¹Emerson J. Speyerer,¹Mark S. Robinson,¹Aaron Boyd,¹Victor H. Silva,²Samuel Lawrence
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2023.115506]
¹School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85282, United States of America
²NASA Lyndon B. Johnson Space Center, Houston, TX 77058, United States of America
Copyright Elsevier

The Ultraviolet/Visible (UVVIS) camera on the Clementine spacecraft provided a global, multispectral view of the Moon. Scientists commonly use individual observations and derived products (optical maturity, mineral abundance, etc.) over 25 years later, addressing questions concerning the composition and relative age of surface features. However, since the mission concluded, our knowledge of lunar topography and the locations of features on the surface have improved with results from the Lunar Reconnaissance Orbiter (LRO) and Gravity Recovery and Interior Laboratory (GRAIL) missions. Before this work, cross-mission comparisons were impaired by spatial offsets between the derived products, which are as large as 2.5 km in some regions. Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) images, acquired under similar lighting conditions, were used as a cartographic reference. We used image-based feature-matching algorithms to automatically derive control points to improve the positional accuracy of each UVVIS observation with the LROC WAC basemap. From ground control points, we calculate a precise camera model (focal length, optical distortion, etc.) for the UVVIS camera and update the pointing for each UVVIS image. Using the updated geometric information and projecting the UVVIS image to the LOLA global shape model, we map the five-band multispectral UVVIS mosaic, the optical maturity map, and FeO and TiO2 abundance maps. We also analyze pitch observations of the polar regions to investigate the influence phase angle has on the derived optical maturity. The new images are registered to the GRAIL-based LRO geodetic framework within a WAC pixel (Ground Sampling Distance ~75 m; average UVVIS sigma0 = 0.084), creating a foundational geospatial data product that does not require any manual interpretation or nonlinear warping of map products to align with the current lunar reference frame.

¹Liang Zhang,²Xubing Zhang,¹Maosheng Yang,¹Xiao Xiao,³Denggao Qiu,³Jianguo Yan,¹Long Xiao,^1,2Jun Huang
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2023.115505]
¹State Key Laboratory of planetary processes and mineral resources, School of Earth Sciences, China University of Geosciences (Wuhan), Wuhan 430074, China
²School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
³State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China
⁴Chinese Academy of Sciences Center for Excellence in Comparative Planetology, Hefei 230026, China
Copyright Elsevier

In the past, global maps of major oxides and magnesium number (Mg #) on the lunar surface had been derived from spectral data of remote sensing images, combined with “ground truth” geochemical information from Apollo and Luna samples. These compositional maps provide insights into the chemical variations of different geologic units, revealing the regional and global geologic evolution. In this study, we produced new maps of five major oxides (i.e., Al2O3, CaO, FeO, MgO, and TiO2) and Mg # using imaging spectral data from the KAGUYA multiband imager (MI) and the one-dimensional convolutional neural network (1D-CNN) algorithm. We took advantage of recently acquired geochemical information from China’s Chang’E-5 (CE-5) samples. We used the coefficients of determination (R2) and Root Mean Squared Error (RMSE) as model evaluation indicators. We compared the results with the models used by Wang et al. (2021) and Xia et al. (2019). Our study shows that the 1D-CNN algorithm model used in this study had a higher degree of fit and smaller dispersion between the “ground truth” value of geochemical information and the predicted value of spectral data. The 1D-CNN algorithm generally performs better in describing the complex nonlinear relationship between spectra and chemical components. In addition, we present regions of mare domes in Mairan Dome (43.76°N, 49.90°W) and irregular mare patches (IMPs) in Sosigenes (8.34°N, 19.07°E) to demonstrate the geologic implications of these new maps. With the highest spatial resolution (~ 59 m/pixel), these new maps of five major oxides and Mg # will serve as an important guide in future studies of lunar geology.