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CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO 2 fertilization
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Abstract
Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO 2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change. Here, we introduce CEDAR-GPP, the first global machine-learning-upscaled GPP product that incorporates the direct CO 2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05° resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO 2 effects. Our machine learning models effectively predict monthly GPP ( R 2 ∼ 0.72), the mean seasonal cycles ( R 2 ∼ 0.77), and spatial variabilities ( R 2 ∼ 0.63) based on cross-validation at flux sites. After incorporating the direct CO 2 effects, the predicted long-term GPP trend across global flux towers substantially increases from 3.1 to 4.5–5.4 gC m −2 yr −1 , which aligns more closely with the 7.7 gC m −2 yr −1 trend detected from eddy covariance data. While the global patterns of annual mean GPP, seasonality, and interannual variability generally align with existing satellite-based products, CEDAR-GPP demonstrates higher long-term trends globally after incorporating CO 2 fertilization and reflected a strong temperature control on direct CO 2 effects. The estimated global GPP trend is 0.57–0.76 PgC yr −1 from 2001 to 2018 and 0.32–0.34 PgC yr −1 from 1982 to 2018. Estimating and validating GPP trends in data-scarce regions, such as the tropics, remains challenging, underscoring the importance of ongoing ground-based monitoring and advancements in modeling techniques. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem–climate interactions. The CEDAR-GPP product is available at https://doi.org/10.5281/zenodo.8212706 (Kang et al., 2024).
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“As many sites are situated in heterogeneous landscapes, featuring multiple land cover types and diverse vegetation conditions, the mismatch between the fixed satellite pixel grid and the measurement footprint introduce substantial biases to the upscaling estimation [43], [50].”
From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [43]
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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 [43]
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MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006
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A constraint on historic growth in global photosynthesis due to rising CO2
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
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MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006
Myneni, R. B., Knyazikhin, Y. V., & Park, T. (2015) · Earth Observing System Data and Information System · doi:10.5067/modis/mcd15a3h.006 · Cited by 108
Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity
Kauwe, M. G. D., Keenan, T. F., Medlyn, B. E., Prentice, I. C., & Terrer, C. (2016) · Nature Climate Change · doi:10.1038/nclimate3105 · Cited by 100
Self-citationAtmospheric deposition, CO2, and change in the land carbon sink
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Representativeness of FLUXNET Sites Across Latin America
Villarreal, S., & Vargas, R. (2021) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg006090 · Cited by 92
Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion
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Warm Winter 2020 Team, & ICOS Ecosystem Thematic Centre (2022) · ICOS Carbon Portal · doi:10.18160/2g60-zhak · Cited by 73
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Mapping the global distribution of C4 vegetation using observations and optimality theory
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Self-citationTechnical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set
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MYD11A1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006
Wan, Z., Hook, S., & Hulley, G. (2015) · Earth Observing System Data and Information System · doi:10.5067/modis/myd11a1.006 · Cited by 56
Environmental controls on the light use efficiency of terrestrial gross primary production
Bloomfield, K. J., Stocker, B. D., Keenan, T. F., & Prentice, I. C. (2022) · Global Change Biology · doi:10.1111/gcb.16511 · Cited by 52
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How representative are FLUXNET measurements of surface fluxes during temperature extremes?
van der Horst, S. V. J., Pitman, A. J., Kauwe, M. G. D., Ukkola, A., Abramowitz, G., & Isaac, P. R. (2019) · Biogeosciences · doi:10.5194/bg-16-1829-2019 · Cited by 28
NOAA GML CCGG Group (2019) · Global Monitoring Laboratory · doi:10.15138/yaf1-bk21 · Cited by 28
AmeriFlux US-Uaf University of Alaska, Fairbanks
Ueyama, M., Iwata, H., & Harazono, Y. (2018) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1480322 · Cited by 27
ISLSCP II C4 VEGETATION PERCENTAGE
Berry, J. A., Collatz, G. J., DeFries, R., & Still, C. J. (2009) · doi:10.3334/ornldaac/932 · Cited by 27
Using automated machine learning for the upscaling of gross primary productivity
Gaber, M., Kang, Y., Schurgers, G., & Keenan, T. F. (2024) · Biogeosciences · doi:10.5194/bg-21-2447-2024 · Cited by 26
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AmeriFlux US-Prr Poker Flat Research Range Black Spruce Forest
Iwahana, G., Kobayashi, H., Ikawa, H., & Suzuki, R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1246153 · Cited by 20
FLUXNET2015 US-SRM Santa Rita Mesquite
Scott, R. L. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440090 · Cited by 20
FLUXNET2015 US-PFa Park Falls/WLEF
Desai, A. R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440089 · Cited by 19
Drought-2018 ecosystem eddy covariance flux product for 52 stations in FLUXNET-Archive format
Drought 2018 Team, & ICOS Ecosystem Thematic Centre (2020) · ICOS Carbon Portal · doi:10.18160/yvr0-4898 · Cited by 19
AmeriFlux FLUXNET-1F US-MMS Morgan Monroe State Forest
Novick, K. A., & Phillips, R. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1854369 · Cited by 18
Knohl, A., Tiedemann, F., Kolle, O., Schulze, E., Anthoni, P. M., Kutsch, W. L., Herbst, M., & Siebicke, L. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440150 · Cited by 17
AmeriFlux FLUXNET-1F US-Me2 Metolius mature ponderosa pine
Hanson, C., Still, C., & Law, B. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1854368 · Cited by 16
FLUXNET2015 BR-Sa1 Santarem-Km67-Primary Forest
SALESKA, S. R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440032 · Cited by 16
AmeriFlux US-Dk2 Duke Forest-hardwoods
Oishi, C., Novick, K. A., & Stoy, P. C. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1246047 · Cited by 15
FLUXNET2015 US-WCr Willow Creek
Desai, A. R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440095 · Cited by 15
FLUXNET2015 US-Ne2 Mead - irrigated maize-soybean rotation site
Suyker, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440085 · Cited by 15
Guoyi, Z., & Yan, J. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440139 · Cited by 15
Ourcival, J. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440164 · Cited by 15
MCD43C4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 0.05Deg CMG V006
Schaaf, C., & Wang, Z. (2015) · Earth Observing System Data and Information System · doi:10.5067/modis/mcd43c4.006 · Cited by 15
plotbiomes: R package for plotting Whittaker biomes with ggplot2
Ştefan, V., & Levin, S. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7145244 · Cited by 15
FLUXNET2015 US-Twt Twitchell Island
Baldocchi, D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440106 · Cited by 14
FLUXNET2015 US-Cop Corral Pocket
Bowling, D. R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440100 · Cited by 14
FLUXNET2015 CA-SF3 Saskatchewan - Western Boreal, forest burned in 1998
Amiro, B. D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440048 · Cited by 14
FLUXNET2015 US-Ton Tonzi Ranch
Baldocchi, D., & Ma, S. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440092 · Cited by 14
FLUXNET2015 CH-Oe1 Oensingen grassland
Ammann, C. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440135 · Cited by 14
FLUXNET2015 CN-HaM Haibei Alpine Tibet site
Tang, Y., Kato, T., & Du, M. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440190 · Cited by 14
FLUXNET2015 AU-DaP Daly River Savanna
Beringer, J., & Hutley, L. B. (2016) · UWA Profiles and Research Repository (University of Western Australia) · doi:10.18140/flx/1440123 · Cited by 14
FLUXNET2015 IT-CA2 Castel d'Asso2
Sabbatini, S., Arriga, N., Gioli, B., & Papale, D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440231 · Cited by 14
Schneider, K., & Schmidt, M. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440217 · Cited by 14
AmeriFlux CA-TP4 Ontario - Turkey Point 1939 Plantation White Pine
Arain, M. A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1246012 · Cited by 13
Desai, A. R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440076 · Cited by 13
FLUXNET2015 AU-Wac Wallaby Creek
Beringer, J., Hutley, L. B., McGuire, D., & K.T., P. U. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440127 · Cited by 13
Beringer, J., & Hutley, L. B. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440197 · Cited by 13
FLUXNET2015 CN-Sw2 Siziwang Grazed (SZWG)
Shao, C. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440212 · Cited by 13
FLUXNET2015 DE-SfN Schechenfilz Nord
Klatt, J., Schmid, H. P., Mauder, M. R., & Steinbrecher, R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440219 · Cited by 13
FLUXNET2015 ES-LgS Laguna Seca
Reverter, B. R., Sánchez‐Cañete, E. P., & Kowalski, A. S. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440225 · Cited by 13
AmeriFlux FLUXNET-1F US-ARM ARM Southern Great Plains site- Lamont
Biraud, S. C., Fischer, M., Chan, S., & Torn, M. S. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1854366 · Cited by 12
FLUXNET2015 CA-NS7 UCI-1998 burn site
Goulden, M. L. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440042 · Cited by 12
FLUXNET2015 US-Me3 Metolius-second young aged pine
Law, B. E. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440080 · Cited by 12
Ardö, J., Tahir, B. A. E., & ElKhidir, H. A. M. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440186 · Cited by 12
FLUXNET2015 AU-ASM Alice Springs
Cleverly, J., & Eamus, D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440194 · Cited by 12
FLUXNET2015 AU-DaS Daly River Cleared
Beringer, J., & Hutley, L. B. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440122 · Cited by 12
FLUXNET2015 GL-ZaH Zackenberg Heath
Lund, M., Jackowicz-Korczyński, M., & Abermann, J. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440224 · Cited by 12
Dong, G. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440209 · Cited by 12
AmeriFlux FLUXNET-1F US-KFS Kansas Field Station
Brunsell, N. A. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1881585 · Cited by 11
AmeriFlux US-Ho1 Howland Forest (main tower)
Ouimette, A., & Ruzol, R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1246061 · Cited by 11
FLUXNET2015 US-Ha1 Harvard Forest EMS Tower (HFR1)
Munger, J. W. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440071 · Cited by 11
Ewers, B. E., & Pendall, E. G. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440115 · Cited by 11
Dolman, A. J., Van Der Molen, M., Parmentier, F.-J., Marchesini, L. B., Dean, J. F., van Huissteden, K., & Maximov, T. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440182 · Cited by 11
Wohlfahrt, G., Hammerle, A., & Hörtnagl, L. J. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440121 · Cited by 11
Matteucci, G. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440167 · Cited by 11
Kang, Y., Gaber, M., Bassiouni, M., Lu, X., & Keenan, T. F. (2023) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8212706 · Cited by 11
Self-citationA Practical Algorithm for Correcting Topographical Effects on Global GPP Products
Xie, X., Chen, J. M., Yuan, W., Guan, X., Jin, H., & Leng, J. (2023) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2023jg007553 · Cited by 10
AmeriFlux FLUXNET-1F US-Ro1 Rosemount- G21
Baker, J., Griffis, T., & Griffis, T. J. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1881588 · Cited by 10
Charting the Future of the FLUXNET Network
Delwiche, K., Nelson, J. A., Kowalska, N., Moore, C. E., Shirkey, G., Tarín, T., Cleverly, J., & Keenan, T. F. (2024) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-23-0316.1 · Cited by 10
Self-citationAmeriFlux US-Dk3 Duke Forest - loblolly pine
Oishi, C., Novick, K. A., & Stoy, P. C. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1246048 · Cited by 10
FLUXNET2015 US-Myb Mayberry Wetland
Sturtevant, C., Szutu, D., Baldocchi, D., Matthes, J. H., Oikawa, P. Y., & Chamberlain, S. D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440105 · Cited by 10
FLUXNET2015 BR-Sa3 Santarem-Km83-Logged Forest
Goulden, M. L. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440033 · Cited by 10
FLUXNET2015 US-Whs Walnut Gulch Lucky Hills Shrub
Scott, R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440097 · Cited by 10
Posse, G., Lewczuk, N. A., Richter, K., & Cristiano, P. M. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440192 · Cited by 10
FLUXNET2015 CZ-BK2 Bily Kriz grassland
Šigut, L., Havránková, K., Jocher, G., Pavelka, M., & Janouš, D. (2016) · ASEP · doi:10.18140/flx/1440144 · Cited by 10
Aurela, M., Tuovinen, J.-P., Hatakka, J., Lohila, A., Mäkelä, T., Rainne, J., & Lauria, T. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440160 · Cited by 10
AmeriFlux FLUXNET-1F US-Rms RCEW Mountain Big Sagebrush
Flerchinger, G. N. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1881587 · Cited by 9
AmeriFlux FLUXNET-1F US-Tw1 Twitchell Wetland West Pond
Valach, A. C., Shortt, R., Szutu, D., Eichelmann, E., Knox, S., Hemes, K. S., Verfaillie, J., & Baldocchi, D. (2021) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1832165 · Cited by 9
FLUXNET2015 US-Ne1 Mead - irrigated continuous maize site
Suyker, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440084 · Cited by 9
FLUXNET2015 US-WPT Winous Point North Marsh
Chen, J., & Chu, H. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440116 · Cited by 9
FLUXNET2015 NL-Hor Horstermeer
Dolman, A. J., Hendriks, D., Parmentier, F.-J., Marchesini, L. B., Dean, J. F., & van Huissteden, K. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440177 · Cited by 9
Meyer, W., Cale, P., Koerber, G. R., Ewenz, C., & Sun, Q. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440195 · Cited by 9
FLUXNET2015 CN-Cha Changbaishan
Zhang, J., & Han, S. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440137 · Cited by 9
Arndt, S. K., Hinko‐Najera, N., & Griebel, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440207 · Cited by 9
FLUXNET2015 JP-SMF Seto Mixed Forest Site
Kotani, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440239 · Cited by 9
Bernhofer, C., Grünwald, T., Moderow, U., Hehn, M., Eichelmann, U., & Prasse, H. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440220 · Cited by 9
FLUXNET2015 MY-PSO Pasoh Forest Reserve (PSO)
Kosugi, Y., & Takanashi, S. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440240 · Cited by 9
AmeriFlux FLUXNET-1F US-Rws Reynolds Creek Wyoming big sagebrush
Flerchinger, G. N. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1881592 · Cited by 8
AmeriFlux FLUXNET-1F US-Mpj Mountainair Pinyon-Juniper Woodland
Litvak, M. E. (2021) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1832161 · Cited by 8
AmeriFlux CA-Ca3 British Columbia - Pole sapling Douglas-fir stand
Black, T. A. (2018) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/1480302 · Cited by 8
AmeriFlux FLUXNET-1F US-UMB Univ. of Mich. Biological Station
Classen, A., Gough, C., Bohrer, G., Lenters, J., & Curtis, P. S. (2023) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.17190/amf/2204882 · Cited by 8
FLUXNET2015 US-Blo Blodgett Forest
Goldstein, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440068 · Cited by 8
FLUXNET2015 US-Syv Sylvania Wilderness Area
Desai, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440091 · Cited by 8
FLUXNET2015 US-Ne3 Mead - rainfed maize-soybean rotation site
Suyker, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440086 · Cited by 8
FLUXNET2015 US-Var Vaira Ranch- Ione
Baldocchi, D., Ma, S., & Xu, L. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440094 · Cited by 8
FLUXNET2015 IT-Ro2 Roccarespampani 2
Papale, D., Tirone, G., Valentini, R., Arriga, N., Belelli, L., CONSALVO, C., Dore, S., Manca, G., Mazzenga, F., Sabbatini, S., et al. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440175 · Cited by 8
FLUXNET2015 IT-Noe Arca di Noe - Le Prigionette
Spano, D., Duce, P., Marras, S., Sirca, C., Arca, A., Zara, P., & Ventura, A. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440171 · Cited by 8
Paul Berbigier, E., & Loustau, D. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440163 · Cited by 8
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Belelli, L., Papale, D., & Valentini, R. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.18140/flx/1440184 · Cited by 8
Research works printed but not linked: 2
- Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, California, USA, 4768–4777, https://dl.acm.org/doi/10.5555/3295222.3295230 (last access: 31 March 2025), 2017. · DOI
- Zhang, Y.: A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000–2020), National Tibetan Plateau Data Center [data set], 10.11888/Ecolo.tpdc.271751., 2021. · DOI
Cited by
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Prithvi-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
“However, these approaches face challenges due to scale mismatches between in-situ measurements and satellite data, as well as generalization issues, which could potentially be mitigated by using foundation models like Prithvi that embed generalized representations of high-resolution data [39].”
Self-citationLi, C., Zhang, D., Zhang, S., Wen, Y., Wang, W., Chen, Y., & Peng, J. X. (2025) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2024jg008605 · Cited by 16
Bilir, T. E., Bloom, A. A., Konings, A. G., Liu, J., Parazoo, N. C., Quetin, G. R., Norton, A., Worden, M. A., Levine, P. A., Ma, S., et al. (2025) · AGU Advances · doi:10.1029/2025av001689 · Cited by 8
Self-citationChu, H., Metzger, S., Ouyang, Z., Griebel, A., Yi, K., Durden, D., Wolf, S., Kasak, K., Rey‐Sánchez, C., Rodriguez, L. C. H., et al. (2026) · Global Change Biology · doi:10.1111/gcb.70887 · Cited by 7
Generalizing Tree–Level Sap Flow Across the European Continent
Loritz, R., Wu, C. H., Klotz, D., Gauch, M., Kratzert, F., & Bassiouni, M. (2024) · Geophysical Research Letters · doi:10.1029/2023gl107350 · Cited by 7
Self-citation
Metadata
- Authors
- Kang, Y., Bassiouni, M., Gaber, M., Lu, X., & Keenan, T. F.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- University of Copenhagen (DK)
- Venue
- Earth System Science Data
- Publisher
- Copernicus GmbH
- Published
- 2025-06-30
- Volume
- 17
- Issue
- 6
- Pages
- 3009-3046
- ISSN
- 1866-3508 (ISSN-L)
- 1866-3516
- OpenAlex
- W4411792583
- Cited by
- 22
- References
- 292
- Citations by year
- 2026: 11 · 2025: 8
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Earth system science data (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- EarthArXiv (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Energy (DE-SC0021023)
- National Aeronautics and Space Administration (80NSSC21K1705, 80NSSC24K1562, 80NSSC20K1801, 80NSSC25K7327)
- U.S. Department of Agriculture (2023-67012-40086)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Keywords
- gross primary productivity, CO2 fertilization, terrestrial carbon sink, eddy covariance flux towers, seasonal cycle, interannual variability, temperature effects on photosynthesis, satellite observations, data-scarce regions, climate-ecosystem interactions, uncertainty estimation
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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