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Cited by 136Plant Water Relations and Carbon DynamicsAtmospheric and Environmental Gas DynamicsRemote Sensing in Agriculture

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

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Nelson, Jacob A., Walther, Sophia, Gans, Fabian, Kraft, Basil, Weber, Ulrich, Novick, Kimberly, Buchmann, Nina, Migliavacca, Mirco +77 more·2024·Biogeosciences
DOI 10.5194/bg-21-5079-2024

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Abstract

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO 2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ET T ), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr −1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr −1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10 3 ± 0.9×10 3 km 3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

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  • Present-day GPP estimates from the ESMs were evaluated against the FLUXCOM-X-BASE and MUSES GPP products.

    “We evaluate the present-day GPP estimates against FLUXCOM-X-BASE GPP product 31 , which uses machine learning trained on eddy covariance data to generate global estimates, and MUltiscale Satellite remotE Sensing (MUSES) GPP product 32 .”

    From Amazon dieback beyond the 21st century under high-emission scenarios by Earth System models (2025) · cites this paper as [31]

  • A combination of SAR and multispectral imagery is superior to multispectral imagery alone for biomass estimation, especially in locations with high biomass.

    “This result is consistent with existing findings that a combination of SAR and multispectral imagery is superior to multispectral imagery alone for biomass estimation, especially in locations with high biomass [48] [39].”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [48]

    Section 5 (page unavailable) proposes using SAR and lidar products related to biomass and canopy height to improve future carbon-flux estimates. Section 2.3 (page unavailable) describes the spectral imagery used, but neither passage compares SAR-plus-multispectral imagery with multispectral imagery alone for biomass estimation, including in high-biomass locations. Searches for that comparison found no supporting passage. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

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References 286 · Cited by 136

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  89. Euskirchen, E.: AmeriFlux FLUXNET-1F US-BZo Bonanza Creek Old Thermokarst Bog, [data set], https://doi.org/10.17190/amf/1881571, 2022a. a · DOI
  90. Euskirchen, E.: AmeriFlux FLUXNET-1F US-BZB Bonanza Creek Thermokarst Bog, [data set], https://doi.org/10.17190/amf/1881569, 2022b. a · DOI
  91. Euskirchen, E.: AmeriFlux FLUXNET-1F US-BZF Bonanza Creek Rich Fen, [data set], https://doi.org/10.17190/amf/1881570, 2022c. a · DOI
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  95. Ewers, B. and Pendall, E.: FLUXNET2015 US-Sta Saratoga, [data set], https://doi.org/10.18140/flx/1440115, 2016. a · DOI
  96. Fares, S.: FLUXNET2015 US-Lin Lindcove Orange Orchard, [data set], https://doi.org/10.18140/flx/1440107, 2016. a · DOI
  97. Flerchinger, G.: AmeriFlux FLUXNET-1F US-Rwf RCEW Upper Sheep Prescibed Fire, [data set], https://doi.org/10.17190/amf/1881591, 2022a. a · DOI
  98. Flerchinger, G.: AmeriFlux FLUXNET-1F US-Rws Reynolds Creek Wyoming Big Sagebrush, [data set], https://doi.org/10.17190/amf/1881592, 2022b. a · DOI
  99. Flerchinger, G.: AmeriFlux FLUXNET-1F US-Rms RCEW Mountain Big Sagebrush, [data set], https://doi.org/10.17190/amf/1881587, 2022c. a · DOI
  100. Flerchinger, G. and Reba, M.: AmeriFlux FLUXNET-1F US-Rwe RCEW Reynolds Mountain East, [data set], https://doi.org/10.17190/amf/1871143, 2022. a · DOI
  101. Forsythe, J., Kline, M., and O’Halloran, T.: AmeriFlux FLUXNET-1F US-HB1 North Inlet Crab Haul Creek, [data set], https://doi.org/10.17190/amf/1832160, 2021. a · DOI
  102. Franssen, H. H., Stöckli, R., Lehner, I., Rotenberg, E., and Seneviratne, S.: Energy balance closure of eddy-covariance data: A multisite analysis for European FLUXNET stations, Agr. Forest Meteorol., 150, 1553–1567, https://doi.org/10.1016/j.agrformet.2010.08.005, 2010. a · DOI
  103. Fratini, G., McDermitt, D. K., and Papale, D.: Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037–1051, https://doi.org/10.5194/bg-11-1037-2014, 2014. a · DOI
  104. Friedl, M. and Sulla-Menashe, D.: MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, https://doi.org/10.5067/MODIS/MCD12C1.006, 2015. a · DOI
  105. Garcia, A., Di Bella, C., Houspanossian, J., Magliano, P., Jobbágy, E., Posse, G., Fernández, R., and Nosetto, M.: FLUXNET2015 AR-SLu San Luis, [data set],https://doi.org/10.18140/flx/1440191, 2016. a · DOI
  106. Goldstein, A.: FLUXNET2015 US-Blo Blodgett Forest, [data set], https://doi.org/10.18140/flx/1440068, 2016. a · DOI
  107. Goslee, S.: AmeriFlux FLUXNET-1F US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site, [data set], https://doi.org/10.17190/amf/1881582, 2022. a · DOI
  108. Gough, C., Bohrer, G., and Curtis, P.: FLUXNET2015 US-UMB Univ. Of Mich. Biological Station, [data set], https://doi.org/10.18140/flx/1440093, 2016. a · DOI
  109. Gough, C., Bohrer, G., and Curtis, P.: AmeriFlux FLUXNET-1F US-UMd UMBS Disturbance, [data set], https://doi.org/10.17190/amf/1881597, 2022. a · DOI
  110. Goulden, M.: FLUXNET2015 CA-NS2 UCI-1930 Burn Site, [data set], https://doi.org/10.18140/flx/1440037, 2016a. a · DOI
  111. Goulden, M.: FLUXNET2015 CA-NS3 UCI-1964 Burn Site, [data set], https://doi.org/10.18140/flx/1440037, 2016b. a · DOI
  112. Goulden, M.: FLUXNET2015 CA-NS4 UCI-1964 Burn Site Wet, [data set], https://doi.org/10.18140/flx/1440039, 2016c. a · DOI
  113. Goulden, M.: FLUXNET2015 BR-Sa3 Santarem-Km83-Logged Forest, [data set], https://doi.org/10.18140/flx/1440033, 2016d. a · DOI
  114. Goulden, M.: FLUXNET2015 CA-NS6 UCI-1989 Burn Site, [data set], https://doi.org/10.18140/flx/1440041, 2016e. a · DOI
  115. Goulden, M.: FLUXNET2015 CA-NS7 UCI-1998 Burn Site, [data set], https://doi.org/10.18140/flx/1440042, 2016f. a · DOI
  116. Goulden, M.: FLUXNET2015 CA-NS5 UCI-1981 Burn Site, [data set], https://doi.org/10.18140/flx/1440040, 2016g. a · DOI
  117. Griffis, T. and Roman, T.: AmeriFlux FLUXNET-1F PE-QFR Quistococha Forest Reserve, [data set], https://doi.org/10.17190/amf/1832157, 2021. a · DOI
  118. Gruening, C., Goded, I., Cescatti, A., Manca, G., and Seufert, G.: FLUXNET2015 IT-SRo San Rossore, [data set], https://doi.org/10.17190/amf/1832157, 2016a. a · DOI
  119. Gruening, C., Goded, I., Cescatti, A., and Pokorska, O.: FLUXNET2015 IT-Isp Ispra ABC-IS, [data set], https://doi.org/10.18140/flx/1440234, 2016b. a · DOI
  120. Hansen, B. U.: FLUXNET2015 GL-NuF Nuuk Fen, [data set], https://doi.org/10.18140/flx/1440222, 2016. a · DOI
  121. Heinsch, F., Zhao, M., Running, S., Kimball, J., Nemani, R., Davis, K., Bolstad, P., Cook, B., Desai, A., Ricciuto, D., Law, B., Oechel, W., Kwon, H., Luo, H., Wofsy, S., Dunn, A., Munger, J., Baldocchi, D., Xu, L., Hollinger, D., Richardson, A., Stoy, P., Siqueira, M., Monson, R., Burns, S., and Flanagan, L.: Evaluation of Remote Sensing Based Terrestrial Productivity from MODIS Using Regional Tower Eddy Flux Network Observations, IEEE Trans. Geosci. Remote Sens., 44, 1908–1925, 2006. a
  122. Hinkle, R.: AmeriFlux FLUXNET-1F US-KS3 Kennedy Space Center (Salt Marsh), [data set], https://doi.org/10.17190/amf/1881586, 2022. a · DOI
  123. Hollinger, D.: AmeriFlux FLUXNET-1F US-Ho2 Howland Forest (West Tower), [data set], https://doi.org/10.17190/amf/1881581, 2022. a · DOI
  124. Huang, X., Xiao, J., Wang, X., and Ma, M.: Improving the Global MODIS GPP Model by Optimizing Parameters with FLUXNET Data, Agr. Forest Meteorol., 300, 108314, https://doi.org/10.1016/j.agrformet.2020.108314, 2021. a · DOI
  125. Huggins, D.: AmeriFlux FLUXNET-1F US-CF1 CAF-LTAR Cook East, [data set], https://doi.org/10.17190/amf/1832158, 2021. a · DOI
  126. Huggins, D.: AmeriFlux FLUXNET-1F US-CF3 CAF-LTAR Boyd North, [data set], https://doi.org/10.17190/amf/1881573, 2022a. a · DOI
  127. Huggins, D.: AmeriFlux FLUXNET-1F US-CF4 CAF-LTAR Boyd South, [data set], https://doi.org/10.17190/amf/1881575, 2022b. a · DOI
  128. Huggins, D.: AmeriFlux FLUXNET-1F US-CF2 CAF-LTAR Cook West, [data set], https://doi.org/10.17190/amf/1881573, 2022c. a · DOI
  129. Joiner, J. and Yoshida, Y.: Satellite-Based Reflectances Capture Large Fraction of Variability in Global Gross Primary Production (GPP) at Weekly Time Scales, Agr. Forest Meteorol., 291, 108092, https://doi.org/10.1016/j.agrformet.2020.108092, 2020. a · DOI
  130. Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a · DOI
  131. Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M., Vaccari, F., and Williams, C.: Global Patterns of Land-Atmosphere Fluxes of Carbon Dioxide, Latent Heat, and Sensible Heat Derived from Eddy Covariance, Satellite, and Meteorological Observations, J. Geophys. Res., 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011. a, b, c, d · DOI
  132. Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM Ensemble of Global Land-Atmosphere Energy Fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019. a, b, c, d · DOI
  133. Jung, M., Nelson, J., Migliavacca, M., El-Madany, T., Papale, D., Reichstein, M., Walther, S., and Wutzler, T.: Technical note: Flagging inconsistencies in flux tower data, Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, 2024. a, b · DOI
  134. Klatt, J., Schmid, H. P., Mauder, M., and Steinbrecher, R.: FLUXNET2015 DE-SfN Schechenfilz Nord, [data set], https://doi.org/10.18140/flx/1440219, 2016. a · DOI
  135. Knohl, A., Tiedemann, F., Kolle, O., Schulze, E.-D., Anthoni, P., Kutsch, W., Herbst, M., and Siebicke, L.: FLUXNET2015 DE-Lnf Leinefelde, [data set], https://doi.org/10.18140/flx/1440150, 2016. a · DOI
  136. Knox, S.: AmeriFlux FLUXNET-1F CA-DB2 Delta Burns Bog 2, [data set], https://doi.org/10.17190/amf/1881564, 2022. a · DOI
  137. Kobayashi, H. and Suzuki, R.: FLUXNET2015 US-Prr Poker Flat Research Range Black Spruce Forest, [data set], https://doi.org/10.18140/flx/1440113, 2016. a · DOI
  138. Kosugi, Y. and Takanashi, S.: FLUXNET2015 MY-PSO Pasoh Forest Reserve (PSO), [data set], https://doi.org/10.18140/flx/1440240, 2016. a · DOI
  139. Kotani, A.: FLUXNET2015 JP-SMF Seto Mixed Forest Site, [data set], https://doi.org/10.18140/flx/1440239, 2016a. a · DOI
  140. Kotani, A.: FLUXNET2015 JP-MBF Moshiri Birch Forest Site, [data set], https://doi.org/10.18140/flx/1440238, 2016b. a · DOI
  141. Kurc, S.: AmeriFlux FLUXNET-1F US-SRC Santa Rita Creosote, [data set], https://doi.org/10.17190/amf/1871145, 2022. a · DOI
  142. Kusak, K., Sanchez, C., Szutu, D., and Baldocchi, D.: AmeriFlux FLUXNET-1F US-Snf Sherman Barn, [data set], https://doi.org/10.17190/amf/1854371, 2022. a · DOI
  143. Kutzbach, L.: AmeriFlux FLUXNET-1F AR-TF1 Rio Moat Bog, [data set], https://doi.org/10.17190/amf/1818370, 2021. a · DOI
  144. Köhler, P., Frankenberg, C., Magney, T. S., Guanter, L., Joiner, J., and Landgraf, J.: Global Retrievals of Solar‐Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO‐2, Geophys. Res. Lett., 45, e2020GL087541, https://doi.org/10.1029/2020GL087541, 2018. a, b · DOI
  145. Law, B.: FLUXNET2015 US-Me3 Metolius-second Young Aged Pine, [data set], https://doi.org/10.18140/flx/1440080, 2016a. a · DOI
  146. Law, B.: FLUXNET2015 US-Me6 Metolius Young Pine Burn, [data set], https://doi.org/10.18140/flx/1440099, 2016b. a · DOI
  147. Law, B.: FLUXNET2015 US-Me1 Metolius – Eyerly Burn, [data set], https://doi.org/10.18140/flx/1440078, 2016c. a · DOI
  148. Law, B.: FLUXNET2015 US-Me5 Metolius-first Young Aged Pine, [data set], https://doi.org/10.18140/flx/1440082, 2016d. a · DOI
  149. Law, B.: FLUXNET2015 US-Me4 Metolius-old Aged Ponderosa Pine, [data set], https://doi.org/10.18140/flx/1440081, 2016e. a · DOI
  150. Law, B.: AmeriFlux FLUXNET-1F US-Me2 Metolius Mature Ponderosa Pine, [data set], https://doi.org/10.17190/amf/1854368, 2022. a · DOI
  151. Leuning, R., van Gorsel, E., Massman, W. J., and Isaac, P. R.: Reflections on the surface energy imbalance problem, Agr. Forest Meteorol., 156, 65–74, https://doi.org/10.1016/j.agrformet.2011.12.002, 2012. a · DOI
  152. Liddell, M. J.: FLUXNET2015 AU-Rob Robson Creek, Queensland, Australia, [data set], https://doi.org/10.18140/flx/1440203, 2016. a · DOI
  153. Lindauer, M., Steinbrecher, R., Wolpert, B., Mauder, M., and Schmid, H. P.: FLUXNET2015 DE-Lkb Lackenberg, [data set], https://doi.org/10.18140/flx/1440214 2016. a · DOI
  154. Litvak, M.: AmeriFlux FLUXNET-1F US-Mpj Mountainair Pinyon-Juniper Woodland, [data set], https://doi.org/10.17190/amf/1832161, 2021. a · DOI
  155. Litvak, M.: AmeriFlux FLUXNET-1F US-Wjs Willard Juniper Savannah, [data set], https://doi.org/10.17190/amf/1832161, 2022. a · DOI
  156. Liu, H., Huang, M., and Chen, X.: AmeriFlux FLUXNET-1F US-Hn3 Hanford 100H Sagebrush, [data set], https://doi.org/10.17190/amf/1881580, 2022. a · DOI
  157. Lohila, A., Aurela, M., Tuovinen, J.-P., Hatakka, J., and Laurila, T.: FLUXNET2015 FI-Jok Jokioinen, [data set], https://doi.org/10.18140/flx/1440159, 2016. a · DOI
  158. Lund, M., Jackowicz-Korczyński, M., and Abermann, J.: FLUXNET2015 GL-ZaH Zackenberg Heath, [data set], https://doi.org/10.18140/flx/1440224, 2016a. a · DOI
  159. Lund, M., Jackowicz-Korczyński, M., and Abermann, J.: FLUXNET2015 GL-ZaF Zackenberg Fen, [data set], https://doi.org/10.18140/flx/1440223, 2016b. a · DOI
  160. Macfarlane, C., Lambert, P., Byrne, J., Johnstone, C., and Smart, N.: FLUXNET2015 AU-Gin Gingin, [data set], https://doi.org/10.18140/flx/1440199, 2016. a · DOI
  161. Manca, G. and Goded, I.: FLUXNET2015 IT-PT1 Parco Ticino Forest, [data set], https://doi.org/10.18140/flx/1440172, 2016. a · DOI
  162. Margolis, H. A.: FLUXNET2015 CA-Qfo Quebec – Eastern Boreal, Mature Black Spruce, [data set], https://doi.org/10.18140/flx/14400452016. a · DOI
  163. Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a · DOI
  164. Massman, B.: FLUXNET2015 US-GBT GLEES Brooklyn Tower, [data set], https://doi.org/10.18140/flx/1440118, 2016. a · DOI
  165. Massman, B.: AmeriFlux FLUXNET-1F US-GLE GLEES, [data set], https://doi.org/10.17190/amf/1871136, 2022. a · DOI
  166. Matamala, R.: FLUXNET2015 US-IB2 Fermi National Accelerator Laboratory- Batavia (Prairie Site), [data set], https://doi.org/10.18140/flx/1440072, 2016. a · DOI
  167. Matteucci, G.: FLUXNET2015 IT-Col Collelongo, [data set], https://doi.org/10.18140/flx/1440167, 2016. a · DOI
  168. Mauder, M., Foken, T., and Cuxart, J.: Surface-Energy-Balance Closure over Land: A Review, Bound.-Lay. Meteorol., 177, 395–426, https://doi.org/10.1007/s10546-020-00529-6, 2020. a · DOI
  169. McCaughey, H.: FLUXNET2015 CA-Gro Ontario – Groundhog River, Boreal Mixedwood Forest, [data set], https://doi.org/10.18140/flx/1440034, 2016. a · DOI
  170. McGloin, R., Šigut, L., Havránková, K., Dušek, J., Pavelka, M., and Sedlák, P.: Energy balance closure at a variety of ecosystems in Central Europe with contrasting topographies, Agr. Forest Meteorol., 248, 418–431, https://doi.org/10.1016/j.agrformet.2017.10.003, 2018. a · DOI
  171. Merbold, L., Rebmann, C., and Corradi, C.: FLUXNET2015 RU-Che Cherski, [data set], https://doi.org/10.18140/flx/1440181, 2016. a · DOI
  172. Meyer, W., Cale, P., Koerber, G., Ewenz, C., and Sun, Q.: FLUXNET2015 AU-Cpr Calperum, [data set], https://doi.org/10.18140/flx/1440195, 2016. a · DOI
  173. Meyers, T.: FLUXNET2015 US-LWW Little Washita Watershed, [data set], https://doi.org/10.18140/flx/1440070, 2016a. a · DOI
  174. Meyers, T.: FLUXNET2015 US-Goo Goodwin Creek, [data set], https://doi.org/10.18140/flx/1440070, 2016b. a · DOI
  175. Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a · DOI
  176. Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, 2016. a · DOI
  177. Munassar, S., Rödenbeck, C., Koch, F.-T., Totsche, K. U., Gałkowski, M., Walther, S., and Gerbig, C.: Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system, Atmos. Chem. Phys., 22, 7875–7892, https://doi.org/10.5194/acp-22-7875-2022, 2022. a · DOI
  178. Munger, J. W.: FLUXNET2015 US-Ha1 Harvard Forest EMS Tower (HFR1), [data set], https://doi.org/10.18140/flx/1440071, 2016. a · DOI
  179. Musavi, T., Migliavacca, M., Reichstein, M., Kattge, J., Wirth, C., Black, T. A., Janssens, I., Knohl, A., Loustau, D., Roupsard, O., Varlagin, A., Rambal, S., Cescatti, A., Gianelle, D., Kondo, H., Tamrakar, R., and Mahecha, M. D.: Stand Age and Species Richness Dampen Interannual Variation of Ecosystem-Level Photosynthetic Capacity, Nat. Ecol. Evol., 1, 0048, https://doi.org/10.1038/s41559-016-0048, 2017. a · DOI
  180. Nelson, J. A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., and Zhang, W.: FLUXCOM-X-BASE, ICOS [data set], https://doi.org/10.18160/5NZG-JMJE, 2023. a · DOI
  181. Network, N.: AmeriFlux FLUXNET-1F US-xBR NEON Bartlett Experimental Forest (BART), [data set], https://doi.org/10.17190/amf/1881598, 2022. a · DOI
  182. Nouvellon, Y.: FLUXNET2015 CG-Tch Tchizalamou, [data set], https://doi.org/10.18140/flx/1440142, 2016. a · DOI
  183. Novick, K. and Phillips, R.: AmeriFlux FLUXNET-1F US-MMS Morgan Monroe State Forest, [data set], https://doi.org/10.17190/amf/1854369, 2022. a · DOI
  184. Oikawa, P.: AmeriFlux FLUXNET-1F US-EDN Eden Landing Ecological Reserve, [data set], https://doi.org/10.17190/amf/1832159, 2021. a · DOI
  185. Olesen, J.: FLUXNET2015 DK-Fou Foulum, [data set], https://doi.org/10.18140/flx/1440154, 2016. a · DOI
  186. Ourcival, J.-M.: FLUXNET2015 FR-Pue Puechabon, [data set], https://doi.org/10.18140/flx/1440164, 2016. a · DOI
  187. Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., and Yakir, D.: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation, Biogeosciences, 3, 571–583, https://doi.org/10.5194/bg-3-571-2006, 2006. a · DOI
  188. Papale, D., Tirone, G., Valentini, R., Arriga, N., Belelli, L., Consalvo, C., Dore, S., Manca, G., Mazzenga, F., Sabbatini, S., and Stefani, P.: FLUXNET2015 IT-Ro2 Roccarespampani 2, [data set], https://doi.org/10.18140/flx/1440175, 2016. a · DOI
  189. Pendall, E. and Griebel, A.: FLUXNET2015 AU-Cum Cumberland Plains, [data set], https://doi.org/10.18140/flx/1440196, 2016. a · DOI
  190. Pilegaard, K. and Ibrom, A.: FLUXNET2015 DK-Eng Enghave, [data set], https://doi.org/10.18140/flx/1440153, 2016. a · DOI
  191. Posse, G., Lewczuk, N., Richter, K., and Cristiano, P.: FLUXNET2015 AR-Vir Virasoro, [data set], https://doi.org/10.18140/flx/1440192, 2016. a · DOI
  192. Poveda, F. D., Ballesteros, A. L., Cañete, E. P. S., Ortiz, P. S., Jiménez, M. R. M., Priego, O. P., and Kowalski, A. S.: FLUXNET2015 ES-Amo Amoladeras, [data set], https://doi.org/10.18140/flx/1440156, 2016. a · DOI
  193. Randerson, J., Van der werf, G., Giglio, L., Collatz, G., and Kasibhatla, P.: Global Fire Emissions Database, Version 4.1 (GFEDv4), [data set], https://doi.org/10.3334/ornldaac/1293, 2017. a, b · DOI
  194. Rannik, U., Peltola, O., and Mammarella, I.: Random uncertainties of flux measurements by the eddy covariance technique, Atmos. Meas. Tech., 9, 5163–5181, https://doi.org/10.5194/amt-9-5163-2016, 2016. a · DOI
  195. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the Separation of Net Ecosystem Exchange into Assimilation and Ecosystem Respiration: Review and Improved Algorithm, Glob. Change Biol., 11, 1424–1439, 2005. a, b
  196. Reverter, B. R., Perez-Cañete, E. S., and Kowalski, A. S.: FLUXNET2015 ES-Ln2 Lanjaron-Salvage Logging, [data set], https://doi.org/10.18140/flx/1440225, 2016a. a · DOI
  197. Reverter, B. R., Perez-Cañete, E. S., and Kowalski, A. S.: FLUXNET2015 ES-LgS Laguna Seca, [data set], https://doi.org/10.18140/flx/1440225, 2016b. a · DOI
  198. Rey-Sanchez, C., Wang, C., Szutu, D., Hemes, K., Verfaillie, J., and Baldocchi, D.: AmeriFlux FLUXNET-1F US-Bi2 Bouldin Island Corn, [data set], https://doi.org/10.18140/flx/1440225, 2022a. a · DOI
  199. Rey-Sanchez, C., Wang, C., Szutu, D., Shortt, R., Chamberlain, S., Verfaillie, J., and Baldocchi, D.: AmeriFlux FLUXNET-1F US-Bi1 Bouldin Island Alfalfa, [data set], https://doi.org/10.17190/amf/1871134, 2022b. a · DOI
  200. RI, I.: Ecosystem Final Quality (L2) Product in ETC-Archive Format – Release 2021-1, [data set], https://doi.org/10.18160/fzmy-pg92, 2021. a · DOI

200 of 268 shown

Authors
Nelson, J. A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L., Ibrom, A., Papale, D., Göckede, M., Duveiller, G., Knohl, A., Hörtnagl, L., Scott, R. L., Dušek, J., Zhang, W., Hamdi, Z. M., Reichstein, M., Aranda-Barranco, S., Ardö, J., Op de Beeck, M., Billesbach, D., Bowling, D., Bracho, R., Brümmer, C., Camps-Valls, G., Chen, S., Cleverly, J. R., Desai, A., Dong, G., El-Madany, T. S., Euskirchen, E. S., Feigenwinter, I., Galvagno, M., Gerosa, G. A., Gielen, B., Goded, I., Goslee, S., Gough, C. M., Heinesch, B., Ichii, K., Jackowicz-Korczynski, M. A., Klosterhalfen, A., Knox, S., Kobayashi, H., Kohonen, K.-M., Korkiakoski, M., Mammarella, I., Gharun, M., Marzuoli, R., Matamala, R., Metzger, S., Montagnani, L., Nicolini, G., O'Halloran, T., Ourcival, J.-M., Peichl, M., Pendall, E., Ruiz Reverter, B., Roland, M., Sabbatini, S., Sachs, T., Schmidt, M., Schwalm, C. R., Shekhar, A., Silberstein, R., Silveira, M. L., Spano, D., Tagesson, T., Tramontana, G., Trotta, C., Turco, F., Vesala, T., Vincke, C., Vitale, D., Vivoni, E. R., Wang, Y., Woodgate, W., Yepez, E. A., Zhang, J., Zona, D., & Jung, M.
Affiliations
  • Max Planck Institute for Biogeochemistry (DE)
  • Indiana University Bloomington (US)
  • ETH Zurich (CH)
  • European Commission (BE)
  • Universität Innsbruck (AT)
  • Czech Academy of Sciences, Global Change Research Institute (CZ)
  • Technical University of Denmark (DK)
  • CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT)
  • University of Göttingen (DE)
  • Southwest Watershed Research Center (US)
  • German Centre for Integrative Biodiversity Research (DE)
  • Universidad de Granada (ES)
  • Lund University (SE)
  • University of Nebraska–Lincoln (US)
  • University of Utah (US)
  • University of Florida (US)
  • Johann Heinrich von Thünen-Institut (DE)
  • Chinese Academy of Sciences (CN)
  • University of Wisconsin–Madison (US)
  • Shanxi University (CN)
  • University of Alaska Fairbanks (US)
  • Agenzia Regionale Protezione Ambiente Valle d'Aosta (IT)
  • Università Cattolica del Sacro Cuore (IT)
  • University of Antwerp (BE)
  • Agricultural Research Service (US)
  • Virginia Commonwealth University (US)
  • University of Liège (BE)
  • Chiba University (JP)
  • University of British Columbia (CA)
  • Japan Agency for Marine-Earth Science and Technology (JP)
  • Finnish Meteorological Institute (FI)
  • University of Helsinki (FI)
  • University of Münster (DE)
  • Argonne National Laboratory (US)
  • Battelle (US)
  • Free University of Bozen-Bolzano (IT)
  • Università degli Studi della Tuscia (IT)
  • Clemson University (US)
  • Centre National de la Recherche Scientifique (FR)
  • Swedish University of Agricultural Sciences (SE)
  • Western Sydney University (AU)
  • GFZ Helmholtz Centre for Geosciences (DE)
  • Forschungszentrum Jülich (DE)
  • Woodwell Climate Research Center (US)
  • Edith Cowan University (AU)
  • University of Copenhagen (DK)
  • UCLouvain (BE)
  • Sapienza University of Rome (IT)
  • Arizona State University (US)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • San Diego State University (US)
Corresponding author
Walther, Sophia
ORCID
Venue
Biogeosciences
Publisher
Copernicus GmbH
Published
2024-11-18
Volume
21
Issue
22
Pages
5079-5115
ISSN
  • 1726-4170 (ISSN-L)
  • 1726-4189
DOI
10.5194/bg-21-5079-2024
OpenAlex
W4404490237
Cited by
136
References
286
Citations by year
2026: 67 · 2025: 66 · 2024: 3
FWCI
23.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Biogeosciences (publishedVersion, cc-by)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • Digital Access to Libraries (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UnitusOpen (Tuscia University) (submittedVersion, cc-by)
  • Työväentutkimus Vuosikirja (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
  • UnissResearch (Università degli Studi di Sassari) (submittedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
  • Open Repository and Bibliography (University of Liège) (submittedVersion, cc-by)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion)
  • PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, other-oa)
  • Scopus (Elsevier) (submittedVersion, cc-by)
  • Open Research (University of Surrey) (submittedVersion, cc-by)
  • OpenAgrar (submittedVersion, cc-by)
Funders
  • National Science Foundation (2149988, 1932900, DEB LTREB 1354370, 2011257, DEB-0425328, DEB-0724514, DEB-0830997, 1936752, 1503912, 1107892, 1822420, 2313772)
  • Deutsche Forschungsgemeinschaft (INST 186/1118-1 FUGG)
  • Niedersächsische Ministerium für Wissenschaft und Kultur (ZN 3679)
  • Horizon 2020 (774124, 951288, 855187, 958927, 776810, 820852, 776186)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (40FA40_154245, 146373, 407340_172433, 200021_197357, 100018L_200918, 20FI21_148992, 20FI20_173691, 20FI20_198227)
  • ETH Zürich Foundation (DONA)
  • Eidgenössische Technische Hochschule Zürich (FEVER ETH-27 19-1)
  • Agencia Estatal de Investigación (PID2020-117825GB-C21, PID2020-117825GB-C22)
  • Ministerio de Universidades (FPU19/01647)
  • Swedish National Space Agency (2021-00144)
  • Australian Research Council (DE190101182)
  • Research Council of Finland (N-PERM 341349, 337549)
  • Ministerstvo Školství, Mládeže a Tělovýchovy (LM2023048)
  • Ministry of Education, Culture, Sports, Science and Technology (JPMXD1420318865)
  • Helmholtz Association (VH-NG-821)
  • Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-04199, ALLRP 555468-20)
  • European Space Agency (Vad3e mecum, 4000134598/21/I-NB, 4000123553/18/I-NB)
  • HORIZON EUROPE Excellent Science (101059548, 20FI20_173691, 101079192, 101056921)
  • Vetenskapsrådet (2021-00644)
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
terrestrial carbon fluxes, water flux, net ecosystem exchange, gross primary productivity, evapotranspiration, transpiration, eddy covariance, carbon cycle, interannual variability, surface-atmosphere exchange, satellite observations, terrestrial biosphere

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