Weekly Glacier Flow Estimation from Dense Satellite Time Series Using Adapted Optical Flow Technology

Contemporary optical remote sensing satellites or constellations of satellites can acquire imagery at sub-weekly or even daily timescales. Thus, these systems facilitate the potential for within-season velocity estimation of glacier surfaces. State-of-the-art techniques for displacement estimation are based on matching image pairs and are thus constrained by the need of significant displacement and/or preservation of the surface over time. Consequently, such approaches cannot benefit entirely from the increasing satellite revisit times. Here, we explore an approach that is fundamentally different from image correlation or similar techniques and exploits the concept of optical flow. Our goal is to assess if this concept could overcome above current limitations of image matching and thus give new insights in glacier flow dynamics. We implement two different methods of optical flow, and test these on the SPOT5 Take5 dataset over Kronebreen, Svalbard and over Kaskawulsh Glacier, Yukon. For Kaskawulsh Glacier we are able to extract seasonal velocity variation, that temporally coincide with events of increased air temperatures. Furthermore, even for the cloudy dataset of Kronebreen, we were able to extract spatio-temporal trajectories which correlate well with measured GPS flow paths. Because the underlying concept is simple and computationally efficient due to data-reduction, our methodology can easily be used for exploratory regional studies of several glaciers or estimation of small and slow flowing glaciers.