Enhanced Crop Discrimination and Monitoring Using Compact-Polarimetric SAR Signature Analysis From RADARSAT Constellation Mission

With the rapid advancements in SAR systems aiming for operational capabilities, crop characterization using Compact-Polarimetric (CP) Synthetic Aperture Radar (CP-SAR) data has gained considerable attention. This study thoroughly assesses the potential usefulness of C-band SAR data in CP mode using the RADARSAT Constellation Mission (RCM) for crop monitoring. The research unfolds across two separate phases: (1) extensive crop scattering characterization and (2) crop classification. In the first part, we introduce three descriptors: compact-polarimetric SAR signature ($CPS$), differential compact-polarimetric signature ($DCPS$), and the Geodesic Distance ($GD$) between signatures, to characterize the scattering pattern of four crop types: Soybean, Hay, Corn, and Cereal. We then derive the μ parameter and employ it in the$\mu -\chi$decomposition method. Time-series investigation of the proposed descriptors and the three power components:$P_{s}$,$P_{d}$, and$P_{v}$provides valuable insights into the scattering responses exhibited by crops, facilitating a robust assessment and tracking of their growing cycle, thus enabling the potential for improving crop discrimination. In the second part, we employ the$\mu -\chi$and$m-\chi$decompositions and wave descriptors to extract a stack of CP features for crop mapping. Combining diverse feature types and leveraging single and multi-date RCM images, classification experiments yield an optimal classification map with an overall accuracy of 89.71%, particularly when utilizing features extracted from multi-date datasets. This study illustrates a substantial effort in crop classification, underscoring the potential of the RCM Circular Polarization Synthetic Aperture Radar (CP-SAR) mission. Furthermore, our findings emphasize the potential of CP-SAR data from the RCM mission in contributing to precision agriculture and sustainable crop management practices.

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