Comparison of gap-filling methods for CO2 flux data




CO2 flux, Gap filling, Missing data


Data collected by sensors are always subject to possible failures, whether due to power failure, external interference, among others.. Moreover, much of the data is not considered during the filtering process because it is physically inconsistent. These failures result in the need to implement various methods of data processing, with a focus on filling in missing records. In the case of CO2 flux data, filling in the missing data is crucial to obtain annual data and the carbon balance. The REddyProc package is widely used and documented in terms of filling this type of data. However, modern methods have been increasingly explored to optimize this process. In this study, we compare data filling between the REddyProc package and the KNN Imputer method. Preliminary results show that the REddyProc package has better statistical indices when filling CO2 streams compared to the KNN method.


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Author Biographies

Bernardo Ivo Goltz, Universidade Federal de Santa Maria

Electrical engineering student

Daniele Morgenstern Aimi, Universidade Federal de Santa Maria

Medical Physics. Holds a PhD in physics

Alecsander Mergen, Universidade Federal de Santa Maria

Physicist, Master in Physics

Vanessa de Arruda Souza, Federal University of Rio Grande do Sul

Meteorologist, PhD in Remote Sensing

Gustavo Pujol Veeck, Universidade Federal de Santa Maria

Physicist, Master in Physics

Tiago Bremm, Universidade Federal de Santa Maria

Physicist, Master in Meteorology

Michel Baptistella Stefanello, Universidade Federal de Santa Maria

Physicist, PhD in Physics

Débora Regina Roberti, Universidade Federal de Santa Maria

Physicist, PhD in Physics


BAGGIO, R. Estratégias de manejo adaptativo para os Campos Sulinos. 2017. 129 p. Tese (Doutorado em Ecologia) — Universidade Federal do Rio Grande do Sul, Porto Alegre, 2017.

BALDOCCHI, D. et al. The challenges of measuring methane fluxes and concentrations over a peatland pasture. Agricultural and Forest Meteorology, v. 153, p. 177–187, 2012.

BÉZIAT, P.; CESCHIA, E.; DEDIEU, G. Carbon balance of a three crop succession over two cropland sites in South West France. Agricultural and Forest Meteorology, v. 149, n. 10, p. 1628– 1645, mar. 2009.

FALGE, E., et al., Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, v. 107, p. 43–69. 2001

FOKEN, T et al., 2004. Handbook of Micrometeorology: A Guide for surface flux measurement and analysis: Chapter 9: POST-FIELD DATA QUALITY CONTROL, Handbook of Micrometeorology.

IPCC. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. [s.l: s.n.].

IPCC. I. P. ON C. C. Mitigation of climate change. In Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: [s.n.].

MARSHALL, G. Statistical methods in the atmospheric sciences, second edition D. S. Wilks. 1995. International Geophysics Series, Vol 59, Academic Press, 464pp. ISBN-10: 0127519653. ISBN-13: 978-0127519654. Meteorological Applications, v. 14, n. 2, p. 205–205, jun. 2007.

MONCRIEFF, J. B. et al. A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. Journal of Hydrology, 1997.

MONCRIEFF, J. et al. Averaging, detrending, and filtering of eddy covariance time series, in Handbook of micrometeorology. Handbook of Micrometeorology: A Guide for surface flux measurement and analysis, 2004.

PANCHASARA, H.; SAMRAT, N. H.; ISLAM, N. Greenhouse Gas Emissions Trends and Mitigation Measures in Australian Agriculture Sector—A Review. Agriculture, v. 11, n. 2, p. 85, 20 jan. 2021.

REICHSTEIN, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, v. 11, n. 9, p. 1424– 1439, set. 2005.

SCIKIT-LEARN: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.

VICKERS, D.; MAHRT, L. Quality control and flux sampling problems for tower and aircraft data. Journal of Atmospheric and Oceanic Technology, 1997.

WEBB, E. K.; PEARMAN, G. I.; LEUNING, R. Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society, 1980. WILCZAK, J. M.; ONCLEY, S. P.; STAGE, S. A. Sonic anemometer tilt correction algorithms. Boundary-Layer Meteorology, 2001.

WUTZLER, T. et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences, 2018.



How to Cite

Goltz, B. I., Aimi, D. M., Mergen, A., Souza, V. de A., Veeck, G. P., Bremm, T., Stefanello, M. B., & Roberti, D. R. (2023). Comparison of gap-filling methods for CO2 flux data. Ciência E Natura, 45(esp. 2), e80997.

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