Partial Least Squares Modeling of Lunar Surface FeO Content with Clementine Ultraviolet-Visible Images
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To accurately predict the iron abundance of the Moon has long been the goal for lunar remote sensing studies. In this paper, we present a new iron model based on partial least squares regression (PLS) method and apply this model to map the global lunar iron distribution using Clementine ultraviolet-visible (UVVIS) dataset. Our iron model has taken into account of more calibration sites other than Apollo and Luna sample-return sites and stations (i.e., the six additional highland or immature sites) in combination with more spectral bands (5 bands and 2 band ratios), in order to derive reliable FeO content and improve the robustness of the PLS model. By comparing the PLS-derived iron map with Lucey’s band-ratio FeO map and Lawrence’s Lunar Prospector (LP) FeO map, the differences are mostly within 1 wt% in FeO content. Moreover, PLS-derived FeO is more consistent with LP’s result which was derived by direct measurement of Fe gamma-ray line (7.6 MeV) rather than the Lucey’s experiential algorithm applying only two bands (750, 950 nm) of Clementine UVVIS dataset. With a global mode of 5.1 wt%, PLS-derived iron map is also validated by FeO abundances of lunar feldspathic meteorites and in support of the lunar magma ocean hypothesis.
KeywordsLunar iron content Partial least squares regression (PLS) Spectroscopy Clementine UVVIS
This work was supported by the National Natural Science Foundation of China (11003012, U1231103), the Natural Science Foundation of Shandong Province (ZR2011AQ001), Independent Innovation Foundation of Shandong University (2013ZRQP004), and Graduate Innovation Foundation of Shandong University at WeiHai, GIFSDUWH (yjs13026).
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