Evaluating intra-season multi-temporal data organization strategies for land cover classification in irrigation districts using multimodal remote sensing data

The integration of multimodal and multi-temporal remote sensing data has become an important approach for improving land cover classification in agricultural irrigation districts. However, how different feature organization strategies influence classification performance remains insufficiently explored. This study systematically investigates the effects of multimodal feature combinations, temporal organization strategies, and temporal accumulation periods on land cover classification. An ensemble learning-based classification framework was developed to quantitatively evaluate these factors using multi-temporal Sentinel-1 SAR, Sentinel-2 imagery, and derived spectral indices. Two representative irrigation districts in China, the Hongqi Canal Irrigation District and the Changshou Irrigation District, were selected as study areas. Experimental results demonstrate that the organization strategy of multimodal and multi-temporal features significantly affects classification accuracy. Specifically, optical MSI features generally contributed more to classification performance than SAR features, while multimodal integration provided complementary information that improved robustness and spatial consistency. Temporal ordering of multimodal features influenced model performance, and longer temporal accumulation periods enhanced classification accuracy by better capturing crop phenological dynamics, although diminishing returns were observed beyond a certain period. Overall, this study provides a systematic evaluation of multimodal and multi-temporal feature organization strategies and offers practical guidelines for optimizing feature construction in irrigation district land cover classification.