Partial Least Squares Modeling of Lunar Surface FeO Content with Clementine Ultraviolet-Visible Images - Planetary Exploration and Science: Recent Results and Advances - page 1

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

Lingzhi Sun and Zongcheng Ling

Abstract 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.

Keywords Lunar iron content ￿ Partial least squares regression (PLS) ￿

Spectroscopy ￿ Clementine UVVIS

1.1 Introduction

As one of the major rock-forming elements, iron is closely related to lunar mafic
mineral assemblages and rock types; thus the accurate estimation of iron abundance
would provide important information of lunar geochemistry, petrogenesis, as well