Spectral Mixture Modeling with Laboratory Near-Infrared Data II: Effects of Grain Size and Implications for Europa

Spectral analysis using linear mixture (LM) and radiative transfer-based (RT) intimate mixture modeling based on Hapke theory at near-infrared wavelengths are applied to estimate the abundance of surface materials on Europa. Previously, Emran (2026) compared these approaches against the laboratory spectra of H 2 O ice and H 2 SO 4 times 8H 2 O mixtures with about 100 mu m grains. Here, the effect of particle size on spectral modeling accuracy was assessed using laboratory spectra of H 2 O ice mixtures with small (about 70 mu m spherical) and coarse (about 1 mm irregular) grains, measured over the about 1.2-2.5 mu m wavelength range at 100 K and 120 K (Stephan et al., 2021). Modeled abundance estimates at both temperatures show consistent trends across all mixing ratios, with only minor temperature-dependent variations. The discrepancy in abundance estimates from both LM and RT models remains within plus or minus 10% across all mixtures, with the error reduced to plus or minus 5% when fine grains dominate. Across all mixtures, the average difference between RT- and LM-derived abundance estimates remains within plus or minus 2% for mixtures containing both small and large grains. In contrast, mixtures composed solely of smaller grains render larger deviations between the models, with RT producing more accurate estimates (Emran, 2026) -- indicating that the presence of coarse H 2 O ice grains minimizes abundance differences between LM and RT modeling. Thus, I posit that Hapke-based RT modeling is the preferred spectral modeling approach -- regardless of grain size or compositional mixture -- for constraining Europa's surface composition. Nonetheless, LM modeling remains a reliable approach for compositional analysis of terrains containing H 2 O ice with about mm-sized grains.