Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces.Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon,water and energy fluxes between the vegetation and near-surface atmosphere.Therefore,an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon,water and energy balance of terrestrial ecosystems.Phenological studies have developed rapidly under global change conditions,while the research of phenology modeling is largely lagged.Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues.In this review,we first summarized the drivers of plant phenology and overviewed the development of plant phenology models.Finally,we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.