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|Title:||Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms||Authors:||Tramontana, Gianluca
Schwalm, Christopher R.
Arain, M. Altaf
|Keywords:||Machine learning;Carbon fluxes;Energy fluxes;FLUXNET;Remote sensing;FLUXCOM||Issue Date:||2016||Publisher:||Copernicus Publications||Source:||Tramontana, G. et al. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. "Biogeosciences" 13 (14): 4291–4313||Project:||info:eu-repo/grantAgreement/EC/FP7/283080||Abstract:||
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.
|Appears in Collections:||DIBAF - Archivio della produzione scientifica|
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