Geoscientific Instrumentation, Methods and Data Systems
Geoscientific Instrumentation, Methods and Data Systems
GI
Articles | Volume 10, issue 1
Articles | Volume 10, issue 1
https://doi.org/10.5194/gi-10-123-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-10-123-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 1
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, Jason Beringer, Matthias Leopold, Ian McHugh, James Cleverly, Peter Isaac, and Azizallah Izady

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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.
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