Several orthonormal projections onto various bases have been proposed to analyze Polarimetric Synthetic Aperture Radar (PolSAR) data. These individual projections frequently lead to several additional ambiguities for target scattering characterization. Therefore, considerable confusion is common when using unsupervised classification approaches to classify targets. In this study, we project the scattering information onto several realizations of the normalized scattering configuration without imposing an orthogonality requirement. Using the full-polarimetric AIRSAR data over San Francisco, USA, we first compute the spectrum of the scattering-type parameter, θFP, and subsequently use it to categorize various land-cover targets.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi -baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences.

This study proposes the spectrum analysis of the scattering-type parameter, θFP, to characterize different land cover targets. Many orthonormal projections of the scattering information onto distinct polarization bases have been proposed in the literature. However, these conventional orthonormal basis projection techniques often provide various target characterization ambiguities and classification. In this work, we propose projecting the target coherency matrix onto several random realizations of the normalized scattering configuration without restriction to the orthogonality constraint. This unique approach helps enhanced target characterization from polarimetric SAR data. We show the efficacy of our proposed technique over different land cover types using the C-band RADARSAT-2 data over SF and the L-band ALOS-2 data over Mumbai.

The contribution of Polarimetric SAR Interferometry (PolInSAR) observables to crop-type classification is investigated in this letter. The focus is set on characteristic parameters of the Coherence Region (CoRe), i.e. the representation in the polar plot of the PolInSAR data. For this purpose, time series of dual-pol HH-VV single-pass TanDEM-X bistatic data acquired over an agricultural area in Spain are exploited. In the experiment, up to 13 different crop types are evaluated. Crop classification is performed by means of the well-known Random Forest algorithm. The retrieved accuracy metrics highlight the potential of the evaluated PolInSAR descriptors for this application. Some PolInSAR features have proven to be enough representative of the scene, such as the Trace Coherence, which yields a classification accuracy of 75% and 87% at pixel and field level, respectively, on its own. Using all the PolInSAR parameters jointly as input features, classification reaches around 90% and 94% accuracy at pixel and field level, respectively. However, there are some PolInSAR feature subsets, e.g. the coherence measured at the Pauli channels or the foci of the ellipse which represents the CoRe, which yield accuracy levels very close to these maxima. These results demonstrate the suitability of the PolInSAR parameters for crop-type classification. Results are further improved when both polarimetric and PolInSAR features are combined, reaching 94% and 96% accuracy at pixel and field level, respectively.

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.

The first results of a project aimed at estimating forest height over the entire Spain by means of TanDEM-X data are shown and discussed in this work. Four test sites representative of the Spanish forests are introduced, as well as the data used for validation of results. Results obtained over one of the test sites (in Teruel province) are presented here. Among the challenges found in the project, the influence of slope in mountain areas and the relatively short height of the forest canopy make this work distinctive to previous projects employing TanDEM-X data for estimation of heights in tropical, boreal and temperate regions.

This paper compares single-pol SAR interferometric and polarimetric SAR interferometric strategies for the estimation of agricultural crop parameters using bistatic TanDEM-X data. An experiment is conducted with data acquired over rice fields in Spain during the Science Phase of this mission. Results show that PolInSAR-based approach and a method based on the coherence amplitude provide then most accurate height estimates. Results derived from single-pol approaches depend on the varying signal penetration of HH and VV channels at different growth phases. Inversion schemes based on the interferometric phase and the complex coherence are also analyzed and provide good results in some cases.

This article evaluates the effect of the double-bounce (DB) decorrelation term that appears in single-pass bistatic acquisitions, as in the TanDEM-X system, on the inversion of scene parameters by means of polarimetric SAR interferometry (PolInSAR). The retrieval of all scene parameters involved in the Random Volume over Ground (RVoG) model (i.e., ground topography, vegetation height, extinction, and ground-to-volume ratios) is affected by this term when the radar response from the ground is dominated by the DB. The estimation error in all these parameters is analyzed by means of simulations over a wide range of system configurations and scene variables for both agricultural crops and forest scenarios. Simulations demonstrate that the inclusion of the DB term, which complicates the inversion algorithm, is necessary for the angles of incidence shallower than 30° to achieve an estimation error below 10% in vegetation height and to avoid a significant underestimation in the ground-to-volume ratios. At steep incidences, this decorrelation term does not affect the estimation of vegetation height and ground-to-volume ratios. Regarding the extinction, this parameter is intrinsically not well estimated, since most retrieved values are close to the initial guesses employed for the optimization algorithm, regardless of the use or not of the DB decorrelation term. Finally, these findings are compared with the experimental results from the TanDEM-X data acquired over the rice fields in Spain for the available system parameters (baseline and incidence angle) of the acquired data set.

In bistatic acquisitions the presence of a double-bounce contribution at the ground affects the interferometric coherence with a decorrelation factor which is usually overlooked in studies employing polarimetric SAR interferometry. The standard acquisition mode of TanDEM-X is bistatic, so the influence of this contribution in the estimation of scene parameters (ground topography and vegetation height) is studied here. The analysis is carried out both with simulations and real data acquired over rice fields during the science phase of TanDEM-X. Results show that the error in height and topography is small when incidence angle is below 30 degrees, but may become noticeable for shallower incidences.

Polarimetric SAR interferometry has been recently applied with TanDEM-X data to the retrieval of vegetation height in rice fields, which constitutes the first demonstration of this technique applied to agricultural crops with satellite data [1]. In this work we extend that study by including an analysis of the effect of incidence angle and baseline on the retrieval of height. The study is based on the exploitation of 6 time series of TanDEM-X acquisitions during its science phase: 3 over Sevilla (Spain), with incidence angles of 22, 30 and 39 degrees, and 3 over Ipsala (Turkey), with incidence angles of 30, 36 and 44 degrees.