Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements

Laurent Bigaignon (1, 2) , Rémy Fieuzal (2) , Claire Delon (1) , Tiphaine Tallec (2)

1 LAERO - Laboratoire d'aérologie
2 CESBIO - Centre d'études spatiales de la biosphère

Continuous N 2 O flux acquisition is crucial to enrich our knowledge of the complex mechanisms underlying the annual greenhouse gas budget and to refine their estimation. N 2 O flux measurement methodologies at high temporal resolution, i.e. micro-meteorology methodologies, are still scarce and may exacerbate the lack of important data, especially during the night if the required turbulent conditions are not met. The static and automated chamber methodologies also lead to numerous gaps in a time series due to low sampling frequency, hardware malfunctions, chambers removal during field operations or filtering of low-quality measurements. There is a strong need to define a generic and realistic N 2 O flux gap-filling methodology, especially since there is no consensus on the methodology to be used. In this study, we investigated the effect of using either the traditional linear interpolation methodology alone, either an Artificial Neural Networks (ANN) methodology alone or the combination of both on gap-filled daily N 2 O flux dynamics and annual budget. All three methodologies were tested on daily N 2 O flux time series measured with automated chambers over 5 years from 2012 to 2016 on a southwestern France crop site following a wheat-maize rotation. On average over the studied period, the results showed better statistical scores using the ANN methodology alone than using the linear interpolation methodology alone, with R² and RMSE of 0.84 and 12.4 gN ha −1 d −1 and of 0.68 and 17.4 gN ha −1 d −1 , respectively. However, whereas the use of ANN methodology reproduced well high measured N 2 O fluxes, it induced overestimation on low measured N 2 O fluxes where the use of the linear interpolation methodology was relevant. To overcome that issue and to take advantages of both methodologies we propose a new one which mixes both. On average, using the mixed methodology did not increase statistical scores compared to the ANN one, with a R² and a RMSE of 0.84 and 12.4 gN ha −1 d −1 respectively for both, but for periods with low measured N 2 O fluxes using the mixed methodology improved the statistical scores and the observed daily flux dynamic.

Mots clés: Greenhouse Gases; Soil; Chambers; Crops; Agriculture; Greenhouse Gases

Domaines: Sciences de l'environnement

Laurent Bigaignon, Rémy Fieuzal, Claire Delon, Tiphaine Tallec. Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements. Agricultural and Forest Meteorology, 2020, ⟨10.1016/j.agrformet.2020.108037⟩. ⟨hal-03024131⟩