Reflectance spectroscopy (300-2500 nm) at various scales for mineral and rock identification

We discussed the possibilities and open questions concerning laboratory reflectance spectroscopy, spectroscopic measurements in the field, hyperspectral image data from spacecraft, and integration of multiscale data. Open questions included: ( 1) bulk-rock spectral complexity, which provides the geologic basis for every spectroscopic analysis; ( 2) criteria for laboratory and field spectra classification, as a tool for ( 3) end-member selection for image data classification; ( 4) peculiar spectral characteristics of Mount Etna basalts; and ( 5) effects of remote-sensing data quality. The last three items emerged during a multiscale survey on the Mount Etna volcano. Our laboratory spectroscopic analyses, supported by specific petrographic analyses, showed the relationship between absorption-band frequency and spectrally active functional groups and the unexpected effects of bulk-rock composition on this relationship. We studied the muscovite Al-OH band in quartzite and micaschist and Fe2+ band in pyroxene-bearing cumulates. Laboratory reflectance spectra of rocks were classified using the concept of spectro-facies. In the case of metamorphic rocks, the result was a tree-structure of rock spectral classes mainly based on the predominant vibrational processes. In the case of basalts, characterized by an overall similarity in their composition, the classes were determined on the basis of overall shape of the spectral curve and on electronic process intensities. Here, we report first results of multiscale data integration for the Mount Etna volcano. Etna rocks consist of basalts, with very low albedo and variable degrees of alteration, and recent lava flows are characterized by overall low reflectance in both ASTER ( advanced spaceborne thermal emission and reflection radiometer) and Hyperion color-composite images. We carried out Spectral Angle Mapper (SAM) classification of Hyperion images, where individual field spectra represented suitable end members for classification of recent lava and pyroclastic deposits. We used field spectra linear combinations to classify mixed pixels and to approximate the classification of altered and oxidized effusive products. Only two laboratory spectral classes coincided with field spectra classes; laboratory spectra were mainly used for spectral features attribution. The overall spectral shape of some of these spectra is still under study. Noise level in Hyperion data precluded the identification of subtle diagnostic iron absorption bands.