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. These systems have the potential to facilitate intra-seasonal, short-term surface velocity variations across a range of ice masses. Current techniques for displacement estimation are based on matching image pairs with sufficient displacement and/or preservation of the surface over time and consequently, do not benefit from an increase in satellite revisit times. Here, we explore an approach that is fundamentally different from image correlation or similar approaches and engages the concept of optical flow. Our goal is to assess whether this technique could overcome the limitations of image matching and yield new insights in glacier flow dynamics. We implement two different methods of optical flow, and test these implementations utilizing the SPOT5 Take5 dataset at two glaciers: Kronebreen, Svalbard and Kaskawulsh Glacier, Yukon. At Kaskawulsh Glacier, we extract intra-seasonal velocity variations that are synchronous with episodes of increased air temperature. Moreover, even for the cloudy dataset of Kronebreen, we can extract spatio-temporal trajectories that correlate well with measured GPS flow paths. Since the underlying concept is simple and computationally efficient due to data-reduction, our optical flow methodology can be rapidly adapted for a range of studies from the investigation of large scale ice sheet dynamics down to the estimation of displacements over small and slow flowing glaciers.