Quantifying lunar soil composition with partial least squares modeling of reflectance Original Research Article

Retrieval of lunar soil composition is commonly achieved through optical remote sensing in which spectral characteristics of returned lunar samples are related to their constituents. Partial least squares (PLS) and principal component regression (PCR) were applied to the dataset characterized by the Lunar Soil Characterization Consortium (LSCC) to estimate the content of FeO, Al2O3 and TiO2 in the soils. The goal of this study was to test whether the conversion of reflectance to single scattering albedo (SSA) via Hapke’s radiative transfer model is able to improve the performance of PLS and PCR. Results from PLS and PCR modeling of SSA spectra indicate that the conversion does not necessarily improve the performance of PLS and PCR, and this depends on the chemical considered, the way to select the number of optimal factors, and how the data were pretreated. The conversion failed to accommodate the large deviation of highland samples with low FeO, TiO2 and high Al2O3.