Physics
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003agufm.p11a..05g&link_type=abstract
American Geophysical Union, Fall Meeting 2003, abstract #P11A-05
Physics
5400 Planetology: Solid Surface Planets, 5410 Composition, 5464 Remote Sensing, 5480 Volcanism (8450), 5494 Instruments And Techniques
Scientific paper
Remotely sensed data are used to classify the lunar maria on the basis of "color"; where mature maria with steep UV-VIS continuum slopes are defined as "red", and those with relatively flatter continuum slopes are termed "blue". The canonical view is that large apparent color variations are due to TiO2 concentration, as TiO2 is the primary variable in lunar basalts, ranging from <1 wt.% to >14 wt.% TiO2. This interpretation was based on the observation of compositional and color extremes juxtaposed in Mare Serenitatis and Tranquillitatis. Lunar Prospector (LP) data allows direct testing of the assumption of the control of lunar UVVIS color by TiO2 in the mare. We compare LP gamma-ray (GRS) and neutron spectrometer (NS) data for TiO2 with Clementine Spectral Reflectance (CSR) data in order to examine basalts with a full range of TiO2 concentrations. Areas were selected on the basis of uniform color over 2-degree areas, to match the resolution of the LPGRS data. First we conducted a comparison between LP-GRS and LP-NS data. This comparison yields an approximate two-to-one correlation (the NS data exhibiting higher values), a significant amount of scatter (R2 = 0.63), and an offset +1.7 TiO2 NS data. This is an important observation as it shows that even direct measurements of surface TiO2 contain considerable uncertainty, even for the best possible scenario for determining TiO2 contents in the maria. A comparison of LPGRS TiO2 and CSR color data yields a poor correlation (R2 = 0.53), that is, UV-VIS color is a poor predictor of LPGRS TiO2 in mature mare. A comparison of LPNS and CSR color data, however, yields a better correlation (R2 = 0.85). On the basis of this preliminary analysis, we conclude that TiO2 controls UVVIS color in the mare. However, we will continue this analysis at the higher resolution accessible to LPNS data to further explore the correlation between data sets. We will present an empirical model that uses a relationship between LPNS and CSR UV-VIS data to predict TiO2 contents globally and at full Clementine resolution (250 m/pixel).
Gillis Jeffery J.
Lucey Paul G.
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