Phenological models are essential for predicting the timing of key life cycle events in plants, yet most applications rely on coarse‐resolution remote sensing products and focus on broad vegetation types. These approaches, often based on satellite-derived vegetation indices are effective at large spatial scales but lack the resolution needed to resolve individual tree responses. Such limitations are especially important in urban areas, where species composition, land cover, and local microclimates vary dramatically across short distances. As a result, there is a need for finer-scale phenological data and models that can capture species-specific and site-level variation in phenology within cities. High-resolution remote sensing-based observations of phenological events at the individual tree level could be used to parameterize phenological models, which would enable more accurate predictions and deeper insights into how different species respond to local environmental conditions. Here, we integrate high‐resolution PlanetScope NDVI (3 m) and XIS-temperature data (90 m) to parameterize phenological models for predicting leaf out timing of four oak species (Quercus alba, Q. rubra, Q. robur, and Q. velutina) at the individual tree level across New York City. Using the estimated date of green up for individual trees from 2018 to 2023, we evaluated six commonly used phenology models with 7,186 oak trees. The Thermal Time (TT) and Alternating (AT) models showed the best overall performance across species, with RMSE values of 5.39 (TT) for Q. rubra and 5.05 (AT) for Q. velutina. RMSE differences among the four best‐performing models were small (~2 days), and all models outperformed both the null and linear (LIN) models. Across species, Q. rubra and Q. velutina also showed the highest prediction accuracy, with values closely aligning to the 1:1 line. Spatially, leaf out occurred earlier in Manhattan and western Queens and later in the Bronx and eastern Queen, consistent with local temperature patterns. These results demonstrate the feasibility of combining high‐resolution satellite‐derived phenology with fine‐scale climate data to produce species‐specific predictions in heterogeneous urban environments. This approach has the potential to advance urban forestry planning, allergenic pollen forecasting, and assessments of climate change impacts on urban ecosystems.