Articles | Volume 10, issue 1
Geosci. Instrum. Method. Data Syst., 10, 123–140, 2021
https://doi.org/10.5194/gi-10-123-2021
Geosci. Instrum. Method. Data Syst., 10, 123–140, 2021
https://doi.org/10.5194/gi-10-123-2021

Research article 28 Jun 2021

Research article | 28 Jun 2021

A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers

Atbin Mahabbati et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Atbin Mahabbati on behalf of the Authors (29 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (02 Feb 2021) by Jean Dumoulin
RR by Thomas Wutzler (16 Feb 2021)
RR by Anonymous Reviewer #2 (25 Feb 2021)
ED: Publish subject to minor revisions (review by editor) (02 Mar 2021) by Jean Dumoulin
AR by Atbin Mahabbati on behalf of the Authors (31 Mar 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (18 Apr 2021) by Jean Dumoulin
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Short summary
We reviewed eight algorithms to estimate missing values of environmental drivers and three major fluxes in eddy covariance time series. Overall, machine-learning algorithms showed superiority over the rest. Among the top three models (feed-forward neural networks, eXtreme Gradient Boost, and random forest algorithms), the latter showed the most solid performance in different scenarios.