Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

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2019
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Abstract
Methane flux (FCH ) measurements using the eddy covariance technique have increased over the past decade. FCH measurements commonly include data gaps, as is the case with CO and energy fluxes. However, gap-filling FCH data is more challenging than other fluxes due to its unique characteristics including multi-driver dependency, variabilities across multiple time scales, non-stationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH . However, there is still no consensus regarding FCH gap-filling methods due to limited comparative research. We are not aware of applications of machine learning (ML) algorithms beyond ANN to FCH datasets. Here, we compare the performance of MDS and three machine learning algorithms (ANN, random forest (RF), and support vector machine (SVM)) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multi-driver dependency of FCH and reduce the internal complexity of algorithmic structures. We applied this approach to five benchmark FCH datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH budget. Therefore, the approach used for FCH gap-filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems interactions with global change processes.
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kim2019gapfillingglobal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kim, Yeonuk;Johnson, Mark S;Knox, Sara Helen;Black, T Andrew;Dalmagro, Higo J;Kang, Minseok;Kim, Joon;Baldocchi, Dennis;
Journal Global change biology
Year 2019
DOI
10.1111/gcb.14845
URL
Keywords
Machine learning support vector machine artificial neural network random forest comparison of gap-filling techniques eddy covariance marginal distribution sampling methane flux

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