Characterizing the Surface of Mars In the Near-Infrared

Jessica Caroline Snyder


Within the Martian atmosphere today are ubiquitous dust aerosols and thin, cirrus-style water-ice clouds. To determine the water content of the clouds, using a full radiative transfer model, from spectral images of Mars, we must first understand how light is reflected from the surface. In order to do so, we begin by using principal component analysis (PCA) to search for characteristic surface spectral endmembers; the combination of these will described how light is reflected over all parts of the surface on a global scale. For this research, we used near-infrared (1.5–4.1 µm) images taken at the NASA Infrared Telescope Facility between 1994 and 1995.  We chose 32 specific wavelengths where most gasses in the Martian atmosphere are not active.  The images were cylindrically remapped and co-registered, then run through PCA to find the eigenvectors which characterize the surface data alone. After doing this for several sets of data, the eigenvectors were compared to check for consistency, which would indicate a characteristic model for the surface over all timescales exists. We present here today the results of these comparisons, as well as a comparison of these new results to previous median results.



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