Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes

Antje M. Moffat, Dario Papale, Markus Reichstein, David Y. Hollinger, Andrew D. Richardson, Alan G. Barr, Clemens Beckstein, Bobby H. Braswell, Galina Churkina, Ankur R. Desai, Eva Falge, Jeffrey H. Gove, Martin Heimann, Dafeng Hui, Andrew J. Jarvis, Jens Kattge, Asko Noormets, Vanessa J. Stauch

Research output: Contribution to journalArticlepeer-review

693 Scopus citations


We review 15 techniques for estimating missing values of net ecosystem CO2 exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe. The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night. The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semi-parametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of ±25 g C m-2 year-1.

Original languageEnglish (US)
Pages (from-to)209-232
Number of pages24
JournalAgricultural and Forest Meteorology
Issue number3-4
StatePublished - Dec 10 2007
Externally publishedYes


  • Carbon flux
  • Eddy covariance
  • Gap-filling comparison
  • Net ecosystem exchange (NEE)
  • Review of gap-filling techniques

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science


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