Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites


  • David A. Wood DWA Energy Limited, Lincoln, United Kingdom



Variations in net ecosystem exchange (NEE) of carbon dioxide, and the variables influencing it, at woodland sites over multiple years determine the long term performance of those sites as carbon sinks. In this study, weekly-averaged data from two AmeriFlux sites in North America of evergreen woodland, in different climatic zones and with distinct tree and understory species, are evaluated using four multi-linear regression (MLR) and seven machine learning (ML) models. The site data extend over multiple years and conform to the FLUXNET2015 pre-processing pipeline. Twenty influencing variables are considered for site CA-LP1 and sixteen for site US-Mpj. Rigorous k-fold cross validation analysis verifies that all eleven models assessed generate reproducible NEE predictions to varying degrees of accuracy. At both sites, the best performing ML models (support vector regression (SVR), extreme gradient boosting (XGB) and multi-layer perceptron (MLP)) substantially outperform the MLR models in terms of their NEE prediction performance. The ML models also generate predicted versus measured NEE distributions that approximate cross-plot trends passing through the origin, confirming that they more realistically capture the actual NEE trend. MLR and ML models assign some level of importance to all influential variables measured but their degree of influence varies between the two sites. For the best performing SVR models, at site CA-LP1, variables air temperature, shortwave radiation outgoing, net radiation, longwave radiation outgoing, shortwave radiation incoming and vapor pressure deficit have the most influence on NEE predictions. At site US-Mpj, variables vapor pressure deficit, shortwave radiation incoming, longwave radiation incoming, air temperature, photosynthetic photon flux density incoming, shortwave radiation outgoing and precipitation exert the most influence on the model solutions. Sensible heat exerts very low influence at both sites. The methodology applied successfully determines the relative importance of influential variables in determining weekly NEE trends at both conifer woodland sites studied.


Eddy covariance, FLUXNET2015, Weekly NEE trends, Variable importance, Correlation comparisons, NEE prediction


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How to Cite

Wood, D. A. (2022). Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites. Research in Ecology, 4(2), 24–50.





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