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 
patterns. 
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. NATURAL HAZARDS MONITORING AND 
CHARACTERIZATION WITH INSAR 
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