Martian Surface Mineralogy from 0.8-1.05 Micrometer TIGER Spectro-Imagery Measurements: Evidence for Compositional Heterogeneities in Terra Sirenum/Tharsis Montes Formation

Details Martian Surface Mineralogy from 0.8-1.05 Micrometer TIGER Spectro-Imagery Measurements: Evidence for Compositional Heterogeneities in Terra Sirenum/Tharsis Montes Formation Martian Surface Mineralogy from 0.8-1.05 Micrometer TIGER Spectro-Imagery Measurements: Evidence for Compositional Heterogeneities in Terra Sirenum/Tharsis Montes Formation

Mar 1996

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996lpi....27..817m&link_type=abstract

Lunar and Planetary Science, volume 27, page 817

Physics

1

Mineralogy, Processes: Surface, Spectro-Imagery, Tharsis Region

Scientific paper

New 0.84-1.02 microns reflectance spectra of Mars are derived from TIGER spectro-imaging measurements obtained at Mauna Kea CFH observatory during the 1990 opposition, from a systematic survey of the Tharsis volcanic region at 200-250 km spatial resolution. The spectral interval is spanned by 240 wavelength channels, with a sampling of about 8 Angstroms. Besides a complementarity with the ISM imaging spectrometer mapping at a larger spatial resolution, the continuous wavelength coverage of the Tharsis province is of particular interest as it permits to study the spectroscopic variability through significative albedo markings. In particular, the dichotomy separating the southern cratered highlands of Terra Cimmeria/Terra Sirenum from the younger volcanic terrains of north Tharsis is investigated. Primary goals of this work are to identify the physico-chemical origins of the observed spectral variations, to map the surface units based on their spectroscopic properties, and to draw inferences in terms of compositional heterogeneities of bright and dark materials. Detailed information regarding the ferric and ferrous mineralogy of undocumented or poorly documented soils is presented, on the basis of a reliable set of about 1500 highly-resolved spectra (R~600) and with the application of mixture modeling and spectral variable analyses to the dataset.

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