Time–Frequency Feature Selection Guided by Phenological Synchronization for Apple Orchard Identification

Accurate spatial mapping of apple orchards is essential for regional agricultural planning and sustainable management; however, large-scale, multi-year mapping remains challenging because spectral confusion and climate-driven inter-annual phenological variation complicate interpretation. To address these challenges, we propose a Phenological Synchronization and Time–Frequency Feature Selection framework (FSITF) for high-precision apple orchard identification using Sentinel-2 time-series observations. We evaluated the framework in Shandong and Shaanxi provinces, two major apple-producing regions in China. FSITF consists of three main components: phenological synchronization, time–frequency feature construction, and discriminative feature selection. First, we develop a phenological synchronization strategy based on derivative analysis and correlation-based temporal alignment to reduce inter-annual and regional temporal discrepancies in phenological trajectories, thereby generating standardized cross-year phenological trajectories. Second, Harmonic Analysis is combined with temporal phenological metrics to construct a complementary time–frequency feature representation, enabling the characterization of complex perennial growth dynamics from both temporal evolution and periodic variation perspectives. Third, a dual-metric feature selection strategy integrating Jeffries–Matusita distance-based class separability and Random Forest-based feature importance is developed to identify informative features and reduce high-dimensional redundancy. Experimental results demonstrate that FSITF achieves overall accuracies exceeding 93% and Kappa coefficients above 0.85 in both study regions, outperforming conventional approaches without phenological synchronization and feature optimization. These results indicate that integrating phenological alignment, time–frequency feature representation, and orchard-oriented feature selection can effectively improve the accuracy and robustness of regional perennial crop mapping, providing a reliable framework for large-scale orchard monitoring.