On the Estimation of Ground and Volume Polarimetric Covariances in Forest Scenarios With SAR Tomography

Abstract

A two-layer model composed by ground and volume contributions has been proven suitable to describe the 3-D backscattering signatures of forest scenarios in a number of experiments. Under this hypothesis, the purpose of this letter is to investigate how synthetic aperture radar tomography (TomoSAR) can be used to estimate ground and volume polarimetric covariances and with which performance. An algorithm which is able to overcome the intrinsic ambiguity in the estimation problem is proposed, and it is shown to be a reliable alternative to the poorly performing full-rank Capon beamformer for estimating the ground polarimetric covariances. This performance improvement can be achieved, for instance, if an a priori knowledge of the ground topography (or an accurate estimate of it) is available. This analysis is carried out by processing an L-band TomoSAR stack acquired by the DLR’s E-SAR sensor over the temperate forest site of Traunstein.