## 3 Prediction of Short-Term Land Surface Phenology Using Machine Learning  
### Prediction of Short-Term Vegetation Phenology Using Machine Learning  
Machine learning is increasingly utilized for vegetation phenology prediction in agriculture, leveraging vast datasets generated from various sources such as satellite imagery, drones, and ground sensors (Figure 2). Machine learning models effectively handle the variability in vegetation phenology across different regions by integrating diverse environmental and spatial data, enabling them to adapt to local conditions and phenological responses [94]. A literature review of scientific studies focusing on short-term vegetation phenology prediction reveals a variety of methodologies and applications that leverage advanced modeling techniques. [PERSON] and [PERSON] [95] developed a framework for short-term phenological predictions of vegetation abundance using multivariate adaptive regression splines (MARS) in the Upper Colorado River Basin. Their study highlighted the ability of MARS to handle complex, nonlinear relationships between environmental variables and vegetation dynamics, achieving high predictive accuracy with R\({}^{2}\) values ranging from 91.62% to 97.22%. Similarly, a study by [PERSON] et al. [96] utilized gated recurrent unit (GRU) models for forest phenology prediction, demonstrating that this approach effectively captured temporal dependencies in phenological changes and provided accurate forecasts over a 60-day horizon. The GRU model's simplicity and efficiency made it a preferred method for predicting forest growth in response to climate variations. Additionally, a convolutional neural network-recurrent neural network (CNN-RNN) framework was introduced by [PERSON] et al. [97] for crop yield prediction, which also incorporated environmental data to enhance understanding of phenological responses across different agricultural settings. This hybrid model outperformed traditional methods by accurately capturing the interactions between climatic factors and crop management practices, emphasizing the importance of integrating machine learning techniques in phenological studies [97]. Based on the above, machine learning methods for predicting short-term vegetation phenology can be categorized into supervised techniques such as regression models (e.g., linear regression, random forest), time series models (e.g., GRU, RNN), and hybrid techniques (CNN-RNN). Figure 1: The number of land surface phenology indexed in Web of Science Core Collection (WoSCC) based on various global open Earth observation satellite missions. The selection of environmental covariates is mandatory in the prediction of short-term vegetation phenology, which refers to the timing of biological events in vegetation, as well as for predictive mapping in general [98]. The most common environmental covariates used in vegetation phenology prediction include temperature, precipitation, soil moisture, and solar radiation [99]. Temperature is a primary factor influencing the timing of phenological events, with warmer temperatures often leading to earlier flowering and leaf-out, while precipitation patterns significantly affect soil moisture, as fluctuations in rainfall can alter phenological responses [100]. Soil moisture itself is a key covariate, as it directly impacts plant hydration and nutrient uptake, influencing overall vegetative development. Additionally, solar radiation is vital for photosynthesis and can affect the vigor of plants, thereby impacting their phenological timing [101]. Other important covariates may include land use, elevation, and even bioclimatic factors that capture complex interactions within ecosystems. Climate change significantly impacts vegetation phenology in agricultural settings, altering the timing of key growth stages and affecting crop productivity. Rising temperatures, a hallmark of climate change, have been linked to advanced spring phenology and delayed autumn phenology, resulting in an extended growing season for many crops [102].