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Library

CarbonDew Library Initiative

We're embarking on a project to compile a comprehensive collection of resources where direct flux measurements, specifically those using eddy covariance approaches, have been applied for immediate and tangible societal benefits.

CarbonDew Library

Real-World Use Is Accelerating

Scientific publications documenting such uses rose from about 0.6 per year in 2002-2006 to nearly 10 per year in 2022-2026, while related news coverage rose from none to almost 6 items per year (Table 1). Together, these form the 160+ entries now tracked by the CarbonDew Library.

Literature References
Scientific publicationsAvg. per year
  1. 2022–2026*9.8
  2. 2017–20216.6
  3. 2012–20164.2
  4. 2007–20111.8
  5. 2002–20060.6
News itemsAvg. per year
  1. 2022–2026*5.8
  2. 2017–20212.6
  3. 2012–20161.0
  4. 2007–20110.6
  5. 2002–20060.0
Table 1
PeriodScientific publications · TotalAvg. per yearNews items · TotalAvg. per year
2022–2026*499.8295.8
2017–2021336.6132.6
2012–2016214.251.0
2007–201191.830.6
2002–200630.600.0

Table 1. Use of direct flux measurements in real-world applications for immediate societal benefits. * 2022–2026 is a partial period. Bars share one scale (0–9.8 per year).

Source: CarbonDew Newsletter · October 2026 · Four-Year Newsletter & Impact Update

If you've come across any publications, conference presentations, white papers, or websites that fit this description, we'd be thrilled if you could share them with us at founders@carbondew.org

Here's what we're looking for:

  • Any type of publication, presentation, or web resource that utilize eddy covariance, eddy accumulation, relaxed eddy accumulation, or disjunct eddy covariance.

  • Measurements that have been used for practical non-academic applications such as farm applications, forestry, gas leak detection, landfill modification, etc., as well as resources that have contributed to the development of regulatory policies, standards, protocols, and more.

  • Ideas and concepts for the measurements to be used for any type of practical non-academic applications.

Whenever possible, please send us links to full texts and open-access websites. If that's not possible, regular references are perfectly fine too.

We kindly ask you to focus on resources that are directly related to our cause. While we appreciate the broader context, for this project, we're specifically interested in concrete examples rather than hypothetical or philosophical insights.

While we may expand our focus to include other methods such as soil chambers and gradients in the future, our current focus is on the eddy methods.

Thank you so much for your time and contribution to this initiative. Your support is invaluable to us and the broader community we serve!

Literature References

  • Abraha, M., Gelfand, I., Hamilton, S.K., Chen, J. and Robertson, G.P., 2019. Carbon debt of field-scale conservation reserve program grasslands converted to annual and perennial bioenergy crops. Environmental Research Letters, 14(2), p.024019. DOI: 10.1088/1748-9326/aafc10
  • Al-Qasim, A.S., Alzayani, A., Almuaibid, A., Yousef, A., Burba, G., Fratini, G., Griessbaum, F. and Facius, D., 2023. The intelligent Carbon Leakage Surveillance (iCLS) System. Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC), Paper SPE-216502-MS. DOI: 10.2118/216502-MS
  • Alfieri, J.G., 2024. USDA LTAR Common Experiment measurement: surface fluxes of carbon dioxide, water vapor, and energy with the eddy covariance method. Version 1. protocols.io. DOI: 10.17504/protocols.io.5jyl8211dl2w/v1
  • Amiro, B.D., Barr, A.G., Barr, J.G., Black, T.A., Bracho, R., Brown, M., Chen, J., Clark, K.L., Davis, K.J., Desai, A.R. et al., 2010. Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research: Biogeosciences, 115(G4). DOI: 10.1029/2010JG001390
  • Anić, M., Ostrogović Sever, M.Z., Alberti, G., Balenović, I., Paladinić, E., Peressotti, A., Tijan, G., Večenaj, Ž., Vuletić, D. and Marjanović, H., 2018. Eddy covariance vs. biometric based estimates of net primary productivity of pedunculate oak (Quercus robur L.) forest in Croatia during ten years. Forests, 9(12), p.764. DOI: 10.3390/f9120764
  • Arias-Ortiz, A., Oikawa, P.Y., Carlin, J., Masqué, P., Shahan, J., Kanneg, S., Paytan, A. and Baldocchi, D.D., 2021. Tidal and nontidal marsh restoration: a trade-off between carbon sequestration, methane emissions, and soil accretion. Journal of Geophysical Research: Biogeosciences, 126(12), e2021JG006573. DOI: 10.1029/2021JG006573
  • Baldocchi, D.D., 2020. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Global Change Biology, 26(1), pp.242–260. DOI: 10.1111/gcb.14807
  • Bautista, N., Marino, B.D.V. and Munger, J.W., 2021. Science to commerce: a commercial-scale protocol for carbon trading applied to a 28-year record of forest carbon monitoring at the Harvard Forest. Land, 10(2), p.163. DOI: 10.3390/land10020163
  • Burba, G., 2022. Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications. Lincoln, NE: LI-COR Biosciences, 702 pp. ISBN: 978-0-578-97714-0. See at LI-COR
  • Burba, G., 2025a. Harvesting Carbon: Fields & Grasslands. A guide to utilizing direct flux measurements for assessment and verification of carbon sequestration and GHG emission rates over areas. ESS Open Archive, 42 pp. DOI: 10.22541/essoar.173884298.81896551/v1
  • Burba, G., 2025b. Harvesting Carbon: Forests, Orchards, & Wooded Wetlands. A guide to utilizing direct flux measurements for assessment and verification of carbon sequestration and GHG emission rates over areas. ESS Open Archive, 45 pp. DOI: 10.22541/essoar.173884304.47041905/v1
  • Burba, G., 2025c. Harvesting Carbon: Lakes, Ponds, & Wetlands. A guide to utilizing direct flux measurements for assessment and verification of carbon sequestration and GHG emission rates over areas. ESS Open Archive, 46 pp. DOI: 10.22541/essoar.174438462.24731911/v1
  • Burba, G., Brooke, A., Doggett, L., Feese, K., Goodding, J., Johnson, D., Leibbrandt, P., Miller, T., Rice, K. and Witt, M., 2021. Using eddy covariance method in disciplines beyond micrometeorology for scientific, regulatory, and commercial applications. EGU General Assembly 2021, EGU21-6272. DOI: 10.5194/egusphere-egu21-6272
  • Burba, G., Madsen, R. and Feese, K., 2013. Eddy covariance method for CO₂ emission measurements in CCUS applications: principles, instrumentation and software. Energy Procedia, 40, pp.329–336. DOI: 10.1016/j.egypro.2013.08.038
  • Burba, G., McDermitt, D., Xu, L., Li, J., Green, R., Chanton, J. and Welding, K., 2016. Using automated eddy-covariance stations for studying landfill methane emissions. Air & Waste Management Association, Air Quality Measurement Methods and Technology Conference 2016, pp.159–170. See at ResearchGate
  • Burba, G., Metzger, S., Awada, T., Demidov, O., Desai, A., Desjardins, R., Durden, D., Elston, J., Granat, R., Hemes, K., Kannenberg, S., Kayode, J.S., Koeppel, A., Koren, G., Lee, S.C., Mutambala, L., Mwape, A., Pashkin, V., Runkle, B., Schödel, S., Senthilvalavan, P., Shanahan, J. and Surendran, U.P., 2023. CarbonDew coordinated response to the draft federal strategy to advance greenhouse gas emissions measurement and monitoring for the agriculture and forest sectors. ESS Open Archive, 8 pp. DOI: 10.22541/essoar.169418018.80680586/v2
  • Campioli, M., Malhi, Y., Vicca, S., Luyssaert, S., Papale, D., Peñuelas, J., Reichstein, M., Migliavacca, M., Arain, M.A. and Janssens, I.A., 2016. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nature Communications, 7, p.13717. DOI: 10.1038/ncomms13717
  • Ceschia, E., Béziat, P., Dejoux, J.F., Aubinet, M., Bernhofer, C., Bodson, B., Buchmann, N., Carrara, A., Cellier, P., Di Tommasi, P. et al., 2010. Management effects on net ecosystem carbon and GHG budgets at European crop sites. Agriculture, Ecosystems & Environment, 139(3), pp.363–383. DOI: 10.1016/j.agee.2010.09.020
  • Chen, S., Liu, L., Ma, Y., Zhuang, Q. and Shurpali, N.J., 2024. Quantifying global wetland methane emissions with in situ methane flux data and machine learning approaches. Earth's Future, 12(11), e2023EF004330. DOI: 10.1029/2023EF004330
  • Christen, A., 2014. Atmospheric measurement techniques to quantify greenhouse gas emissions from cities. Urban Climate, 10, pp.241–260. DOI: 10.1016/j.uclim.2014.04.006
  • Chu, H., Luo, X., Ouyang, Z., Chan, W.S., Dengel, S., Biraud, S.C., Torn, M.S., Metzger, S., Kumar, J., Arain, M.A. et al., 2021. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology, 301–302, p.108350. DOI: 10.1016/j.agrformet.2021.108350
  • Davis, K.J., Deng, A., Lauvaux, T., Miles, N.L., Richardson, S.J., Sarmiento, D.P., Gurney, K.R., Hardesty, R.M., Bonin, T.A., Brewer, W.A. et al., 2017. The Indianapolis Flux Experiment (INFLUX): a test-bed for developing urban greenhouse gas emission measurements. Elementa: Science of the Anthropocene, 5, p.21. DOI: 10.1525/elementa.188
  • Delwiche, K.B., Knox, S.H., Malhotra, A., Fluet-Chouinard, E., McNicol, G., Feron, S., Ouyang, Z., Papale, D., Trotta, C., Canfora, E. et al., 2021. FLUXNET-CH₄: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands. Earth System Science Data, 13(7), pp.3607–3689. DOI: 10.5194/essd-13-3607-2021
  • Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C.S.B., Masson, V., Oleson, K.W., Velasco, E. and Wouters, H., 2017. Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city. Quarterly Journal of the Royal Meteorological Society, 143(704), pp.1581–1596. DOI: 10.1002/qj.3028
  • Dengel, S., Levy, P.E., Grace, J., Jones, S.K. and Skiba, U.M., 2011. Methane emissions from sheep pasture, measured with an open-path eddy covariance system. Global Change Biology, 17(12), pp.3524–3533. DOI: 10.1111/j.1365-2486.2011.02466.x
  • Desai, A.R., 2024. The invisible hand of carbon dioxide on forest productivity. Eos Editors' Highlights, 11 July. See at Eos
  • Deshmukh, C., Serça, D., Delon, C., Tardif, R., Demarty, M., Jarnot, C., Meyerfeld, Y., Chanudet, V., Guédant, P., Rode, W., Descloux, S. and Guérin, F., 2014. Physical controls on CH₄ emissions from a newly flooded subtropical freshwater hydroelectric reservoir: Nam Theun 2. Biogeosciences, 11(15), pp.4251–4269. DOI: 10.5194/bg-11-4251-2014
  • Diaz, M.B., Roberti, D.R., Carneiro, J.V., de Arruda Souza, V. and de Moraes, O.L.L., 2019. Dynamics of the superficial fluxes over a flooded rice paddy in southern Brazil. Agricultural and Forest Meteorology, 276–277, p.107650. DOI: 10.1016/j.agrformet.2019.107650
  • Dilley, M., Grimes, D. and Austin, M., 2024. Building trust in carbon markets through atmospheric-based greenhouse gas monitoring and verification. Research Report, March 2024. Zug: Hyphen Global AG, 41 pp. See at Hyphen (PDF)
  • Environmental Research & Education Foundation (EREF), 2026. Quantifying methane emissions from landfills using eddy covariance. Funded research project (2 years, US$249,773), Auburn University and State University of New York at Albany. See at EREF
  • Felber, R., Münger, A., Neftel, A. and Ammann, C., 2015. Eddy covariance methane flux measurements over a grazed pasture: effect of cows as moving point sources. Biogeosciences, 12(12), pp.3925–3940. DOI: 10.5194/bg-12-3925-2015
  • Gioli, B., Toscano, P., Lugato, E., Matese, A., Miglietta, F., Zaldei, A. and Vaccari, F.P., 2012. Methane and carbon dioxide fluxes and source partitioning in urban areas: the case study of Florence, Italy. Environmental Pollution, 164, pp.125–131. DOI: 10.1016/j.envpol.2012.01.019
  • Goettemoeller, J. and Gas Analysers Team, 2016. Tools for mapping greenhouse gas emissions in urban environments. International Environmental Technology, Sept/Oct 2016, 2 pp. See at Envirotech Online
  • Gold Standard, 2025. NMC 143 – Eddy covariance-based methodology for continuous quantification of greenhouse gas fluxes. Methodology concept (developer: Hyphen Global AG), published 10 January 2025. Geneva: Gold Standard Foundation. See at Gold Standard
  • Gurney, K. and Shepson, P., 2021. The power and promise of improved climate data infrastructure. Proceedings of the National Academy of Sciences, 118(35), e2114115118. DOI: 10.1073/pnas.2114115118
  • Haniff, M.H., Ibrahim, A., Jantan, N.M., Shahabudin, N., Mos, H., Afifah, A.R. and Yusup, Y., 2018. Influence of eddy covariance sensor height above the oil palm canopy on CO₂ and energy fluxes. Journal of Oil Palm Research, 30(1), pp.94–100. DOI: 10.21894/jopr.2018.0005
  • Haniff, M.H., Ibrahim, A., Jantan, N.M., Shahabudin, N., Mos, H. and Yusup, Y., 2016. Carbon dioxide and energy fluxes above an oil palm canopy in peninsular Malaysia. International Journal of Agronomy and Agricultural Research, 9(2), pp.137–146. See at ResearchGate
  • Hatala, J.A., Detto, M., Sonnentag, O., Deverel, S.J., Verfaillie, J. and Baldocchi, D.D., 2012. Greenhouse gas (CO₂, CH₄, H₂O) fluxes from drained and flooded agricultural peatlands in the Sacramento-San Joaquin Delta. Agriculture, Ecosystems & Environment, 150, pp.1–18. DOI: 10.1016/j.agee.2012.01.009
  • Helfter, C., Tremper, A.H., Halios, C.H., Kotthaus, S., Bjorkegren, A., Grimmond, C.S.B., Barlow, J.F. and Nemitz, E., 2016. Spatial and temporal variability of urban fluxes of methane, carbon monoxide and carbon dioxide above London, UK. Atmospheric Chemistry and Physics, 16(16), pp.10543–10557. DOI: 10.5194/acp-16-10543-2016
  • Hemes, K.S., Chamberlain, S.D., Eichelmann, E., Anthony, T., Valach, A., Kasak, K., Szutu, D., Verfaillie, J., Silver, W.L. and Baldocchi, D.D., 2019. Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands. Agricultural and Forest Meteorology, 268, pp.202–214. DOI: 10.1016/j.agrformet.2019.01.017
  • Hemes, K.S., Runkle, B.R.K., Novick, K.A., Baldocchi, D.D. and Field, C.B., 2021. An ecosystem-scale flux measurement strategy to assess natural climate solutions. Environmental Science & Technology, 55(6), pp.3494–3504. DOI: 10.1021/acs.est.0c06421
  • Hill, T., Chocholek, M. and Clement, R., 2017. The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how? Global Change Biology, 23(6), pp.2154–2165. DOI: 10.1111/gcb.13547
  • Hutley, L.B., Leuning, R., Beringer, J. and Cleugh, H.A., 2005. The utility of the eddy covariance techniques as a tool in carbon accounting: tropical savanna as a case study. Australian Journal of Botany, 53(7), pp.663–675. DOI: 10.1071/BT04147
  • ICOS ERIC, 2024. Fluxes – The European Greenhouse Gas Bulletin, Volume 3: Monitor, report & verify emissions to accelerate climate action. Helsinki: ICOS ERIC. DOI: 10.18160/FZ5X-FA8T
  • Jongen, H.J., Steeneveld, G.J., Beringer, J., Christen, A., Chrysoulakis, N., Fortuniak, K., Hong, J., Hong, J.W., Jacobs, C.M.J., Järvi, L., Meier, F., Pawlak, W., Roth, M., Theeuwes, N.E., Velasco, E., Vogt, R. and Teuling, A.J., 2022. Urban water storage capacity inferred from observed evapotranspiration recession. Geophysical Research Letters, 49(3), e2021GL096069. DOI: 10.1029/2021GL096069
  • Knox, S.H., Sturtevant, C., Matthes, J.H., Koteen, L., Verfaillie, J. and Baldocchi, D., 2015. Agricultural peatland restoration: effects of land-use change on greenhouse gas (CO₂ and CH₄) fluxes in the Sacramento-San Joaquin Delta. Global Change Biology, 21(2), pp.750–765. DOI: 10.1111/gcb.12745
  • Lewicki, J.L. and Hilley, G.E., 2009. Eddy covariance mapping and quantification of surface CO₂ leakage fluxes. Geophysical Research Letters, 36(21). DOI: 10.1029/2009GL040775
  • Lewicki, J.L., Hilley, G.E., Fischer, M.L., Pan, L., Oldenburg, C.M., Dobeck, L. and Spangler, L., 2009. Eddy covariance observations of surface leakage during shallow subsurface CO₂ releases. Journal of Geophysical Research: Atmospheres, 114(D12). DOI: 10.1029/2008JD011297
  • Lipson, M., Grimmond, S., Best, M., Chow, W.T.L., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B.G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J.P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E. and Ward, H.C., 2022. Harmonized gap-filled datasets from 20 urban flux tower sites. Earth System Science Data, 14(11), pp.5157–5178. DOI: 10.5194/essd-14-5157-2022
  • Maboni, C., Bremm, T., Aguiar, L.J.G., Scivittaro, W.B., de Arruda Souza, V., Zimermann, H.R., Teichrieb, C.A., de Oliveira, P.E.S., Herdies, D.L., Degrazia, G.A. and Roberti, D.R., 2021. The fallow period plays an important role in annual CH₄ emission in a rice paddy in southern Brazil. Sustainability, 13(20), p.11336. DOI: 10.3390/su132011336
  • Marras, S., Masia, S., Duce, P., Spano, D. and Sirca, C., 2015. Carbon footprint assessment on a mature vineyard. Agricultural and Forest Meteorology, 214–215, pp.350–356. DOI: 10.1016/j.agrformet.2015.08.270
  • McNicol, G., Fluet-Chouinard, E., Ouyang, Z., Knox, S., Zhang, Z., Aalto, T., Bansal, S., Chang, K.-Y., Chen, M., Delwiche, K. et al., 2023. Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): model development, network assessment, and budget comparison. AGU Advances, 4(5), e2023AV000956. DOI: 10.1029/2023AV000956
  • Meili, N., Manoli, G., Burlando, P., Carmeliet, J., Chow, W.T.L., Coutts, A.M., Roth, M., Velasco, E., Vivoni, E.R. and Fatichi, S., 2021. Tree effects on urban microclimate: diurnal, seasonal, and climatic temperature differences explained by separating radiation, evapotranspiration, and roughness effects. Urban Forestry & Urban Greening, 58, p.126970. DOI: 10.1016/j.ufug.2020.126970
  • Metzger, S., Burba, G., Aguilos, M., Bernier, T., Curtis, B., Demidov, O., Durden, D., Elston, J., Hamann, H., Hawkins, J., Kayode, J.S., Kisekka, I., Klein, L., Knox, S., Koren, G., Malone, S., Pierrat, Z., Runkle, B., Schödel, S., Shanahan, J., Shrestha, G., Udayar Pillai, S., Vargas, R., Velasco, E. and Yi, K., 2023a. CarbonDew coordinated response to: the federal strategy to advance an integrated US greenhouse gas monitoring and information system. ESS Open Archive, 6 pp. DOI: 10.22541/essoar.168500353.39806527/v1
  • Metzger, S., Burba, G., Desai, A., Hemes, K., Jaiswal, D., Kayode, J.S., Kisekka, I., Kumar, J., Mitra, B., Mwape, A., Paleri, S., Pullanagari, R.R., Runkle, B. and Schödel, S., 2023b. Carbon Dew: direct greenhouse gas exchange measurements anchor equitable climate solutions worldwide. EGU General Assembly 2023, Vienna, 23–28 April, EGU23-10860. DOI: 10.5194/egusphere-egu23-10860
  • Metzger, S., Romano, N., Weintraub-Leff, S., Burba, G., Oikawa, P. et al., 2023c. CarbonDew: direct greenhouse gas exchange measurements for equitable worldwide emissions trading. Battelle 2023 Conference 'Innovations in Climate Resilience', Columbus, Ohio, 28–30 March. See at CarbonDew
  • Mills, M.B., Malhi, Y., Ewers, R.M., Kho, L.K., Teh, Y.A., Both, S., Burslem, D.F.R.P., Majalap, N., Nilus, R., Huaraca Huasco, W., Cruz, R. et al., 2023. Tropical forests post-logging are a persistent net carbon source to the atmosphere. Proceedings of the National Academy of Sciences, 120(3), e2214462120. DOI: 10.1073/pnas.2214462120
  • Mos, H., Harun, M.H., Jantan, N.M., Hashim, Z., Ibrahim, A.S. and Yusup, Y., 2023. Differences in CO₂ emissions on a bare-drained peat area in Sarawak, Malaysia, based on different measurement techniques. Agriculture, 13(3), p.622. DOI: 10.3390/agriculture13030622
  • Nelson, J.A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L. et al., 2024. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X. Biogeosciences, 21(22), pp.5079–5115. DOI: 10.5194/bg-21-5079-2024
  • Nemitz, E., Mammarella, I., Ibrom, A., Aurela, M., Burba, G.G., Dengel, S., Gielen, B., Grelle, A., Heinesch, B., Herbst, M. et al., 2018. Standardisation of eddy-covariance flux measurements of methane and nitrous oxide. International Agrophysics, 32(4), pp.517–549. DOI: 10.1515/intag-2017-0042
  • Nicolini, G., Antoniella, G., Carotenuto, F., Christen, A., Ciais, P., Feigenwinter, C., Gioli, B., Stagakis, S., Velasco, E., Vogt, R., Ward, H.C., Barlow, J., Chrysoulakis, N., Duce, P., Graus, M., Helfter, C., Heusinkveld, B., Järvi, L., Karl, T., Marras, S., Masson, V., Matthews, B., Meier, F., Nemitz, E., Sabbatini, S., Scherer, D., Schume, H., Sirca, C., Steeneveld, G.J., Vagnoli, C., Wang, Y., Zaldei, A., Zheng, B. and Papale, D., 2022. Direct observations of CO₂ emission reductions due to COVID-19 lockdown across European urban districts. Science of the Total Environment, 830, p.154662. DOI: 10.1016/j.scitotenv.2022.154662
  • Novick, K.A., Metzger, S., Anderegg, W.R.L., Barnes, M., Cala, D.S., Guan, K., Hemes, K.S., Hollinger, D.Y., Kumar, J., Litvak, M., Lombardozzi, D., Normile, C.P., Oikawa, P., Runkle, B.R.K., Torn, M. and Wiesner, S., 2022a. Informing nature-based climate solutions for the United States with the best-available science. Global Change Biology, 28(12), pp.3778–3794. DOI: 10.1111/gcb.16156
  • Novick, K., Williams, C., Runkle, B., Anderegg, W.R.L., Hollinger, D., Litvak, M., Normile, C., Shrestha, G., Almaraz, M., Anderson, C., Barnes, M., Baldocchi, D., Colburn, L., Cullenward, D., Evans, M., Guan, K., Keenan, T., Lamb, R., Larson, L., Oldfield, E., Poulter, B., Reyes, J., Sanderman, J., Selmants, P., Sepulveda Carlo, E., Torn, M.S., Trugman, A. and Woodall, C., 2022b. The science needed for robust, scalable, and credible nature-based climate solutions in the United States: Summary Report. Bloomington, IN: Indiana University. DOI: 10.5967/8rgp-tc11
  • Papale, D., Christen, A., Davis, K., Feigenwinter, C., Gioli, B., Järvi, L., Matthews, B., Velasco, E. and Vogt, R., 2022. Eddy covariance flux observations. Chapter B.i. In: IG3IS Urban Greenhouse Gas Emission Observation and Monitoring Good Research Practice Guidelines. GAW Report No. 275. Geneva: World Meteorological Organization, pp.114–123. See at WMO
  • Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M. et al., 2020. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, 7, p.225. DOI: 10.1038/s41597-020-0534-3
  • Peddinti, S.R. and Kambhammettu, B.V.N.P., 2019. Dynamics of crop coefficients for citrus orchards of central India using water balance and eddy covariance flux partition techniques. Agricultural Water Management, 212, pp.68–77. DOI: 10.1016/j.agwat.2018.08.027
  • Prajapati, P. and Santos, E.A., 2017. Measurements of methane emissions from a beef cattle feedlot using the eddy covariance technique. Agricultural and Forest Meteorology, 232, pp.349–358. DOI: 10.1016/j.agrformet.2016.09.001
  • Rebmann, C., Aubinet, M., Schmid, H.P., Arriga, N., Aurela, M., Burba, G., Clement, R., De Ligne, A., Fratini, G., Gielen, B. et al., 2018. ICOS eddy covariance flux-station site setup: a review. International Agrophysics, 32(4), pp.471–494. DOI: 10.1515/intag-2017-0044
  • Roberti, D.R., Mergen, A., Gotuzzo, R.A., Veeck, G.P., Bremm, T., Marin, L., de Quadros, F.L.F. and Jacques, R.J.S., 2024. Sustainability in natural grassland in the Brazilian Pampa biome: livestock production with CO₂ absorption. Sustainability, 16(9), p.3672. DOI: 10.3390/su16093672
  • Roth, M., Jansson, C. and Velasco, E., 2017. Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city. International Journal of Climatology, 37(5), pp.2679–2698. DOI: 10.1002/joc.4873
  • Runkle, B.R.K., Suvočarev, K., Reba, M.L., Reavis, C.W., Smith, S.F., Chiu, Y.-L. and Fong, B., 2019. Methane emission reductions from the alternate wetting and drying of rice fields detected using the eddy covariance method. Environmental Science & Technology, 53(2), pp.671–681. DOI: 10.1021/acs.est.8b05535
  • Rutledge, S., Wall, A.M., Mudge, P.L., Troughton, B., Campbell, D.I., Pronger, J., Joshi, C. and Schipper, L.A., 2017. The carbon balance of temperate grasslands part II: the impact of pasture renewal via direct drilling. Agriculture, Ecosystems & Environment, 239, pp.132–142. DOI: 10.1016/j.agee.2017.01.013
  • Sabbatini, S., Mammarella, I., Arriga, N., Fratini, G., Graf, A., Hörtnagl, L., Ibrom, A., Longdoz, B., Mauder, M., Merbold, L. et al., 2018. Eddy covariance raw data processing for CO₂ and energy fluxes calculation at ICOS ecosystem stations. International Agrophysics, 32(4), pp.495–515. DOI: 10.1515/intag-2017-0043
  • Schreiner-McGraw, A.P., Ransom, C.J., Veum, K.S., Wood, J.D., Sudduth, K.A. and Abendroth, L.J., 2024. Quantifying the impact of climate smart agricultural practices on soil carbon storage relative to conventional management. Agricultural and Forest Meteorology, 344, p.109812. DOI: 10.1016/j.agrformet.2023.109812
  • Shrestha, O., Khan, A., Torrion, J.A., Sigler, A., McVay, K., Powell, S.P. and Stoy, P.C., 2025. Actual crop coefficients for cereal crops in Montana USA from eddy covariance observations. SSRN preprint. DOI: 10.2139/ssrn.5213667
  • So, K., Rogers, C.A., Li, Y., Arain, M.A. and Gonsamo, A., 2024. Retention forestry as a climate solution: assessing biomass, soil carbon and albedo impacts in a northern temperate coniferous forest. Science of the Total Environment, 947, p.174680. DOI: 10.1016/j.scitotenv.2024.174680
  • Social Carbon Foundation, 2025. SCD0003: Module for eddy covariance-based continuous quantification of greenhouse gas fluxes. Version 1.0, 21 March 2025. SOCIALCARBON Standard, 39 pp. See at Social Carbon (PDF)
  • Stagakis, S., Feigenwinter, C., Vogt, R., Brunner, D. and Kalberer, M., 2023. A high-resolution monitoring approach of urban CO₂ fluxes. Part 2 – surface flux optimisation using eddy covariance observations. Science of the Total Environment, 903, p.166035. DOI: 10.1016/j.scitotenv.2023.166035
  • Talib, A., Desai, A.R., Huang, J., Thom, J., Panuska, J.C. and Stoy, P.C., 2024. Improving parameterization of an evapotranspiration estimation model with eddy covariance measurements for a regional irrigation scheduling program. Agricultural and Forest Meteorology, 350, p.109967. DOI: 10.1016/j.agrformet.2024.109967
  • Teets, A., Fraver, S., Hollinger, D.Y., Weiskittel, A.R., Seymour, R.S. and Richardson, A.D., 2018. Linking annual tree growth with eddy-flux measures of net ecosystem productivity across twenty years of observation in a mixed conifer forest. Agricultural and Forest Meteorology, 249, pp.479–487. DOI: 10.1016/j.agrformet.2017.08.007
  • Thomas, R.Q., Boettiger, C., Carey, C.C., Dietze, M.C., Johnson, L.R., Kenney, M.A., McLachlan, J.S., Peters, J.A., Sokol, E.R., Weltzin, J.F., Willson, A. and Woelmer, W.M., 2023. The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), pp.112–113. DOI: 10.1002/fee.2616
  • Valach, A.C., Kasak, K., Hemes, K.S., Anthony, T.L., Dronova, I., Taddeo, S., Silver, W.L., Szutu, D., Verfaillie, J. and Baldocchi, D.D., 2021. Productive wetlands restored for carbon sequestration quickly become net CO₂ sinks with site-level factors driving uptake variability. PLOS ONE, 16(3), e0248398. DOI: 10.1371/journal.pone.0248398
  • van Ramshorst, J.G.V., Knohl, A., Callejas-Rodelas, J.Á., Clement, R., Hill, T.C., Siebicke, L. and Markwitz, C., 2024. Lower-cost eddy covariance for CO₂ and H₂O fluxes over grassland and agroforestry. Atmospheric Measurement Techniques, 17(20), pp.6047–6071. DOI: 10.5194/amt-17-6047-2024
  • Vargas, R., Loescher, H.W., Arredondo, T., Huber-Sannwald, E., Lara-Lara, R. and Yépez, E.A., 2012. Opportunities for advancing carbon cycle science in Mexico: toward a continental scale understanding. Environmental Science & Policy, 21, pp.84–93. DOI: 10.1016/j.envsci.2012.04.003
  • Vargas, R., Yépez, E.A., Andrade, J.L., Ángeles, G., Arredondo, T., Castellanos, A.E., Delgado-Balbuena, J., Garatuza-Payán, J., González del Castillo, E., Oechel, W., Rodríguez, J.C., Sánchez-Azofeifa, A., Velasco, E., Vivoni, E.R. and Watts, C., 2013. Progress and opportunities for monitoring greenhouse gases fluxes in Mexican ecosystems: the MexFlux network. Atmósfera, 26(3), pp.325–336. DOI: 10.1016/S0187-6236(13)71079-8
  • Veeck, G.P., Dalmago, G.A., Bremm, T., Buligon, L., Jacques, R.J.S., Fernandes, J.M., Santi, A., Vargas, P.R. and Roberti, D.R., 2022. CO₂ flux in a wheat–soybean succession in subtropical Brazil: a carbon sink. Journal of Environmental Quality, 51(5), pp.899–915. DOI: 10.1002/jeq2.20362
  • Velasco, E., 2021. Impact of Singapore's COVID-19 confinement on atmospheric CO₂ fluxes at neighborhood scale. Urban Climate, 37, p.100822. DOI: 10.1016/j.uclim.2021.100822
  • Velasco, E., 2022. Services and limits of urban vegetation to mitigate climate change. Invited seminar, Department of Architecture, National University of Singapore, Singapore, 14 January.
  • Velasco, E., 2023a. Urban flux towers: applications and challenges. AmeriFlux Annual Meeting 2023, Harvard Forest, Petersham, Massachusetts, USA, 4–6 October.
  • Velasco, E., 2023b. Urban warming, climate change and atmospheric pollution. First Colloquium on Urban Climates, Institute of Geography, National Autonomous University of Mexico, Mexico City, Mexico, 25 August.
  • Velasco, E., 2025. Urban flux towers as a tool for devising better climate policies for cities. IG3IS Webinar Series (Q3–Q4 2025), World Meteorological Organization, 11 November. See at WMO IG3IS | Recording
  • Velasco, E., Lamb, B., Pressley, S., Allwine, E., Westberg, H., Jobson, B.T., Alexander, M., Prazeller, P., Molina, L. and Molina, M., 2005a. Flux measurements of volatile organic compounds from an urban landscape. Geophysical Research Letters, 32(20). DOI: 10.1029/2005GL023356
  • Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A. and Molina, L.T., 2014. Sources and sinks of carbon dioxide in a neighborhood of Mexico City. Atmospheric Environment, 97, pp.226–238. DOI: 10.1016/j.atmosenv.2014.08.018
  • Velasco, E., Pressley, S., Allwine, E., Westberg, H. and Lamb, B., 2005b. Measurements of CO₂ fluxes from the Mexico City urban landscape. Atmospheric Environment, 39(38), pp.7433–7446. DOI: 10.1016/j.atmosenv.2005.08.038
  • Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Coons, T., Foster, W., Jobson, B.T., Westberg, H., Ramos, R., Hernández, F., Molina, L.T. and Lamb, B., 2009. Eddy covariance flux measurements of pollutant gases in urban Mexico City. Atmospheric Chemistry and Physics, 9(19), pp.7325–7342. DOI: 10.5194/acp-9-7325-2009
  • Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L.T. and Lamb, B., 2011. Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign. Theoretical and Applied Climatology, 103(3–4), pp.501–517. DOI: 10.1007/s00704-010-0314-7
  • Velasco, E. and Roth, M., 2010. Cities as net sources of CO₂: review of atmospheric CO₂ exchange in urban environments measured by eddy covariance technique. Geography Compass, 4(9), pp.1238–1259. DOI: 10.1111/j.1749-8198.2010.00384.x
  • Velasco, E., Roth, M., Norford, L. and Molina, L.T., 2016. Does urban vegetation enhance carbon sequestration? Landscape and Urban Planning, 148, pp.99–107. DOI: 10.1016/j.landurbplan.2015.12.003
  • Velasco, E., Roth, M., Tan, S.H., Quak, M., Nabarro, S.D.A. and Norford, L., 2013. The role of vegetation in the CO₂ flux from a tropical urban neighbourhood. Atmospheric Chemistry and Physics, 13(20), pp.10185–10202. DOI: 10.5194/acp-13-10185-2013
  • Velasco, E., Segovia, E. and Roth, M., 2023. High-resolution maps of carbon dioxide and moisture fluxes over an urban neighborhood. Environmental Science: Atmospheres, 3(7), pp.1110–1123. DOI: 10.1039/D2EA00108J
  • Verra, 2026. Tool for quantifying emission reductions, removals, and carbon stock changes with atmospheric-based monitoring (methodology development ID M0444; proposed by Hyphen Global AG). Status: on hold as of 1 April 2026. Washington, DC: Verra. See at Verra
  • Vogel, E., Davis, K.J., Wu, K., Miles, N.L., Richardson, S.J., Gurney, K.R., Monteiro, V., Roest, G.S., Kenion, H.C.R. and Horne, J.P., 2024. Using eddy-covariance to measure the effects of COVID-19 restrictions on CO₂ emissions in a neighborhood of Indianapolis, IN. Carbon Management, 15(1), p.2365900. DOI: 10.1080/17583004.2024.2365900
  • Wall, A.M., Campbell, D.I., Mudge, P.L. and Schipper, L.A., 2020. Temperate grazed grassland carbon balances for two adjacent paddocks determined separately from one eddy covariance system. Agricultural and Forest Meteorology, 287, p.107942. DOI: 10.1016/j.agrformet.2020.107942
  • Wang, X., Che, T., Xiao, J., Wang, T., Tan, J., Zhang, Y., Ren, Z., Geng, L., Wang, H., Xu, Z., Liu, S. and Li, X., 2025. A post-processed carbon flux dataset for 34 eddy covariance flux sites across the Heihe River basin, China. Earth System Science Data, 17(4), pp.1329–1346. DOI: 10.5194/essd-17-1329-2025
  • Ward, P.R., Micin, S.F. and Fillery, I.R.P., 2012. Application of eddy covariance to determine ecosystem-scale carbon balance and evapotranspiration in an agroforestry system. Agricultural and Forest Meteorology, 152, pp.178–188. DOI: 10.1016/j.agrformet.2011.09.016
  • Wiesner, S., Desai, A.R., Duff, A.J., Metzger, S. and Stoy, P.C., 2022. Quantifying the natural climate solution potential of agricultural systems by combining eddy covariance and remote sensing. Journal of Geophysical Research: Biogeosciences, 127(9), e2022JG006895. DOI: 10.1029/2022JG006895
  • Williams, C.A., Andrews, A., Brown, M., Davis, K.J., Hoffman, F., Larson, L., Poulter, B., Shrestha, G., Sundquist, E.T., Wei, Y. et al., 2023. 2022 North American Carbon Program Science Implementation Plan. Report of the North American Carbon Program. Washington, DC: US Carbon Cycle Science Program. DOI: 10.5065/kwe1-w815
  • Xia, Y., Sanderman, J., Watts, J.D., Machmuller, M.B., Mullen, A.L., Rivard, C., Endsley, A., Hernandez, H., Kimball, J., Ewing, S.A. et al., 2025. Coupling remote sensing with a process model for the simulation of rangeland carbon dynamics. Journal of Advances in Modeling Earth Systems, 17(3), e2024MS004342. DOI: 10.1029/2024MS004342
  • Xu, L., Lin, X., Amen, J., Welding, K. and McDermitt, D., 2014. Impact of changes in barometric pressure on landfill methane emission. Global Biogeochemical Cycles, 28(7), pp.679–695. DOI: 10.1002/2013GB004571
  • Yi, K., Torn, M.S., Senay, G.B., Wang, L., Suvočarev, K., Fisher, J.B., Ahmadi, A., Chu, H., Bohrer, G., Moore, G.W. et al., 2026. Bioclimatic reorganization of carbon–water–energy coupling revealed by eddy covariance and interpretable machine learning. Global Change Biology Communications, 1(2), e70012. DOI: 10.1002/gcb4.70012
  • Yuan, J., Jose, S., Hu, Z., Pang, J., Hou, L. and Zhang, S., 2018. Biometric and eddy covariance methods for examining the carbon balance of a Larix principis-rupprechtii forest in the Qinling Mountains, China. Forests, 9(2), p.67. DOI: 10.3390/f9020067
  • Yusup, Y., Ramli, N.K., Kayode, J.S., Yin, C.S., Hisham, S., Mohamad Isa, H. and Ahmad, M.I., 2020. Atmospheric carbon dioxide and electricity production due to lockdown. Sustainability, 12(22), p.9397. DOI: 10.3390/su12229397
  • Zalakeviciute, R., Alexander, M.L., Allwine, E., Jimenez, J.L., Jobson, B.T., Molina, L.T., Nemitz, E., Pressley, S.N., VanReken, T.M., Ulbrich, I.M., Velasco, E. and Lamb, B.K., 2012. Chemically-resolved aerosol eddy covariance flux measurements in urban Mexico City during MILAGRO 2006. Atmospheric Chemistry and Physics, 12(16), pp.7809–7823. DOI: 10.5194/acp-12-7809-2012
  • Zeri, M., Anderson-Teixeira, K., Hickman, G., Masters, M., DeLucia, E. and Bernacchi, C.J., 2011. Carbon exchange by establishing biofuel crops in Central Illinois. Agriculture, Ecosystems & Environment, 144(1), pp.319–329. DOI: 10.1016/j.agee.2011.09.006
  • Zhuravlev, R., Dara, A., dos Santos, A.L.D., Demidov, O. and Burba, G., 2022. Globally scalable approach to estimate net ecosystem exchange based on remote sensing, meteorological data, and direct measurements of eddy covariance sites. Remote Sensing, 14(21), p.5529. DOI: 10.3390/rs14215529

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