Accurate large-scale crop mapping is fundamental to agricultural management. However, in Liaoning Province, undulating terrain and fragmented fields make fine crop classification challenging. In particular, corn and soybean have overlapping phenologies, which can lead to spectral and structural confusion in conventional optical–SAR feature spaces and limit mapping accuracy. This study proposes a fine crop mapping framework integrating optical phenotypic, microwave structural, and meteorological time-series features. To overcome the curse of dimensionality caused by high-dimensional heterogeneous data, an adaptive feature truncation mechanism based on the transition pattern of the marginal-gain curve was designed. Additionally, a pyramid multi-scale sliding window algorithm was constructed to optimize meteorological features, achieving dimensionality reduction and precise identification of phenologically sensitive windows. The results indicate that: (1) The multi-scale feature selection strategy effectively eliminates redundant variables and maximizes the inter-class discriminability of core features, significantly improving computational efficiency and classification performance. (2) High-frequency meteorological features provide key physiological constraints. Specifically, mid-May shortwave radiation, early October precipitation, and early August growing degree days constitute the core environmental–physiological features for distinguishing confused crops, helping to mitigate the spectral confusion of dryland crops. (3) Driven by the multi-source features, the Support Vector Machine (SVM) exhibits the optimal generalization robustness for processing high-dimensional structured data, yielding an overall classification accuracy of 91.80% and a Kappa coefficient of 0.8905. This framework provides a reliable methodological reference for high-precision crop monitoring in large-scale complex planting areas.
Dong et al. (Thu,) studied this question.