Passive Corner Reflectors (PCR) are often used in spaceborne SAR interferometry as benchmarks. The main goal of the use of PCRs in DInSAR deformation monitoring is to provide pixels with a high and stable response to be used as reference to estimate the deformation when natural persistent scatters are not available. The use of PCRs at C band is not always suitable, especially in areas as glaciers, snow covered regions, and mountain slopes, where accessibility and PCRs’ installation can be very time and cost consuming, or where harsh weather conditions can jeopardize their performance. An alternative to PCRs is Active Reflectors (AR), more compact and lighter apparatus, which need a power source, and are often susceptible to the natural air temperature variations which can affect the stability of their response. The study presented here reports on the use of an AR designed to operate with Sentinel-1 SAR data, installed with some PCRs aimed at comparing the performance of the two approaches. The AR was designed and implemented to provide a fair performance/cost benefit to make feasible the setup of a dense network. Images covering almost one year have been processed to compare the performance of a prototype installed close to our center. A real campaign was also carried out installing an AR together with a network of PCRs in a site, located in a mountain area of Andorra, where a landslide occurred in 2018, and where a monitoring based on DInSAR is ongoing.
Estimating unknown absolute phase from a wrapped observation is a challenging and ill-posed problem that possibly leads to misinterpretation of interferometric SAR (InSAR) deformation results. In this study, we introduce a quality index to cluster post-phase unwrapping multi-master InSAR timeseries outputs based on the estimated phase residuals and redundancy of network of interferograms. The index is supposed to indicate the reliability of a timeseries, including the identification of persistent scatterers (PSs) possibly affected by phase unwrapping jumps. The algorithm was tested on two Sentinel-1 interferometric datasets with 622,991 and 95,398 PSs, generated from the PSI processing chain PSIG of the geomatics division of CTTC. Promising result have been achieved-especially in identifying erroneous PSs with phase unwrapping jumps. Along with existing temporal phase consistency checking algorithms, the approach could provide rich information toward a better interpretation of the deformation timeseries results.
Full coverage and continuous deformation information retrieval are key aspects for dam health diagnosis. Ground-based synthetic aperture radar (GB-SAR) interferometry is used for the remote monitoring of the Geheyan Dam, China. Although the monitoring of a dam with ground-based interferometry is not an innovation, specific issues have been found out in the case study discussed due to the large dimension of the monitored structure. More than 400 images were used for interferogram generation. Radar signals reflected from the dam were carefully analyzed: a sort of tunneling effect caused by multireflection is observed, and deformations caused by water level and temperature variations were detected during a six-day monitoring campaign. Radar monitoring results were compared to the data recorded by plummets installed in the dam. The agreement between the displacements retrieved from interferometric data and the plummets demonstrates the capability of GB-SAR for deformation monitoring, with the advantage of large area coverage.
A new approach to Persistent Scatterer Interferometry (PSI) data processing and analysis implemented in the PSI chain of the Geomatics (PSIG) Division of CTTC is used in this work. The flexibility of the PSIG procedure allowed evaluating two different processing chains of the PSIG procedure. A full PSIG procedure was implemented in the TerraSAR-X dataset while a reduced PSIG procedure was applied to the nine Sentinel-1 images available at the time of processing. The performance of the PSIG procedure is illustrated using X-band and C-band Sentinel-1 data and several examples of deformation maps covering different types of deformation phenomena are shown.
The PSIG procedure is a new approach to Persistent Scatterer Interferometry (PSI), which is implemented in the in-house
PSI chain of the Geomatics Division of the CTTC. The PSIG procedure has been successfully tested over urban, rural
and vegetated areas using X-band SAR data. This paper briefly describes the main steps of the procedure, mainly
focusing on the two key processing steps of the approach. The first one is a selection of Persistent Scatterers (PS)
consisting in a candidate Cousin PS (CPS) selection based on a phase similitude criteria that allows a correct phase
unwrapping and a phase unwrapping consistency check. The second key element is a 2+1D phase unwrapping algorithm,
which consists in a 2D phase unwrapping followed by a 1D phase unwrapping that allows the detection and correction of
unwrapping errors. The results of the CPS selection and the 2+1D phase unwrapping obtained using a stack of 28
TerraSAR-X StripMap images over the metropolitan area of Barcelona are shown.
This paper focuses on the geometric applications of the SAR interferometry, i.e. the generation of digital elevation models (interferometric SAR, InSAR) and the monitoring of deformations (differential interferometric SAR, DInSAR). The InSAR and DInSAR techniques have in common most of the processing steps of their procedures. In this paper we describe a general DInSAR procedure for deformation monitoring. Furthermore, we discuss the phase unwrapping,
which represents a key processing step for both the InSAR and DInSAR techniques. The second part of the paper describe in depth the DInSAR results based on ERS images, which were obtained on the estimation of the co-seismic field associated with a series of earthquakes occurred in Central Italy in 1997.
The synergetic use of optical (SPOT stereo images) and radar (interferometric SAR images) data for DEM generation is addressed. The paper presents a complete interferometric SAR (InSAR) procedure and investigates the potentiality of SPOT data to support such a procedure and to improve the quality of the terrain surface reconstruction using a flexible data fusion approach.
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