Validating a Lunar Roughness-Based Thermal Correction with Diviner Temperature Observations
Abstract
The nature of hydration on the lunar surface has been debated since several spacecraft independently detected an absorption feature in the 3 µm wavelength region about a decade ago. The 3-micron spectral feature is commonly associated with vibrational modes of OH/H₂O bonds, but differing thermal emission removal corrections can yield conflicting interpretations of the band strength and hydration source. In the near-infrared, where thermal emission begins to dominate the radiance of a surface, it is critical to accurately determine the temperature of the surface to remove thermal emission, specifically for wavelengths longer than ~2.5 µm. Airless bodies like the Moon can also sustain large anisothermalities due to surface roughness, complicating wavelength-dependent emission and potentially biasing interpretations of reflectance spectra. In this work, we leverage the complete spatial and hourly temporal thermal infrared coverage of the LRO Diviner Lunar Radiometer to provide strong empirical constraints on the validity of our roughness-based thermal model.
The Moon Mineralogy Mapper (M³) aboard Chandrayaan-1 currently has the most complete spectral coverage of the lunar surface out to 3 μm. Here, we use the roughness-based thermal model employed in Bandfield et al. (2018) to remove emitted radiance from M³ spectra. We build upon previous work by validating the surface temperatures predicted by the model using Diviner-derived surface brightness temperatures over the same locations and local times as the M³ observations. We show that the roughness-based thermal correction can accurately and precisely predict surface temperatures across a wide range of lunar surfaces (swirls, cold spots, highlands and mare regolith) and observation geometries. Our roughness-based correction and empirical validation framework will directly benefit next-generation lunar missions like the recently selected SIMPLEx mission, Lunar TrailBlazer, which will acquire hyperspectral imagery from the visible out to beyond 3 microns contemporaneously with multi-spectral thermal infrared imagery. Bandfield, J. L., et al. "Widespread distribution of OH/H₂O on the lunar surface inferred from spectral data." Nature geoscience 11.3 (2018): 173.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.P33F3500T
- Keywords:
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- 0758 Remote sensing;
- CRYOSPHERE;
- 5410 Composition;
- PLANETARY SCIENCES: SOLID SURFACE PLANETS;
- 5464 Remote sensing;
- PLANETARY SCIENCES: SOLID SURFACE PLANETS