Application of the Trace Coherence to HH-VV PolInSAR TanDEM-X Data for Vegetation Height Estimation

Citation

Romero-Puig N, Marino A & Lopez-Sanchez JM (2022) Application of the Trace Coherence to HH-VV PolInSAR TanDEM-X Data for Vegetation Height Estimation. IEEE Transactions on Geoscience and Remote Sensing, 60, Art. No.: 4404210. https://doi.org/10.1109/TGRS.2021.3101016

Abstract
This article investigates, for the first time, the inclusion of the operator Trace Coherence (TrCoh) in polarimetric and interferometric synthetic aperture radar (SAR) methodologies for the estimation of biophysical parameters of vegetation. A modified inversion algorithm based on the well-known Random Volume over Ground (RVoG) model, which employs the TrCoh, is described and evaluated. In this regard, a different set of coherence extrema is used as input for the retrieval stage. In addition, the proposed methodology improves the inversion algorithm by employing analytical solutions rather than approximations. Validation is carried out exploiting single-pass HH-VV bistatic TanDEM-X data, together with reference data acquired over a paddy rice area in Spain. The added value of the TrCoh and the convenience of the use of analytical solutions are assessed by comparing with the conventional polarimetric SAR interferometry (PolInSAR) algorithm. Results demonstrate that the modified proposed methodology is computationally more effective than current methods on this dataset. For the same scene, the steps required for inversion are computed in 6 min with the conventional method, while it only takes 6 s with the proposed approach. Moreover, vegetation height estimates exhibit a higher accuracy with the proposed method in all fields under evaluation. The root-mean-squared error reached with the modified method improves by 7 cm with respect to the conventional algorithm.

Keywords
Coherence; Covariance matrices; Synthetic aperture radar; Scattering; Estimation; Decorrelation; Vegetation mapping

Journal
IEEE Transactions on Geoscience and Remote Sensing: Volume 60

People

Dr Armando Marino

Associate Professor, Biological and Environmental Sciences