Principles of Remote Sensing

[1] We examined the lunar swirls using data from the Moon Mineralogy Mapper (M 3 ). The improved spectral and spatial resolution of M 3 over previous spectral imaging data facilitates distinction of subtle spectral differences, and provides new information about the nature of these enigmatic features. We characterized spectral features of the swirls, interswirl regions (dark lanes), and surrounding terrain for each of three focus regions: Reiner Gamma, Gerasimovich, and Mare Ingenii. We used Principle Component Analysis to identify spectrally distinct surfaces at each focus region, and characterize the spectral features that distinguish them. We compared spectra from small, recent impact craters with the mature soils into which they penetrated to examine differences in maturation trends on-and off-swirl. Fresh, on-swirl crater spectra are higher albedo, exhibit a wider range in albedos and have well-preserved mafic absorption features compared with fresh off-swirl craters. Albedoand mafic absorptions are still evident in undisturbed, on-swirl surface soils, suggesting the maturation process is retarded. The spectral continuum is more concave compared with off-swirl spectra; a result of the limited spectral reddening being mostly constrained to wavelengths less than ∼1500 nm. Off-swirl spectra show very little reddening or change in continuum shape across the entire M 3 spectral range. Off-swirl spectra are dark, have attenuated absorption features, and the narrow range in off-swirl albedos suggests off-swirl regions mature rapidly. Spectral parameter maps depicting the relative OH surface abundance for each of our three swirl focus regions were created using the depth of the hydroxyl absorption feature at 2.82 mm. For each of the studied regions, the 2.82 mm absorption feature is significantly weaker on-swirl than off-swirl, indicating the swirls are depleted in OH relative to their surroundings. The spectral characteristics of the swirls and adjacent terrains from all three focus regions support the hypothesis that the magnetic anomalies deflect solar wind ions away from the swirls and onto off-swirl surfaces. Nanophase iron (npFe 0 ) is largely responsible for the spectral characteristics we attribute to space weathering and maturation, and is created by vaporization/deposition by micrometeorite impacts and sputtering/reduction by solar wind ions. On the swirls, the decreased proton flux slows the spectral effects of space weathering (relative to nonswirl regions) by limiting the npFe 0 production mechanism almost exclusively to micrometeoroid impact vaporization/deposition. Immediately adjacent to the swirls, maturation is accelerated by the increased flux of protons deflected from the swirls.Citation: Kramer, G. Y., et al. (2011), M 3 spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies,

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“…As the grain size becomes larger, more light is absorbed and the reflectance drops. From Clark [1999].…”

Section: Discussionmentioning

confidence: 99%

“…With increasing grain size, absorption depth at first increases, reaches a peak, then decreases, while reflectance decreases and absorption feature width increases. Data from Clark [1999].…”

Section: Discussionmentioning

confidence: 99%

M³ spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

et al. 2011

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“…The SWIR spectral features of Classes 3, 4, 5, and 6 are indicative of the Al-rich mica minerals muscovite and/or illite, which are indistinguishable at this spectral resolution [64]. Small differences in the exact feature positions between the classes reveal the extent of substitution for aluminum in the crystal structure, e.g., [65,66]; the~2.2 µm minimum of less Al-rich compositions (e.g., Class 5) is shifted to longer wavelengths than the more Al-rich compositions (e.g., Classes 3 and 6). These classes are mapped in areas within metamorphic, granitoid, and siliciclastic rock units, as well as in a few spots in sedimentary units (Figure 8a).…”

Section: Aviris Swirmentioning

confidence: 95%

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

McDowell

Kruse

2016

Remote Sensing

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Abstract:We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR,~0.4-1 µm), shortwave infrared (SWIR,~1-2.5 µm), and longwave infrared (LWIR,~8-13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach.

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“…To obtain an optimal solution, the dual problem can be solved using Lagrangian duality and thus the solution's linearly separable support vector. The optimal solution of the machine is equivalent to solving the objective function of equation (6). When solving the optimal problem of an SVM including a kernel function, the dual objective function to be solved is described by equation ( 7):…”

Section: Model Evaluationmentioning

confidence: 99%

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

Wang

et al. 2021

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Principles of Remote Sensing

Cited by 15 publications

References 2 publications

M³ spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

M³ spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

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Principles of Remote Sensing

Cited by 15 publications

References 2 publications

M3 spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

M3 spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

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Product

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M³ spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies

M³ spectral analysis of lunar swirls and the link between optical maturation and surface hydroxyl formation at magnetic anomalies