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

Zhu, Songyan; Clement, Robert; McCalmont, Jon; Davies, Christian A.; Hill, Timothy

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 (1.44 MB)

Date

2022-03-01

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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