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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 8, NO. 4, JULY 2011
Comparison of Persistent Scatterers and Small
Baseline Time-Series InSAR Results: A Case
Study of the San Francisco Bay Area
Piyush Shanker, Francesco Casu, Howard A. Zebker, and Riccardo Lanari
Abstract—Time-series interferometric synthetic aperture radar
(InSAR) methods estimate the spatiotemporal evolution of deformation over large areas by incorporating information from
multiple SAR interferograms. Persistent scatterer (PS) and small
baseline (SB) methods, which identify areas where the surface is
least affected by geometric and temporal decorrelation, represent
two families of time-series InSAR techniques to study successfully
a wide spectrum of ground deformation phenomena worldwide.
However, little is known comparatively about the performance
of PS and SB techniques applied to the same region. Here, we
compare quantitatively and cross validate the time-series InSAR results generated using two representative algorithms—the
maximum likelihood PS method and the small baseline subset
algorithm—in selected test sites, over the San Francisco Bay
Area imaged by European Remote Sensing (ERS) sensors during
1995–2000. We present line of sight (LOS) velocities and deformation time series using both techniques and show that the root
mean squared differences of the estimated mean velocities and
deformation from each method are about 1 mm/year and 5 mm,
respectively. These values are within expected noise levels and
a characteristic of the pixel selection parameters for both the
time-series techniques. We validate our deformation estimates
against creep measurements from alignment arrays along the
Hayward Fault and show that our estimates agree to within
0.5 mm/year LOS velocity and 1.5 mm LOS displacement.
Index Terms—Interferometric synthetic aperture radar
(InSAR), persistent scatterers, San Francisco Bay, small
baseline (SB).
I. I NTRODUCTION
T
IME-SERIES interferometric synthetic aperture radar
(InSAR) techniques are extensions of conventional
InSAR for analyzing the temporal evolution of Earth surface
displacements. These techniques use multiple SAR acquisitions
to generate deformation time series and enable us to overcome
some limitations of conventional InSAR, such as the presence
of uncorrelated phase noise terms such as atmospheric propagation delays, to reduce errors in deformation estimates. Current
multitemporal InSAR algorithms can be broadly classified into
two categories—persistent scatterer (PS) [1] and small baseline
Manuscript received May 3, 2010; revised September 21, 2010; accepted
November 14, 2010. Date of publication January 19, 2011; date of current
version June 24, 2011. This work was supported by the National Aeronautics
and Space Administration Earth System Science Fellowship.
P. Shanker and H. A. Zebker are with the Department of Electrical Engineering and the Department of Geophysics, Stanford University, Stanford,
CA 94305 USA (e-mail: [email protected]).
F. Casu and R. Lanari are with the Istituto per il Rilevamento Elettromagnetico dell’Ambiente-Consiglio Nazionale delle Ricerche, 80124 Napoli, Italy
(e-mail: [email protected]).
Digital Object Identifier 10.1109/LGRS.2010.2095829
(SB) [2] methods, depending on the method of selecting pixels
with reliable phase measurements. In PS techniques, interferograms are formed using a single master scene and analyzed
at single look resolution to maximize the signal to clutter ratio
of resolution cells containing a single dominant scatterer—the
clutter is the part of the echo from all of the non-PS scattering
elements within the cell. The SB methods use the most highly
correlated areas to derive the deformation signal from multilooked interferograms, which reduces speckle and improves
the phase estimate [2]. Although multilooking is not mandatory
[3], it is very helpful in reducing data volume and noise levels.
In SB algorithms, a network of redundant interferograms with
perpendicular baseline values below a threshold is used to limit
the effects of geometric decorrelation.
Both PS and SB methods have been successfully used over
the last couple of years to study various phenomena like fault
creep, landslides, and groundwater-induced subsidence [4]–[7].
Various independent validation studies [8]–[11] demonstrate
deformation time-series retrieval accuracy. In particular, the
PSIC4 project resulted in a comparison of time-series InSAR
techniques by various research groups anonymously within
Europe [11]. The Terrafirma project [10] results compared six
PS processing chains over a common area including the intermediate processing products but did not include comparisons
with any small baseline subset algorithm (SBAS) products.
Very few intercomparisons between the two families of timeseries techniques at a regional scale have been attempted [12].
In this letter, we present a quantitative comparison of
deformation time series using two different multitemporal
InSAR algorithms—Stanford University’s maximum likelihood PS (MLPS) selection algorithm [13] in the Stanford
method for PS (StaMPS) framework [14] and the Istituto
per il Rilevamento Elettromagnetico dell’Ambiente-Consiglio
Nazionale delle Ricerche’s (IREA-CNR) SBAS [2], both applied to a common European Resource Sensing data set covering the San Francisco Bay Area. We determine the noise
floor for each method by analyzing tandem SAR acquisitions.
Moreover, we statistically cross compare the retrieved mean
velocity maps and time series. Finally, we validate our results
by comparing the deformation estimates against creep measurements from the U.S. Geological Survey (USGS) alignment
array [15] along the Hayward Fault.
II. DATA AND M ETHODOLOGY
We compare the deformation time-series processed over
three subareas in the San Francisco Bay Area [Fig. 1(a)] using
the MLPS (PS family) and SBAS algorithms (SB family).
We analyzed a total of 43 SAR acquisitions acquired during
1545-598X/$26.00 © 2011 IEEE
SHANKER et al.: COMPARISON OF PS AND SB TIME-SERIES INSAR RESULTS
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Fig. 1. (a) Locations of three subareas used in the time-series comparison, shown overlaid on the SRTM DEM of the region. The largest square represents the
total area covered by the frame analyzed using the SBAS method. (b) Time-baseline plot of the 43 ERS scenes (vertices) common to both data sets, a scene from
December 1997 (square) was used for the master geometry. Edges between vertices represent the 124 interferograms used in the SBAS analysis; in the MLPS case
all the images interfere with the master acquisition.
descending passes of European Remote Sensing-1/2 satellites
(Track 70, Frame 2853) between May 1995 and December
2000. All products were produced in a common geometry
corresponding to an acquisition from Dec 1997. We used
42 common-master interferograms for the PS analysis and
124 multilooked interferograms (20 azimuth looks and 4 range
looks), corresponding to the network shown in Fig. 1(b), for the
SBAS analysis. There are no significant time gaps in the data
set, and there are no disconnected subsets of SAR scenes in
the interferogram network [Fig. 1(b)]. This area has previously
been studied using both techniques [5], [7], [13] but with
different sets of SAR acquisitions.
The SAR images were processed independently at Stanford
and IREA-CNR. We averaged the results from the PS analysis
over a 20-azimuth and 4-range pixel window to match the
resolution of the SBAS analysis. We processed the PS data
in three different areal subsets due to the large data volume
while processing the SBAS data as a complete frame [see
Fig. 1(a)]. We unwrapped the PS data set using the stepwise3-D phase-unwrapping algorithm [14] and the SBAS data set
interferogram network shown in Fig. 1(b) with the extended
minimum cost flow (E-MCF) algorithm [16]. Our comparison
requires a common reference point, which we chose as the pixel
with the maximum average temporal coherence [14], [16]. We
also use a 400-day temporal filter to mitigate the effects of the
seasonal variation of the groundwater levels [7].
First, we examined the tandem pairs in the generated deformation time series to estimate the noise level assuming
that the deformation signal does not change significantly over
24 h. This provides a quantitative measure of our ability to
correct for temporally uncorrelated noise sources, such as orbit
errors and atmospheric propagation delays, by computing the
average standard deviation of the phase differences.
We then compared the PS and SBAS time series pixel by
pixel, computing the mean and standard deviation of the difference in mean line-of-sight (LOS) velocities as estimated by PS
and SBAS techniques. We also report the average standard de-
TABLE I
N OISE F LOOR OF THE PS AND SBAS T ECHNIQUES U SING THE T HREE
TANDEM PAIRS OF DATA IN O UR S ERIES OF 43 SAR S CENES
viation between the time series of commonly selected coherent
pixels, following [8]–[11].
Finally, we compared our estimated deformation time series
against creep measurements from eight stations of a USGS
alignment array [15]. The alignment array stations are located
a few kilometers apart on the Hayward Fault corresponding to
the San Leandro subarea that we processed. The creep meter
measurements were projected onto radar LOS and directly
compared against the estimated time series following [5].
III. R ESULTS AND D ISCUSSION
We used a phase noise threshold of 5 mm in the MLPS
algorithm [13] for identifying the PS and a temporal coherence
threshold of 0.7 [16] for identifying the SBAS pixels. Both the
techniques identified a similar network of coherent pixels (85%
in common), mostly in the urban areas.
Table I shows the statistics from the tandem pair analysis in
our three subareas and indicates an average of 3–4-mm noise in
PS results and 2-mm noise in SBAS results. Assuming that the
noise was contributed equally by each scene, this corresponds
to 2–3-mm noise level in PS and 1.5-mm noise level in SBAS
per SAR scene.
In Fig. 2, we present the mean deformation velocity maps using the two techniques. Both the PS and the SBAS approaches
identify the major deformation features in the San Francisco
Bay Area. These include the subsidence of the San Francisco
International (SFO) airport runway [SFO airport subarea, SFO
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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 8, NO. 4, JULY 2011
Fig. 2. LOS velocity in the three different regions of the San Francisco Bay Area as estimated using (left) the MLPS method and (middle) the SBAS method.
The common reference point is indicated by a black triangle in each of the images. (Right) The differences between the velocity estimates are also shown. The
stations for which individual time-series are shown in Fig. 3 are marked by circles. The areas across the fault used to compute the differential subsidence across
the Silver Creek Fault (c3) are also shown.
on Fig. 2(a3)], the uplift due to the San Leandro synform, rapid
subsidence of many areas near the bay such as near Bay Farm
Island (BFI) [San Leandro subarea, BFI on Fig. 2(b3)], the
differential uplift across the Silver Creek Fault (SCF) in San
Jose [South Bay subarea, SCF on Fig. 2(c3)], and the creep
across the Hayward Fault near Oakland and Fremont [Fig. 2(b)
and (c), respectively].
In addition to comparing the mean deformation velocities,
we also directly compared the estimated time series for select
stations shown in Fig. 2(a3)–(c3). The time series for differential subsidence across the Silver Creek Fault [Fig. 3(c3)] was
produced by averaging the rectangular regions on either side of
the fault and computing their difference. We also show regional
subsidence along the San Francisco Bay including Candle Stick
Point [Fig. 3(a1)], Bay Farm Island [Fig. 3(b1)], Alameda Ferry
[Fig. 3(b2)], and Shoreline Park [Fig. 3(c1)].
We observe few, if any, systematic differences between the
two techniques. We note discrepancies of more than 2 mm/year
for rapidly subsiding reclaimed areas located near the Bay, such
as the runway of the SFO airport [Fig. 3(a2)], the edge of BFI
[Fig. 3(b1)], and the Shoreline Park [Fig. 3(c1)]. We attribute
these disagreements to the use of different phase-unwrapping
algorithms in the standard codes [14], [16]. We analyzed the
MLPS interferograms using the several unwrapping algorithms
available in the StaMPS framework and found that the pseudo3-D algorithm best matched the independent ground-truth
measurements. Hence, we used the pseudo-3-D unwrapping
algorithm for all our comparisons. SBAS data have been unwrapped via the E-MCF algorithm only [16]. The discrepancies
could also be a consequence of spatial filtering in StaMPS [14],
where deformation is derived using a combination of spatial
and temporal low-pass filter on the unwrapped data. If the
dimensions of the filter are large, some localized subsidence
features are smoothed out. This filtering does not affect our
estimates of fault creep as the physical dimensions of these
features are significantly larger than those of the applied filters.
We also observe some boundary effects in the processing.
The three PS data sets were processed individually to reduce
the data volume, while the entire ERS frame was processed
for the SBAS analysis. As a result, the PS pixels near the subset
boundaries are less constrained in the phase unwrapping step
than in the SBAS data set. Hence, the differences between the
SHANKER et al.: COMPARISON OF PS AND SB TIME-SERIES INSAR RESULTS
595
Fig. 3. Estimated LOS time-series for three different stations for each of the three different regions in the San Francisco Bay Area using the MLPS method and
the SBAS method. The difference between the deformation estimates for each of the stations is also shown. c3 represents the average difference across the Silver
Creek Fault (SCF) computed in the areas highlighted in Fig. 2(c3).
velocity estimates near the edges are more than 1 mm/year
higher than over other parts of the image.
A systematic phase ramp is observed in the South Bay region
[Fig. 2(c)], which is located in the near-range region of the
SAR scene. This may be due to uncompensated phase ramps in
individual interferograms or may be indicative of an orbit error
in the vertical direction in the master SAR scene used for PS
analysis. Such problems can be overcome over regions where
multiple global positioning system stations or other geodetic
observations distributed over the imaged area are available.
Moreover, the time series estimated using the PS and SBAS
techniques physically represents the motion of the dominant
scatterer in the resolution element and the average motion of the
several scatterers in the resolution element, respectively. These
values could be physically different.
Table II shows the statistics of the difference in deformation
estimates from the two time-series InSAR techniques. We
observed (not shown) that the error statistics are normally distributed and, hence, characterized solely by mean and standard
deviation. We also report the average standard deviation as this
statistic has been widely used in other studies for quantifying
estimation errors and differences. The average difference in
estimated LOS velocities is on the order of 1 mm/year, and the
estimated LOS time series is 5 mm.
TABLE II
D ISCREPANCY B ETWEEN LOS V ELOCITIES AND T IME S ERIES U SING
THE MLPS AND THE SBAS T ECHNIQUES , FOR THE T HREE
S UBREGIONS IN THE S AN F RANCISCO BAY A REA
In Fig. 4, we present the estimated LOS creep from the
PS and SBAS techniques, along with the actual creep measurements from the USGS alignment array. Creep meters are
insensitive to vertical deformation, whereas InSAR is most
sensitive to the vertical component by almost a factor of
three. Hence, any local vertical deformation will significantly
affect the time-series InSAR estimates. Table III shows the
differences between InSAR and creep meter measurements for
eight stations along the Hayward Fault, an average of about
1.5 mm for LOS displacement and 0.5 mm/year for LOS
velocity.
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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 8, NO. 4, JULY 2011
Fig. 4. Comparison of the time-series InSAR creep estimates along the Hayward Fault with USGS alignment array observations [16] for three selected stations
[(a) HLSA. (b) H73A. (c) HPAL]. The error statistics for all the eight stations located in the analyzed region is shown in Table III. Both techniques successfully
capture the change in creep rate in mid 1997 for HPAL station.
TABLE III
C OMPARISON OF THE E STIMATED PS AND SBAS T IME S ERIES W ITH
C REEP M EASUREMENTS F ROM THE USGS A LIGNMENT A RRAY AT
E IGHT D IFFERENT S TATIONS A LONG THE H AYWARD FAULT
IV. C ONCLUSION
Intercomparison of Stanford University’s MLPS selection
algorithm in the StaMPS framework and IREA-CNR’s SBAS
technique shows that the PS and SBAS techniques both identify
deformation features in the Bay Area, with similar coverage. In
addition, the average discrepancy of 1 mm/year in the estimated
LOS velocity and 5 mm in LOS time series is consistent with
those estimated by the European Space Agency’s (ESA) PSIC-4
study [11], the Terrafirma validation project [10], and by other
independent studies [5], [8]. We also compared the accuracy
of fault creep estimates from the two time-series InSAR techniques against creep meter measurements and found agreement
within 1.5-mm LOS displacement and 0.5 mm/year velocity,
similar to that reported in [5]. Here, we focused on the quantification of the discrepancies between the PS and SBAS timeseries techniques, representing the first of a set of comparative
studies aimed at understanding these observed differences.
More detailed comparative studies such as the Terrafirma
project [10], including the comparison of the estimated results
in geophysical parametric domain, are needed to fully understand the limitations of and the differences between the various
time-series InSAR techniques. Similarly, comparative studies
using X-band and L-band sensors will help us better understand
the effects of wavelength, resolution, and orbit repeat periods
on time-series InSAR algorithms. ESA’s supersite initiative
will enable these intercomparative studies and validate InSAR
against other geodetic measurements.
ACKNOWLEDGMENT
The authors would like to thank Prof. P. Segall for inviting
F. Casu to spend time with his group at the Geophysics
Department, Stanford University, Stanford, CA, and making
this project possible. The authors would also like to thank
T. R. Lauknes from Northern Research Institute, Tromso, for
providing the generalized SAR (gSAR) processor to process
the PS data set.
R EFERENCES
[1] A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 1, pp. 8–20,
Jan. 2001.
[2] P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm
for surface deformation monitoring based on small baseline differential
SAR interferograms,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11,
pp. 2375–2383, Nov. 2002.
[3] A. Hooper, “A multi-temporal InSAR approach combining both persistent
scatterer and small baseline approaches,” Geophys. Res. Lett., vol. 35,
p. L16302, 2008, doi: 10.1029/2008GL034654.
[4] R. Bürgmann, G. Hilley, A. Ferretti, and F. Novali, “Resolving vertical
tectonics in the San Francisco Bay Area from permanent scatterer InSAR
and GPS analysis,” Geology, vol. 34, no. 3, pp. 221–224, Mar. 2006,
doi: 10.1130/G22064.1.
[5] R. Lanari, F. Casu, M. Manzo, and P. Lundgren, “Application of
SBAS-DInSAR technique to fault creep: A case study of the Hayward
Fault, California,” Remote Sens. Environ., vol. 109, no. 1, pp. 20–28,
Jul. 2007.
[6] G. Hilley, R. Burgmann, A. Ferretti, F. Novali, and F. Rocca, “Dynamics
of slow-moving landslides from permanent scatterer analysis,” Science,
vol. 304, no. 5679, pp. 1952–1955, Jun. 2004.
[7] R. Lanari, F. Casu, M. Manzo, G. Zeni, P. Berardino, M. Manunta, and
A. Pepe, “An overview of the small baseline subset algorithm: A DInSAR
technique for surface deformation analysis,” Pure Appl. Phys., vol. 164,
no. 4, pp. 637–661, 2007.
[8] F. Casu, M. Manzo, and R. Lanari, “A quantitative assessment of the
SBAS algorithm performance for surface deformation retrieval from
DInSAR data,” Remote Sens. Environ., vol. 102, no. 3/4, pp. 195–210,
Jun. 2006.
[9] A. Ferretti, G. Savio, R. Barzaghi, A. Borghi, S. Musazzi, F. Novali,
C. Prati, and F. Rocca, “Submillimeter accuracy of InSAR time series:
Experimental validation,” IEEE Trans. Geosci. Remote Sens., vol. 45,
no. 5, pp. 1142–1153, May 2007.
[10] M. Crosetto, O. Monserrat, N. Adam, A. Parizzi, C. Bremmer,
S. Dortland, R. F. Hanssen, and F. J. van Leijen, Final report of the
validation of existing processing chains in Terrafirma stage 2, Terrafirma project, ESRIN/Contract no. 19366/05/I-E. [Online]. Available:
http://www.terrafirma.eu.com/product_validation.htm
[11] D. Raucoules, B. Bourgine, M. de Michele, G. Le Cozannet, L. Closset,
C. Bremmer, J. G. Veldkamp, D. Tragheim, L. Bateson, M. Crosetto,
M. Agudo, and M. Engdahl, “Validation and intercomparison of Persistent Scatterers Interferometry: PSIC4 project results,” J. Appl. Geophys.,
vol. 68, no. 3, pp. 335–347, Jul. 2009.
[12] T. R. Lauknes, P. Shanker, J. Dehls, H. Zebker, I. Henderson, and
Y. Larsen, “Detailed landslide mapping in northern Norway with smallbaseline and persistent scatterer interferometric SAR time-series methods,” Remote Sens. Environ., vol. 114, no. 9, pp. 2097–2109, Sep. 2010.
[13] P. Shanker and H. Zebker, “Persistent scatterer selection using maximum likelihood estimation,” Geophys. Res. Lett., vol. 34, p. L22 301,
2007.
[14] A. Hooper, “Persistent scatterer radar interferometry for crustal deformation studies and modeling of volcano deformation,” Ph.D. dissertation,
Stanford Univ. Press, Stanford, CA, 2006.
[15] F. S. McFarland, J. J. Lienkaemper, and S. J. Caskey, Data from theodolite measurements of creep rates on San Francisco Bay Region Faults,
California, 1979–2009, U.S. Geological Survey, Reston, VA, Openfile Rep. 2009-1119, v 1.1. [Online]. Available: http://pubs.usgs.gov/
of/2009/1119
[16] A. Pepe and R. Lanari, “On the extension of minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2374–2383,
Sep. 2006.