J. J. Gillis

Mapping iron in the lunar mare: An improved approach

[1] Previous methods for mapping iron (Lucey et al., 1995, 1998, 2000a) give inconsistent results in terms of suppressing the effects of maturity, making it difficult to distinguish true units of contrasting iron from regions that simply show the residual effects of incomplete separation of maturity. These methods rely on an assumption that materials of similar iron content but varying maturity form radial trends on a plot of VIS reflectance versus NIR/VIS ratio. Recently, the assumption of radial behavior of maturity trends in mare regions has been called into question by Staid and Pieters (2000). They observed trends in the mare that appeared more parallel in nature. In this work we study nearly 10,000 craters in six mare regions and model the effects of maturity with radiative transfer theory to quantify and better understand the spectral behavior of maturity variations in the mare. We confirm that the maturity trends in mare regions are more parallel than radial, and we exploit this fact to develop a new algorithm for determination of iron content in mare regions. This new mare iron algorithm better compensates for maturity than previous methods, and uncertainties due to maturity variations are less than 0.5 wt% FeO. Absolute uncertainties are similar to previous algorithms (1.5 wt% FeO). Results from this work provide the ability to detect iron anomalies in the ejecta of craters and as a result more confidently map vertical stratigraphy in the lunar mare in terms of iron composition.

An imaging radiometer for measurement of lunar polar cold trap temperatures

The LRO Radiometer Investigation is an experiment proposed for NASA’s Lunar Reconnaisance Orbiter mission that will use a simple but extremely sensitive radiometer to measure the temperatures of the region of permanent shade at the lunar poles. Temperature governs the ability of these surfaces to act as cold traps, and tightly constrains the identity and lifetimes of potential volatile resources. The LRO Radiometer will also measure the night time temperature of the Moon, and use the extensive modeling experience of the team to use these data to produce maps of meter-scale rocks that constitute a significant hazard to landing and operations. The LRO Radiometer also supports LRO objectives by measuring the global abundance of meter scale rocks at 1 km resolution. This measurement is accomplished in four (4) months of observations.

Is random noise causing a poor correlation between the Lunar Prospector TiO2 data and Clementine UVVIS-color ratio?

The ability to measure TiO2 remotely is important for mapping the composition lunar basalt flows globally, and for placing lunar samples into a regional and global geologic context. Comparing Clementine UVVIS-ratio (415/750 nm) with Lunar Prospector derived TiO2 data, however, yields a less than ideal correlation, which would suggest that either the UVVIS ratio has poor predictive capabilities with respect to TiO2 composition or poor accuracy of the Lunar Prospector TiO2 data. Established uncertainties of the Clementine UVVIS data are approximately 1%, while the reported relative errors for Lunar Prospector neutron spectrometer data are on the order of 5%. Thus, we investigate the possibility of whether the greater uncertainty of the Lunar Prospector neutron data could cause the poor correlation between the two data sets. The sensitivity of the TiO2-UVVIS correlation to data accuracy was measured by adding randomly-distributed noise to the Clementine UVVIS data, and then comparing this modified Clementine data with the “noiseless” Clementine data. The comparison was then evaluated for the level of noise needed to produce a similar amount of scatter observed in the Lunar Prospector TiO2 and Clementine UVVIS-ratio trend. The results of this study indicate that Lunar Prospector would have to possess significantly more than 5% uncertainty to match the observed poor correlation between Lunar Prospector and Clementine data sets. On this basis, we concluded that algorithms that depend solely upon correlations between UV and visible spectral parameters and TiO2 concentration have inherently poor predicting power.