6533b7d5fe1ef96bd1263acb

RESEARCH PRODUCT

Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

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Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE) ~ 1.8 g C m−2 d−1). However, when predictions were based on only remotely sensed data the accuracy was close to the optimum (ρ ~ 0.8; RMSE ~ 1.9 g C m−2 d−1) and to a commonly used light use efficiency model (MOD17) with parameters optimised for the applied study sites (the MOD17 +, ρ ~ 0.79; RMSE ~ 2.04 g C m−2 d−1). Remotely sensed data were key drivers for the accurate prediction of GPP in ecosystems with high variability of green biomass over the phenological cycle (e.g., deciduous broad-leaved forests) or highly affected by the human management (e.g. croplands). In contrast, in the ecosystems with low variability of greenness (e.g., evergreen broad-leaved forests), the predictions were poor when meteorological information were not used. At a European spatial scale, when modelled grids of meteorological, land cover and fPAR data were used as inputs, the propagation of their uncertainty, not accounted in the models training, had significant effects on the uncertainty of the mean annual GPP. At this scale, the effects of meteorological uncertainty were higher than the misclassification error. These findings suggested that a strategy based on satellite-measured data could be a favourable improvement for the spatial upscaling of GPP, because avoiding the propagation of the uncertainties of the modelled grids.