Estimation of lunar elemental abundances using Clementine UVVIS+NIR dataстатья
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Дата последнего поиска статьи во внешних источниках: 18 июля 2013 г.
Аннотация:In this study we propose a regression model for the estimation of lunar elemental abundances from
spectral features extracted from Clementine multispectral imagery in the visible and near-infrared
domain. We extract a set of spectral features, including the continuum slope, the FWHM of the ferrous
absorption trough near 1000 nm, and the wavelengths and relative depths of the absorption minima and
inflection points present in the trough. As a ‘‘ground truth’’ for the elemental abundances we rely on the
Lunar Prospector gamma ray spectrometer (LP GRS) data. With respect to the elemental abundances of the
Apollo and Luna landing sites independently derived from returned samples, the best examined regression
model is a second-order polynomial. The proposed regression-based approach allows an estimation of the
elemental abundances of Ca, Al, Fe, Mg, and O at an accuracy of about 1 wt% and some tenths of a weight
percent for Ti. Weexamine the influence of calibration of the Clementine UVVIS+NIR data and find that its
effect on the results obtained with the regression approach is minor. Furthermore, we define a threeendmember
model which allows the petrographic mapping of the lunar surface materials in terms of their
Fe, Mg, and Al abundances. We examine the global distribution of Mg-rich rocks, the distribution of
cryptomaria, and the occurrence of aluminous mare basalts in the Frigoris region. A possible regional
compositional anomaly in northwestern Oceanus Procellarum is found, which corresponds to an extended
area displaying spectral characteristics consistent with mare basalt containing significant amounts of
olivine. On local scales, we examine in terms of our regression model the highland craters Proclus and
Tycho, the compositionally anomalous central peaks of the craters Copernicus and Bullialdus, and the
pyroclastic deposits on the floor of Alphonsus and on the northern rim of Petavius. As a general result, we
show that the regression-based approach allows the detection of the main lunar terrain classes and rock
types based on multispectral imagery in the visible and near-infrared domain.