Optimizing Multi-Sensor Sentinel Feature Subsets for Crop Mapping with Spatial Cross-Validation Control

Accurate crop mapping is important for agricultural monitoring and land management; yet, identifying robust and compact feature subsets from high-dimensional multi-sensor remote sensing data remains challenging, particularly in heterogeneous agricultural landscapes affected by spatial autocorrelation. Although combining multi-sensor data provides complementary spectral and structural information, traditional workflows often neglect spatial dependence during feature evaluation, leading to over-optimistic validation metrics and spatially unstable feature subsets. To address this issue, this study proposes a hierarchical feature selection and subset optimization framework for crop mapping by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. A total of 135 multi-sensor features were constructed, including spectral bands, vegetation indices, SAR metrics, texture descriptors, and phenological statistics. To improve feature compactness and spatial robustness, a multi-stage selection strategy combining correlation-based redundancy removal, spatial cross-validation (SCV) control, Boruta, recursive feature elimination (RFE), L1 regularization, SHapley Additive exPlanations (SHAP), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed. Results showed that temporal and phenological features contributed more strongly to crop discrimination than static spectral or SAR features, while multi-sensor integration further improved classification stability. Notably, the proposed framework reduced the feature space from 135 to 12 variables while slightly improving classification performance. The final optimized model achieved an overall accuracy (OA) of 96.98% under SCV and generated spatially consistent crop maps at 10 m resolution. The framework provides an efficient and scalable solution for fine-scale crop mapping in complex agricultural regions and demonstrates the practical potential of incorporating spatial dependence control into feature selection for large-scale agricultural monitoring applications.