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


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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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