Repeat-pass Synthetic Aperture Radar Research Articles

Repeat pass imaging-based velocity estimation of Siachen glacier in J&K Himalaya using Sentinel-2 and Landsat-8 data: A comparison with SAR observations

In this study optical data from satellites Sentinel-2 (S-2), Landsat-8 (L-8), and synthetic aperture radar (SAR) data from Sentinel-1(S-1) have been used to estimate the surface velocity of the Siachen Glacier in J&K Himalaya. Repeat-pass optical and SAR data have been processed employing sub-pixel offset tracking (SOT) approach to decipher surface velocity of glaciers during 2013–2018. It is observed that SOT derived 2-D surface velocity using S-2 and L-8 data pairs show more than 70 % correlation over the entire stretch of the glacier. High correlation among velocities from S-2 and L-8 over two years validates one sensor against another for glacier surface movement studies in the Himalaya. This study reveals that the velocity of the glacier varies from ∼120 ma−1 to ∼151 ma−1 during 2013–2018. This study results the movement of glacier using optical sensor and SAR sensor over same geographical zone and similar time zone to evaluate the feature extraction and sensor impact on feature extraction. We have found that all sensors have produced similar results, since data is collected near time frame.

Polarimetric SAR pixel offset tracking for large-gradient landslide displacement mapping

Interferometry and pixel offset tracking (POT) are two major technologies to measure ground deformation with repeat-pass synthetic aperture radar (SAR) observations. The SAR POT method can tolerate a much larger deformation gradient than interferometric SAR (InSAR), and thus it has been widely applied in monitoring earthquakes, volcanoes, landslides, glaciers, etc. However, there is an intrinsic trade-off between matching accuracy and detail preserving in terms of matching window size for the traditional SAR POT method using only single-polarization intensity. Taking advantage of polarimetric scattering properties, this study develops a novel POT method, named PCM-POT, towards mitigating the dependence of offset estimations on matching window size. It employs a generalized likelihood ratio test (GLRT) upon polarimetric covariance matrices (PCMs) to evaluate the similarity of two candidate pixels and estimate high-resolution surface displacements with high accuracy. The effectiveness and advantage of PCM-POT are demonstrated by an experiment of retrieving the displacements of the Slumgullion landslide from a pair of full-polarimetric UAVSAR images. Comparisons with the traditional POT indicate that using polarimetric scattering properties can reduce the uncertainty in offset estimations over heterogeneous sliding areas. Further experiments suggest that PCM-POT is also suitable for dual-polarimetric satellite SAR data.

Differential Tropospheric Delay Estimation by Simultaneous Multi-Angle Repeat-Pass InSAR

Tropospheric delays are one of the main contributors to the interferometric phase in synthetic aperture radar (SAR) interferometry. When the phase contributions from surface deformation, topography, and ionospheric delays are negligible or known, the interferogram can be used to estimate the differential tropospheric delay (DTD), which can help to improve tropospheric delay predictions from weather models and in situ measurements. In conventional repeat-pass interferometric SAR (InSAR), however, the estimation of the DTD can still be significantly hindered by baseline errors. In addition, a single interferogram provides only relative DTDs, as the delays can be retrieved up to an unknown offset. To address such issues, this article presents a method for the estimation of DTDs on large scales by using repeat-pass simultaneous multi-angle SAR systems. Complementary simultaneous observations of the correlated troposphere from multiple angles are used to retrieve estimates of the absolute DTD and, at the same time, to mitigate the effect of baseline knowledge errors. Finally, a performance evaluation is presented for the Harmony Earth Explorer 10 candidate mission. A centimeter-level absolute accuracy and a submillimeter-level relative accuracy of the DTD estimation are achieved under the multistatic Harmony case when at least one companion satellite has an inter-satellite distance longer than 300 km to provide enough sensitivity.

Multipass Interferometric ISAR for Three-Dimensional Space Target Reconstruction

The 2-D inverse synthetic aperture radar (ISAR) images of space targets vary with the viewing angles and are not suitable for automatic target recognition (ATR), thus requiring 3-D reconstruction techniques. Conventional 3-D space target reconstruction techniques either use monostatic radar with incoherent processing for target retrieval or exploit multistatic radar but coherent processing for target reconstruction. In this article, we combined aspects of monostatic radar and coherent processing to investigate the coherent reconstruction of 3-D space target reflectivity with monostatic radar. Space objects periodically revisit the same ground area. Multipass interferometric synthetic aperture radar (multipass InSAR) uses repeat-pass synthetic aperture radar (SAR) acquisitions on the same ground area with a spaceborne SAR system for 3-D ground surface reconstruction. Motivated by multipass InSAR, we used multipass ISAR acquisitions on the same space target with monostatic ground radar for 3-D space target reconstruction. We proposed a multipass interferometric ISAR (multipass InISAR) algorithm that exploits inverse synthetic aperture technology for range-azimuth focusing and interferometry for the third dimension reconstruction. The elevation estimation performance of the algorithm is analyzed. Simulation results on the Tiangong-2 space lab, Jason-3 geodetic satellite, and ENVISAT remote sensing satellite validate the effectiveness of the proposed algorithm.

Assessing the Impact of Positioning Errors in Car-Borne Repeat-Pass SAR Interferometry With a Controlled Rail-Based Experiment

Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1–2 cm using a local GNSS reference station and up to 10–15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.

Earth-Based Repeat-Pass SAR Interferometry of the Moon: Spatial–Temporal Baseline Analysis

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area.

Global time series and temporal mosaics of glacier surface velocities derived from Sentinel-1 data

Abstract. Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modeling and glacier mass flux computations. Since 2014, repeat-pass synthetic aperture radar (SAR) data have been acquired by the Sentinel-1 satellite constellation as part of the Copernicus program of the EU (European Union) and ESA (European Space Agency). It enables global, near-real-time-like and fully automatic processing of glacier surface velocity fields at up to 6 d temporal resolution, independent of weather conditions, season and daylight. We present a new global data set of glacier surface velocities that comprises continuously updated scene-pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution. The velocity information is derived from archived and new Sentinel-1 SAR acquisitions by applying a well-established intensity offset tracking technique. The data set covers 12 major glacierized regions outside the polar ice sheets and is generated in an HPC (high-performance computing) environment at the University of Erlangen-Nuremberg. The velocity products are freely accessible via an interactive web portal that provides capabilities for download and simple online analyses: http://retreat.geographie.uni-erlangen.de (last access: 6 October 2021). In this paper, we give information on the data processing and how to access the data. For the example region of Svalbard, we demonstrate the potential of our products for velocity time series analyses at very high temporal resolution and assess the quality of our velocity products by comparing them to those generated from very high-resolution TerraSAR-X SAR and Landsat-8 optical (ITS_LIVE, GoLIVE) data. The subset of Sentinel-1 velocities for Svalbard analyzed in this paper is accessible via the GFZ Potsdam Data Services under the DOI https://doi.org/10.5880/fidgeo.2021.016 (Friedl et al., 2021). We find that Landsat-8 and Sentinel-1 annual velocity mosaics are in an overall good agreement, but speckle tracking on Sentinel-1 6 d repeat acquisitions derives more reliable velocity measurements over featureless and slow-moving areas than the optical data. Additionally, uncertainties of 12 d repeat Sentinel-1 mid-glacier scene-pair velocities have less than half (< 0.08 m d−1) of the uncertainties derived for 16 d repeat Landsat-8 data (0.17–0.18 m d−1).

Multiple Statistics Contributing to Few-Sample Deep Learning for Subtle Trace Detection in High-Resolution SAR Images

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods.

Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation

The paper addresses the temporal stability of distributed targets, particularly referring to vegetation, to evaluate the degradation affecting synthetic aperture radar (SAR) imaging and repeat-pass interferometry, and provide efficient SAR simulation schemes for generating big dataset from wide areas. The models that are mostly adopted in literature are critically reviewed, and aim to study decorrelation in a range of time (from hours to days), of interest for long-term SAR, such as ground-based or geosynchronous, or repeat-pass SAR interferometry. It is shown that none of them explicitly account for a decorrelation occurring in the short-term. An explanation is provided, and a novel temporal decorrelation model is proposed to account for that fast decorrelation. A formal method is developed to evaluate the performance of SAR focusing, and interferometry on a homogenous, stationary scene, in terms of Signal-to-Clutter Ratio (SCR), and interferometric coherence. Finally, an efficient implementation of an SAR simulator capable of handling the realistic case of heterogeneous decorrelation over a wide area is discussed. Examples are given by assuming two geostationary SAR missions in C and X band.

Dual Frequency Orbiter-Radar System for the Observation of Seas and Tides on Titan: Extraterrestrial Oceanography from Satellite

Saturn’s largest moon, Titan, is believed to have a ~100 km thick ice shell above a global ocean of liquid water. Organic materials, including liquid hydrocarbon lakes and seas in its polar terrain, cover Titan’s surface, which makes it a world of two oceans. The RADAR instrument on board Cassini, was able to probe lakes and seas during few dedicated altimetric observations, revealing its capability to work as a sounder. Herein, we describe the design of, and scientific motivation for, a dual frequency X/Ka-band radar system that is able to investigate Titan’s subsurface liquid water ocean, as well as the depth and composition of its surface liquid hydrocarbon basins. The proposed system, which could take advantage of the telecommunications dish, can operate as a sounder, as Synthetic Aperture Radar (SAR) able to map the surface at tens meters of scale resolution, and when data are acquired from close-adjacent orbits, as a repeat-pass SAR interferometer (InSAR). The instrument, which is based on the architecture of the Cassini RADAR, can also characterize Titan’s interior by using geophysical measurements of the tidal amplitude to derive high accuracy estimates of the Love number h2 from a 1500 km circular orbit.

Bara Shigri and Chhota Shigri glacier velocity estimation in western Himalaya using Sentinel-1 SAR data

ABSTRACTIn this article, we have presented surface velocity estimation of Chhota Shigri (CS) and Bara Shigri (BS) glaciers in the Himalaya using C-band synthetic aperture radar (SAR) data from Sentinel-1 (S-1). Repeat-pass SAR image subpixel offset tracking has been used to generate velocity vectors from yearly and seasonal S-1 data sets. SAR offset tracking on the basis of maximum intensity correlation provided the two-dimensional (2-D) movement of the both CS and BS glaciers of Chandra valley in western Himalaya. Movement of the glacier has been observed in the terminus to ablation zone during 2014–2018. S-1-derived movement is compared with in-situ measurement over CS glacier, and it has shown approximately 25% deviation during 2015–2016 with respect to field measurements from 2009 to 2010. Similar to previous decade observations, S-1 offset tracking movement results of recent three years have shown a decreasing trend.

Modeling and Robust Estimation for the Residual Motion Error in Airborne SAR Interferometry

Due to the limited accuracy of the current navigation systems, uncompensated motion errors during airborne synthetic aperture radar (SAR) preprocessing, i.e., the residual motion error (RME), cause undesirable phase errors in the final interferogram. Especially in airborne repeat-pass interferometric SAR (InSAR), the removal of RME is critical for topographic mapping. In this letter, based on the geometry of a single-baseline interferogram, a model is first developed for describing the relationship between the time-varying baseline parameters and the interferometric phase errors. A robust estimation is then employed to estimate the RME-induced phase errors. The performance of the proposed method was validated by the use of P- and L-band single-baseline interferograms acquired by the airborne E-SAR system. The results showed that the phase artifacts in the initial differential interferograms can be greatly mitigated. In addition, the corrected interferograms acquired in the P- and L-bands were used to estimate the digital elevation model (DEM). After correction, the root-mean-square errors (RMSEs) of the two DEMs with respect to the light detection and ranging (LiDAR) DEM were 2.6 and 4.6 m, respectively, which are improvements of 48.0% and 63.8%. Furthermore, even in the case of low coherence, the proposed method can still work well.

Baseline Estimation Using DEM Prior Knowledge and Capability Analysis for Gaofen-3 Repeat-Pass SAR Interferometry

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.

Retrieval of time series three-dimensional landslide surface displacements from multi-angular SAR observations

A major limitation for wide application of Synthetic Aperture Radar (SAR) remote sensing in mapping landslide surface displacements is the intrinsic gap between the ultimate objective of measuring three-dimensional displacements and the limited capability of detecting only one or two-dimensional displacements by repeat-pass SAR observations of identical imaging geometries. Although multi-orbit SAR observations of dissimilar viewing geometries can be jointly analyzed to inverse the three-dimensional displacements, the reliability of inversion results might be highly questionable in case of continuous motion because of the usually asynchronous acquisitions of multi-orbit SAR datasets. Aiming at this problem, we proposed an approach of retrieving time series three-dimensional displacements from multi-angular SAR datasets for step-like landslides in the Three Gorges area in this article. Firstly, time series displacements of a common ground target in the azimuth and line-of-sight (LOS) direction can be estimated using traditional methods of SAR interferometry (InSAR) and SAR pixel offset tracking (POT), respectively. Then, a spline fitting and interpolation procedure was employed to parameterize the displacement history in the sliding/dormant periods of step-like landslides and estimate displacements from multi-angular observations for identical date series. Finally, three-dimensional displacements can be inverted from these synchronized multi-angular measured displacements in traditional ways. As a case study, the proposed method was applied to retrieve the three-dimensional displacements history of the Shuping landslide in the Three Gorges area, China. Comparisons between SAR-measured displacements and measurements of global positioning system (GPS) showed good agreement. Furthermore, temporal correlation analyses suggest that reservoir water level fluctuation and rainfall are the two most important impact factors for the Shuping landslide stability.

An evaluation of processing InSAR Sentinel-1A/B data for correlation of mining subsidence with mining induced tremors in the Upper Silesian Coal Basin (Poland)

The Satellite Radar Interferometry is one of the common methods that allow to measure the land subsidence caused by the underground black coal excavation. The interferometry images processed from the repeat-pass Synthetic Aperture Radar (SAR) systems give the spatial image of the terrain subjected to the surface subsidence over mining areas. Until now, the InSAR methods using data from the SAR Systems like ERS-1/ERS-2 and Envisat-1 were limited to a repeat-pass cycle of 35-day only. Recently, the ESA launched Sentinel-1A and 1B, and together they can provide the InSAR coverage in a 6-day repeat cycle. The studied area was the Upper Silesian Coal Basin in Poland, where the underground coal mining causes continuous subsidence of terrain surface and mining tremors (mine-induced seismicity). The main problem was with overlapping the subsidence caused by the mining exploitation with the epicentre tremors. Based on the Sentinel SAR images, research was done in regard to the correlation between the short term ground subsidence range border and the mine-induced seismicity epicentres localisation.

Phase Calibration of Airborne Tomographic SAR Data via Phase Center Double Localization

Synthetic aperture radar (SAR) data collected over a 2-D synthetic aperture can be processed to focus the illuminated scatterers in the 3-D space, using a number of signal processing techniques generally grouped under the name of SAR tomography (TomoSAR). A fundamental requirement for TomoSAR processing is to have precise knowledge of the platform position along the 2-D synthetic aperture. This requirement is not easily met in the case where the 2-D aperture is formed by collecting different flight lines (i.e., 1-D apertures) in a repeat-pass fashion, which is the typical case of airborne and spaceborne TomoSAR. Subwavelength platform position errors give rise to residual phase screens among different passes, which hinder coherent focusing in the 3-D space. In this paper, we propose a strategy for calibrating repeat-pass tomographic SAR data that allows us to accurately estimate and remove such residual phase screens in the absence of reference targets and prior information about terrain topography and even in the absence of any point- or surface-like target within the illuminated scene. The problem is tackled by observing that multiple flight lines provide enough information to jointly estimate platform and target positions, up to a roto-translation of the coordinate system used for representing the imaged scene. The employment of volumetric scatterers in the calibration process is enabled by the phase linking algorithm, which allows us to represent them as equivalent phase centers. The proposed approach is demonstrated through numerical simulations, in order to validate the results based on the exact knowledge of the simulated scatterers, and using real data from the ESA campaigns AlpTomoSAR, BioSAR 2008, and TropiSAR. A cross-check of the results from simultaneous P- and L-band acquisitions from the TropiSAR data set indicates that the dispersion of the retrieved flight trajectories is limited to a few millimeters.

Joining a Discrete Radiative Transfer Model and a Kernel Retrieval Algorithm for Soil Moisture Estimation From SAR Data

This paper investigates the problem of retrieving soil moisture under crops using Synthetic Aperture Radar (SAR) data. First, we simulated the time series of L-band SAR signals over agricultural fields using a discrete radiative transfer model (RTM). Full growth cycles of winter wheat, maize, and sugar beet fields sampled during the AgriSAR2006 (Agricultural bio/geophysical retrievals from frequent repeat pass SAR and optical imaging) field campaign were considered. A generally good correspondence between the simulated crop backscattering coefficients and those measured by the airborne L-band E-SAR (Experimental-SAR) system was observed with an average rootmean-square error (RMSE) of 2.32 dB. The highest RMSE of 3.63 dB was obtained by the RTM simulations of 11V polarized signals in the wheat field, whereas the smallest RMSE of 1.63 dB is achieved in RTM simulations of 11V backscattering coefficients in the field of sugar beet. All discrepancies were critically discussed and interpreted. Then, soil moisture was estimated using a nonlinear inversion technique, support vector regression (ν-SVR). The model was trained with the backscatter model simulations obtained by the RTM. For all fields considered, the RMSE of the predicted soil moisture was smaller than 5.5% Vol. and the corresponding correlation coefficient (r) was equal to or higher than 0.71.

Man-Made Change Detection Using High-Resolution Cosmo-SkyMed SAR Interferometry

The technique of coherent change detection (CCD) in repeat-pass synthetic aperture radar (SAR) imagery has the potential to detect very subtle scene changes. With the advent of high-resolution space-borne SAR images, CCD has received a lot of interest. In CCD, the interferometric coherence is evaluated and analyzed to detect changes. The sample coherence estimator is biased, especially for low-coherence values, which complicates CCD map analysis and interpretation. The bias decreases when the number of samples used to evaluate the coherence increases. However, a large number of samples causes the loss of the small size changes. In this work, an adaptive method for coherence estimation is described and evaluated. The local fringe frequency is used as an additional change indicator to adapt the number of samples used to estimate the coherence. The results obtained using Cosmo-SkyMed (CSK) SAR images indicate that the proposed adaptive method improves detection performance while preserving the small size changes.

Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

The phenological stages of onion fields in the first year of growth are estimated using polarimetric observables and single-polarization intensity channels. Experiments are undertaken on a time series of RADARSAT-2 C-band full-polarimetric synthetic aperture radar (SAR) images collected in 2009 over the Barrax region, Spain, where ground truth information about onion growth stages is provided by the European Space Agency (ESA)-funded agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) field campaign conducted in that area. The experimental results demonstrate that polarimetric entropy or copolar coherence when used jointly with the cross-polarized intensity allows unambiguously distinguishing three phenological intervals.

Phase Offset Calculation for Airborne InSAR DEM Generation Without Corner Reflectors

Digital elevation model (DEM) generation through interferometric processing of synthetic aperture radar (SAR) data requires the calculation of a constant phase offset present in the unwrapped interferograms. This operation is usually carried out by exploiting the external information provided by GPS measurements in correspondence of corner reflectors (CRs) properly deployed over the illuminated area. This is, however, expensive in terms of cost and time. Moreover, deployment of CRs along with the corresponding in situ GPS measurements can be difficult (if not impossible) in unfriendly areas or in natural disaster scenarios. To circumvent these limitations, we address in this work the estimation of the required phase offset by exploiting a low-accuracy external DEM, without using CRs. More specifically, a two-step approach is proposed. The first step exploits the synthetic phase computed by means of the external DEM and represents a straightforward extension of the procedure that is usually applied in the presence of CRs. Subsequently, in order to refine the achieved solution, a second step is introduced. It is based on a least squares approach that properly exploits the difference between the available low-accuracy DEM and the interferometric DEM generated by means of the phase offset value roughly estimated through the first step. The presented approach is very easy to implement and allows us to achieve an accurate and fast estimate of the needed phase offset, even in the presence of an external DEM affected by a vertical bias and/or a planar shift. The algorithm performances improve in the presence of a large variation of the look angle, as it generally happens in airborne systems. On the other side, the effectiveness of the algorithm may be impaired by the possible presence of artifacts in the unwrapped interferograms, such as those due to the residual motion errors typical of repeat-pass airborne SAR scenarios. Accordingly, the proposed solution is particularly suitable for single-pass interferometric airborne SAR systems, as demonstrated through the presented experimental results achieved on real data.