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Phenologies of the vegetated land surface are being used increasingly fordiagnosis and prognosis of climate change consequences. Current prospectiveand retrospective phenological models stand far apart in their approaches tothe subject. We report on an exploratory attempt to implement a phenologicalmodel based on a new event driven concept which has both diagnostic andprognostic capabilities in the same modeling framework. This Event DrivenPhenological Model (EDPM) is shown to simulate land surface phenologies andphenophase transition dates in agricultural landscapes based on assimilationof weather data and land surface observations from spaceborne sensors. Themodel enables growing season phenologies to develop in response to changingenvironmental conditions and disturbance events. It also has the ability toingest remotely sensed data to adjust its output to improve representationof the modeled variable. We describe the model and report results of initialtesting of the EDPM using Level 2 flux tower records from the Amerifluxsites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulatingthe dynamics of normalized difference vegetation index based on flux towerdata, the predictions by the EDPM show good agreement (RMSE < 0.08;<i>r</i><sup>2</sup> > 0.8) for maize and soybean during several growing seasons atdifferent locations. This study presents the EDPM used in the companionpaper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate dailyactual evapotranspiration over multiple growing seasons

A new concept for simulation of vegetated land surface dynamics – Part 1: The event driven phenology model