Temporal and geometrical decorrelation often prevents SAR interferometry from being an operational tool for surface deformation monitoring and topographic profile reconstruction. Moreover, atmospheric disturbances can strongly compromise the accuracy of the results. The authors present a complete procedure for the identification and exploitation of stable natural reflectors or permanent scatterers (PSs) starting from long temporal series of interferometric SAR images. When, as it often happens, the dimension of the PS is smaller than the resolution cell, the coherence is good even for interferograms with baselines larger than the decorrelation one, and all the available images of the ESA ERS data set can be successfully exploited. On these pixels, submeter DEM accuracy and millimetric terrain motion detection can be achieved, since atmospheric phase screen (APS) contributions can be estimated and removed. Examples are then shown of small motion measurements, DEM refinement, and APS estimation and removal in the case of a sliding area in Ancona, Italy. ERS data have been used.

Permanent Scatterer SAR Interferometry (PSInSAR) aims to identify coherent radar targets exhibiting high phase stability over the entire observation time period. These targets often correspond to point-wise, man-made objects widely available over a city, but less present in non-urban areas. To overcome the limits of PSInSAR, analysis of interferometric data-stacks should aim at extracting geophysical parameters not only from point-wise deterministic objects (i.e., PS), but also from distributed scatterers (DS). Rather than developing hybrid processing chains where two or more algorithms are applied to the same data-stack, and results are then combined, in this paper we introduce a new approach, SqueeSAR, to jointly process PS and DS, taking into account their different statistical behavior. As it will be shown, PS and DS can be jointly processed without the need for significant changes to the traditional PSInSAR processing chain and without the need to unwrap hundreds of interferograms, provided that the coherence matrix associated with each DS is properly “squeezed” to provide a vector of optimum (wrapped) phase values. Results on real SAR data, acquired over an Alpine area, challenging for any InSAR analysis, confirm the effectiveness of this new approach.

High-resolution interferometric synthetic aperture radar (InSAR) permanent scatterer data allow us to resolve the rates and variations in the rates of slow-moving landslides. Satellite-to-ground distances (range changes) on landslides increase at rates of 5 to 7 millimeters per year, indicating average downslope sliding velocities from 27 to 38 millimeters per year. Time-series analysis shows that displacement occurs mainly during the high-precipitation season; during the 1997–1998 El Niño event, rates of range change increased to as much as 11 millimeters per year. The observed nonlinear relationship of creep and precipitation rates suggests that increased pore fluid pressures within the shallow subsurface may initiate and accelerate these features. Changes in the slope of a hill resulting from increases in the pore pressure and lithostatic stress gradients may then lead to landslides.

Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km² straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.

This paper presents the results of a blind experiment that is performed using two pairs of dihedral reflectors. The aim of the experiment was to demonstrate that interferometric synthetic aperture radar (InSAR) measurements can indeed allow a displacement time series estimation with submillimeter accuracy (both in horizontal and vertical directions), provided that the data are properly processed and the impact of in situ as well as atmospheric effects is minimized. One pair of dihedral reflectors was moved a few millimeters between SAR acquisitions, in the vertical and east-west (EW) directions, and the ground truth was compared with the InSAR data. The experiment was designed to allow a multiplatform and multigeometry analysis, i.e., each reflector was carefully pointed in order to be visible in both Envisat and Radarsat acquisitions. Moreover, two pairs of reflectors were used to allow the combination of data gathered along ascending and descending orbits. The standard deviation of the error is 0.75 mm in the vertical direction and 0.58 mm in the horizontal (EW) direction. GPS data were also collected during this experiment in order to cross-check the SAR results