Feature Engineering for estimating the maturity of lunar soils from spectroscopic data

Existing algorithms for estimating the maturity of lunar soils are not optimized for data from any of the orbital sensors which are currently active. This paper addresses this issue by proposing an algorithm for estimating soil maturity (I_S/FeO) using spectroscopic data at the spectral resolution of the Moon Mineralogy Mapper (M³). As part of this method, four key spectral parameters for estimating I_S/FeO are identified and used to train a Support Vector Regression (SVR) model. The physical significance of each parameter is discussed, and the equation of the predictive hyperplane is provided for increased transparency. The proposed method outperforms state-of-the-art algorithms and returns a coefficient of determination (R²) of 0.92 over the Lunar Soil Characterization Consortium (LSCC) dataset.