Post contributed by Dr. Alexander Barrett, School of Physical Sciences, The Open University, UK.

Transverse Aeolian Ridges (TARs) are a distinctive aeolian landform, commonly found on the surface of Mars (Balme et al., 2008; Bourke et al., 2006). They consist of metre to decimetre scale granular ripples, which form transverse (i.e. perpendicular) to the direction of the prevailing wind. Similar to terrestrial megaripples, TARs are believed to form through surface creep of coarse particles. Understanding the distribution of TARs is important for understanding the aeolian environment in which they formed (Favaro et al., 2021), and for planning rover missions (Balme et al., 2018). TARs present a potential hazard when they occur in rover landing sites. Consequently, a team at the Open University has trained a machine learning model called NOAH-H (developed by SciSys Ltd. for the European Space Agency; Woods et al. 2020) to identify these features, and distinguish them from other common surface textures (Barrett et al. 2021).

Image 1 shows bedforms in Oxia Planum. The size and prevalence of bedforms in this area is important, since it is the landing site for the ExoMars Rosalind Franklin Rover (Vago et al., 2017), which will arrive in 2023 to search for signs of past and present life in this once water-rich area (Quantin-Nataf et al., 2021). Large bedforms, or continuous regions of loose aeolian sand, could form navigational hazards, limiting the directions in which the rover can progress, or the science targets that it can access. We need to have a good understanding of the distribution of TARs in the immediate area where the rover will land, however this is not precisely known in advance and could be anywhere within a large landing ellipse. Mapping all of the TARs in the potential landing area by hand is impossible in the time available, even for a large team.

Image 1: Large TARs at the planned ExoMars Rosalind Franklin rover landing site at Oxia Planum, Mars (High Resolution Imaging Science Experiment (HiRISE) Image ESP_048292-1985. a) translucent layer showing Machine Learning produced terrain classification, overlying the red-band HiRISE image from which the classification was made. The area is dominated by rugged and fractured bedrock, while topographic lows contain non-bedrock material. This has been shaped into TARs of various sizes and degrees of continuity. The divide between bedrock and non-bedrock textures is well defined, as are the perimeters of patches of TARS. The transitions between textured, fractured and rugged bedrock are less well identified, as these features form a continuum, and so some areas have characteristics of multiple classes. HiRISE Image Credit NASA/JPL/University of Arizona.