Abstract
Spring vegetation phenology determines the onset of the growing season. Changes in spring vegetation phenology alter the length of the growing season and thereby affect ecosystem productivity and regional and global carbon and energy balances. Satellite-derived vegetation indices have long been used as proxies for representing the status of terrestrial vegetation.
However, the modeling of such large scale vegetation phenology dynamics is still a big challenge because the underlying mechanisms of vegetation phenology process are still unclear. To date, the performance of vegetation phenology models at global scale is rarely examined.
Within this project, global-scale vegetation phenology models will be developed based on specieslevel models. Bayesian model comparisons will subsequently be conducted to select the most parsimonious vegetation phenology model for global carbon cycle models. In addition, remote sensing-based phenological dates will be compared to ground observations at species level to answer whether the satellite images capture the phenology dynamics observed in situ. This project also aims to explore the recent controversial debate on the amplitude of the advancement of spring phenology since the 1980s. The present study will make a step forward in the study of vegetation phenology and will have important implications for the ecological modeling community by suggesting the most optimal phenology model.
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