Observations of the northern seasonal polar cap on Mars II: HiRISE photometric analysis of evolution of northern polar dunes in spring


We present an overview of our analyses of HiRISE observations of spring evolution of selected dune areas of the north polar erg. The north polar erg is covered annually by seasonal volatile ice layer, a mixture of CO2 and H2O with mineral dust contamination. In spring, this layer sublimes creating visually enigmatic phenomena, e.g. dark and bright fan-shaped deposits, dark–bright–dark bandings, dark down-slope streaks, and seasonal polygonal cracks. Similar phenomena in southern polar areas are believed to be related to the specific process of solid-state greenhouse effect. In the north, it is currently unclear if the solid-state greenhouse effect is able to explain all the observed phenomena especially because the increased influence of H2O on the time scales of this process has not yet been quantified. HiRISE observations of our selected locations show that the ground exhibits a temporal behaviour similar to the one observed in the southern polar areas: a brightening phase starting close to the spring equinox with a subsequent darkening towards summer solstice. The resolution of HiRISE enabled us to study dunes and substrate individually and even distinguish between different developments on windward and slip face sides of single dunes. Differences in the seasonal evolution between steep slip faces and flatter substrate and windward sides of dunes have been identified and compared to CRISM data of CO2 and H2O distributions on dunes. We also observe small scale dark blotches that appear in early observations and tend to sustain a low reflectivity throughout the spring. These blotches can be regarded as the analogue of dark fan deposits in southern polar areas, leading us to the conclusion that both martian polar areas follow similar spring evolutions.

Michael Aye
Michael Aye
Research Scientist in Planetary Science

My research interests include remote sensing of surfaces, related machine learning studies and open source software.