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.

Data sets

A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers A. Mahabbati https://doi.org/10.26182/5f292ee80a0c0

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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.