wang, X., & Dong, X. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.121158 · Cited by 34
Responding time scales of vegetation production to extreme droughts over China
Deng, Y., Wu, D., Wang, X., & Xie, Z. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108630 · Cited by 35
He, X., Li, Y., Liu, S., Xu, T., Chen, F., Li, Z., Zhang, Z., Liu, R., Song, L., Xu, Z., et al. (2023) · Hydrology and earth system sciences · doi:10.5194/hess-27-1583-2023 · Cited by 33
Bu, J., Gan, G., Chen, J., Su, Y., Yuan, M., Gao, Y., Domingo, F., López‐Ballesteros, A., Migliavacca, M., El-Madany, T. S., et al. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.113999 · Cited by 39
Self-citationUpscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning
Reitz, O., Graf, A., Schmidt, M., Ketzler, G., & Leuchner, M. (2020) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg005814 · Cited by 33
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century
Smallman, T. L., Milodowski, D. T., Neto, E. S., Koren, G., Ometto, J. P., & Williams, M. (2021) · Earth System Dynamics · doi:10.5194/esd-12-1191-2021 · Cited by 42
Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates
Pickering, M., Cescatti, A., & Duveiller, G. (2022) · Biogeosciences · doi:10.5194/bg-19-4833-2022 · Cited by 36
Zhu, S., McCalmont, J. P., Cardenas, L. M., Cunliffe, A. M., Olde, L., Signori‐Müller, C., Litvak, M. E., & Hill, T. C. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109365 · Cited by 33
Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss
Li, J., Bevacqua, E., Wang, Z., Sitch, S. A., Arora, V. K., Arneth, A., Jain, A. K., Goll, D. S., Tian, H., & Jakob Zscheischler, A. (2023) · Communications Earth & Environment · doi:10.1038/s43247-023-00869-4 · Cited by 37
Self-citationConstraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches
Upton, S., Reichstein, M., Gans, F., Peters, W., Kraft, B., & Bastos, A. (2024) · Atmospheric chemistry and physics · doi:10.5194/acp-24-2555-2024 · Cited by 32
Self-citationJin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., & Piao, S. L. (2024) · Earth system science data · doi:10.5194/essd-16-2857-2024 · Cited by 35
Climate drivers of the variations of vegetation productivity in India
Verma, A., Chandel, V., & Ghosh, S. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac7c7f · Cited by 32
Khruschev, S., Plyusnina, T. Y., Antal, T. K., Pogosyan, S. I., Riznichenko, G. Y., & Rubin, A. B. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-00982-2 · Cited by 32
MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations
Nathaniel, J., Liu, J., & Gentine, P. (2023) · Scientific Data · doi:10.1038/s41597-023-02349-y · Cited by 33
Self-citationShi, H., Luo, G., Hellwich, O., Xie, M., Zhang, C., Zhang, Y., Wang, Y., Yuan, X., Ma, X., Zhang, W., et al. (2022) · Biogeosciences · doi:10.5194/bg-19-3739-2022 · Cited by 31
A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
Shangguan, W., Xiong, Z., Nourani, V., Li, Q., Lu, X., Li, L., Huang, F., Zhang, Y., Sun, W., & Dai, Y. (2023) · Forests · doi:10.3390/f14050913 · Cited by 31
Global sensitivities of forest carbon changes to environmental conditions
Besnard, S., Santoro, M., Cartus, O., Fan, N., Linscheid, N., Nair, R. K. F., Weber, U., Koirala, S., & Carvalhais, N. (2021) · Global Change Biology · doi:10.1111/gcb.15877 · Cited by 35
Self-citationInferring causal relations from observational long-term carbon and water fluxes records
Díaz, E., Adsuara, J. E., Moreno‐Martínez, Á., Piles, M., & Camps‐Valls, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-05377-7 · Cited by 33
Self-citationGlobal carbon flux dataset generated by fusing remote sensing and multiple flux networks observation
Yuan, Q., Wang, X., Che, T., & Li, J. (2025) · Scientific Data · doi:10.1038/s41597-025-05672-8 · Cited by 33
Assessing Model Predictions of Carbon Dynamics in Global Drylands
Fawcett, D., Cunliffe, A. M., Sitch, S. A., O’Sullivan, M., Anderson, K., Brazier, R. E., Hill, T. C., Anthoni, P. M., Arneth, A., Arora, V. K., et al. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.790200 · Cited by 36
Self-citationZheng, Y., Zhao, W., Chen, A., Chen, Y., Chen, J., & Zhu, Z. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170439 · Cited by 30
Yang, Z., Huang, Y., Duan, Z., & Tang, J. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.110154 · Cited by 30
He, L., Rosa, L., Lobell, D. B., Wang, Y., Yin, Y., Doughty, R., Yao, Y., Berry, J. A., & Frankenberg, C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2306507120 · Cited by 30
Kernel methods and their derivatives: Concept and perspectives for the earth system sciences
Johnson, J. E., Laparra, V., Pérez-Suay, A., Mahecha, M. D., & Camps‐Valls, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235885 · Cited by 31
Self-citationKim, D., Ha, K., & Yeo, J. (2021) · Earth s Future · doi:10.1029/2020ef001697 · Cited by 29
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) · doi:10.5194/essd-2020-307 · Cited by 29
Self-citationIncreasing Optimum Temperature of Vegetation Activity Over the Past Four Decades
Wang, Y., Sarmah, S., Singha, M., Chen, W., Ge, Y., Liáng, L., Goswami, S., & Niu, S. (2024) · Earth s Future · doi:10.1029/2024ef004489 · Cited by 32
Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland
Guevara-Escobar, A., González-Sosa, E., Cervantes‐Jiménez, M., Suzán-Azpiri, H., Queijeiro-Bolaños, M. E., Carrillo‐Ángeles, I. G., & Cambrón-Sandoval, V. H. (2021) · Biogeosciences · doi:10.5194/bg-18-367-2021 · Cited by 28
Huang, B., Lu, F., Wang, X., Wu, X., Zhang, L., & Ouyang, Z. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac3871 · Cited by 28
An end-to-end satellite-based GPP estimation model devoid of meteorological and land cover data
Zhu, W., Zhao, C., & Xie, Z. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109337 · Cited by 28
Uniform upscaling techniques for eddy covariance FLUXes (UFLUX)
Zhu, S., Quaife, T. L., & Hill, T. C. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2312266 · Cited by 28
European Carbon Uptake has Not Benefited From Vegetation Greening
Liu, X., He, B., Guo, L., Huang, L., Yuan, W., Chen, X., Hao, X., Xie, X., Zhang, Y., Zhong, Z., et al. (2021) · Geophysical Research Letters · doi:10.1029/2021gl094870 · Cited by 27
Understanding and Monitoring Chemical and Biological Soil Degradation
Saljnikov, E., Lavrishchev, A. V., Römbke, J., Rinklebe, J., Scherber, C., Wilke, B., Tóth, T., Blum, W. E. H., Behrendt, U., Eulenstein, F., et al. (2021) · Innovations in landscape research · doi:10.1007/978-3-030-85682-3_3 · Cited by 27
Fan, N., Reichstein, M., Koirala, S., Ahrens, B., Mahecha, M. D., & Carvalhais, N. (2022) · Nature Geoscience · doi:10.1038/s41561-022-01074-2 · Cited by 28
Self-citationArora, V. K., Seiler, C., Wang, L., & Kou‐Giesbrecht, S. (2023) · Biogeosciences · doi:10.5194/bg-20-1313-2023 · Cited by 34
Sun, W., Luo, X., Fang, Y., Shiga, Y. P., Zhang, Y., Fisher, J. B., Keenan, T. F., & Michalak, A. M. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02093-x · Cited by 27
The biological basis for using optical signals to track evergreen needleleaf photosynthesis
Pierrat, Z., Magney, T. S., Cheng, R., Maguire, A. J., Wong, C. Y. S., Nehemy, M. F., Rao, M. P., Nelson, S. E., Williams, A. F., Grosvenor, J. A. H., et al. (2024) · BioScience · doi:10.1093/biosci/biad116 · Cited by 27
Tu, H., Jiapaer, G., Yu, T., Zhang, L., Chen, B., Lin, K., & Li, X. (2023) · Remote Sensing · doi:10.3390/rs15092471 · Cited by 26
Wieder, W. R., Butterfield, Z., Lindsay, K., Lombardozzi, D., & Keppel‐Aleks, G. (2021) · Global Biogeochemical Cycles · doi:10.1029/2021gb007034 · Cited by 26
Self-citationPrikaziuk, E., Migliavacca, M., Su, Z., & van der Tol, C. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113324 · Cited by 25
Self-citationPatra, P. K., Hajima, T., Saito, R., Chandra, N., Yoshida, Y., Ichii, K., Kawamiya, M., Kondo, M., Ito, A., & Crisp, D. (2021) · Progress in Earth and Planetary Science · doi:10.1186/s40645-021-00420-z · Cited by 25
Self-citationZhu, J., Wang, Q., He, N., & Yu, G. (2021) · Environmental Research · doi:10.1016/j.envres.2021.111787 · Cited by 25
Yang, Y., Bloom, A. A., Ma, S., Levine, P. A., Norton, A., Parazoo, N. C., Reager, J. T., Worden, J. R., Quetin, G. R., Smallman, T. L., et al. (2022) · Geoscientific model development · doi:10.5194/gmd-15-1789-2022 · Cited by 25
Importance of the memory effect for assessing interannual variation in net ecosystem exchange
Liu, W., He, H., Wu, X., Ren, X., Zhang, L., Shi, L., Feng, L., Wang, Y., & Lv, Y. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109691 · Cited by 25
Huang, C., He, W., Liu, J., Nguyen, N. T., Yang, H., Lv, Y., Chen, H., & Zhao, M. (2024) · Journal of Geophysical Research Atmospheres · doi:10.1029/2023jd040418 · Cited by 25
Self-citationA consistent budgeting of terrestrial carbon fluxes
Dorgeist, L., Schwingshackl, C., Bultan, S., & Pongratz, J. (2024) · Nature Communications · doi:10.1038/s41467-024-51126-x · Cited by 24
Self-citationChen, Z., Huntzinger, D. N., Liu, J., Piao, S. L., Wang, X., Sitch, S. A., Friedlingstein, P., Anthoni, P. M., Arneth, A., Bastrikov, V., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/abfac1 · Cited by 26
Self-citationEndsley, K. A., Kimball, J. S., & Reichle, R. H. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002804 · Cited by 24
Controls on timescales of soil organic carbon persistence across sub‐Saharan Africa
von Fromm, S. F., Döetterl, S., Butler, B. M., Aynekulu, E., Berhe, A. A., Haefele, S. M., McGrath, S. P., Shepherd, K. D., Six, J., Tamene, L. D., et al. (2023) · Global Change Biology · doi:10.1111/gcb.17089 · Cited by 27
The weak land carbon sink hypothesis
Randerson, J. T., Li, Y., Fu, W., Primeau, F., Kim, J. E., Mu, M., Hoffman, F. M., Trugman, A. T., Yang, L., Wu, C., et al. (2025) · Science Advances · doi:10.1126/sciadv.adr5489 · Cited by 29
Tracking Global Patterns of Drought‐Induced Productivity Loss Along Severity Gradient
Wang, Y., Fu, Z., Hu, Z., & Niu, S. (2022) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2021jg006753 · Cited by 23
He, W., Jiang, F., Ju, W., Byrne, B., Xiao, J., Nguyen, N. T., Wu, M., Wang, S., Wang, J., Rödenbeck, C., et al. (2023) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003150 · Cited by 24
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
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
