The proposal aims at building and applying a novel bottom-up modelling system for better estimates of European biosphere carbon balance components that integrates data from CARBOEurope eddy covariance flux towers, remote sensing via the EOS MODIS sensor, European soils, and meteorological observations. For that purpose, eddy covariance net ecosystem carbon exchange data from boreal to Mediterranean sites will be separated into the components gross carbon uptake and ecosystem respiration. Models that compute these major fluxes will be parameterised with this data via a newly developed robust inverse modelling tool that is insensitive to outliers in the data and that yields uncertainty estimates of the model parameters. European-scale spatial data sets (remotely sensed vegetation properties, land cover, soil, meteorology) will be adapted to drive the parameterised models for the estimation of spatially distributed carbon balance components. Subsequently, a factorial error analysis of these spatial carbon flux estimates will be performed at the pixels corresponding to the European flux tower sites and an uncertainty analysis will be commenced. Thus, for the first time a robust inverse modelling tool will be developed and European soil information will be integrated to estimate European scale carbon balances. The project will be carried out at one of the core centres of European carbon cycle research, where the applicant will gain international experience via optimal integration into the international carbon cycle research network. Through the project the applicant will sharpen his skills in modern computational methods, applied vegetation remote sensing and spatial data integration, and will receive complementary training in neural network modelling for environmental applications. He will follow and co-develop the scientific progress via active participation in carbon cycle related workshops organised by the host institution.
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