Journal
BIOGEOSCIENCES
Volume 13, Issue 14, Pages 4291-4313Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/bg-13-4291-2016
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Funding
- EU [GA 283080, 283080, 640176]
- ERC [647423]
- Ministry of the Environment of Japan [2-1401]
- JAXA Global Change Observation Mission (GCOM) project [115]
- National Aeronautics and Space Administration (NASA) [NNX12AP74G, NNX10AG01A, NNX11AO08A]
- Natural Sciences and Engineering Research Council (NSREC) of Canada
- GEISpain project - Spanish Ministry of Economy and Competitiveness [CGL2014-52838-C2-1-R]
- European Union ERDF funds
- European Commission [300083]
- US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program [DE-FG02-04ER63917, DE-FG02-04ER63911]
- CarboEuropeIP
- FAO-GTOS-TCO
- iLEAPS
- Max Planck Institute for Biogeochemistry
- National Science Foundation
- University of Tuscia
- US Department of Energy
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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 (R-2 < 0.5), ecosystem respiration (R-2 > 0.6), gross primary production (R-2 > 0.7), latent heat (R-2 > 0.7), sensible heat (R-2 > 0.7), and net radiation (R-2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R-2 < 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.
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