Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana, Martin Jung, Christopher R. Schwalm, Kazuhito Ichii, Gustau Camps-Valls, Botond Ráduly, Markus Reichstein, M. Altaf Arain, Alessandro Cescatti, Gerard Kiely, Lutz Merbold, Penelope Serrano-Ortiz (+3 others)
2016 Biogeosciences  
<p><strong>Abstract.</strong> Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across
more » ... rent ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;lt;<span class="thinspace"></span>0.5), ecosystem respiration (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;gt;<span class="thinspace"></span>0.6), gross primary production (<i>R</i><sup>2</sup>&amp;gt;<span class="thinspace"></span>0.7), latent heat (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;gt;<span class="thinspace"></span>0.7), sensible heat (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;gt;<span class="thinspace"></span>0.7), and net radiation (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;gt;<span class="thinspace"></span>0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;gt;<span class="thinspace"></span>0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (<i>R</i><sup>2</sup><span class="thinspace"></span>&amp;lt;<span class="thinspace"></span>0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.</p>
doi:10.5194/bg-13-4291-2016 fatcat:fc2i5rktnnedpkzbrg56yh7vom