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Stable gap-filling for longer eddy covariance data gaps : A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes

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dc.contributor.authorZhu, Songyan
dc.contributor.authorClement, Robert
dc.contributor.authorMcCalmont, Jon
dc.contributor.authorDavies, Christian A.
dc.contributor.authorHill, Timothy
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.date.accessioned2022-12-27T00:09:42Z
dc.date.available2022-12-27T00:09:42Z
dc.date.embargoedUntil2022-12-27
dc.date.issued2022-03-01
dc.descriptionAcknowledgments The authors thank the FLUXNET and the research groups for providing the CC-BY-4.0 (Tier one) open-access eddy covariance data (https://fluxnet.org/login/?redirect_to=/data/download-data/). They also thank the ReddyProc (https://cran.r-project.org/web/packages/REddyProc/index.html) team and scikit-learn (https://scikit-learn.org/stable/install.html) team for the packages that help the implementation and validation for gap-filling approaches. Songyan Zhu would like to acknowledge a Shell funded PhD studentship and Timonthy Hill acknowledge funding from a joint UK NERC-FAPESP grant no. NE/S000011/1 & FAPESP-19/07773-1.en
dc.description.statusPeer revieweden
dc.format.extent10
dc.format.extent1511489
dc.identifier212223795
dc.identifier29684c0c-9836-406b-840c-4ec6ff15a922
dc.identifier85121984662
dc.identifier.citationZhu, S, Clement, R, McCalmont, J, Davies, C A & Hill, T 2022, 'Stable gap-filling for longer eddy covariance data gaps : A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes', Agricultural and Forest Meteorology, vol. 314, 108777. https://doi.org/10.1016/j.agrformet.2021.108777en
dc.identifier.doi10.1016/j.agrformet.2021.108777
dc.identifier.issn0168-1923
dc.identifier.otherRIS: urn:522F3033020A19B1F03AA9B9E8CC5B31
dc.identifier.otherORCID: /0000-0002-5978-9574/work/107063447
dc.identifier.urihttps://hdl.handle.net/2164/19784
dc.identifier.vol314en
dc.language.isoeng
dc.relation.ispartofAgricultural and Forest Meteorologyen
dc.subjectSDG 13 - Climate Actionen
dc.subjectSDG 15 - Life on Landen
dc.subjectGlobal land ecosystemsen
dc.subjectCarbon exchangeen
dc.subjectEddy covarianceen
dc.subjectLong gapsen
dc.subjectRobust gap-fillingen
dc.subjectQH301 Biologyen
dc.subjectNatural Environment Research Council (NERC)en
dc.subjectNE/S000011/1en
dc.subject19/07773-1.en
dc.subjectSupplementary Informationen
dc.subject.lccQH301en
dc.titleStable gap-filling for longer eddy covariance data gaps : A globally validated machine-learning approach for carbon dioxide, water, and energy fluxesen
dc.typeJournal articleen

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