Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal spatial resolutions with large swath width. Generally, phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or eigendecomposition. Therefore, approach fails to utilize complete polarization information of scattered wave. study, we propose a target characterization parameter, θxP that utilizes 2D Barakat degree matrix. We also an unsupervised clustering scheme using scattering entropy, HxP. time-series Sentinel-1 data canola wheat fields over Canadian test site show sensitivity morphology at different stages. During initial growth stages, values low due vegetation density. contrast, advanced observe decreased appearance complex canopy structure. Similarly, effectiveness HxP/θxP plane is evident plots. This innovative framework beneficial for operational use agricultural applications.