Nonlinear Spectral Mixture Modeling to Estimate Water-Ice Abundance of Martian Regolith
Abstract
We present a novel technique to estimate the abundance of water-ice in the Martian permafrost using Phoenix Surface Stereo Imager multispectral data. In previous work, Cull et al. (2010) estimated the abundance of water-ice in trenches dug by the Mars Phoenix lander by modeling the spectra of the icy regolith using the radiative transfer methods described in Hapke (2008) with optical constants for Mauna Kea palagonite (Clancy et al., 1995) as a substitute for unknown Martian regolith optical constants. Our technique, which uses the radiative transfer methods described in Shkuratov et al. (1999), seeks to eliminate the uncertainty that stems from not knowing the composition of the Martian regolith by using observations of the Martian soil before and after the water-ice has sublimated away. We use observations of the desiccated regolith sample to estimate its complex index of refraction from its spectrum. This removes any a priori assumptions of Martian regolith composition, limiting our free parameters to the estimated real index of refraction of the dry regolith at one specific wavelength, ice grain size, and regolith porosity. We can then model mixtures of regolith and water-ice, fitting to the original icy spectrum to estimate the ice abundance. To constrain the uncertainties in this technique, we performed laboratory measurements of the spectra of known mixtures of water-ice and dry soils as well as those of soils after desiccation with controlled viewing geometries. Finally, we applied the technique to Phoenix Surface Stereo Imager observations and estimated water-ice abundances consistent with pore-fill in the near-surface ice. This abundance is consistent with atmospheric diffusion, which has implications to our understanding of the history of water-ice on Mars and the role of the regolith at high latitudes as a reservoir of atmospheric H2O.
- Publication:
-
AAS/Division for Planetary Sciences Meeting Abstracts #49
- Pub Date:
- October 2017
- Bibcode:
- 2017DPS....4940003G