The motion of sea ice is not only of interest for ships navigating arctic or antarctic waters, but is also important for analyses of parameters describing the interaction between ocean, sea ice and atmosphere, such as heat exchange or salinity transport. Sea ice drift can be estimated from a sequence of satellite images at different spatial scales and with different temporal separation. We determine sea ice motion using a pattern-based multi-scale cascaded motion tracking approach. High-resolution SAR data, specifically Envisat and RS2 images, are analyzed for the purpose of algorithm testing and development. Our present work focuses on the reliability of the drift product and on other parameters that can be derived from the velocity tracking, such as sea ice deformation. Due to the scarcity of in-situ ice measurements of ice drift, it is a challenging task to validate the derived displacement field. Without using external data, the reliability of the result can be estimated using a consistency check called backmatching, but this is computationally very expensive. Another approach estimates a set of parameters for image pattern quality that can be directly calculated during algorithm runtime. This method significantly reduces the computational load of our drift field reliability estimate. In our presentation, we will describe a detailed error analysis of sea ice displacement maps generated from SAR data. A method to estimate the accuracy of displacement vectors using statistical methods and texture analysis will be introduced. In this way, we can assess the reliability of the ice drift maps even when there is no reference data available. Furthermore, we will evaluate the possibility to combine SAR data of different polarizations to improve the quality of the resulting drift maps and analyze sea ice deformation parameters obtained from the calculated displacement field.