REMOTE SENSING ASSESSMENT OF LUNAR RESOURCES

The utilization of space resources is necessary to foster growth and sustainability of human activities in space. The distribution of lunar resources will shape planning permanent settlements by affecting decisions about where to locate a lunar base. Mapping the location of such resources, however, is not the limiting factor in selecting a site for a lunar base. It is indecision about which resources to use that leaves the location uncertain. A wealth of remotely sensed data exists that, when integrated, can provide necessary information for identifying targets for future detailed exploration. High spatial resolution (~200 m/p) maps showing the distribution of FeO and TiO₂ contents, to ~1 wt% accuracy, and abundances of major minerals (plagioclase, pyroxene, ilmenite, and olivine) are derived from Clementine multispectral images. SMART-1 will soon augment the Clementine data with high-spatial, low-spectral resolution UV-Vis, and low-spatial, high-spectral near infrared data. SMART-1 will also provide the first global x-ray data for Mg, Fe, Si, Al. Direct measurements of Fe and Ti at lower-spatial resolution than Clementine were acquired by Lunar Prospector gamma-ray (60 km/pixel) and neutron spectrometers (15 km/pixel). Distribution maps of FeO, TiO₂, and mineralogy, while not necessarily resources themselves, can be correlated with a particular resource(s) of interest because of well-understood and consistent element-element and element-mineral correlations revealed through over 30 years of sample studies. For example, readable oxygen is associated with pyroclastic deposits and areas enriched in ilmenite. Moreover, ilmenite is a good proxy for H³⁺ as it is efficient at retaining solar wind-implanted volatiles. Hydrogen is also concentrated at the poles; however, there is a discrepancy between Clementine and Earth-based radar concerning the existence of ice at the south pole of the Moon. We have integrated these various distribution maps and applied weighting coefficients in order to emphasize diverse ancillary information (e.g., resource of interest, its proximity to other resources, orbital parameters, landing safety, and environmental conditions). The result is a multi-variable correlation matrix that will assist in targeting future landing missions for in-situ studies and potential lunar bases.