## 3 Methods  
We apply the offset tracking technique on SAR image pairs of repeat pass orbits to derive surface displacements [20], using an in-house developed software system. The offset tracking technique is less sensitive to temporal stability of the target phase (coherence) than the interferometric SAR method (InSAR) [21]. We checked the interferometric coherence for the 12-day repeat pass S1 SAR image pairs. Snowfall and redistribution of surface snow due to wind are the main causes for temporal decorrelation during winter. The coherence in the S1 data set varies significantly between individual repeat tracks. Along the ice sheet margins and the southern section of the ice sheet, coherence is rather low. Further restrictions for the application of InSAR arise from ambiguities in zones of strong velocity gradients such as the margins of fast flowing ice streams due to the failure in resolving individual phase cycles. The feasibility for resolving velocity gradients depends on the radar signal bandwidth in slant range and on the interferometric time span [22]. For S1 IW mode data phase ambiguities in shear zones prohibit the application of InSAR on fast-flowing outlet glaciers. Whereas InSAR provides the displacement only in slant range (radar line-of-sight, LOS), offset tracking delivers the components of the surface displacement vector along the flight track (azimuth) and in LOS. The two-dimensional displacement vector in radar imaging geometry is projected onto the surface defined by a digital elevation model (DEM). The resulting surface displacement vector in map projection is defined by two horizontal components (easting, northing) and the vertical component. The vertical component results from the elevation difference between the start position and end position \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{\begin{tabular}{c} **SAR** \\ **Frequency** \\ **(GHz)** \\ \end{tabular} } & **Repeat** & **Imaging** & **Swath** & **Incidence** & \
\begin{tabular}{c} **Nominal** \\ **(km)** \\ \end{tabular} & \multirow{2}{*}{
\begin{tabular}{c} **Pixel Spacing** \\ **Gr \(\times\) Az (m)** \\ \end{tabular} } \\  & **(GHz)** & & & & & & \\ \hline Sentinel-1 & 5.4 & 12 & IW & 250 & 30–42 & 5 \(\times\) 20 & 2.4 \(\times\) 13.9 \\ TerraSAR-X & 9.6 & 11 & SM & 30 & 28–42 * & 2 \(\times\) 3.3 & 0.9 \(\times\) 1.95 \\ ALOS & 1.27 & 46 & FBS & 70 & 34 & 7 \(\times\) 5 & 4.7 \(\times\) 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the SAR data used in this study. Pixel spacing refers to single look complex (SLC) data as used for velocity retrievals. Nominal spatial resolution specified for single look data. Gr: Ground range; Az: azimuth; LOS: line-of sight. * includes angles for different tracks. of the LOS and azimuth displacement vector projected onto the DEM. We use data of the Greenland Ice Mapping Project (GIMP) digital elevation model (DEM) posted at a 30 m grid [23]. For generating the S1 Greenland-wide velocity map (Figure 1) we resample the DEM to a 250 m grid applying bi-linear interpolation. If a pixel is covered by multiple swaths, averaging is performed at pixel scale. The S1, TerraSAR-X and PALSAR velocity data sets used for comparisons on sub-sections of the west coast are posted at a 30 m grid, using the 30 m grid of the DEM for geocoding. Because of different spatial resolutions we perform the cross-correlation with the different SAR products on templates (windows) of different size to calculate two-dimensional offsets (Table 2). The selected window sizes are based on the outcome of performance tests on various outlet glaciers, taking into account signal correlation and the feasibility of resolving gradients in velocity. For Sentinel-1 the rectangular shape of the matching window accounts for the different spatial resolutions in LOS and azimuth.