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Mapping without the sun
Zhang, Jixian

InSAR application is hampered by the presence of atmospheric
delay anomalies. Differences in atmospheric temperature,
pressure, and water vapor content at the two observation times
can cause differing path delays and consequent anomalies in an
InSAR deformation image. Atmospheric delay anomalies can
reduce the accuracy of InSAR-derived deformation
measurements from several millimeters under ideal conditions
to 2-3 centimeters under more typical conditions, thus
obscuring subtle deformation that holds clues to the cause of the
deformation. The atmospheric delays that hamper InSAR
accuracy can be lessened by routinely estimating water-vapor
content using a high-resolution weather model, a continuous
global positioning system (CGPS) network, or other satellite
sensors such as the Moderate Resolution Imaging
Spectroradiometer (MODIS), Advanced Spacebome Thermal
Emission and Reflection Radiometer (ASTER), and European
Medium Resolution Imaging Spectrometer (MERIS) to improve
InSAR deformation measurements (Li et al., 2003; Foster et al.,
2006). In addition, multi-temporal InSAR images should be
used to reduce artifacts due to atmospheric delay anomalies,
orbit errors, DEM-induced artifacts, and loss of coherence
measurements to improve the accuracy of deformation
measurements. Stacking and least-squares inversion approaches,
which take into account covariance characteristics of data
distribution, can be applied to multi-temporal InSAR images to
reduce atmospheric delay anomalies and improve temporal
sampling in order to reveal transient, dynamic deformation
Persistent Scatterer (PS) InSAR (PSInSAR) (Ferretti et al.,
2001; Hooper et al., 2007) represents one of the most
significant advances in InSAR research. PSInSAR uses unique
characteristics of atmospheric delay anomalies and the
distinctive backscattering of certain ground targets (called PS)
to improve the accuracy of conventional InSAR deformation
measurements from 10-20 mm to 2-3 mm (Ferretti et al., 2001).
The SAR backscattering signal of a PS target has broadband
spectra in the frequency domain, implying that the radar phase
of this kind of scatterer correlates over much longer temporal
intervals and over much larger baseline separations than other
scatterers. As a result, if the backscattering return of a pixel is
dominated by PS(s), this pixel is always coherent over long
time intervals. At PS pixels, the difficulty of decorrelation in
conventional InSAR can therefore be overcome. In addition, the
atmospheric contribution is rather smooth spatially and is
independent over time. At PS pixels, the atmospheric
contribution to the backscattered signal can be identified and
removed from the data using a multi-interferogram approach.
Therefore, the ultimate goal of PSInSAR processing is to
separate the different contributions (surface deformation,
atmospheric delay anomaly, DEM error, orbit error, and
decorrelation noise) by means of least-squares estimations and
iterations, taking into account the spatio-temporal distribution
and the correlation between PS samples. After removing errors
due to atmospheric anomaly, orbit error, and DEM error,
deformation histories at PS points can be appreciated at
millimeter accuracy. PSInSAR has been applied successfully to
monitor landslides, urban subsidence, fault movement, and
volcanic deformation.
One of the drawbacks of the current InSAR technique is the
lack of temporal sampling of InSAR images. In many cases,
documenting the transient behavior of deformation is critical for
hazard-mitigation purposes. However, transient volcano
deformation can be short-lived, and many times it cannot be
captured with InSAR due to long sensor-revisit times (e.g., 35
days for ERS and Envisat, 44 days for JERS-1, 24 days for
Radarsat-1, and 46 days for ALOS). The SARs onboard the
Envisat, Radarsat-1, and ALOS radar satellites are capable of
acquiring images in both strip and scan modes. In strip mode,
radar-antenna pointing is fixed along the flight path, and the
antenna footprint covers a strip (swath) on the surface beneath
the orbital track. As a result, the size of an image is limited in
the cross-track direction to about 100 km. Currently, strip-mode
SAR images are used for InSAR deformation mapping. Scan
mode SAR (ScanSAR) is achieved by periodically switching
the antenna look angle into neighboring subswaths in the cross
track direction, thereby drastically increasing the size of the
accessible image swath to about 400-500 km. Because
ScanSAR can acquire more frequent observations of a given
study area than is possible with strip-mode SAR, interfeometric
ScanSAR (Guamieri and Rocca, 1999) images significantly
increase the number of interferometric observations in a fixed
time frame, improving the temporal resolution of deformation
mapping. This makes ScanSAR InSAR a very attractive tool for
monitoring transient deformation signals.
Past spacebome radar sensors such as ERS-1, ERS-2, JERS-1,
and Radarsat-1 are all single-polarized radars (i.e., radar signals
are vertically or horizontally transmitted and vertically or
horizontally received, respectively); therefore, the radar signal
from these sensors only partially captures the scattering
properties of targets on the surface. If a sensor is equipped with
a fully-polarized radar (i.e., radar signals are both vertically and
horizontally transmitted and both vertically and horizontally
received), such as the radar sensors onboard the Japanese
ALOS satellite and future radar satellites, the resulting fully-
polarized radar image can be related to the signatures of known
elemental targets, making it possible to infer the type of
scattering that is taking place (e.g., Touzi et al., 2004). The
polarization signatures of the vegetation canopy, the lower
vegetation, and the ground are quite different and can be
separated using polarimetric analysis. The optimization
procedure can be deployed to maximize the interferometric
coherence between two polarimetric radar images to reduce the
effect of baseline and temporal decorrelation on the
interferogram. Then, through a coherent target decomposition
approach that separates radar backscattering returns coming
from the canopy top, the bulk volume of the forest, and the
ground surface, one can derive the difference of interferometric
phase measurements that leads to the height difference between
the physical scatterers possessing these mechanisms (Cloude
and Papathanassiou, 1998). Often, physical radar backscattering
models over different vegetation types can be developed to
calculate the canopy height, the bare-earth topography, the
mean volume extinction coefficient that is related to canopy
density, and other canopy structural parameters based on
measurements from a polarimetric InSAR image. The use of
multiple polarimetric InSAR (Pol-InSAR) images with different
baselines improves estimates of the canopy height, canopy
density, stem biomass, and ground height.
2.1 Imaging earthquake displacement
InSAR was first used to map the ground surface displacement
caused by the 1992 Landers earthquake (Massonnet et al.,
1993). Using a pair of SAR images, one before the earthquake
and the other after the earthquake, InSAR can map a co-seismic
deformation field, which can be used to estimate earthquake