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We developed a portable ultra-wideband radar system capable of reconfigurable operation in multiple frequency bands (separate or simultaneous) spanning from microwaves through millimeter waves. The instrument provides a compact solution for fine-resolution measurements of elevation changes and superficial snow/firn thickness from low-altitude, mid-sized airborne platforms. In this article, we provide an overview of the radar system design and its performance during laboratory testing. We demonstrate its application in aerial surveys of snow layer thickness at S/C bands, dual-band airborne altimetry at Ku-/Ka-bands, and present first-order comparisons with coincident airborne lidar data.

Monitoring properties of ice sheets in polar regions is one of the main challenges in glaciology. There is a large amount of heterogeneous radar data from the polar regions that have been gathered through expensive missions. However, retrieving meaningful information from this large volume of data is still a great challenge. With the advancement of machine learning techniques in recent years, many scientists are eager to take advantage of these algorithms and techniques to explore and mine Arctic and Antarctic data. These advancements, however, have happened mainly in the area of supervised learning where the models are data hungry and require large amounts of annotated data. Generating simulated data can be an effective and inexpensive approach to provide large labeled datasets for training machine learning models. In this work, we explore two approaches to simulate arctic snow radar echogram images, namely a radar scattering physics based approach combined with some statistical measures and a purely data-driven approach based on a conditional generative adversarial network. Using several image comparison metrics, we analyze the utility of both methods for the purpose of simulating echograms. Our results show that the physics simulator generates images with good structural similarities, while the purely data-driven approach achieves better textural similarities for simulated image. Finally, we also show that by augmenting our real dataset by the simulated echograms, we can improve our deep learning model for tracking internal layers of snow.

In Rodriguez-Morales et al., 2021, Fig. 1 appeared with some flaws related to a production error. For the sake of clarity, here we list the inconsistencies we identified and provide a revised version of the figure. This erratum does not affect any of the claims, results, discussions, or conclusions in the original manuscript.