On 26 January 2014 at 13:55 UTC, an Mw 6.0 earthquake struck the island of Cephalonia, Greece, followed five hours later by an Mw 5.3 aftershock and by an Mw 5.9 event on 3 February 2014 at 03:08 UTC (National Observatory of Athens, Institute of Geodynamics [NOA-GI]). The epicenter of theMw 6.0 event was relocated 2 km east of the town of Lixouri, and that of the Mw 5.9 event at the tip of the Gulf of Argostoli, in the northern part of the Paliki peninsula (Fig. 1; Karastathis et al., 2014; Papadopoulos et al., 2014). Extensive structural damage and widespread environmental effects were induced throughout the Paliki peninsula and along the eastern coast of the Gulf of Argostoli (Valkaniotis et al., 2014). Quays, sidewalks, and piers were damaged in the waterfront areas of the towns of Lixouri and Argostoli, the island capital, and liquefactions, road failures, rock falls, and small landslides were observed.Most of the latter effects took place in the aftermath of the 26 January 2014 event and were reactivated one week later by the 3 February earthquake. In this paper, we derive the 3D surface deformation field associated with the 3 February 2014Mw 5.9 event based on the application of three different measurement techniques to Synthetic Aperture Radar (SAR) acquisitions from the Italian Space Agency (Agenzia Spaziale Italiana [ASI]) COSMO– SkyMed satellites and the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) Tan- DEM-X satellite. We then model the main seismic sources and their associated slip distributions, comparing the latter with improved hypocenter relocations, which allows us to speculate on the possible rupture mechanism. Finally, we discuss the contribution of our findings to the characterization of the seismogenic sources of this region

We present 17 velocity records derived from in situ stand-alone single-frequency Global Positioning System (GPS) receivers placed on eight marine-terminating ice sheet outlet glaciers in South, West and North Greenland, covering varying parts of the period summer 2009 to summer 2012. Common to all the observed glacier velocity records is a pronounced seasonal variation, with an early melt season maximum generally followed by a rapid mid-melt season deceleration. The GPS-derived velocities are compared to velocities derived from radar satellite imagery over six of the glaciers to illustrate the potential of the GPS data for validation purposes. Three different velocity map products are evaluated, based on ALOS/PALSAR data, TerraSAR-X/Tandem-X data and an aggregate winter TerraSAR-X data set. The velocity maps derived from TerraSAR-X/Tandem-X data have a mean difference of 1.5% compared to the mean GPS velocity over the corresponding period, while velocity maps derived from ALOS/PALSAR data have a mean difference of 9.7%. The velocity maps derived from the aggregate winter TerraSAR-X data set have a mean difference of 9.5% to the corresponding GPS velocities. The data are available from the GEUS repository at doi:10.5280/GEUS000001.

In recent years, the Sentinel-1 satellites have provided a data archive of unprecedented volume, delivering C-band Synthetic Aperture Radar (SAR) acquisitions over most of the polar ice sheets with a repeat-pass period of 6–12 days using Interferometric Wide swath (IW) imagery acquired in Terrain Observation by Progressive Scans (TOPS) mode. Due to the added complexity of TOPS-mode interferometric processing, however, Sentinel-1 ice velocity measurements currently rely exclusively on amplitude offset tracking, which generates measurements of substantially lower accuracy and spatial resolution than would be possible with Differential SAR Interferometry (DInSAR). The main difficulty associated with TOPS interferometry lies in the spatially variable azimuth phase contribution arising from along-track motion within the scene. We present a Sentinel-1 interferometric processing chain, which reduces the azimuth coupling to the line-of-sight phase signal through a spatially adaptive coregistration refinement incorporating azimuth velocity measurements. The latter are based on available ice velocity mosaics, optionally supplemented by Burst-Overlap Multi-Aperture Interferometry. The DInSAR processing chain is demonstrated for a large drainage basin in Northeast Greenland, encompassing the Northeast Greenland Ice Stream (NEGIS), and integrated with state-of-the-art offset tracking measurements. In the ice sheet interior, the combined DInSAR and offset tracking ice velocity product provides a spatial resolution of $50\times 50\mathrm{m}$ and 1-sigma accuracies of 0.18 and 0.44 m/y in the x and y components respectively, compared to GPS.