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The quantification of forest Gross Primary Productivity (GPP) has been the focus of many scientific studies (e.g. carbon cycle, climate change, etc.). Current remote sensing-based models (i.e., the MODIS MOD_17 model), rely on the accurate meteorological data, specific vegetation parameter, the applicability and explicability of remote sensing data. In this study, the original MODIS GPP products were...
A strategy of data assimilation using the refined remote sensing product for the process-based model (Biome-BGC) in order to improve the simulated carbon fluxes was proposed. Firstly, we applied the optimized the remote-sensing-based MODIS MOD_17 GPP (MOD_17) model to calibrate the process-based Biome-BGC model. This incorporation strategy for the parameterization of Biome-BGC has been proved to be...
An approach was used to incorporate the forest carbon flux for Qilian Mountains by ecological-process-based model (Biome-BGC), and remote-sensing-based model (MODIS-PSN). The calibration phase, aiming at setting the ecophysiological parameters to effectively simulate the daily GPP behavior of the Qilian Mountains, was proceeded by adjusting the 8 day GPP outputs obtained from Biome-BGC using the optimized...
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