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

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dc.creatorTramontana, Gianluca
dc.creatorJung, Martin
dc.creatorSchwalm, Christopher R.
dc.creatorIchii, Kazuhito
dc.creatorCamps-Valls, Gustau
dc.creatorRáduly, Botond
dc.creatorReichstein, Markus
dc.creatorArain, M. Altaf
dc.creatorCescatti, Alessandro
dc.creatorKiely, Gerard
dc.creatorMerbold, Lutz
dc.creatorSerrano-Ortiz, Penelope
dc.creatorSickert, Sven
dc.creatorWolf, Sebastian
dc.creatorPapale, Dario
dc.date2016-07-29
dc.date2023-03-10T14:34:29Z
dc.date2023-03-10T14:34:29Z
dc.date.accessioned2026-06-27T17:18:21Z
dc.descriptionAbstract. 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 different 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 (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 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.
dc.identifierhttps://hdl.handle.net/10568/129400
dc.identifier.urihttp://hdl.handle.net/123456789/148321
dc.languageen
dc.publisherCopernicus GmbH
dc.rightsOpen Access
dc.sourceTramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.; Ichii, Kazuhito; Camps-Valls, Gustau; Ráduly, Botond; Reichstein, Markus; Arain, M. Altaf; Cescatti, Alessandro; Kiely, Gerard; Merbold, Lutz; Serrano-Ortiz, Penelope; Sickert, Sven; Wolf, Sebastian; Papale, Dario. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13: 4291-4313
dc.subjectcarbon
dc.subjectenergy
dc.subjectcarbon dioxide
dc.subjectalgorithms
dc.titlePredicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
dc.typeJournal Article

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Instituto Internacional de Investigaciones Pecuarias (ILRI)