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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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      Zhu_etal_AFM_Stable_Gap_Filling_AAM.pdf (1.441Mb)
      Publication date
      01/03/2022
      Author
      Zhu, Songyan
      Clement, Robert
      McCalmont, Jon
      Davies, Christian A.
      Hill, Timothy
      Metadata
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      Citation
      Zhu , 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.2021.108777
      URI
      https://hdl.handle.net/2164/19784
      DOI
      https://doi.org/10.1016/j.agrformet.2021.108777
      Rights
      © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
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