Due to the challenging illumination conditions of the lunar polar regions, mineralogic maps have generally been constrained to within 0°–70°N/S. Here we generate a gridded reflectance data cube from the Kaguya Spectral Profiler measurements for each polar region and calibrate it to absolute reflectance using data from the Lunar Orbiter Laser Altimeter. We use this data set to derive the first quantitative mineral maps of iron oxide (FeO), the optical maturity parameter (OMAT), and nanophase iron poleward of 50°N/S at a spatial resolution of 1 km pixel^–1. We evaluate potential latitudinal trends in space weathering and optical maturity and characterize the polar geology, with an emphasis on the Artemis region (84°–90°S). The maps of FeO are in excellent agreement with the abundances measured by the Lunar Prospector and provide an increased level of detail, such as the excavation of high- and low-FeO material by De Forest crater. The OMAT maps offer a fantastic view of both polar regions. They highlight small fresh craters, walls, and central peaks, as well as rays extending through multiple degrees of latitude, such as those from Tycho and De Forest, which extend into the Artemis region. Both polar regions are characterized by the ubiquitous presence of noritic anorthosites and anorthositic norite. Low-calcium pyroxene is largely the dominant mafic mineral present. The Artemis region has relatively homogeneous FeO and plagioclase content at the observed spatial resolution. The lowest FeO values are found near Shackleton, between Shoemaker and Faustini, and on the central peak of Amundsen crater.

The exploration of the lunar south polar region and the ground truthing of polar volatiles is one of the top priorities for several space agencies and private partners. Here we use Moon Mineralogy Mapper surficial water ice detections to investigate the location of water-ice-bearing permanently shaded regions (PSRs) near the south pole. We extract a variety of parameters such as their temperature regime, slope, hydrogen content, number of ice detections, depth stability for water ice and dry ice, and mobility aspects. We identify 169 water-ice-bearing PSRs and use their characteristics to identify sites that allow us to access the highest abundances of volatiles, sites that can be visited to characterize the lateral or vertical distribution of volatiles (water ice and dry ice), and sites that allow for the fastest recovery of a scientifically interesting sample. Collectively, 37 PSRs are identified as sites of interest, including 11 that would address more than one mission objective and may be, for that reason, higher-priority targets of exploration. These PSRs are found in Shoemaker, Faustini, Cabeus, Malapert, Nobile, Sverdrup, Wiechert J, and Haworth craters, as well as three unnamed craters (PSRs 57, 120, and 89). These sites are all located within 6° of the south pole. We present case studies for a relatively short traverse mission (20–50 km) to PSR 89, a medium-length traverse (∼100 km) to Sverdrup 1, and a longer traverse (∼300 km) to Cabeus that can serve as a guide in planning upcoming exploration missions.

Impact cratering on the Moon and the derived size-frequency distribution functions of lunar impact craters are used to determine the ages of unsampled planetary surfaces across the Solar System. Radiometric dating of lunar samples provides an absolute age baseline, however, crater-chronology functions for the Moon remain poorly constrained for ages beyond 3.9 billion years. Here we present U–Pb geochronology of phosphate minerals within shocked lunar norites of a boulder from the Apollo 17 Station 8. These minerals record an older impact event around 4.2 billion years ago, and a younger disturbance at around 0.5 billion years ago. Based on nanoscale observations using atom probe tomography, lunar cratering records, and impact simulations, we ascribe the older event to the formation of the large Serenitatis Basin and the younger possibly to that of the Dawes crater. This suggests the Serenitatis Basin formed unrelated to or in the early stages of a protracted Late Heavy Bombardment.

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.

Key Points: We study the global roughness at baselines between 330 m and 1,350 km and identify scale breaks near 1, 42, and 338 km We analyze three scaling regimes 330–659 m, 1–21 km, and 42–169 km, which correspond to different impact, volcanic, and tectonic events A local roughness analysis of complex craters, basins, rilles, and light plains shows the behavior of geologic units and sloped terrain