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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

dc.contributor.authorZhu, Songyan
dc.contributor.authorClement, Robert
dc.contributor.authorMcCalmont, Jon
dc.contributor.authorDavies, Christian A.
dc.contributor.authorHill, Timothy
dc.date.accessioned2023-09-12T23:13:22Z
dc.date.available2023-09-12T23:13:22Z
dc.date.issued2022-03-01
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.issn0168-1923
dc.identifier.otherRIS: urn:522F3033020A19B1F03AA9B9E8CC5B31
dc.identifier.otherORCID: /0000-0002-5978-9574/work/107063447
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/19784
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.format.extent10
dc.format.extent1511489
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
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1016/j.agrformet.2021.108777
dc.date.embargoedUntil2022-12-27
dc.identifier.vol314en


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