Figure 14. Mean spectral SSA plots from FRT00021C92 are shown for the regions of interest for the Mount Desert Island and Sands of Forvie. ROI coverages are shown overlain onto a portion of the HiRISE color mosaic. The Mount Desert Island ROI covers 3709 pixels whereas the Sands of Forvie ROI covers 1709 pixels at 12 m/pixel. Thick lines for the spectral plots were derived from the full log maximum likelihood processing of the data, whereas thin lines represent the means for the data before the processing. The wavelength regions between 0.62 and 0.72 µm are not included in analyses because they correspond to the instrument’s blocking filter region. RGB labels correspond to the bands used to make the CRISM false color images displayed in Figures 1-3. Wavelength coverages for HiRISE and Mastcam color mosaics are also shown.
Both the MDI and SoF mean spectra converge for wavelengths shorter than ~0.75 µm result from the spectral dominance of ferric iron oxides and oxyhydroxides at shorter wavelengths (e.g., He et al., 2022). This is consistent with the observed reddish-brown colors in Mastcam images from both sites, given that both Mastcam and HiRISE color images are associated with these wavelengths, as shown on Figure 14. Johnson et al. (2004) show that thin coatings of iron oxides become less opaque at longer wavelengths over the spectral range shown in Figure 14. This phenomenon explains why the longer wavelength segments of the spectra are dominated by broad absorptions associated with ferrous silicate minerals. Thus, for detailed analyses of the mineralogy of MDI and SoF ROIs, we restrict the wavelength range to 0.9 to 2.5 µm where the data are controlled by the spectral properties of ferrous silicates within the deposits.
6 CRISM Spectral Endmembers Using Checkerboard Unmixing
The next step in analysis of MDI dune and SoF sand sheet spectra was to consider the spectral heterogeneity. There were two reasons for pursuing this topic. First was to understand the areal heterogeneity of the sand deposits for purposes of understanding the dynamics of deposition and transport. The second was to evaluate further contributions of local bedrock grains on the margins of the sand deposits, providing a much larger areal extent than obtained using APXS measurements. To pursue these objectives, we used a linear checkerboard unmixing approach with only L data (1.03 to 2.5 µm, 219 bands). This is because calculations with the combined S and L data showed that subtle pixel-to-pixel misregistration offsets at the joins of the two spectra strongly affected endmember selections. As explained in Section 2, the SMACC method takes the spectral data set and searches for endmembers that bound the hyperdimensional swarm. The program then treats each pixel and its respective spectrum as a linear combination of the endmember spectra.
The ROIs for the two sand deposits were run through the SMACC procedure as one data set under the simplifying assumption that the two deposits have the same mineral phases in different proportions to explain the differences for the long wavelength portions of the spectra. In fact, three endmembers explain 90% of the variance in the combined data set. The remaining 10% was found to be associated with aberrant spectra with unrealistic spectral shapes, presumably associated with ill-behaved portions of the 2D CRISM detector array (Murchie et al., 2007). Figure 15 shows the three endmember spectra, along with a color-coded relative abundance map. The endmember spectra include one (red trace) that is unlike the mean spectra shown in Figure 14, with a rise from 1.03 to 1.5 µm that curves to flat values at longer wavelengths. The abundance map indicates high proportions of this endmember at the margins of the two deposits and is interpreted to be a mixture of local bedrock and regolith incorporated into the basaltic sands, as shown in Figure 15. Examples of this local contamination can be seen as partially buried bedrock in Mastcam color images from MDI and SoF sand deposit locations (Figures 5 and 8).