evaluating scansar interferometry deformation time series using bursted stripmap data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 6, JUNE 2011 2335 Evaluating ScanSAR Interferometry Deformation Time Series Using Bursted Stripmap Data Sean M. Buckley, Member, IEEE, and Krishnavikas Gudipati, Student Member, IEEE Abstract—We demonstrate scanning synthetic aperture radar (ScanSAR) advanced radar interferometry processing for surface deformation time series analysis. We apply the small baseline subsets (SBAS) technique to ScanSAR data synthesized from 40 ERS-1 and ERS-2 stripmap SAR images over known deforma- tion in Phoenix, Arizona. The strategy is to construct a burst pat- tern similar to Envisat ScanSAR data for two scenarios, namely, an idealized 100% burst overlap case and a realistic variable-burst synchronization case in which any image pair has at least 50% burst overlap. We find this latter scenario to be reasonable based on an assessment of the effect of burst overlap on Phoenix inter- ferometric phase coherence. The differences between the variable burst overlap ScanSAR and stripmap SAR SBAS-derived pixel velocities have a mean of 0.02 cm/year and a standard deviation of 0.02 cm/year. It is noted that one can expect SBAS velocity and displacement one-sigma errors of 0.1 cm/year and 0.5 cm, respec- tively, from multilooked stripmap data. We observe that 96% and 99% of the variable burst overlap ScanSAR pixel velocities are within ±0.1 and ±0.2 cm/year (one- and two-sigma), respectively, of our stripmap SAR pixel velocities. These results are similar to those reported for SBAS analysis applied to low-resolution mul- tilook interferograms derived from coherence-preserving down- sampling of stripmap data. We also find that the rms deviations between variable burst overlap ScanSAR and stripmap SBAS displacement estimates are 0.40 ± 0.30 cm. 68% and 94% of the variable burst overlap ScanSAR pixel displacements are within ±0.5 and ±1.0 cm, respectively, of the stripmap displacements. Index Terms—Deformation time series, interferometric syn- thetic aperture radar (InSAR), small baseline subsets (SBAS), scanning synthetic aperture radar (ScanSAR). I. I NTRODUCTION T HERE is increasing interest in exploiting the scanning synthetic aperture radar (ScanSAR) imaging mode cur- rently available from the TerraSAR-X, ALOS, Radarsat-2, and Envisat satellites [1]. Scientists routinely use ScanSAR radio- metric backscatter data for sea and lake ice mapping and ocean monitoring [2], [3]. In addition, recent studies demonstrate Manuscript received January 24, 2010; revised August 19, 2010; accepted October 3, 2010. Date of publication January 30, 2011; date of current version May 20, 2011. This work was supported in part by the National Aeronautics and Space Administration under Grant NNX08AF62G. S. M. Buckley was with the Department of Aerospace Engineering and Engi- neering Mechanics and the Center for Space Research, The University of Texas at Austin, Austin, TX 78712-0235 USA. He is currently with the Jet Propulsion Laboratory, Pasadena, CA 91109 USA (e-mail: [email protected]). K. Gudipati was with the Department of Aerospace Engineering and Engi- neering Mechanics and the Center for Space Research, The University of Texas at Austin, Austin, TX 78712-0235 USA. He is currently with ExxonMobil, Houston, TX 77002 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2010.2102360 Fig. 1. ScanSAR acquisition pattern. ScanSAR interferometry for measuring surface topography and deformation in individual interferograms [4]–[7]. However, there is little in the way of published research [8] on the use of ScanSAR data in advanced interferometric SAR (InSAR) techniques. These approaches [9]–[21] exploit tens of SAR images to mitigate atmospheric and decorrelation effects. We assess the performance of ScanSAR data in an advanced InSAR time series analysis. This paper represents an extension of our preliminary work reported in [22]. We provide in this section an overview of ScanSAR imaging and the limitations associated with its use in radar interferometry studies. We also describe the small baseline subsets (SBAS) technique [10] used in performing our InSAR time series analysis. The ScanSAR imaging mode provides greater slant range co-1verage than the stripmap SAR mode by illuminating a number of subswaths (Fig. 1). For example, whereas Envisat advanced SAR (ASAR) stripmap data consists of a single 100-km-wide swath, the ScanSAR swath contains five sub- swaths and spans 400 km [23]. The wider coverage is achieved by electronically steering the antenna through a set of prede- termined elevation angles. Bursts, groups of consecutive radar pulses along a given subswath, are processed into a continuous SAR image. However, the total time a target is illuminated by a set of ScanSAR bursts is a small fraction of the stripmap synthetic aperture exposure time. This results in a significant reduction in the ScanSAR image azimuth (along-track) resolu- tion as compared to stripmap images. ScanSAR repeat-pass interferometry is constrained by a need for sufficient burst overlap for the ScanSAR image pair [24], [25]. Consider a target imaged by a single ScanSAR burst from a reference and secondary orbit path [Fig. 2(a)]. The burst du- ration and viewing angles through which the sensor images the target determines the azimuth (Doppler) frequency response. The secondary image burst should align with the reference 0196-2892/$26.00 © 2011 IEEE

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Page 1: Evaluating ScanSAR Interferometry Deformation Time Series Using Bursted Stripmap Data

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 6, JUNE 2011 2335

Evaluating ScanSAR Interferometry DeformationTime Series Using Bursted Stripmap Data

Sean M. Buckley, Member, IEEE, and Krishnavikas Gudipati, Student Member, IEEE

Abstract—We demonstrate scanning synthetic aperture radar(ScanSAR) advanced radar interferometry processing for surfacedeformation time series analysis. We apply the small baselinesubsets (SBAS) technique to ScanSAR data synthesized from40 ERS-1 and ERS-2 stripmap SAR images over known deforma-tion in Phoenix, Arizona. The strategy is to construct a burst pat-tern similar to Envisat ScanSAR data for two scenarios, namely, anidealized 100% burst overlap case and a realistic variable-burstsynchronization case in which any image pair has at least 50%burst overlap. We find this latter scenario to be reasonable basedon an assessment of the effect of burst overlap on Phoenix inter-ferometric phase coherence. The differences between the variableburst overlap ScanSAR and stripmap SAR SBAS-derived pixelvelocities have a mean of 0.02 cm/year and a standard deviationof 0.02 cm/year. It is noted that one can expect SBAS velocity anddisplacement one-sigma errors of 0.1 cm/year and 0.5 cm, respec-tively, from multilooked stripmap data. We observe that 96% and99% of the variable burst overlap ScanSAR pixel velocities arewithin ±0.1 and ±0.2 cm/year (one- and two-sigma), respectively,of our stripmap SAR pixel velocities. These results are similar tothose reported for SBAS analysis applied to low-resolution mul-tilook interferograms derived from coherence-preserving down-sampling of stripmap data. We also find that the rms deviationsbetween variable burst overlap ScanSAR and stripmap SBASdisplacement estimates are 0.40 ± 0.30 cm. 68% and 94% of thevariable burst overlap ScanSAR pixel displacements are within±0.5 and ±1.0 cm, respectively, of the stripmap displacements.

Index Terms—Deformation time series, interferometric syn-thetic aperture radar (InSAR), small baseline subsets (SBAS),scanning synthetic aperture radar (ScanSAR).

I. INTRODUCTION

THERE is increasing interest in exploiting the scanningsynthetic aperture radar (ScanSAR) imaging mode cur-

rently available from the TerraSAR-X, ALOS, Radarsat-2, andEnvisat satellites [1]. Scientists routinely use ScanSAR radio-metric backscatter data for sea and lake ice mapping and oceanmonitoring [2], [3]. In addition, recent studies demonstrate

Manuscript received January 24, 2010; revised August 19, 2010; acceptedOctober 3, 2010. Date of publication January 30, 2011; date of current versionMay 20, 2011. This work was supported in part by the National Aeronauticsand Space Administration under Grant NNX08AF62G.

S. M. Buckley was with the Department of Aerospace Engineering and Engi-neering Mechanics and the Center for Space Research, The University of Texasat Austin, Austin, TX 78712-0235 USA. He is currently with the Jet PropulsionLaboratory, Pasadena, CA 91109 USA (e-mail: [email protected]).

K. Gudipati was with the Department of Aerospace Engineering and Engi-neering Mechanics and the Center for Space Research, The University of Texasat Austin, Austin, TX 78712-0235 USA. He is currently with ExxonMobil,Houston, TX 77002 USA (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2010.2102360

Fig. 1. ScanSAR acquisition pattern.

ScanSAR interferometry for measuring surface topography anddeformation in individual interferograms [4]–[7]. However,there is little in the way of published research [8] on the useof ScanSAR data in advanced interferometric SAR (InSAR)techniques. These approaches [9]–[21] exploit tens of SARimages to mitigate atmospheric and decorrelation effects.

We assess the performance of ScanSAR data in an advancedInSAR time series analysis. This paper represents an extensionof our preliminary work reported in [22]. We provide in thissection an overview of ScanSAR imaging and the limitationsassociated with its use in radar interferometry studies. We alsodescribe the small baseline subsets (SBAS) technique [10] usedin performing our InSAR time series analysis.

The ScanSAR imaging mode provides greater slant rangeco-1verage than the stripmap SAR mode by illuminating anumber of subswaths (Fig. 1). For example, whereas Envisatadvanced SAR (ASAR) stripmap data consists of a single100-km-wide swath, the ScanSAR swath contains five sub-swaths and spans 400 km [23]. The wider coverage is achievedby electronically steering the antenna through a set of prede-termined elevation angles. Bursts, groups of consecutive radarpulses along a given subswath, are processed into a continuousSAR image. However, the total time a target is illuminated bya set of ScanSAR bursts is a small fraction of the stripmapsynthetic aperture exposure time. This results in a significantreduction in the ScanSAR image azimuth (along-track) resolu-tion as compared to stripmap images.

ScanSAR repeat-pass interferometry is constrained by a needfor sufficient burst overlap for the ScanSAR image pair [24],[25]. Consider a target imaged by a single ScanSAR burst froma reference and secondary orbit path [Fig. 2(a)]. The burst du-ration and viewing angles through which the sensor images thetarget determines the azimuth (Doppler) frequency response.The secondary image burst should align with the reference

0196-2892/$26.00 © 2011 IEEE

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2336 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 6, JUNE 2011

Fig. 2. (a) ScanSAR repeat-pass imaging geometry. (b) Azimuth (Doppler)frequency response of a target in a single burst relative to the (dashed line)full-aperture Doppler bandwidth. The shaded area represents the overlappingportion of the azimuth spectra.

image burst such that the burst azimuth spectra overlap[Fig. 2(b)]. Increased interferometric coherence is achievedwith increased burst overlap. In addition, common band fil-tering [26] in azimuth will improve interferogram coherenceby discarding the nonoverlapping portion of the azimuth spec-tra. This results in decreased phase noise at the expense ofdegrading the already coarse ScanSAR azimuth resolution.To routinely achieve adequate ScanSAR interferometry burstoverlap requires accurate estimates and predictions of thesatellite position and precise timing of the ScanSAR burstingscheme [27].

In this paper, we synthesize ScanSAR data by intentionallyomitting raw data lines from stripmap SAR data. This choiceallows us to control the amount of burst overlap for a giveninterferogram image pair. We use this approach to implementdifferent burst overlap scenarios in our time series analysis.This choice also facilitates a comparison between ScanSAR andstripmap SAR SBAS results.

The SBAS approach [10] generates a time series of deforma-tion maps by inverting a set of interferograms. Conventionalinterferometry processing is applied to pairs of images withlimits on their perpendicular baseline and temporal separation.

These thresholds reduce the effects of spatial and temporaldecorrelation in the interferograms. The images are resampledto a common geometry and the usual two-pass InSAR steps offorming the multilooked differential interferograms, filtering,and unwrapping are performed.

SBAS analysis is applied to unwrapped differential inter-ferograms. A topographic phase error and long-term lineardeformation velocity are estimated for each pixel. The topo-graphic phase error is due to inaccuracies in the digital elevationmodel (DEM) used in forming the differential interferograms.These estimated components are removed from the differentialinterferograms to obtain wrapped residual differential interfer-ograms which are spatially filtered and unwrapped. Finally,topography-corrected displacement phase maps are created bysumming the long-term linear velocity trend and the unwrappedresidual phase maps.

A time series is estimated by inverting the database oftopography-corrected displacement phase maps using a singu-lar value decomposition approach. The time series consists ofphase maps corresponding to each of the SAR acquisition datesand containing atmospheric artifacts and cumulative deforma-tion since the first SAR acquisition date. Atmospheric phasescreens (APS) for each date are estimated and removed byapplying a sequence of spatial low-pass and temporal high-pass filters to the phase maps. The SBAS final results consistof a mean velocity map and a time series of displacementphase maps that are corrected for DEM errors and atmosphericeffects.

The objectives of this paper are to assess the effect ofScanSAR burst overlap on interferometric phase coherenceand to characterize the relative performance of ScanSAR andstripmap data in an SBAS time series analysis. The rest ofthis paper is organized as follows. In Section II, we describethe synthesis and processing of ScanSAR data in this paper.We also provide details of the ScanSAR and stripmap InSARprocessing. In Section III, we present the results of a case studyto assess the effect of burst overlap on the quality of ScanSARInSAR phase in comparison to the original stripmap InSARphase. The processing details and results obtained from theapplication of the SBAS technique to synthesized ScanSARdata and their comparison with the original Phoenix stripmapresults are given in Section IV. Concluding remarks appear inSection V.

II. DATA SYNTHESIS, IMAGE FORMATION,AND InSAR PROCESSING

We use a burst pattern similar to the Envisat ASAR WideSwath Mode to synthesize ScanSAR data from ERS-1/2stripmap scenes. Each burst consists of 60 raw data lines witha gap of 240 lines between consecutive bursts. The 300-pulseburst cycle results in 3 to 4 looks per synthetic aperture. Themidswath azimuth bandwidth of one burst is 74 Hz which is20 times less than the stripmap azimuth bandwidth and compa-rable to the 62-Hz bandwidth of the Envisat ASAR Wide SwathMode product. This results in an azimuth resolution of 100 m.The slant range resolution of the synthesized ScanSAR data isthe same as the ERS-1/2 stripmap data.

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BUCKLEY AND GUDIPATI: EVALUATING SCANSAR INTERFEROMETRY DEFORMATION TIME SERIES 2337

We adjust the burst synchronization between a pair ofScanSAR images based on the registration and Doppler cen-troid information from the original stripmap data processing.The amount of burst overlap is determined by the choice of thestripmap azimuth line at which we start the burst pattern foreach synthesized image. For a given reference image associatedwith a ScanSAR interferometry image pair, we vary the degreeof burst overlap by choosing different starting azimuth lines forthe secondary image burst pattern factoring in the azimuth off-set and amount of deskew between the reference and secondarystripmap images.

The ScanSAR data are processed with a combination therange-Doppler algorithm [28] and the modified SPECAN algo-rithm [29]. We use the Doppler centroid values estimated fromthe stripmap data. Each burst is range compressed and correctedfor range cell migration. We utilize the known stripmap antennabeam pattern to compensate for the azimuth-dependent ampli-tude gain in the processed burst [30]. The azimuth compressionis performed with the modified SPECAN deramping operationand a chirp-z transformation [29]. This approach providesconvenient control of the image azimuth spacing, which wechoose to be ten times that of the stripmap data. We store theprocessed data as individual burst images and generate coherentimages by stitching together the processed bursts.

The first step in the ScanSAR interferometry processing isto register the burst images. However, the coarse ScanSARazimuth resolution complicates the processing. An ampli-tude correlation approach results in generally unsatisfactoryazimuth registration and phase banding in the subsequentinterferogram [7].

We employ the following approach for registering oursynthesized ScanSAR data. Continuous intensity images arecreated from the processed ScanSAR images by incoher-ently stitching together the bursts. We also generate from thestripmap reference image a multilook intensity image with thesame range and azimuth spacing as the ScanSAR data. Thisimage has significantly less speckle noise than the ScanSARimages. We then use an amplitude correlation technique todetermine registration offsets between the ScanSAR intensityimages and the multilook stripmap intensity image. The burstmode data then are resampled to the stripmap image. Finally,a second amplitude correlation process is applied to the re-sampled ScanSAR multilook images to determine any residualoffsets.

The remaining ScanSAR interferometry processing stepsconsist of interferogram formation, topographic phase removal,interferogram filtering, and unwrapping. Burst interferogramsare formed through complex conjugate multiplication of cor-responding resampled reference and secondary image bursts.A continuous interferogram is created by coherently stitchingtogether the burst interferograms and taking two looks inrange. We remove the topographic phase contribution usinga DEM derived from the Shuttle Radar Topography Mission(SRTM) (http://www2.jpl.nasa.gov/srtm). The differential in-terferograms are filtered with an adaptive spectral filter [31]with a 64 × 64 window size and a filter weight of 1. Weunwrap the phase with a minimum cost flow algorithm witha correlation coefficient threshold of 0.5.

Fig. 3. Phoenix, Arizona ERS-2 stripmap differential interferogram formedfrom data acquired on March 15 and December 20, 1999. Decorrelated areasare shown in grayscale. The phase represents land subsidence associated withgroundwater withdrawal.

Stripmap interferograms are generated using the originalSAR scenes from which the ScanSAR data are synthesized.These interferograms are formed with two range looks and tenazimuth looks so as to have the same spacing as the ScanSARinterferograms. We utilize the ScanSAR interferometry pro-cessing parameter choices through the rest of the stripmapInSAR processing.

III. CHARACTERIZING THE EFFECT OF BURST OVERLAP

We present here the results of a limited case study to assessthe effect of burst overlap on ScanSAR interferometry. Ourinterest is in determining the degree to which burst overlapresults in a useable interferogram. For a given stripmap imagepair, ScanSAR data are synthesized with varying amounts ofburst overlap and processed. We then calculate the differencebetween the ScanSAR and stripmap unwrapped differentialinterferometric phases. It should be noted that we elected notto perform azimuth common band filtering in the case studypresented in this section. Consequently, the results representa conservative lower bound on the amount of burst overlapneeded for a viable ScanSAR interferogram at its finest azimuthresolution.

The case study presented in this section considers a 9-mo150-m perpendicular baseline interferogram over a portion ofPhoenix, Arizona (Fig. 3). The area is generally arid, urbanized,and well-suited for interferometry. Localized areas spanningseveral kilometers are experiencing land subsidence associ-ated with aquifer compaction caused by excessive groundwaterwithdrawal [32]–[35].

We form ScanSAR interferograms synthesized from theoriginal stripmap data with 100%, 80%, 60%, and 40% burstoverlap (Fig. 4). The deformation features are visible in all

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2338 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 6, JUNE 2011

Fig. 4. Phoenix ScanSAR differential interferograms for different burst overlap cases: (a) 100%, (b) 80%, (c) 60%, (d) 40%. Decorrelated areas are shown ingrayscale. The ScanSAR data are synthesized from the ERS-2 stripmap SAR data used to form the interferogram shown in Fig. 3. Azimuth command filtering isnot applied.

cases, albeit with increasing amounts of decorrelated areas asburst overlap decreases.

We assess the ScanSAR performance by taking profiles (LineAA′ in Figs. 3 and 4) through the interferograms (Fig. 5). Wecompute the mean and standard deviation of the phase differ-ence between the ScanSAR and stripmap interferogram profiles(Table I). The overall phase trend is visible as burst overlapdecreases despite increasing phase noise (i.e., phase standarddeviation in Table I). Based on an inspection of the ScanSARand stripmap phase profiles and their statistics, we consider50% burst overlap as an approximate threshold for obtaininguseable Phoenix ScanSAR interferograms. However, we notethat the phase profiles noise statistics might be improved byadditional phase filtering than we performed here. For example,one could repeatedly filter the differential interferogram orimplement azimuth common band filtering as we do in theSBAS analysis.

IV. SBAS PROCESSING AND RESULTS

This section describes the ScanSAR data sets and SAR,InSAR, and SBAS processing for evaluating ScanSAR de-formation time series analysis. We utilize 40 ERS-1/2 SARimages collected over Phoenix between July 1992 and October2000. ScanSAR data are synthesized and processed with thebursting scheme and modified SPECAN algorithm described inSection II.

We synthesize ScanSAR data for two burst overlap cases.In the first scenario, referred to as the 100% burst overlapcase, registration offsets between the original stripmap imagesare used to synthesize ScanSAR data with burst cycles suchthat there is 100% burst overlap between all interferometryimage pairs. This represents an idealized ScanSAR imagingscenario and gives the best possible ScanSAR SBAS time seriessolution.

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BUCKLEY AND GUDIPATI: EVALUATING SCANSAR INTERFEROMETRY DEFORMATION TIME SERIES 2339

Fig. 5. Profile AA′ taken through the Phoenix unwrapped differential inter-ferograms in Figs. 3 and 4.

TABLE ISCANSAR AND STRIPMAP INTERFEROGRAM PROFILE

PHASE DIFFERENCE STATISTICS

In the second scenario, referred to as the variable burstoverlap case, we randomly stagger the ScanSAR data burstcycles to ensure that there is at least 50% burst overlap betweenany given image pair. This represents a more realistic ScanSARimaging scenario. For example, the European Space Agencyinitiated an Envisat Wide Swath burst synchronization strategyin September 2006 which resulted in a 90% probability of anytwo ScanSAR acquisitions having at least 50% overlap [27].

The data are processed with the two-pass InSAR approachand an SRTM DEM. We consider 101 interferograms withperpendicular baselines less than 130 meters and temporalbaselines less than 4 years. As part of the ScanSAR processing,we employ azimuth common band filtering and create inter-ferograms with approximately 100-m slant range and azimuthspacing by taking four range looks and two azimuth looks. Theinterferograms are smoothed with an adaptive spectral filter[31] with a 32 × 32 window size and 0.5 filter weight andunwrapped with a minimal cost flow algorithm.

We apply the SBAS technique described in Section I to thestripmap, 100% burst overlap, and variable burst overlap datasets. All pixels with correlation coefficients greater than 0.5 inat least 70% of the interferograms are processed. As part ofestimating the APS, we use a 2 km × 2 km window for low-pass spatial filtering and a Kaiser filter [36] to reject nonlinearsignals having time periods greater than 40 days.

Several Phoenix metropolitan area subsidence features arevisible in the SBAS mean velocity maps in the 100% burst over-

Fig. 6. SBAS line-of-sight mean velocity measured over Phoenix usingScanSAR data synthesized from ERS-1/2 stripmap data. The velocity map isdraped over an intensity image derived from the incoherent sum of all ERS-1/2intensity imagery. Decorrelated areas are shown in grayscale. Subsidencefeatures A, B, and C are in the cities of Peoria, Glendale, and Scottsdale,respectively. The black rectangle corresponds to the region shown in Fig. 8.

lap and variable burst overlap scenarios (Fig. 6). We observeepisodic nonlinear deformation superimposed on an overallconstant-velocity trend in the SBAS time series for single pixelsnear the center of subsidence features A, B, and C (Fig. 7). Forexample, the Peoria relative uplift events generally occur duringthe winter months.

In the absence of GPS or leveling data concurrent with theSAR measurements, we compare our ScanSAR results withan SBAS time series generated from multilooked stripmapinterferograms, as well as with statistics reported in SBASstudies using low-resolution multilooked stripmap data [37]and multilooked stripmap data [38]. Starting from a databaseof stripmap interferograms created from the same 101 im-age pairs used for the ScanSAR data synthesis, we derive astripmap SBAS time series solution. All stripmap images areregistered to the same stripmap master reference image towhich all the burst mode images are registered. In this way,all ScanSAR and stripmap interferograms and derived timeseries solutions are aligned, enabling a pixelwise comparison ofthe two.

We evaluate the ScanSAR SBAS results by considering thenumber of coherent pixels, the mean deformation velocity, andthe deformation time series. Coherent pixels are identified aspixels with a correlation coefficient greater than 0.5 in at least70% of the interferograms. We observe a 4% reduction in thenumber of coherent pixels available for SBAS processing ingoing from the stripmap to the ScanSAR imaging mode.

We generate ScanSAR and stripmap SBAS velocity differ-ences for the coherent pixels [Fig. 8(a)]. For the 100% burstoverlap case, the mean and standard deviation of velocitydifferences computed over all the coherent pixels for the regionshown in Fig. 3 are 0.01 and 0.02 cm/year, respectively. Thevelocity difference mean and standard deviation for the variableburst overlap case are both 0.02 cm/year. These velocity devia-tions are comparable to those reported in [37] for a comparisonof low-resolution multilook stripmap and nominal resolutionmultilook stripmap SBAS analyses. Furthermore, our velocity

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2340 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 6, JUNE 2011

Fig. 7. SBAS line-of-sight deformation time series for single pixels in subsidence features shown in Fig. 6. (a) Subsidence feature A (Peoria) variable burstoverlap. (b) Subsidence feature A (Peoria) 100% burst overlap. (c) Subsidence feature B (Glendale) variable burst overlap. (d) Subsidence feature B (Glendale)100% burst overlap. (e) Subsidence feature C (Scottsdale) variable burst overlap. (f) Subsidence feature C (Scottsdale) 100% burst overlap.

deviations are within the expected range for the SBAS tech-nique (0.05 to 0.25 cm/year) reported in [38].

We also compute the root mean square (rms) differencebetween ScanSAR and stripmap SBAS time series at eachcoherent pixel [Fig. 8(b)]. In order to do this, the time seriessolutions are adjusted such that both series have a zero linear-fit intercept at the first date of time series. The rms then iscalculated using differences in displacement estimates at eachdate of the time series. For the 100% burst overlap case, themean and standard deviation of the rms error over the wholeimage are 0.30 and 0.25 cm, respectively. For the variable burstoverlap case, the mean rms is 0.40 cm with a standard deviationof 0.30 cm. Higher rms differences are observed for pixels at theboundaries of decorrelated regions [Fig. 8(b)], indicating a needfor a higher correlation threshold in the phase unwrapping step.

To relate our ScanSAR results with stripmap SBAS evalua-tions [37], [38], we also calculate the percentage of pixels forwhich the ScanSAR to stripmap SBAS velocity and deforma-tion time series differences are within one and two standarddeviations (reported in the previous paragraph). Based on the

work in [38], the expected one-sigma errors for velocity anddisplacement derived from SBAS multilooked stripmap dataanalyzes are 0.1 cm/year and 0.5 cm, respectively. For our100% burst overlap and variable burst overlap cases, we findthat 96% and 99% of velocity differences are within ±0.1 and±0.2 cm/year, respectively. These results show that we recoverdeformation velocities from ScanSAR data at a comparablelevel to SBAS stripmap data analysis. Our velocity errors arealso consistent with those reported in [37] for SBAS analysisapplied to low-resolution (200-m slant range resolution and200-m azimuth resolution) multilook interferograms derivedfrom coherence-preserving downsampling of stripmap datawherein 87% and 99% of velocity differences are within ±0.1and ±0.2 cm/year, respectively. The greater percentage of pix-els within the velocity one-sigma in our analysis as comparedto the analysis in [37] might be explained by the finer resolutionof our data.

Expectedly, our ScanSAR SBAS analysis accurately recov-ers displacements in fewer pixels than we accurately recoverdeformation velocities. For the displacement time series in the

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BUCKLEY AND GUDIPATI: EVALUATING SCANSAR INTERFEROMETRY DEFORMATION TIME SERIES 2341

Fig. 8. Comparison of ScanSAR SBAS variable burst overlap and stripmapSBAS results. (a) The difference between ScanSAR SBAS variable burstoverlap and stripmap SBAS velocity estimates. (b) The rms difference betweenthe ScanSAR SBAS variable burst overlap and stripmap SBAS displacementestimates.

100% burst overlap case, 87% and 98% of coherent pixels haverms deviations within ±0.5 and ±1.0 cm, respectively. For thevariable burst overlap case, 69% and 95% coherent pixels arewithin ±0.5 and ±1.0 cm, respectively. For the low-resolutionmultilook stripmap SBAS deformation time series deviationsreported in [37], 98% and 99% of coherent pixels are within±0.5 and ±1.0 cm, respectively. Hence, we find that in theScanSAR SBAS variable burst overlap case there are morepixels with large displacement deviations than that reported forlow-resolution multilook stripmap SBAS data processing. Webelieve that the difference is explained by the azimuth filteringperformed in the downsampling of the stripmap data in theanalysis in [37]. Their low-pass filtering around the Dopplercentroid preserves the coherence of the original stripmap datawhereas our analysis utilizes synthesized ScanSAR data.

V. CONCLUSION

This paper provides a first assessment of the use of ScanSARdata in advanced InSAR time series analysis. We successfullyapply SBAS processing to a database of ScanSAR imagessynthesized from ERS-1/2 stripmap SAR data. We utilize arealistic burst overlap scenario wherein any two ScanSARimages have at least 50% burst overlap. We find this obser-vation strategy to produce useable interferograms for observ-ing kilometer-scale land subsidence features in the Phoenix,Arizona metropolitan area.

We find that SBAS analysis of C-band ScanSAR data similarto the Envisat Wide Swath Mode is capable of recovering defor-mation velocities on the order of 0.1 cm/year and displacementson the order of a centimeter. We observe rms differences of0.02 ± 0.04 cm/year in ScanSAR SBAS velocity estimatesrelative to stripmap SBAS results derived from the original

Phoenix stripmap data. ScanSAR SBAS time series displace-ment estimates exhibit rms differences of 0.40 ± 0.30 cmrelative to stripmap SBAS results. Hence, caution must be usedwhile drawing inferences about small nonlinear displacementssince departures from the linear velocity trend of less than acentimeter are within the observed noise level for the ScanSARSBAS displacement we calculated for the Phoenix data. Thislimitation may be overcome by considering adjacent satellitetracks that provide overlapping ScanSAR data and a combineddenser time sampling. In addition, one might consider the trade-off between resolution and azimuth common band filtering andthe choices in filtering the ScanSAR differential interferograms.

ACKNOWLEDGMENT

The authors thank the European Space Agency for pro-viding the original ERS-1 and ERS-2 SAR data (copyright1992–2000). The authors also thank the Arizona Departmentof Water Resources with funding from the NASA Earth Sci-ence Applications Program (BAA-01-OES-01) and throughthe WInSAR Consortium with funding from NASA, NSF,and USGS for the SAR data purchases. The authors wouldalso like to thank the USGS National Map Seamless Server(http://seamless.usgs.gov) for the SRTM DEM data. Finally,the authors also thank D. Yang for his contributions to thedevelopment of our SBAS processing software and the anony-mous reviewers for their helpful comments. In memory ofProf. K. Clint Slatton.

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Sean M. Buckley (M’96) received the B.S. degree in aeronautical and as-tronautical engineering from the University of Illinois Urbana-Champaign,Champaign, in 1992 and the M.S. and Ph.D. degrees in aerospace engineeringfrom The University of Texas at Austin, Austin, in 1994 and 2000, respectively.

From 2000 to 2004, he was a Research Engineer/Science Associate with theCenter for Space Research at The University of Texas at Austin. From 2004to 2010, he was an Assistant Professor with the Department of AerospaceEngineering and Engineering Mechanics at The University of Texas at Austin.He is currently employed with the radar science and engineering section at theJet Propulsion Laboratory, Pasadena, CA. His research interests are in satelliteand aircraft remote sensing including interferometric synthetic aperture radaralgorithm development and deformation studies.

Krishnavikas Gudipati (S’04) received the B.Tech. degree in civil engineeringfrom the Indian Institute of Technology, Bombay, India, in 1997, the M.S.degree in geological sciences from The University of Texas at Austin, Austin,in 2003, and the Ph.D. degree in aerospace engineering from The University ofTexas at Austin in 2009.

He is currently employed with the geophysical processing group at Exxon-Mobil, Houston, TX.