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 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 CPS (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
$\mu$
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
$P_{v}$
provide 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 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.