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This study investigates the application of coherence and backscattering, derived from time-series Sentinel-1 synthetic aperture radar (SAR) imagery of a crop season (18 scenes with a 12-day revisit cycle), for crop growth monitoring and classification in the agricultural region of Southwestern Ontario, Canada. To fulfill this goal, we initially analyze the temporal behavior of backscattering and coherence for a variety of crops to gain some insights for classification. Secondly, diverse combinations involving polarization channels, feature types, and image quantities for crop classification are analyzed. The deep analysis of temporal dynamics highlights a stronger correlation between the time-series curves of backscattering and crop growth in comparison to coherence. The VH backscattering and the VV coherence demonstrate a higher sensitivity to the variations in crop growth. The crop mapping results indicate that backscattering produces significantly higher accuracy of crop classification than coherence. Furthermore, the incorporation of coherence features with backscattering can enhance the accuracy, with VV making more pronounced contributions compared to VH. Notably, the most effective classification result is achieved through a scheme that integrates both backscattering coefficients of dual polarization (VV&#x002B;VH) and the VV coherence, achieving a better overall accuracy of 95.33&#x0025; and a kappa coefficient of 0.93. This study concludes that the crucial information provided by the temporal variations in backscattering and coherence to improve crop classification accuracy depends on both the polarization channel and crop type, with coherence playing a supplementary role. Our study consolidates the previous work and provides useful insights into the field of crop classification.

Understanding the Temporal Dynamics of Coherence and Backscattering using Sentinel-1 Imagery for Crop-Type Mapping