In recent years, new algorithms have been proposed to retrieve maximum available information in synthetic aperture radar (SAR) interferometric stacks with focus on distributed scatterers. The key step in these algorithms is to optimally estimate single-master (SM) wrapped phases for each pixel from all possible interferometric combinations, preserving useful information and filtering noise. In this paper, we propose a new method for SM-phase estimation based on the integer least squares principle. We model the SM-phase estimation problem in a linear form by introducing additional integer ambiguities and use a bootstrap estimator for joint estimation of SM-phases and the integer unknowns. In addition, a full error propagation scheme is introduced in order to evaluate the precision of the final SM-phase estimates. The main advantages of the proposed method are the flexibility to be applied on any (connected) subset of interferograms and the quality description via the provision of a full covariance matrix of the estimates. Results from both synthetic experiments and a case study over the Torfajökull volcano in Iceland demonstrate that the proposed method can efficiently filter noise from wrapped multibaseline interferometric stacks, resulting in doubling the number of detected coherent pixels with respect to conventional persistent scatterer interferometry.

The intra-plate deformation of the Upper Rhine Graben (URG) located in Central Europe is investigated using geodetic measurement techniques. We present a new approach to calculate a combined velocity field from InSAR, levelling and GNSS measurements. As the expected tectonic movements in the URG area are small (less than 1 mm a⁻¹), the best possible solutions for linear velocity rates from single-technique analyses are estimated in a first step. Second, we combine the velocity rates obtained from InSAR (line of sight velocity rates in ascending and descending image geometries), levelling (vertical velocity rates) and GNSS (horizontal velocity rates) using least-squares adjustment (LSA). Focusing on the Northern URG area, we analyse SAR data on four different image stacks (ERS ascending, ERS descending, Envisat ascending, Envisat descending) using the Persistent Scatterer (PS) approach. The linear velocity rates in ascending and descending image geometries, respectively, are estimated in an LSA from joint time-series analysis of ERS and Envisat data. Vertical velocity rates from levelling are obtained from a consistent adjustment of more than 40 000 measured height differences using a kinematic displacement model. Horizontal velocity rates in east and north direction are calculated from a time-series analysis of daily coordinate estimates at 76 permanently operating GNSS sites in the URG region. As the locations, at which the measurement data of PS-InSAR, levelling and GNSS reside, do not coincide, spatial interpolation is needed during several steps of the rigorous processing. We use Ordinary Kriging to interpolate from a given set of data points to the locations of interest with a special focus on the modeling and propagation of errors. The final 3-D velocity field is calculated at a 200 m grid, which carries values only close to the location of PS points, resulting in a mean horizontal and vertical precision of 0.30 and 0.13 mm a⁻¹, respectively. The vertical component of the combined velocity field shows a significant subsidence of about 0.5 mm a⁻¹ in the northern part of the graben coinciding with a well-known quaternary basin structure. Horizontal displacement rates of up to 0.8 mm a⁻¹ in southeast direction are observed outside the graben, in reasonable alignment with the average direction of maximum horizontal stress. Within the graben, the velocity directions rotate toward east in the non-subsiding part, while an opposite trend is observed in the subsiding part of the graben. The complexities of the observed velocity field are compatible to the geomechanical situation in our investigation area which is characterized by a transition from a restraining to a releasing bend setting. Glacial isostatic adjustment is another potential source influencing the observed velocity field, as well as anthropogenic signals due to mining, oil exploration and groundwater usage that have been identified in some places.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.