Reflectance spectroscopy (0.3–2.5 µm) at various scales for bulk-rock identification

We discussed the possibilities and open questions concerning laboratory refl ectance spectroscopy, spectroscopic measurements in the fi eld, 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 fi eld spectra classifi cation, as a tool for (3) end-member selection for image data classifi cation; (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 specifi c 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 Fe 2+ band in pyroxene-bearing cumulates. Laboratory refl ectance spectra of rocks were classifi ed using the concept of spectrofacies. 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 fi rst 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 fl ows are characterized by overall low refl ectance in both ASTER (advanced spaceborne thermal emission and refl ection radiometer) and Hyperion color-composite images. We carried out Spectral Angle Mapper (SAM) classifi cation of Hyperion images, where individual fi eld spectra represented suitable end members for classifi cation of recent lava and pyroclastic deposits. We used fi eld spectra linear combinations to classify mixed pixels and to approximate the classifi cation of altered and oxidized effusive products. Only two laboratory spectral classes coincided with fi eld 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 identifi cation of subtle diagnostic iron absorption bands

Accuracy in mineral identification: image spectral and spatial resolutions and mineral spectral properties

Problems related to airborne hyperspectral image data are reviewed and the requirements for data analysis applied to mineralogical (rocks and soils) interpretation are discussed. The variability of mineral spectral features, including absorption position, shape and depth is considered and interpreted as due to chemical composition, grain size effects and mineral association. It is also shown how this variability can be related to well defined geologic processes. The influence of sensor noise and diffuse atmospheric radiance in classification accuracy is also analyzed