Model-Based Decomposition of Dual-Pol SAR Data: Application to Sentinel-1

In this study, we advance a new family of model-based decompositions adapted for dual-pot synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pot decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, nondipole options can he used. The cross-polarized phase delta, and the alpha angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 (S1) covering different scenarios, such as agricultural, forest, urban, and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to S1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for S1, for which dual-pot phase analysis coupled to dense time series offer great opportunities for land-ice monitoring.

Automated summary

In this study, a new model-based decomposition for dual-pot synthetic aperture radar data is developed using the Stokes vector formalism. The decomposition allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. The method is applied to dual-pol data provided by Sentinel-1, showing its potential for key applications such as classification and geo-physical parameter estimation. The study also demonstrates the significant impact of polarized contribution on backscattered state, especially over glaciers.