Partial Least Squares Modeling of Lunar Surface FeO Content with Clementine Ultraviolet-Visible Images - Planetary Exploration and Science: Recent Results and Advances - page 3

end-member (“Optimized origin” in Fig. 1.1 ), which is dark and “red.” However,
Blewettetal.( 1997 ) pointed it out that the directional-hemispherical laboratory
spectra that Lucey used in his algorithm may induce error when applying to the
bidirectional spacecraft measurements. He improved Lucey's method by collecting
image of lunar landing sites and applying them to iron mapping algorithms. Lucey
et al. ( 1998 ) examined and quantified important aspects, e.g., maturity, grain size
and mineralogy, and topographic shading, in his new iron modeling. He then
obtained an improved iron abundance model by using their final processing of
Clementine UVVIS datasets (Lucey et al. 2000 ). Later on, Gillis et al. ( 2004 ) noted
that TiO 2 abundance has an effect on the relationship between Fe content and Fe
parameter, and they optimized this method by adding TiO 2 -sensitive regression
parameters into the regression of iron content. Wilcox et al. ( 2005 ) developed a new
algorithm to determine the iron content in lunar mare regions based on the findings
that the maturity trends in lunar mare area are more parallel than radial. They
collected more than 9,000 craters from mare regions and make a 950/750 nm vs.
750 nm reflectance plot with these data and found the radial trends were disobeyed.
While iron abundance was still orthogonal to maturity trends, the maturity trends
were parallel to each other, suggesting new trends of iron distribution in lunar
mare. Their new iron model has absolute uncertainty similar to Lucey 2000's
model (1.5 wt%), while it allows better compensation for the maturity-induced iron
uncertainties (<0.5 wt%).
Except for NIR/VIS ratio methods mentioned above, many other approaches like
utilizing infrared continuum slope of the spectrum in order to suppress the effect
of topography (Le Mouelic et al. 2002 ) and iron absorption band depth (Fischer
and Pieters 1994 ) have been proposed in the iron modeling. These methods are
limited by the data calibration and quality of Clementine NIR dataset. Statistical
relationships between spectral and chemical abundance of lunar soils have also been
evaluated by Pieters et al. 2002 for their applications of remotely compositional
analysis. She firstly applied principle component analysis (PCA) regression method
with lunar mare soil spectra produced by Lunar Soil Characterization Consortium
(LSCC) to define and evaluate the correlations between chemical abundance and
spectral parameters (Pieters et al. 2002 ). Then she also derived three statistical
relations between spectral and mineral parameters using LSCC data and applied
them to Clementine UVVIS data (Pieters et al. 2006 ).
Although many iron models have been put forward as discussed above, a
quantitatively accurate iron model is still in need, especially for the exploration of
the potentials of multispectral imaging data like Clementine UVVIS and other lunar
hyperspectral datasets (e.g., data from Moon Mineralogy Mapper (M 3 ), Interference
Imaging Spectrometer (IIM), etc.). In this paper, we choose to build iron abundance
models with partial least squares (PLS) regression method. PLS is known as the
second generation of regression method, which performs well in multivariable
regression especially when multiple correlations exist among variables. Li ( 2006 )
made a comparison between PLS and PCA in deriving chemical and mineral
abundances using data from LSCC. He found PLS models use less components