Resolved Hapke parameter maps of the Moon
Abstract
We derived spatially resolved near-global Hapke photometric parameter maps of the Moon from 21 months of Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) multispectral observations using a novel “tile-by-tile method” (1° latitude by 1° longitude bins). The derived six parameters (w,b,c,BS0,hS, andθp) for each tile were used to normalize the observed reflectance (standard angles i = g = 60°, e = 0° instead of the traditional angles i = g = 30°, e = 0°) within each tile, resulting in accurate normalization optimized for the local photometric response. Each pixel in the seven-color near-global mosaic (70°S to 70°N and 0°E to 360°E) was computed by the median of normalized reflectance from large numbers of repeated observations (UV: ∼50 and visible: ∼126 on average). The derived mosaic exhibits no significant artifacts with latitude or along the tile boundaries, demonstrating the quality of the normalization procedure. The derived Hapke parameter maps reveal regional photometric response variations across the lunar surface. The b, c (Henyey-Greenstein double-lobed phase function parameters) maps demonstrate decreased backscattering in the maria relative to the highlands (except 321 nm band), probably due to the higher content of both SMFe (submicron iron) and ilmenite in the interiors of back scattering agglutinates in the maria. The hS (angular width of shadow hiding opposition effect) map exhibits relatively lower values in the maria than the highlands and slightly higher values for immature highland crater ejecta, possibly related to the variation in a grain size distribution of regolith.
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The authors have previously developed LunaNet, a neural network-based crater detector that visually detects craters in a camera image and matches those detected craters to a database of known lunar craters with absolute latitudes and longitudes.
“We have previously developed a system that visually detects craters in a camera image using a neural network and matches those detected craters to a database of known lunar craters with absolute latitudes and longitudes [5].This crater detector is called LunaNet.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [5]
This work improved an existing lunar terrain relative navigation system by replacing the crater detector with LunaNet, a neural network-based crater detector developed in [5].
“In summary, this work improved an existing lunar terrain relative navigation system by replacing the crater detector with LunaNet, a neural network-based crater detector developed in [5].”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [5]
Analysis in [5] demonstrates the high level of robustness of LunaNet to noise and variations in image brightness.
“Analysis in [5] demonstrates the high level of robustness of LunaNet to noise and variations in image brightness.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [5]
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Deep Learning Crater Detection for Lunar Terrain Relative Navigation
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- Authors
- Sato, H., Robinson, M. S., Hapke, B., Denevi, B. W., & Boyd, A. K.
- Affiliations
- Arizona State University (US)
- University of Pittsburgh (US)
- Johns Hopkins University (US)
- Venue
- Journal of Geophysical Research: Planets
- Publisher
- American Geophysical Union (AGU)
- Published
- 2014-08
- Volume
- 119
- Issue
- 8
- Pages
- 1775-1805
- ISSN
- 2169-9097 (ISSN-L)
- OpenAlex
- W2069833494
- Cited by
- 165
- References
- 87
- Citations by year
- 2026: 22 · 2025: 23 · 2024: 11 · 2023: 21 · 2022: 8 · 2021: 8 · 2020: 19 · 2019: 13 · 2018: 10 · 2017: 7 · 2016: 8 · 2015: 11 · 2014: 3 · 2013: 1
- FWCI
- 5.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Geophysical Research Planets (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Planetary Science and Exploration
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astro and Planetary Science
- Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
- Keywords
- Moon, Hapke model, multispectral reflectance, Henyey-Greenstein phase function, opposition effect, lunar maria, lunar highlands, ilmenite, crater ejecta
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