Porosity is a fundamental property of sand deposits found in a wide range of landscapes, from beaches to dune fields. As a primary determinant of the density and permeability of sediments, it represents a central element in geophysical studies involving basin modeling and coastal erosion as well as geoaccoustics and geochemical investigations aiming at the understanding of sediment transport and water diffusion properties of sandy landscapes. These applications highlight the importance of obtaining reliable porosity estimations, which remains an elusive task, notably through remote sensing. In this work, we aim to contribute to the strengthening of the knowledge basis required for the development of new technologies for the remote monitoring of environmentally-triggered changes in sandy landscapes. Accordingly, we employ an in silico investigation approach to assess the effects of porosity variations on the reflectance of sandy landscapes in the visible and near-infrared spectral domains. More specifically, we perform predictive computer simulations using SPLITS, a hyperspectral light transport model for particulate materials that takes into account actual sand characterization data. To the best of our knowledge, this work represents the first comprehensive investigation relating porosity to the reflectance responses of sandy landscapes. Our findings indicate that the putative dependence of these responses on porosity may be considerably less pronounced than its dependence on other properties such as grain size and shape. Hence, future initiatives for the remote quantification of porosity will likely require reflectance sensors with a high degree of sensitivity.