# A Study of Forest Phenology Prediction Based on GRU Models
## 1 Introduction
[PERSON] used Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and carbon flux data, supplemented by phenology camera observation data and meteorological data, to propose a remote sensing-based phenology inversion model [11]. [PERSON] focused on the broad-leaved Korean pine forest of the Changbai Mountain and explored the role of digital camera images in species-scale phenological simulation and community-scale phenology model improvement [12]. [PERSON] et al. incorporated a water stress response function into the wheat growth model, improving the simulation accuracy of wheat phenology periods [13]. Physics-based models can predict change processes but require a large amount of relevant information such as meteorological data, terrain data, soil characteristics, and vegetation over, as well as complex mathematical tools and users with a lot of specialized knowledge [14]. Empirical models, on the other hand, are mainly represented by data-driven models, establishing a direct mapping between input features and output targets without the need to understand the physical processes [15]. Machine learning models belong to the latter category [16]. Deep learning is a new research direction in the field of machine learning, which can acquire the internal rules and representation hierarchy of sample data through training. Currently, deep learning has been applied in the field of phenology analysis, no longer limited to extracting vegetation indices but can directly classify phenological stages by learning the features of time series images. At the same time, deep learning has the ability to handle long time series data, such as recurrent neural networks (RNNs), which are one of the most popular neural networks in the field of deep learning and have been proven to be effective tools for time series prediction [17; 18]. However, the inherent problem of RNN is the vanishing or exploding gradient, which has not significantly improved its performance in sequence prediction [19]. Long short-term memory neural networks (LSTMs) optimize and solve this problem based on RNN, and many studies have shown that LSTMs perform better than traditional RNNs. In recent years, gate recurrent units (GRUs) [20], which have been optimized based on LSTMs, have appeared and shown good performance in natural language processing, with simpler structure and higher operation speed [21; 22]. For example, [PERSON] used a pre-trained convolutional neural network to analyze the phenological stages of six crops such as wheat, cotton, and corn [23] and compared them with the phenological stages obtained from the extracted image texture features and the Naive-Bayes classifier. GRU prediction models have been successfully applied in the prediction of industrial, medical, and agricultural yields [24; 25]. [PERSON] developed a model using Sentinel-2 images time series and the Welch \(t\)-test to identify vegetation removal in the urban-rural interface for fuel management actions to aid firefighting [26]. [PERSON] compared the classification performance and sensitivity of SVM, RF, and CART algorithms for winter wheat identification using Sentinel-2 images, finding SVM and RF to have optimal performance and low sensitivity to parameter changes, while CART was sensitive and had inferior performance [27]. [PERSON] presented a novel deep learning model for pixel-based land cover and crop classification using multi-temporal Sentinel-2 imagery, achieving high accuracy and reducing the need for manual feature engineering [28]. Carbon neutrality is a key path and regulatory target for adaptation and mitigation of climate change, and forests are the mainstay of urban ecological environment and carbon neutrality. There is currently no relevant research directly using deep learning to predict forest phenology. In this study, the GRU model was employed to train GEI data from three locations in the forest area, effectively predicting the vegetation phenology changes.