1. Partial Least Squares Modeling of Lunar Surface FeO Content with Clementine Ultraviolet-Visible Images

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.