Comparison of selected machine learning algorithms to derive glacier velocity maps

Marine-terminating glacier dynamics play a crucial role in understanding the climate system. They connect large ice sheets, oceans, and the atmosphere; thus their changes might deliver important information about the relationship between those systems. One of the factors describing ice dynamics is velocity. Its changes can reflect the processes occurring on and underneath the ice sheet surface. Nowadays, that information is delivered mainly by remote-sensing sensors, including satellite radar images (SAR), which provide timely and continuous data even in isolated areas. Plenty of offset-based algorithms already exist to deliver reliable velocity maps based on satellite products. However, these methods require setting a bunch of processing parameters, and they are usually suitable for only one sensor type. This study investigates possible machine learning solutions for finding corresponding areas on satellite images in order to provide velocity maps in an alternative way. In this work, SAR datasets from Sentinel-1 satellite were used to test two machine learning approaches for glacier velocity retrieval. The first approach is based on utilising convolutional neural networks (CNN) to select similar areas on the image pairs. The input data consist of only two coregistered SAR intensity images, which are augmented in the next processing step. As the model output, the most similar image patch is returned. After selecting corresponding image patches, the offsets in both image axes are determined and calculated into velocity values based on a pixel size and temporal baseline. The second approach investigates the possibility of applying the LightGlue image matching technique to the analysis of SAR data in order to detect similar features and determine their movement. The same input products are used, and methods performance and reliability are assessed. Both techniques are tested on two glaciers with different ice dynamics and locations: one in Greenland and one in Svalbard. The methods are compared in terms of efficiency, information density, and velocity values reliability. The final maps are validated by offset-tracking results processed for the same input images.