The mineralogy and environmental history of Mars are been extensively investigated through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and quantification of different iron oxides present in the Martian terrains. Although such experiments can provide valuable information regarding the presence of these minerals, the scope of the resulting observations may be hindered by logistics and cost-related constraints. We believe that predictive computer simulations can be employed to mitigate some of these constraints and contribute to the generation and validation of hypotheses in this area. Accordingly, we propose the use of SPLITS (Spectral Light Transport Model for Sand) in investigations involving the spectral signatures of iron-rich sand-textured soils found on Mars, and demonstrate its predictive capabilities in this context through comparisons of modeled results with actual measured data.