Synergistic active and passive remote sensing with multi-feature optimization for winter wheat identification in...

Accurate mapping of winter wheat is essential for food security, agricultural management, and climate adaptation strategies. We present a multi-source, phenology-aware approach for identifying winter wheat in the arid oasis agricultural landscape of Changji City (northern Xinjiang) using Sentinel-1 C-band SAR and Sentinel-2 surface reflectance imagery acquired during key growth stages in 2023. We constructed a comprehensive six-category feature set-polarimetric, textural, spectral, vegetation indices, normalized-difference indices, and temporal-difference features-and designed eleven feature-combination schemes for comparative evaluation. A Random Forest-based forward sequential feature selection (RF-FS) procedure was used to identify the most informative predictors. From an initial 171 features, RF-FS selected 27 key variables (an 84.4% reduction), dominated by SWIR bands (B11/B12), a phenology index (NDPI), and SAR polarimetric combinations; moisture-sensitive features comprised 40.7% of the selected subset. The optimized model achieved an overall accuracy (OA) of 94.60% and a Kappa coefficient of 0.9338 (producer's accuracy [PA] = 100%). Temporal analysis indicates that April and May contributed the most discriminative information, corresponding to rapid biomass accumulation and the jointing-heading stages. Applying the optimized classifier yielded an estimated 2023 winter wheat area of approximately 93.39 km2 for Changji, with a relative error of 7.79% compared to statistical records. The results validate a "multi-source synergy, phenology-driven, moisture-sensitive" framework for efficient winter wheat mapping in arid desert-oasis mosaics and provide an operational basis for crop structure monitoring and agricultural risk assessment.