# A Study of Forest Phenology Prediction Based on GRU Models
## 1 Introduction  
Plants exhibit high sensitivity to interannual climate change, and their growth and development are significantly influenced by climate change. As climate change continues to be influenced by human activities such as urbanization, coupled with the exacerbating effects of urban environmental issues such as the urban heat island effect and air pollution, the impact on urban vegetation growth is further intensified. Climate change has had irreversible effects on the life rhythms of many plants, mainly manifested in changes in the phenology of plant growth stages such as budburst, flowering, leaf expansion, peak leaf, autumn coloration, and leaf fall, as well as the shift in phenological synchronicity [1]. Byaltering their phenology, plants adapt to seasonal changes in their surroundings, providing the most direct evidence for climate change and indicating that species and ecosystems are being affected by global environmental changes, thereby limiting the stable provision of their vegetation ecosystem services [2]. Phenological observations will continue to provide data for global change research, and long-term monitoring of the main phenological phases of urban forests will provide a basis for studying the response of natural ecosystems to future climate change. Accurate phenological models will improve the accuracy of predicting ecosystem productivity and gas exchange with the atmosphere, thus enabling accurate predictions of future climate change trends. Therefore, predicting phenological phases is crucial for understanding how plants respond to climate change. Achieving accurate predictions of forest phenology and enhancing the ecological adaptability of vegetation to climate change can reveal the laws of ecosystem carbon cycling and is closely related to assessing the ecological environment and climate forecasting. Phenological prediction models are important tools for quantitatively studying the relationship between climate factors and plant phenology and can be used to simulate trends in vegetation distribution, reconstruct past climate changes, predict future climate changes, and assess risks in agriculture and forestry production. Many predictive models have been developed to explain the historical trends of plant phenology and predict future phenological trends [3; 4]. Currently, vegetation phenological prediction models are often established using different meteorological factors, satellite remote sensing, and ground-based remote sensing parameters. For example, machine learning is used to predict vegetation phenology models using satellite remote sensing and gridded data [5]. [PERSON] et al. proposed the Growth Status Index (SSI) model, which is a classic bioclimatological phenology model index composed of minimum temperature, evaporation demand, and photoperiod [6]. [PERSON] et al. proposed a predictive model for autumn phenology, which revealed the coupling effect of shortened photoperiod and decreased minimum daily temperature on leaf senescence [7]. [PERSON] used machine learning and satellite remote sensing and gridded data to predict vegetation phenology models [5]. [PERSON] installed phenology cameras in the carbon and water flux network to obtain leaf-out and leaf-fall phenology periods and established a phenology model related to canopy leaf area, chlorophyll, and anthocyanin [8]. [PERSON] et al. used rubber tree phenology quantification research and rubber tree observation test data as the basis, combined with crop growth clock model, to establish a rubber tree spring phenology prediction model and developed it into computer software [9]. [PERSON] used phenological observation data of tropical evergreen broad-leaved trees combined with monthly average temperature and precipitation data of the same period and used the integral regression analysis method to establish a phenology prediction model [10].