Stable gap-filling for longer eddy covariance data gaps : A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes
Publication date
01/03/2022Metadata
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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
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© 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/