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Techniques utilizing a suite of interferograms, multi‐interferogram approaches, are aimed at overcoming limitations associated with phase decorrelation and atmospheric effects. The PSInSAR technique (Ferretti et al., 2000, 2001), developed in the late nineties, initiated a “second generation” of algorithms addressing the difficulties related to conventional (single‐pair) analyses. The basic idea is to compare many SAR images and to focus the analysis on high‐quality radar targets, usually referred to as permanent scatterers (PS). Such targets exhibit very stable radar signatures and allow the implementation of powerful filtering procedures to estimate and remove atmospheric disturbances, so that extremely accurate displacement data can be estimated. Common to all geodetic applications, the displacements are computed with respect to a stable reference point.

A recent enhancement of the permanent scatterer technique, the SqueeSAR algorithm (Ferretti et al., 2011), allows for two families of stable points on the Earth's surface, permanent scatterers and distributed scatterers. As noted above, permanent scatterers (PS) are radar targets that are highly reflective (backscatter significant energy), generating very bright pixels in a SAR scene. Permanent scatterers are associated with stable features such as buildings, metallic objects, pylons, antennae, outcrops, and so on. Distributed scatterers (DS) are radar targets usually composed of a localized collection of pixels in the SAR image, all exhibiting a very similar radar signature. Such scatterers usually correspond to rocky areas, detritus, and areas generally free of vegetation. Temporal decorrelation, though still present in distributed scatterers, is small enough to allow for the retrieval of their displacements. Provided enough SAR images are available, one can determine a time series of range change (displacement along the line of sight) regardless of the type of scatterer identified by the algorithm. Thus, estimates of the geographic coordinates of the measurement point (located with a precision of about 1 m), average annual velocity of the measurement point (with a precision dependent on the number of data available, but typically less than 1 mm/yr), and time‐series of scatterer displacement (with a precision typically better than 4 – 5 mm for individual measurements).

In order to successfully perform a multi‐interferogram analysis, a minimum number of satellite images (approximately 10 – 15) are required. This is necessary to create a reliable statistical analysis of the radar returns, making it possible to identify pixels that can be used in the analysis. The higher the number of images acquired and processed, the better the quality of the results. For displacement data associated with permanent and distributed scatterers, a key factor is the distance from the reference point. The relative accuracy can be better than a few millimeters for a distance less than the average correlation length of the atmospheric components (about 4 km at midlatitudes). Average displacement rates can be estimated with a precision better than 1 mm/yr, depending on the number of data available and the temporal span of the acquisitions (Ferretti, 2014).