Tracking Retreat of the North Seasonal Ice Cap on Mars: Results from the THEMIS Investigation

• Main Authors: Ivanov, A. B., Wagstaff, K. L., Ttus, T. N.
• Format: Other/Unknown Material
• Language: unknown
• Published: 2005
• Subjects: Lunar and Planetary Science and Exploration; Midwinter; Ice cap
• Online Access: http://hdl.handle.net/2060/20050169830

The CO2 ice caps on Mars advance and retreat with the seasons. This phenomenon was first observed by Cassini and then confirmed by numerous ground based observations in 19th and 20th centuries. With the advent of the space age observations of the seasonal ice cap were done by all orbiting spacecraft starting with Mariner 7. Viking Orbiters and more recently the Mars Global Surveyor (particularly Mars Orbiter Camera (MOC) and Thermal Emission Spectrometer (TES) instruments) have accumulated significant data on the retreat of the CO2 seasonal cap. During Mars year 2 of THEMIS operations at Mars, we planned an observational campaign in which the THEMIS instrument (onboard the Mars Odyssey spacecraft) repeatedly observed the north seasonal polar cap from midwinter to late spring. THEMIS allows simultaneous observations in both Thermal IR (12.57 m) and Visible wavelengths (0.65 m). One of the goals for this work is to initiate an interannual program for observations of the seasonal ice caps using the THEMIS instrument. The most efficient way to detect the edge between frost and bare ground is directly onboard of the spacecraft. Prior to onboard software design effort, we have developed two groundbased algorithms for automatically finding the edge of the seasonal polar cap in THEMIS IR data. The first algorithm relies on fully calibrated data and can be used for highly reliable groundbased analyses. The second method was specifically developed for processing raw, uncalibrated data in a highly efficient way. It has the potential to enable automatic, onboard detections of the seasonal cap retreat. We have experimentally confirmed that both methods produce similar results, and we have validated both methods against a model constructed from the MGS TES data from the same season.