Paper No. 78-9
Presentation Time: 10:10 AM
FIELD THERMOPHYSICAL EXPERIMENTS OF MARS ANALOG SITES
The identification of features on Mars that may represent the presence of ice, water, or subsurface layering such as recurring slope linae, gullies or indurated crusts are assumed to show a variation in thermal properties relative to surrounding materials if water in some phase is present in or close to the surface. This suggests that a set of observational criteria can be established to determine the presence of water if the limits of detection and the variation in unconsolidated sediments an be established. Our approach has been to conduct a series of field experiments to determine the naturally occurring diurnal temperature curves of fine to coarse grained sediments analogous to features identified on Mars. Utilizing a combination of fixed thermal cameras and contact sensors, we’ve developed a ground network capable of continuous measurements that enhance repeat drone imagery. Early work near SP Crater included collecting thermal imagery of a variable surface including the presence of high thermal inertia material as well as finer grained sediments. The results of this work suggest that the relative difference between materials of different inertias remain apparent in thermal imagery through the day. The latest experiment near Sunset Crater, Flagstaff, AZ where repeat thermal infrared measurements were collected via drone and ground network to characterize the natural variation of a near-homogenous sand-sized sediment at a range of spatial and temporal scales, and measure the variation due to wind, clouds, and changes in solar insolation. Applying water to the surface worked as a test for the detection of a saturated feature. Results suggest that the impact of clouds and wind are observable but ephemeral effects in the diurnal curves, quickly attenuating relative to solar insolation. Repeat maxima and minima temperatures of dry sediments over the course of multiple days suggest that the presence of water is detectable in these datasets.