Analysis of the MESSENGER MASCS photometric targets part II: Photometric variability between geomorphological units

Accepted Manuscript

Analysis of the MESSENGER MASCS Photometric Targets Part II: Photometric Variability Between Geomorphological Units Deborah L. Domingue , Mario D’Amore , Sabrina Ferrari , Jorn ¨ Helbert , Noam R. Izenberg PII: DOI: Reference:

S0019-1035(18)30215-X https://doi.org/10.1016/j.icarus.2018.07.018 YICAR 12967

To appear in:

Icarus

Received date: Revised date: Accepted date:

28 March 2018 11 July 2018 25 July 2018

Please cite this article as: Deborah L. Domingue , Mario D’Amore , Sabrina Ferrari , Jorn ¨ Helbert , Noam R. Izenberg , Analysis of the MESSENGER MASCS Photometric Targets Part II: Photometric Variability Between Geomorphological Units, Icarus (2018), doi: https://doi.org/10.1016/j.icarus.2018.07.018

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Highlights  The spectral variability within similar geomorphological units suggests compositional variations in both relative mineral abundances and in mineral endmembers  Photometric models suggest variations in regolith grain structure across Mercury’s surface.  Photometric models suggest variations in regolith structure (grain size, compaction, topography on micron to centimeter scales).

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Analysis of the MESSENGER MASCS Photometric Targets Part II: Photometric Variability Between Geomorphological Units Deborah L. Domingue1, Mario D’Amore2, Sabrina Ferrari2, Jörn Helbert2, Noam R. Izenberg3 1Planetary

Science Institute, 1700 E. Fort Lowell, Suite 106, Tucson, AZ 85719-2395, USA; for Planetary Research, DLR, Rutherfordstrasse 2, Berlin, Germany; 3The Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, USA

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Abstract This study examines the level of structural uniformity within Mercury’s regolith as a function of geomorphological unit. Using two categories of photometric models (Hapke versus Kaasalainen-Shkuratov), the variation between and within similar geomorphological units are examined with the Mercury Atmosphere and Surface Composition Spectrometer (MASCS) photometry sequence data sets. The results show evidence for variations in the spectral and photometric scattering properties both within similar geomorphological units and between different geomorphological units. The ejecta and cratering units show the largest differences between the modeling results, each indicating variations in different properties. The results for the intercrater materials, smooth materials, and dark materials show consistent results between both models. The variations include possible differences in grain structures, regolith compaction, and surface roughness on micron to millimeter scales.

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1.0 Introduction The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft included a suite of instruments in its payload to study and examine Mercury’s surface. During MESSENGER’s operations these instruments mapped the surface covering a variety of wavelengths, including X-ray, Gamma-ray, and ultraviolet through near-infrared. The X-ray spectrometer (XRS) measurements indicate a surface low in Fe and Ti content, ≤4 wt % and <0.8 wt%, respectively (Nittler et al. 2011). The Gamma-ray Spectrometer (GRS) observations indicate an average Fe abundance of 1.9 ± 0.3 wt % in the northern hemisphere (Evans et al. 2012), commensurate with the XRS observations. Both instruments indicate a high abundance of sulfur, with a global average ranging between 1 – 4 wt% (Nittler et al. 2011, Weider et al. 2012) and a northern hemisphere average of 2.3 ± 0.4 wt % (Evans et al. 2012). The XRS and GRS have mapped the variation in elemental abundance over the northern hemisphere for several of the major elements, such as Mg, Na, Fe, and S, along with carbon and chlorine (Weider et al. 2014, 2015; Peplowski et al. 2014, 2015a, 2015b, Evans et al. 2015). While element composition has been mapped based on the XRS and GRS observations, mineral compositional determinations have remained more elusive. Global analysis of the Mercury Atmosphere and Surface Composition Spectrometer (MASCS) spectra show no mineralogically diagnostic absorption features from 300 nm to 1450 nm (Izenberg et al. 2014). The diagnostic 1-m absorption band indicative of ferrous iron in silicates is not 2

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observed in either the MASCS surface spectra (Izenberg et al. 2014) nor in the Mercury Dual Imaging System’s (MDIS) color observations (Murchie et al. 2015). Izenberg et al. (2014) did detect possible evidence for a oxygen-metal charge transfer (OMCT) ultraviolet absorption in spectral ratios of bright units with the average Mercury spectrum consistent with an upper limit of ~1.8 wt% FeO. MDIS imaged the surface in up to 11-colors and mapped the surface at 1 km/pixel in 8 colors ranging from 400 nm to 1000 nm (Murchie et al. 2015). Examination of the global 8color map (Murchie et al. 2015) and the MASCS spectra (Izenberg et al. 2014) show that most spectral variations across Mercury’s surface are manifested in spectral slope changes and reflectance variations that can be described as a mixture between two spectral endmembers, high-reflectance plains (HRP) and a low-reflectance materials (LRM) (Murchie et al 2015). While no definitive absorption features have been detected in the MASCS spectral data set, there is evidence for broad color features in the MDIS data (Murchie et al 2015, Vilas et al. 2016). Examination of color observations of craters containing hollows regions shows instances of broad color signatures consistent with the presence of MgS (Vilas et al. 2016), though to date no MASCS spectra have shown absorptions diagnostic of sulfide minerals. These signatures are seen at a higher spatial resolution than possible with the MASCS observations, indicating the presence of this material is spatially variable and masked when mixed with other Mercury spectral units (Vilas et al. 2016). The MDIS data also show evidence for a broad, upward curve in the reflectance spectrum centered near 600 nm that is seen in LRM (Murchie et al. 2015, Klima et al. 2018). It has been attributed to the presence of either graphite (in amounts consistent with the GRS observations), nanophase and/or microphase iron or iron sulfide (products of space weathering), or ironbearing phases and carbon from a late accreting carbonaceous veneer (Murchie et al. 2015). In addition to this compositional information, the MDIS imaging data have been examined using photometric modeling methods to decipher the physical properties of the surface. The structure of the regolith is of interest as it provides insight into the formation and processing of the surface. Domingue et al. (2016) applied multiple models to a global photometric data set and found Mercury’s regolith to be, in general, smoother on micrometer scales than the lunar regolith with a narrower particle size distribution and a lower mean particle size. Their modeling results, used primarily to provide a photometric standardization for the construction of the global 8-color mosaic, also suggests that the structure of the regolith grains on Mercury are different than those on the Moon or asteroids (implying differences in regolith processing), and that Mercury’s regolith contains a compositionally distinct component from the lunar regolith (Domingue et al. 2016). Photometric analysis of the MASCS data acquired during the MESSENGER mission’s orbital phase is presented in a sister publication (Domingue et al. 2018) and provides an algorithm to photometrically standardize the MASCS’s Visible and InfraRed Spectrograph (VIRS) spectral data set. Both the MDIS and MASCS/VIRS photometric observations have been examined to provide information on a global, or ‘average surface’, scale. This study uses the MASCS/VIRS photometric data set to examine individual geomorphologic units and the photometric variations within those units in the visible to near-infrared wavelengths.

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The purpose of this study is to examine the level of structural uniformity within Mercury’s regolith as a function of geomorphological unit using the MASCS/VIRS photometric observations. The photometric properties provide evidence for potential variations in regolith processing. Below we give an overview of the data set (Section 2.0), a brief discussion of the models applied (Section 3.0), and the results of the modeling effort (Section 4.0) followed by a discussion of the implications for Mercury’s regolith structure. The goal of this study is to ascertain if the regolith properties, as derived from the photometric characteristics, vary between geomorphological units and if they are homogeneous (or heterogeneous) within each geomorphological units. This study utilizes two different models. Correlations between the results of each model are used to confirm areas of similarity or areas of difference.

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2.0 The Data Set The data set used in this study is the same data described by Domingue et al. (2018) for the examination and derivation of the MASCS/VIRS photometric standardization. The data source and characterization is described by Domingue et al. (2018) and is not repeated here. The focus here is the description of the geomorphological context of the data set, the organization into different data groups for analysis, and the general spectral properties of each data group.

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2.1 Geomorphological Context The geomorphological units present in the MASCS photometric regions include smooth plains, darker smooth plains, intercrater materials, and two units associated with craters and their ejecta. The first of these crater units consists of proximal material. Proximal material includes mixtures of impact melt and ejecta deposits (breccias), shaped rims, walls, and outcropping floors of medium-size craters (labeled crater material, Figure 1). The second crater unit includes radial and discontinued crater ejecta (labeled distal ejecta Figure 1). These are characterized by radially lineated and hummocky, often discontinued terrain, beyond the rims of medium-size craters, most likely emplaced contemporaneously with the impact material. Flat surfaces with relatively high reflectance values (15 to 20% above the global mean, Denevi et al. 2009) and lower impact densities (Strom et al. 2008) than surrounding terrains are defined as smooth plains. These are interpreted to be of volcanic origin (Denevi et al. 2009, 2013). Smooth plains cover most of the floors of medium sized craters (i.e. diameter of 20 – 200 km), but still occur as extensive exposures of intercrater plains. Darker smooth plains are defined as smooth patches of lower albedo with respect to previously defined smooth plains. They mostly fill crater floors and embay rim walls. Intercrater material is characterized by coarse surfaces reworked by multiple impact events of varying size and degradation stage, including material of degraded basins. The distribution of these geomorphological units within the MASCS photometric regions is shown in Figure 1 and their geographic locations are summarized in Table 1. Photometric region 1540 is dominated by intercrater materials, which are mainly related to a 250-km diameter degraded basin located just north of the region. It contains several patches of smooth plains, though sharp distinctions of these unit boundaries are difficult to discern. In addition, bright radially lineated ejecta from Mena crater (photometric region 1542) partially crosscut this region.

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Photometric region 1541 is predominately characterized by intercrater material crosscut by several scarps and possibly hosts small patches of smooth material. This particular region is dominated by impacts of different size and degradation, and is crossed by prominent structures; at least three irregular depressions host high-reflectance prominent hills.

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Figure 1. The MASCS photometric regions are identified in context with the surrounding geology from the MDIS monochrome mosaic. The eastern regions (top) and western region (bottom) are plotted to show their location on the surface and the different geomorphological units present within each region is mapped. The numbers of each unit refer to their identifier in the photometric target data retrieval sequences.

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Mena crater’s bright ejecta dominate photometric region 1542. Prior textures associated with 20 – 40 km craters are still observable, with those features superposing the rim of the 273-km diameter crater Vieira da Silva. Photometric regions 1544 and 1552 overlap and both contain Waters crater. Region 1544 is dominated by the ejecta from this crater, with intercrater material surrounding the western and southern edges. Both photometric regions contain a low-reflectance patch that partly covers Waters and its proximal ejecta. This low-reflectance patch has been interpreted as impact melt that flowed out during the later crater formation (Ostrach et al. 2012; D’Incecco et al. 2013, 2015). Region 1552 contains intercrater plains along with darker smooth plainsand rim/proximal ejecta associated with impacts in the southeast corner of the region. The geomorphological units in these two regions may be overlain by fresh bright crater ejecta. While there is spatial overlap between the two regions, they also each include coverage of terrains not present in the other. Located along the rim of Beethoven basin, photometric region 1543 is dominated by ejecta materials and smooth surface terrains. It is located northeast of Bello crater, and contains radially lineated ejecta from this 130-km diameter crater. To the northwest of Bello is photometric region 1547, which includes smooth plains that fill Beethoven basin. Bello crater is the dominating feature in photometric region 1549, centered on the smooth plains of the crater floor and the proximal ejecta along its rim. Moderate sized impact craters also dominate the geology in photometric regions 1545, 1546, 1548, and 1551. Cézanne crater (65-km diameter) dominates the west portion of region 1545, with smooth plains units along its floor and outwards, where intercrater materials cover the eastern portion. The peak of this crater hosts an elongated depression, which partly consists of a well-shaped bowl crater and has been indicated as a probable volcanic vent (Denevi et al. 2013). An unnamed, 70-km diameter, complex crater fills region 1548 and the northwest portion of region 1546. This crater postdates a highly degraded crater of similar size; whose rim profile is still recognizable in the southeastern portion of the photometric region. The superposed younger crater is filled by smooth material and surrounded by homogeneous radially lineated ejecta. The impact material strictly associated with the rim and the central peak displays a higher reflectance with respect to the infilling and surrounding terrains, possibly due to hollows or the exhumation of different overlays. Both regions fall within the high-magnesium geochemical region (HMR) constrained by Weider et al. (2015), possibly originated by mantle excavation during a large impact event. Fresh radially lineated ejecta from Waters crater could also be present. Similarly, region 1551 is centered on an unnamed, 75-km diameter crater that postdates a northern, highly degraded, narrower crater displaying a similar size, and in turn superposes the ejecta of Nabokov crater, which lies to the northwest. The crater in region 1551 is vertically crossed by relatively fresh, minor impacts. Photometric region 1553 is dominated by the 165-km diameter, peak-ring, Nabokov crater. The impact breccia (i.e. crater material) and the smooth plains are distinguished 7

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based on their texture, but the western portions of both units display a uniformly seamless darker albedo, which has ben recently interpreted as Low Reflectance Material (Klima et al. 2016) exhumed during the impact. In contrast, low-albedo intercrater materials that dominate photometric region 1550 have been interpreted as Low-Reflectance Blue Plains (LRBP) (Denevi et al. 2009), which is often connected to impact melt material. The extended low albedo area could be associated with the formation of the 70-km diameter degraded crater in the upper northwest corner of this region. Due to the high variability of origin, terrain maturity, and the local processes involved, the geomorphologic distinctions are not diagnostic of spectral properties. As discussed above, each photometric region’s geomorphological units need to be contextualized within the spectral and geochemical data available.

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Table 1. Latitude and Longitude Coordinates of Photometric Targets Photometric Region Central Latitude Central East Geomorphological ID # (deg) Longitude (deg) Units Present* ICM, SM, DJ 1540 -3.61 -124.59 ICM 1541 -6.95 58.13 SM, CM, DJ 1542 -0.5 -124.67 DJ 1543 -9.28 -105.82 SM, DJ 1544 -16.75 -115.61 ICM, CM, DJ 1545 -8.64 -122.55 ICM, SM, DJ 1546 -1.17 -106.92 SM, DJ 1547 -17.04 -123.58 SM, CM, SJ 1548 -0.5 -108.44 SM, CM 1549 -18.82 -120.39 ICM 1550 -3.57 63.16 ICM, SM, DJ 1551 -17.17 58.81 ICM, DSM, CM 1552 -9.75 -104.91 SM, DSM, CM 1553 -14.5 55.5 *ICM: Intercrater materials, SM: Smooth plains, DSM: Dark smooth plains, DJ: Distal ejecta, CM: Crater materials

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2.2 Data Analysis Groups We organized The MASCS/VIRS spectral data into two groups. The first group was defined based on geomorphological unit. If the MASCS/VIRS footprint was completely contained within one of the five geomorphological units defined above (see Domingue et al. 2018 Figure 1 for footprint overlap with geomorphological unit), the spectrum corresponding to that footprint was binned into that geomorphological unit’s data set, regardless of which region from which it was acquired. This created a group of data sets representative of the five geomorphological units found in the photometrically targeted areas. The second group of data sets is a further subdivision of the first group by region. The data from each geomorphological unit was divided into additional data sets based on the region from which it was acquired. The spectra in the second group was examined for the distribution in incidence, emission, and phase angle (collectively referred to as 8

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photometric angles) coverage to insure sufficient coverage to constrain the modeling solutions. The resulting data sets are summarized in Table 2. Examples of two data sets from the second group (Figure 2), where the photometric angle coverage was sufficient for modeling and another where the coverage was insufficient for modeling. If any Group Two data set had insufficient photometric angle coverage to constrain the photometric models it was not analyzed and is not listed in Table 2. However, the data was included in the appropriate Group One, geomorphological data set. The division into these two groups of data sets was performed in order to examine the level of heterogeneity in the photometric properties of Mercury’s surface. This addresses the following questions: 1) Are the regolith properties, as derived from the photometric characteristics, from each geomorphological unit different or similar? and 2) Are the photometric characteristics within the same geomorphological units sufficiently homogeneous to infer similar regolith properties?

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Table 2. Organization of the Data Sets Group One Spectra from all photometric regions are combined (Group One) Smooth Plains (9734) Darker Smooth Plains (382) Intercrater Material (11411) Crater Material (5023) Distal Ejecta (17550) Group Two Photometric Region ID Geomorphological Unit Smooth Plains (183) 1540 Intercrater Material (3283) Distal Ejecta (174) 1541 Intercrater Material (3134) Smooth Plains (570) 1542 Crater Material (1130) Distal Ejecta (486) 1543 Distal Ejecta (7448) Smooth Plains (318) 1544 Distal Ejecta (3087) Intercrater Material (260) 1545 Crater Material (847) Distal Ejecta (806) Smooth Plains (415) 1546 Intercrater Material (531) Distal Ejecta (2699) Smooth Plains (646) 1547 Distal Ejecta (1594) Smooth Plains (881) 1548 Crater Material (1231) 9

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Distal Ejecta (535) Smooth Plains (2698) 1549 Crater Material (541) 1550 Intercrater Material (3397) Smooth Plains (1737) 1551 Distal Ejecta (711) Intercrater Material (529) 1552 Darker Smooth Plains (129) Crater Materials (324) Smooth Plains (2089) 1553 Darker Smooth Plains (215) Crater Materials (479) Numbers in parenthesis indicate number of spectra included in the analysis.

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Figure 2. Photometric angle coverage from two photometric regions (1540 top row, 1543 bottom row) for intercrater materials. The left column displays incidence versus emission angle coverage while the right column displays incidence versus phase angle coverage. The poor coverage for photometric region 1543 is insufficient to constrain the photometric models, thus the 1543 intercrater material data set was not analyzed. This data, however, were included in the overall intercrater material data set. 2.3 Spectral Properties of the Geomorphological Units Example spectra for each of the geomorphological units were extracted from several of the photometric regions for study. The spectra for each of the geomorphological units were selected such that the variation in incidence, emission, and phase angle values varied by less than a degree for each angle within each unit (Tables 3 – 7). Minimizing the variations

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in the photometric angles removes variations due to photometry and provides a means to examine the spectral variability within each geomorphological unit within a single region and between regions. In addition, the spectral variability within each geomorphological unit for each region was measured by examining the ratio of the individual spectra with the median spectrum from the region of origin. The results for each geomorphological unit are presented below. The spectra are examined in order to quantify the compositional uniformity (or heterogeneity) within the same geomorphological units and between the different geomorphological units.

33.239 33.018 – 33.710

29.221 29.081 – 29.271

Median phase Phase angle range

78.225 78.163 – 78.359

77.835 77.814 – 77.837

33.444 33.409 – 33.477

30.667 30.638 – 30.694

33.363 33.294 – 33.423

32.873 32.865 – 32.881

77.819 77.816 – 77.820

77.837 77.837 – 77.838

77.825 77.824 – 77.825

77.816 77.816 – 77.817

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Table 3. Photometric Angle Values for the Example Intercrater Material Spectra. Region ID: 1540 1541 1545 1548 1550 1551 Median incidence 47.200 48.631 47.665 47.177 47.055 48.997 Incidence 47.012 – 48.583 – 47.643 – 47.149 – 47.007 – 48.996 – angle range 47.338 48.993 47.690 47.205 47.112 48.998

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Table 4. Photometric Angle Values for the Example Smooth Material Spectra. Region ID: 1540 1542 1544 1549 Median incidence 52.453 52.886 52.653 52.201 Incidence angle 52.340 – 52.885 – 52.582 – 52.020 – range 52.469 52.890 52.726 52.539 25.398 25.373 – 25.510

24.940 24.926 – 24.960

25.198 25.125 – 25.268

25.652 25.272 – 25.838

Median phase Phase angle range

77.837 77.833 – 77.837

77.804 77.800 – 77.806

77.839 77.838 – 77.838

77.825 77.766 – 77.831

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Table 5. Photometric Angle Values for the Example Dark Smooth Material Spectra. Region ID: 1549 1553 Median Incidence 56.5614265 56.342556 12

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56.493 – 56.631

56.313 – 56.372

Median emission Emission angle range

21.293352 21.224 – 21.362

21.5449265 21.516 – 21.575

Median phase Phase angle range

77.838825 77.839 – 77.839

77.8547145 77.855 – 77.855

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Table 6. Photometric Angle Values for the Example Distal Ejecta Spectra Region ID: 1540 1542 1544 Median incidence 46.732 46.888 46.100 Incidence angle 46.704 – 46.877 – 46.003 – range 46.754 46.923 46.196

1548 46.893 46.803 – 46.998

Median emission Emission angle range

31.133 31.104 – 31.172

31.7661 31.669 – 31.865

30.951 30.846 – 31.042

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77.848 77.836 – 77.864

77.830 77.828 – 77.832

77.838 77.837 – 77.838

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Table 7. Photometric Angle Values for the Example Crater Material Spectra Region ID: 1542 1545 1549 1553 Median incidence 55.437 55.883 55.490 55.395 55.076 – 55.636 – 55.003 – 55.000 – Delta incidence 55.988 55.997 55.569 55.498 Median emission

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22.413 21.890 – 22.801

21.996 21.880 – 22.242

22.362 22.284 – 22.854

22.494 22.390 – 22.889

77.840 77.837 – 77.861

77.857 77.855 – 77.857

77.838 77.837 – 77.839

77.855 77.854 – 77.855

Intercrater material. Example spectra from the photometric regions containing intercrater materials (Figure 3) show a range of variability within each region. Note that the majority of regions for this geomorphological unit show spectrally consistent behavior. However, two regions (1540 and 1541) display some amount of spectral variability. This variability is more clearly seen in a subset of spectra from each region ratioed to the median spectrum for that region (Figure 4). Region 1540 shows differences in albedo

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between the selected spectra, and with the median spectrum, but no spectral slope variations. Region 1541, however, displays both albedo differences and spectral slope variations between spectra and with the median spectrum. Albedo variations, without spectral changes (such as slope or absorption features), are indicative of differences in relative abundances of minerals, such as in opaque or glass content, without necessarily requiring different mineral components. Variations in spectral slope however, indicate that there could be either changes in the relative abundances of the mineral components, the addition of another mineral, or both in the mineralogy throughout region 1541. Variations between the different regions (Figure 5) were examined by comparing the median spectrum from each region. The variations in these median spectra are predominately in albedo, though the normalized spectra show some slight spectral differences towards the near infrared. The spectral differences may reflect either compositional (variation in the abundances of a spectrally neutral or weathering component) or photometric effects, though the photometric angle differences are less than a degree in phase (Table 3).

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Figure 4. These graphs are an example of the spectral variability within the intercrater materials from regions 1540 (top) and 1541 (bottom) shown in terms of reflectance (left column), ratioed reflectance with linear slope fit (right column). Displayed is a subset of the spectra shown in Figure 3 (top). The right column graphs display the spectra from the left graph ratioed to the median spectrum from the same region between 365 nm and 785 nm, with a linear fit to the resulting ratio spectrum. Colors are coordinated across graphs for the same region.

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Figure 5. (left) Comparison of the median spectra from each region of intercrater materials shown in Figure 3. These median spectra are representative of intercrater material within the study area. (right) The normalized spectra (unity at 400 nm) show that the differences are predominately in albedo, with slight slope changes towards the near infrared, indicating possible variations in the abundance of a spectrally neutral or space weathering component.

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Smooth Plains. Example spectra from the photometric regions containing smooth plains (Figure 6) also show a range of variability within each region. To further examine this spectral variability a subset of spectra from each region was ratioed to the median spectrum for that region (Figure 7). Each of the four regions show albedo variations within the example spectra in addition to slope variations between the ratioed spectra. As with the intercrater material units, both albedo and slope variations are observed within the individual smooth material units. Differences in albedo are interpreted as an indicator of differences in relative abundances in a spectrally neutral mineral component, such as opaque or glass content. Differences in the ratioed spectrum slopes, however, are interpreted as indicators of both mineral composition and abundance differences. While subtle, these examples show there is compositional variety within this geomorphological unit, even those considered to be part of the same unit. Variations between the different regions (Figure 8) were examined by comparing the median spectrum from each region. The variations in these median spectra reflect compositional differences (both in abundances and mineral components) rather than photometric differences (Table 4). The variations in these median spectra are predominately in albedo, though the normalized spectra show some slight indication of possible spectral differences towards the near infrared. The overall similarity in the normalized median spectra suggests that the differences between the smooth plains regions are dominated by differences in relative abundances of a neutral or space weathering component.

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Figure 7. These graphs are an example of the spectral variability within the smooth plains from regions 1542 (top), 1540 (second row), 1544 (third row), and 1549 (bottom) shown in terms of reflectance (left column), ratioed reflectance with linear slope fit (right column). Displayed is a subset of the spectra shown in Figure 6 (top). The right column graphs display the spectra from the left graph ratioed to the median spectrum from the same region between 365 nm and 785 nm, with a linear fit to the resulting ratio spectrum. Colors are coordinated across graphs for the same region.

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Dark Smooth Plains. Examples of this unit within the photometric regions was limited to three regions, each associated with a different crater (Nabokov in region 1553, Bello in region 1549, and Waters in overlapping regions 1544 and 1552). Only two of the regions (1549 and 1553) were examined in closer detail as they both contained spectra obtained at photometric angles within less than a degree of each other for each of the angles (Table 5). This photometric similarity facilitated comparisons of spectral properties within each region and between the regions (Figure 9). The example spectra of dark smooth plains from region 1553 display less variation in albedo as compared to the example spectra from region 1549. The median spectrum from both regions display differences in albedo (Figure 9), and the normalized median spectra also display spectral differences between 550 – 800 nm. Examination of the spectral variability within the individual units was conducted by ratioing a subset of the spectra to the median spectrum of the respective region (Figure 20

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10). While the spectra from region 1549 show distinct differences in albedo, differences in the slope of the ratioed spectra are smaller than their counter parts for region 1553. As with similar albedo and slope variations seen in the intercrater materials and smooth plains, these are indicative of variations of abundances and compositions both within a single dark smooth plains unit and between these geomorphological units. The dark smooth plains show more spectral variability between regions than the intercrater materials or smooth plains.

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right, show distinct albedo differences. Comparisons of the normalized median spectra, bottom left, also show distinctive spectral differences between 550 – 800 nm, suggesting compositional differences.

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Distal Ejecta. Several of the photometric regions contain geomorphological units of distal ejecta. Four of these regions were examined in closer detail. The photometric similarity between the spectra (Table 6) facilitated comparisons of spectral properties within each region (Figure 11) and between the regions (Figure 12). Each of the four regions display varying ranges of spectral variability, seen in a comparison of the median spectra from each region (Figure 12). The normalized median spectra show variations in slope towards the near infrared, indicative of possible variations in a space-weathering or spectrally neutral component. Distal ejecta from region 1540 show differences in albedo, but are very similar in terms of spectral slope (Figure 13). The remaining three regions display both differences in albedo and differences in the slope of the ratioed spectra. As with similar albedo and slope variations seen in the other geomorphological units, these are indicative of variations of abundances and compositions both within a single distal ejecta unit and between ejecta units.

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Figure 13. Examination of the spectral variability in distal ejecta from photometric regions 1540 (top), 1542 (second row), 1544 (third row), and 1548 (bottom) are shown in terms of reflectance (left column) and ratioed reflectance (right column). Displayed is a subset of the spectra shown in Figure 11. The right column graphs display the spectra from the graph to the left ratioed to the median spectrum from the wavelength range between 365 nm and 785 nm, with a linear fit to the resulting ratio spectrum. Colors are coordinated across graphs for the same region.

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Crater Material. Crater materials are observed in many of the photometric regions. Four of these regions were examined in closer detail. The photometric similarity between these spectra (Table 7) facilitates comparisons of spectral properties within each region (Figure 14) and between the regions (Figure 15). The four regions display varying ranges of spectral variability. A comparison of the median spectra from each region (Figure 15) shows variations in albedo and along with differences in spectral slope, which is more clearly seen in the normalized median spectra. Each region shows differences in albedo and spectral slope (Figure 16) when ratioed to the median spectrum from their region. As with similar albedo and slope variations seen in the other geomorphological units, these are indicative of variations of abundances and compositions both within a single crater material unit and between crater units.

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Figure 14. Comparisons, within each region, of example spectra of crater material acquired within less than one degree of incidence, emission, and phase angle values (Table 7). The black spectrum in each graph represents the median spectrum for that region. . The spurious absorptions seen in region 1542 are artifacts of noise in the initial spectra and demonstrate the variability in the data set.

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Figure 16. Examination of the spectral variability in distal ejecta from photometric regions 1542 (top), 1545 (second row), 1549 (third row), and 1553 (bottom) are shown in terms of reflectance (left column), ratioed reflectance (right colimn). Displayed is a subset of the spectra shown in Figure 14 (top). The right-hand graphs display the spectra from the left graph ratioed to the median spectrum from the region ranging between 365 nm and 785 nm, with a linear fit to the resulting ratio spectrum. Colors are coordinated across graphs for the same region.

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2.4 Implications for Photometric Analysis. Photometric analysis, the examination of changes in reflectance with changes in illumination (incidence angle) and viewing (emission angle) geometry, assumes a one-toone correspondence between reflectance value and a set of photometric angles. More than one reflectance value for a single set of photometric angles will poorly constrain a model. The spectral properties of the data groups display instances where there are multiple reflectance values for a single set of photometric angles. This translates into modeling results where the model parameters are not well constrained, and conclusions from the results must be made with caution. It is still of value to examine the photometric properties of these data groups to derive ‘average’ or ‘representative’ photometric properties to better understand the variability in the physical properties of the regolith in addition to the chemical or compositional properties. The data groups described in this paper were used by Domingue et al. (2018) to derive a globally applicable photometric standardization for the MASCS/VIRS data set. This global photometric standardization provides a mechanism to remove reflectance variations due to illumination and viewing geometry differences within the MASCS/VIRS spectral data set. Domingue et al. (2018) combined the data in two ways: 1) first using all the data into a single data set, and 2) secondly using only those data from the intercrater materials, smooth, and dark smooth plains (excluding the crater and ejecta unit data sets). They examined the application of the Hapke Basic model (Hapke 1981, 1984, 1986, 1993, 2002, 2008, 2012a) and a model based on the work of Kaasalainen et al. (2001) and Shkuratov et al. (2011), hereafter referred to as the Kaasalainen-Shkuratov or KS model. This was similar to the application of these models to the 8-color MDIS photometric data set (Domingue et al. 2016). These models have been applied to the two data set groups, and are described briefly below.

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3.0 Photometric Models The Hapke Basic model, given by (

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( )

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where c is the parameter indicating partition between forward and backward scattering, and b is the amplitude of the scattering component. Due to the narrow range of phase angle available in the MASCS orbital photometric data set, the partition parameter, c, was set to zero. For the derivation of these equations and their full mathematical expression refer to the works by B. Hapke (Hapke 2012a and references therein). Domingue et al. (2016) describe a set of photometric models based on the work of Kaasalainen et al. (2001) and Shkuratov et al. (2011), which were applied to the MDIS data set. The KS model that best described the MDIS data was the version applied to the MASCS/VIRS data to derive a globally applicable photometric standardization (Domingue et al. 2018). The KS model used in those studies was also used here, and is given by )

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where AN is the normal albedo, cl is a partition parameter between the Lommel-Seeliger ( ) and Lambert (cosi) type scattering, and  is the disk-function parameter associated with surface roughness (Shkuratov et al. 2011). For the full description of this equation refer to the works by Akimov (1988), Shkuratov et al. (1983, 2011), Kaasalainen et al. (2001), and Schröder et al. (2013). Each of these two models was applied to the data sets using a least squares grid search to find the optimal value for the model parameters. The grid-search algorithm minimized the value of the root mean square (RMS), defined by ∑

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where N is the number of measurements, is the measured reflectance, and is the model predicted reflectance. The grid-search algorithm is the same used by Domingue et al. (2016, 2018), where each Hapke and KS parameter is varied simultaneously along the grid increments such that all combinations of parameters are considered. The smallest grid increment was 0.01 for all parameters except ̅ , where the smallest grid value was 1. The models were applied to each wavelength data set independently. The data were not weighted, as the uncertainties in the data are similar within each wavelength bin.

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4.0 Results Below we discuss the results from each model for both data groups, presented by geomorphological unit. The detailed graphs of the model parameter results are shown in the Appendix, along with the corresponding error analysis figures. Examination of spectra acquired at similar photometric angle values (Section 2.3) for each of the geomorphological units shows differences that are attributable to compositional variations. These differences, in some cases, can be limited to variations in relative abundances and, in other cases, differences in the mineral end-members. This variability creates multiple reflectance values for a single set of photometric angles, which results in a data set that does not well constrain any photometric model, as reflected by the error estimates. Error estimates quoted for the parameter values are based on the RMS algorithm. For each application of a model, we stored the ten parameter sets with the lowest RMS values at each wavelength. At each wavelength, we calculated the difference between the minimum and maximum values of the stored parameters. The search grid size for the parameter was added to the difference for the final error bar values quoted below and presented in the Appendix. This method differs from the error estimates used by Domingue et al. (2018) for their examination of the global data sets, where they calculated the median difference between the minimum and maximum stored values across all wavelengths prior to adding the grid size to derive their error estimates. There are some general findings, common to many of the geomorphological units, within the modeling results. Many of the results using the Hapke model lead to two solutions sets, one associated with high values of the single scattering albedo (referred to as the high-w solution set) and another associated with low values of the single scattering albedo (referred to as the low-w solution set). This is similar to the results seen for the global data analysis presented by Domingue et al. (2018). The single particle scattering function amplitude parameter associated with the high-w group is commensurate with a nearly isotropic particle scattering function, whereas the amplitude parameter values associated with the low-w group produces a mildly to moderately backward scattering particle scattering function. This correlation was also noted in the analyses of the global data sets (Domingue et al. 2018). In contrast, the Kaasalainen-Shkuratov modeling results display a single parameter solution set across all geomorphological units. As discussed by Domingue et al. (2018), Mercury has been demonstrated to be ~15% darker in standardized reflectance than the lunar nearside (Denevi and Robinson 2008). Comparisons of fresh lunar to fresh mercurian craters show that the crater ray material on Mercury is darker than the lunar highlands (Denevi and Robinson 2008, Denevi et al. 2009, Braden and Robinson 2013, Neish et al. 2013). These general properties lead Domingue et 33

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al. (2018) to focus on the low-w solutions in their applications to the global MASCS data sets. For similar reasons, the following discussion will also focus on the low-w solution sets for the Hapke model, but both solutions sets are presented in Appendix A.

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4.1 Intercrater Material Hapke Model. The variation in single scattering albedo between regions is compared by fitting a polynomial to the low-w solution set, where appropriate, (Figure A1) and then contrasting the resulting polynomials (Figure 17). The variations in single scattering albedo are interpreted in context with the estimated errors (Figure A1). The error in the w values derived for regions 1540 and 1545 indicate that the value for this parameter is not well constrained for these regions. The error values below ~450 nm for region 1550 also indicate poorly constrained single scattering values from the modeling results. The remaining regions show values of w indicative of subtle differences (~0.05) in albedo properties between many regions containing intercrater material units. The single particle scattering function properties between regions is also compared by fitting a polynomial to the amplitude values from the low-w solution set (Figure 17). The initial results (Figure A2) follow a similar pattern as the scattering albedo. Regions 1540 and 1545 have large error estimates (Figure A2) over the whole wavelength range while region 1550 show large error values for wavelengths below ~450 nm. The variation in single particle scattering function between the remaining regions is subtle, often times within the error bars. This suggests that the physical structures of regolith grains within these intercrater material units are similar. The surface roughness (Figure A3) displays median values over all wavelengths ranging from 14° to 26° between the six regions examined, with a median value of 25° for the intercrater material as a combined unit. Comparisons between the surface roughness values for each region is made by fitting a polynomial function to the values within the loww solution set (Figure 17). The error bars for region 1545 show this parameter is not well constrained, and is commensurate with the large variations seen as a function of wavelength. Theoretically this parameter should be constant with wavelength, with subtle variations due to the ability of the data to well constrain the model. Differences outside the error bars are seen between some of the regions, implying regolith roughness variations between the regions. The properties of the combined, intercrater material (Figure A4) are commensurate with a low albedo, mildly backward scattering, surface. The surface roughness values are uniform with wavelength, within the error estimates, and are similar to the effective surface roughness for the Moon (23.4°) derived by Sato et al. (2014). Kaasalainen-Shkuratov Model. Normal albedo values for the intercrater material (Figure A5) vary within the error bars between most of the regions. Comparisons were made by fitting a polynomial function to the normal albedo spectrum (Figure 17). The resulting polynomials are similar (within estimated error), with the exception of region 1552, which appears brighter than similar intercrater material units from the other regions. The estimated errors for the normal albedo are consistent from region to region implying that the KS model describes the data from each region equally well. The value of the partition parameter, cl, (Figure A6) varies greatly from region to region, well outside the estimated error bars, where values near unity indicate more Lommel-Seeliger type scattering. A polynomial function was fit to the partition parameter 34

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values as a function of wavelength and compared (Figure 17) to examine similarities and differences. There are variations, both in magnitude and spectrally, between many of the regions that are outside of the error estimates, suggesting differences in regolith grain structures within the intercrater material units across Mercury’s surface, commensurate with the Hapke modeling results. The KS model parameter associated with surface roughness () displays median values over all wavelengths ranging from 0.375 to 0.600, with a median value of 0.6 for the intercrater material as a whole (Figure A7). Comparisons between the KS model surface roughness values derived for each region was made by fitting a polynomial function to the values as a function of wavelength and contrasting the resulting polynomials (Figure 17) in in conjunction with the error estimates from each region. Variations in magnitude and spectral shape are noted, and are for the most part, within the error estimates. The KS model results for the combined, intercrater material (Figure A8) are also commensurate with a low albedo surface, with a more Lambert-like scattering surface. The parameter associated with surface roughness displays some small variations with wavelength, mostly within the error estimates, similar to the Hapke modeling results.

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4.2 Smooth Material Hapke Model. Nine of the regions examined contained smooth material units. The resulting parameter values have larger error estimates at the short and long wavelength ends for many of the regions (Figure A9). Region 1540 shows large error estimates below ~450 nm and above ~820 nm compared to the mid-wavelengths. Similarly, regions 1546 and 1547 show large errors below ~600 nm. Region 1540 displays larger errors above ~820 nm. Regions 1549 and 1551 show the larger error estimates below ~550 nm, whereas region 1553 displays the larger errors below ~450 nm. A polynomial function was fit to the low-w group for each region containing sufficient wavelength coverage in this solution set (Figure A9). The resulting polynomials are compared (Figure 18) to examine variation in w for the smooth material between the different regions studied. There are variations in w from region to region outside of the error estimates within many of the mid-wavelengths. The variation in single particle scattering function properties show error values that follow similar patterns as the scattering albedo patterns within the regions examined. The errors are larger at the short and long wavelength edges of the spectral region. A polynomial fit to the low-w solution set amplitude values (Figure 17) show variations in particle scattering properties between regions for the smooth material. The differences in the scattering function amplitude, especially within the mid-wavelengths, are outside of the errors (Figure A10), suggesting variation in particle structure within the regolith grains of the different smooth material units. The surface roughness (Figure A11) displays median values over all wavelengths ranging from 4° to 32° between the seven regions examined, with a median value of 25° for the smooth material as a combined unit. A polynomial was fit to the low-w solution set roughness values (Figure 18). The smooth material in region 1548 displays a distinctly smoother surface than the smooth material units from the other regions. The results show a range of roughness values between these smooth material units from region to region. The properties of the combined, smooth material (Figure A12) are similar to those seen for the intercrater materials. They are commensurate with a low albedo, mildly backward scattering, surface. The surface roughness values are uniform with wavelength, within the error estimates, and are similar to the effective surface roughness for the Moon (23.4°) derived by Sato et al. (2014). Kaasalainen-Shkuratov Model. The normal albedo values (Figure A13) for the smooth material vary (~0.1) slightly more from region to region than observed in the intercrater material examples. The resulting normal albedo values were fit with a polynomial as a function of wavelength. The polynomials are compared for each region (Figure 18) to examine variations between the smooth units from different regions. The variations in normal albedo show a narrow range of variation outside the error estimates along with some spectral variability between the smooth material units from different regions (Figure A13). The value of the partition parameter (Figure A14) varies greatly from region to region. Comparisons of polynomial fits as a function of wavelength to the partition parameter values, shown in Figure 18, display a range of both values and spectral characteristics well outside the error estimates. These variations in phase function indicate differences in

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regolith properties ranging from grain size and structure to compaction and compositional differences. The KS model parameter associated with surface roughness () displays median values over all wavelengths ranging from 0.06 to 0.59, with a median value of 0.6 for the smooth material as a combined unit (Figure A15). The variations between regions for this parameter are greater in the smooth material than observed in the intercrater material examples. Polynomials, as a function of wavelength, were fit to the surface roughness results and plotted together to compare variations between different regions (Figure 18). Variations well outside the error estimates are evident between the regions. These variations include spectral differences in this parameter’s properties. The properties of the combined, smooth material (Figure A16), based on the KS model results are similar to those of the intercrater materials, as also seen with the Hapke modeling results. The normal albedo is commensurate with a dark surface, with regolith surface structures relatively uniform over scales ranging from 200 to 1000 nm.

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obtained for regions 1540, 1544, and 1546. The Appendix displays the high-w results for these regions. 4.3 Dark Smooth Material

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Hapke Model. There were only two regions that had sufficient photometric angle coverage to support modeling efforts for the dark smooth material unit. One region is described with a low-w solution set, with very few wavelengths supporting a high-w solution set, while the other region is described with a high-w solution set, with very few wavelengths supporting a low-w solution set. The low-w solutions from both regions are similar, however the high-w solutions are different between the two regions (Figure A17). The error estimates (Figure A17) show larger errors at wavelengths shortward of 400 nm for both regions, and longward of 820 nm for region 1553. This is seen in the error estimates for all the Hapke model parameters. The high-w solution sets show nearly isotropic scattering behavior, whereas the low-w solution set is broadly backward scattering (Figure A18), as seen in the previous units. There is evidence for a wavelength dependence in the scattering behavior, which is indicative of changes in scattering centers (such as cracks and inclusions). The scattering function for these two regions are very different, commensurate with the low-w and highw correlations seen in the previous units. The surface roughness (Figure A19) shows differences between regions, and in the case of the overall dark material unit, variations with wavelength (Figure A20). The median values of the surface roughness parameter are 2.5° and 11° for the individual regions and 31° for the dark smooth plains as a unit, over all wavelengths. Interestingly, the roughness values for the individual regions are within the error estimates. The data for the combined dark smooth material unit includes the date from the two individual regions shown here in addition to the data from the other regions that had insufficient angle coverage to be modeled independently. The combined dark smooth material data set has a large majority of wavelengths in the low-w solutions set (Figure A20). The roughness values for the combined region are much higher and are also outside the error estimates. Kaasalainen-Shkuratov Model. The normal albedo values between the two regions show a difference in spectral slope, with region 1553 becoming brighter with increasing wavelength (Figure A21) more steeply than region 1552. The differences in normal albedo between the two regions are outside of the error estimates. The value of the partition parameter (Figure A22) varies between the two regions, both in magnitude and spectrally. The median values, over all wavelengths, for the two regions are 0.1 and 0.35 with the median value for the combined dark smooth material unit being 0.17. The values of the partition parameter for the two regions examined are well outside the error estimates and indicate regolith grains with very different scattering properties from each other. The KS model parameter associated with surface roughness () displays median values over all wavelengths of 0.16 and 0.64 for the two regions, with a median value of 0.565 for the combined dark smooth material (Figure A23). The differences in the roughness

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parameter also lie well outside the error estimates. The different spectral behavior shows evidence for variation in dark smooth material across Mercury’s surface. The KS modeling results for the combined dark smooth material unit (Figure A24) show normal albedo and surface roughness values commensurate with those obtained for region 1553, even though the data set includes spectra from multiple regions. The partition parameter values, however, display a distinct, nearly linear, variation with wavelength not seen in the regional results.

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Hapke Model. Nine of the photometric regions studied contained examples of distal ejecta. The single scattering albedo for these regions displays a variety of spectral properties (Figure A25), with the modeling results being better constrained in some regions compared to others. Four of the regions examined (1542, 1544, 1547, and 1551) have error estimates that are large over the majority of wavelengths examined for both the single scattering albedo and the single particle scattering function amplitude parameter. region 1540 displays larger errors shortward of ~500 nm compared to the longer wavelengths. The largest error estimates for the surface roughness are seen for regions 1542, 1544, 1545, 1547, and 1551. Region 1540 shows a spectral discontinuity in single scattering albedo values at ~500 nm, whereas three of the regions (1544, 1546, 1548) display a single low-w set of values. Polynomial fits to the low-w solution sets (Figure 19) are compared and display some spectral variations outside the error estimates (Figure A25) between some of the regions. The value of the Hapke scattering function amplitude parameter displays some variation with wavelength (Figure A26). Polynomial fits to the low-w solution sets for this parameter (Figure 19) display variations outside the error estimates (Figure A26) between some of the regions. This is indicative of differences in regolith grain structures on the size scale of the observing wavelengths. The surface roughness (Figure A27) shows differences between regions, with the median values ranging from 6° to 30° for the individual regions and 18° for the combined distal ejecta as a unit (Figure A28), over all wavelengths. Polynomial fits to the low-w solution sets (Figure 20) for this parameter also display variations outside the error estimates (Figure A27) between some of the regions. The combined distal ejecta unit modeling results also display a single, low-w solution set with a nearly spectrally-uniform surface roughness (Figure A28). The particle scattering function amplitude displays a slight variation with wavelength, though the variation is within the error estimates. Kaasalainen-Shkuratov Model. The normal albedo values over the nine regions studied show differences in both albedo and spectral slope (Figure A29). Polynomial fits to the normal albedo, as a function of wavelength, are compared (Figure 18) and display variations in albedo between distal ejecta units from different regions outside of the error estimates (Figure A29). The value of the partition parameter (Figure A30) varies with wavelength for some of the regions and displays a range of values between regions. Polynomial fits to this parameter (Figure 19) display variations outside the error estimates (Figure A30),

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suggesting different regolith scattering properties, such as those from differences from grain size, grain structure, or regolith compaction and composition. The KS model parameter associated with surface roughness () displays median values over all wavelengths between 0.13 and 0.91, with a median value of 0.48 for the distal ejecta unit as a whole (Figure A31). Comparisons of polynomial fits to this parameter (Figure 19) display a wide range of variations outside the error estimates (Figure A31). The KS modeling results of the combined distal ejecta unit (Figure A32) display values median to the range of those from the various regions (Figure 19). The nine regions examined all display a large range in model parameter values, indicating that this geomorphological unit has different regolith properties across the planet.

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obtained for regions 1540, and 1545. The Appendix displays the high-w results for these regions. 4.5 Crater Material

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Hapke Model. Seven of the photometric regions studied contained examples of crater material. Only two regions (1549 and 1553) contained two solutions sets (a low-w and a high-w set). Region 1542 modeling results contained a single, high-w set (w > 0.65 at 600 nm). Region 1545 modeling results contained a single, moderate-w set ( w > 0.45 at 600 nm). Regions 1548, 1551, and 1552 modeling results contained a single, low-w set ( w < 0.3 at 600 nm). These solutions and their associated error estimates are displayed in Figure A33. Comparisons of polynomial fits to the low-w solution sets (Figure 20) do not display large differences outside of the error estimates within the regions with low-w solutions, with the exception of region 1553 (with large errors over the whole wavelength range) and region 1549 below ~480nm. Among the regions with the low-w solution sets, there is some spectral variability in the scattering function amplitude along with some wavelength-averaged differences between regions, however much of the differences are within the error estimates (Figure A34). The error properties for each region are similar to what is seen with the scattering albedo. Polynomial fits to the values are compared (Figure 20) and display little variation outside of the error estimates with the exception of region 1552. This suggests potential variations in regolith grain structures between the crater material units in region 1552 and the other crater material units. The surface roughness (Figure A35) shows differences between regions, with the median values ranging from 0° to 29° for the individual regions and 29° for the crater material as a unit, over all wavelengths. All the regions display roughness values that are uniform with wavelength within the error estimates. Comparisons of polynomial fits (Figure 20) show variations between regions outside the error estimates (Figure A35). The combined crater material unit modeling solutions display two solution sets below ~600 nm (Figure A36). The values for the single scattering albedo and particle scattering function amplitude are unconstrained below ~ 600nm for this combined data set. The surface roughness is uniform with wavelength within the error estimates. Kaasalainen-Shkuratov Model. The normal albedo values over the seven regions studied show differences in both albedo and spectral slope (Figure A37), however the majority of these differences are within the error estimates. Polynomial fits to the normal albedo values (Figure 20) show subtle variations in albedo and spectral characteristics outside of the error estimates (Figure A37) for some of the regions at the longer (>700 nm) wavelengths. The value of the partition parameter (Figure A38) varies in between the regions examined. The values are spectrally flat except for region 1551. There is more wavelengthto-wavelength scatter in the values at the short (<400 nm) and long (>800 nm) wavelength ranges. Comparing polynomial fits to this parameter (Figure 20) shows distinct variation between regions outside of the error estimates (Figure A38) signaling variations in scattering properties of the regolith. The crater material units appear to cluster into three different groups (Figure 19), within the error estimates.

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The KS model parameter associated with surface roughness () displays median values over all wavelengths between 0.01 and 0.74, with a median value of 0.26 for the combined crater material unit (Figure A39). The value of this parameter is spectrally flat with the exception of regions 1551 and 1552, which show spectral variation in the opposite trend. Region 1551 shows parameter values decreasing with wavelength, whereas region 1552 shows parameter values increasing with increasing wavelength. Comparing polynomial fits to this parameter (Figure 20) shows distinct variation between regions outside of the error estimates (Figure A39) signaling variations in regolith properties. The modeling results for the combined crater material (Figure A40) display a normal albedo on the brighter end of the range seen in the individual regions (Figure 20). The surface roughness parameter is uniform with wavelength within the error estimates, but displays larger wavelength-to-wavelength variations shortward of ~420 nm. The partition parameter also displays larger wavelength-to-wavelength variations shortward of ~400 nm, and the spectral variation seen in this parameter is within the error estimates.

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4.6 Geomorphological Comparisons

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Comparisons of the parameter values for the combined data set analysis results for each of the geomorphological units display some interesting contrasts between the two models (Figure 21). The Hapke model’s single scattering albedo is spectrally similar for all the units within the error estimates. The geomorphological units appear to cluster, with the intercrater materials and dark smooth plains showing similar values, the distal ejecta and smooth plains clustering at similar values, and the crater materials displaying unique properties. Within the error estimates the crater materials are distinct from the intercrater materials and dark smooth plains, but overlap with the other two units. The KS model’s normal albedo, in contrast, is similar for all the units within the error estimates. While there are subtle indications of spectral variations, these are all well within the error estimates. The Hapke model’s single particle scattering function displays similar properties for all units well within the error estimates. The value of the scattering function amplitude, within the error values, is nearly uniform with wavelength, with the apparent exception of the dark smooth material. However, the larger error estimates at the short ( < 500 nm) and long (> 820 nm) wavelength ranges leave the amplitude parameter essentially unconstrained, thus removing the apparent spectral variability. The phase function within the KS model, however, shows some variations. The phase function partition parameter values for the dark smooth material are spectrally distinct from all the other units. The distal ejecta, smooth plains, and intercrater materials show similar partition parameter values within the error estimates. The crater materials are different from the distal ejecta and smooth plains, with variations outside the error estimates. However, the crater material and intercrater material values overlap within the error estimates. These results, from both models, suggest that regolith grain properties along with regolith structural characteristics vary across the surface within a narrow range of grain and grain-to-grain properties. The surface roughness parameters from both models show some interesting variations between the geomorphological units. In the case of the Hapke surface roughness parameter, there appears to be three clusters of units. The crater material and dark smooth material are similar within the error estimates. The intercrater material and the smooth material, are also similar within the error estimates. These two clusters also overlap with each other within the error values. The distal ejecta unit, however, is distinct in roughness properties from the crater material and dark smooth material, and displays different values outside the error estimates. In contrast, the distal eject unit Hapke roughness parameter values are within the error estimates of those for the intercrater materials and dark smooth plains. The surface roughness values from the KS modeling results display different associations between units than the Hapke roughness values. While the results suggest some spectral variability, with the exception of the crater materials, all other unit values are within the error estimates of each other. The crater material shows distinct values that are outside the error estimates.

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The parameters found for each of the models is based on comparisons of the RMS values over a grid search. The parameter sets selected were those that provided the lowest RMS values, though the parameter sets with the top ten lowest values were retained for error estimation purposes. Both models performed similarly. As an example, the RMS values for the combined unit analyses (Figures 22 and 23) show only small-scale differences between the two models for all the geomorphological units examined in this study. Common features in the quality of fit between the two models include higher RMS values for the units associated with cratering: distal ejecta and crater materials. Examination of the differences of the RMS values between the two models show that the Hapke model provides a slightly lower RMS value than the KS model for all geomorphological units. However, the differences are insignificant given the number of data points and the magnitude of the differences (Domingue et al. 2018).

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Comparisons of the results from the two models are not straight forward, as there is no exact one-to-one correspondence between the two models’ parameter sets. The derivation of the Hapke model correlates the model parameters to specific surface properties, though laboratory testing of these correlations has not been unambiguously confirmed (Shepard and Helfenstein 2007, Helfenstein and Shepard 2011). The mathematical coupling between the Hapke parameters can cause their values to be affected by multiple surface properties (Shepard and Helfenstein 2007), especially in the form used for this study. However, the MASCS photometric data set does not contain adequate illumination and viewing geometry coverage to support the application of the more parameter intensive versions of the Hapke model, which show indications of better correlations between parameter value and surface properties (Helfenstein and Shepard 2011). The KS model, in contrast, is a more empirical model, with looser correlations between surface properties, such that the parameter values are also influenced by multiple surface properties (Shkuratov et al. 2011). With this said, it is still useful to examine how the values of these parameters change to identify regions of similar or dissimilar regolith structural properties. The results from the analyses can address the basic goal of this study: examining the structural uniformity within the regolith as a function of geomorphological unit. However, correlating the source of the regolith differences requires some understanding of the parameter’s intended relationship with surface properties. Each model incorporates an albedo parameter. The Hapke model includes the single scattering albedo. This corresponds to the scattering albedo of an “average” regolith grain. The KS model incorporates the normal albedo, also known as the geometric albedo, which is the surface reflectance at zero phase angle, and is a property of the regolith as a unit, not an average of the grains of which it is composed. It is more influenced by grain size distribution and porosity. Each model also contains a phase function. The Hapke model incorporates a single particle scattering function, which, in theory, describes the directional scattering probability of an average regolith particle. In contrast, the KS model incorporates a phase function that describes the directional scattering probability of the regolith as a unit. Interpretation of these two parameters in terms of regolith properties is different. The Hapke scattering function amplitude is an indicator of grain structures (the density of scattering centers, such as inclusions, fractures, or mineral phase changes). The KS partition parameter is an indicator of a mixture of regolith structures, such as porosity and grain-to-grain boundaries, though it is also influenced by grain structures. Both models contain a parameter related to surface roughness or tilt. The KS model parameter labeled as surface roughness is only correlated to surface roughness (Shkuratov et al. 2011) and its value can be affected by other surface properties. The Hapke surface roughness parameter’s theoretical basis is the average surface tilt over the scale of a few grains to the detector footprint. It is most sensitive to the scale at which well-defined shadows exist (Shepard and Campbell 1998), which is approximately 100 m on the lunar surface (Helfenstein and Shepard 1999). Both models support roughness variations across Mercury’s surface, within similar geomorphological units and between these units. The results from this study are examined in terms of these three components: albedo, phase/scattering function, and surface roughness. We address the goal of this study, the

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variability of surface characteristics, by examing the modeling results in context of these components as a function of geomorphological unit. Albedo. The results using the Hapke model, for many of the geomorphological units, lead to two solutions sets, one associated with high values of the single scattering albedo and another associated with low values of the single scattering albedo. The following discussion focuses on the low-w solution sets for the Hapke model, for the reasons stated earlier. In general, the single scattering albedo values, within each geomorphological unit, were either similar within the error estimates, or overlapped over a range of values where the brightest and darkest albedos differed outside of the error estimates. This difference, typically was small (on the order of 0.03). This was also the case for the normal albedo values. The only outlier for this generalization was the dark smooth material. Only two units were studied, each displaying albedo from both models that were different from the other outside of the error estimates. This is summarized in Table 8. Phase/Scattering Function. With the exception of the dark smooth material units, both models show different results regarding their phase or scattering functions (Table 8). The single particle scattering function amplitude parameter from the Hapke model follows the same trends within the geomorphological units as the single scattering albedo from the same model. The phase function from the KS model, however, displays a range of variability within the cratering units (distal ejecta and crater material) and the dark smooth material. The smooth material, in this model, displays an overlapping range of values where the largest and smallest values are outside of the error estimates. The intercrater material regions are all similar within the error estimates, with the exception of region 1552. Surface Roughness. Each model tells a different story regarding the surface roughness of the regolith for each geomorphological unit, with the exception of the dark smooth material (Table 8). Both models indicate that the topological properties of the two dark smooth material units examined are similar to each other. For the intercrater material units the KS model predicts that the roughness properties between units is similar within the error estimates, whereas the Hapke model indicates they fall into two groups, with regions 1546 and 1552 being similar and regions 1541 and 1550 showing similar properties. For the remaining geomorphological units the KS model predicts variable properties from region to region, whereas the Hapke model predicts a range of properties, many having similar traits.

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Table 8. Variations in Photometric Properties within the Geomorphological Units Unit w b An cl   Intercrater All similar Material None w/in 2 groups None w/in None w/in None w/in except error est. error est. error est. error est. region 1552 Smooth Overlap Overlap Material range range Overlap (region (region None w/in Overlap Variable range 1548 1548 error est. range outside outside overlap) overlap)

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Variations in the Hapke single scattering albedo, coupled with the differences in the particle scattering function amplitude are indicative of grain composition and structure differences. This includes variations in the density of scattering centers, which can manifest themselves as inclusions, cracks, or mineral phase changes. This can be interpreted as possible differences in agglutinate characteristics or abundances, amorphous rims or glass content, and even submicroscopic weathering product abundance or size. No distinctive differences are seen in the units associated with cratering (distal ejecta and crater materials), nor in the intercrater materials. Possible differences in the smooth material units are indicated, with definitive differences between the two dark smooth material units examined. According to the Hapke modeling results, there are more variations in the surface roughness, predominately on the micron to centimeter scale, within each geomorphological unit. The differences in the normal albedo coupled with differences in the phase function partition parameter values can be interpreted to indicate differences in the inter-particle scattering properties, either due to compaction differences or grain shape and structure changes, including those listed above. The variations in these KS parameters can be interpreted to indicate variability in the compaction and grain size distribution within each of the geomorphological units. Variations in the KS roughness-related parameter also indicates changes in the texture of the regolith. The end result is that both models are indicative of structural changes in both the regolith and regolith particles between similar geomorphological units. Comparisons of the combined units (Figure 24), show that in terms of albedo characteristics the geomorphological units examined are similar, with the exception of the crater material unit. Each of the units are distinct from each other in terms of the scattering and phase functions, suggesting differences in both regolith particle structures and interparticle properties. The parameters associated with surface roughness suggest the intercrater material and smooth material are similar on the micron to centimeter scale, whereas the remaining units are each distinct from the other.

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The results from the photometric models indicate variations in the physical characteristics on the surface of Mercury, both within similar geomorphological units and between these units. The variations can be grouped into three categories: 1) those of the individual grains that comprise the regolith, 2) those of the regolith as a unit (such as compaction and grain size distribution), and 3) the topography on the micron to centimeter scale. In the first category, regarding the structural properties of the individual grains, there is evidence for variations within some of the geomorphological units. The non-crater units, 55

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(the smooth material, dark smooth material, and intercrater material) all show variations from region to region within the same geomorphological unit. The crater units (he distal ejecta and crater material units) appear to have similar regolith grain characteristics within their respective geomorphological unit. This implies that there are processes in addition to the ones forming the geomorphological unit that influence the character of the regolith grains in the potentially older morphological units. These processes may be indicative of the influence of space weathering on the surface. The regolith grain characteristics between geomorphological units also varies, implying that the influence of the unit formation process is still evidence within all units. The regolith properties as a whole, such as compaction and grain size distribution, appear to vary both within the geomorphological units examined and between the units examined. Also implying structural variations in Mercury’s regolith across the surface not correlated with morphological processes. The topographic properties on the grain to centimeter scale also appear to vary within geomorphological unit. These properties are most similar between the intercrater material and smooth material units.

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Intercrater Material. The Hapke model solutions, along with the associated error estimates, are displayed in the following graphs. The Hapke model results can often be segregated between a set of parameters corresponding to a lower value of w and those corresponding with a higher value of w. Polynomial fits to the parameter sets with the lower w values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit.

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0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1541

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1545

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

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M

Sca ering Func on Amplitude (b)

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1000

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Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1550

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1552

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

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400

600 800 Wavelength (nm)

1000

Figure A2. Displayed are (left) the Hapke model particle scattering function amplitude values for the intercrater material units, separated by region, with the corresponding error estimates (right).

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Surface Roughness Errors: 1540 40

35

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20 15

10

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M

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0 400

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1545 40

30

20 15

AC

10

Surface Roughness Errors: 1545 35

20 15 10 5 0

0

400

1000

25

5

200

600 800 Wavlength (nm)

30

CE

25

400

40

PT

35

200

1000

ED

200

Surface Roughness (q)

600 800 Wavlength (nm)

Surface Roughness Errors: 1541

40

Error

Surface Roughness (q)

1541

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Error

Surface Roughness (q)

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600 800 Wavelength (nm)

1000

200

65

400

600 800 Wavlength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1546 40

35

35

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20

15

15

10

10

5

5 0

0 200

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200

1000

1000

35

30

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AN US

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Error

40

20 15

10

10

M

15

5

5

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600 800 Wavelength (nm)

1552 40

30

20 15

AC

10

Surface Roughness Errors: 1552 35

20 15 10 5 0

0

400

1000

25

5

200

600 800 Wavlength (nm)

30

CE

25

400

40

PT

35

200

1000

ED

200

Surface Roughness (q)

600 800 Wavlength (nm)

Surface Roughness Errors: 1550

40

Error

Surface Roughness (q)

1550

400

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Error

Surface Roughness (q)

1546 40

600 800 Wavelength (nm)

1000

200

400

600 800 Wavlength (nm)

1000

Figure A3. Displayed are (left) the Hapke model surface roughness values for the intercrater material units, separated by region, with the corresponding error estimates (right).

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Intercrater Material 0.7

0.5 0.4 0.3 0.2

CR IP T

Single Sca ering Albedo (w)

0.6

0.1 0 200

400

600 800 Wavelength (nm)

1000

Intercrater Material

Surface Roughness Errors: Intercrater Material

40

35

30

30 25

25

20

Error

15

20 15

10

10

5

M

Surface Roughness (q)

AN US

40

35

5 0

0 400

600 800 Wavelength (nm)

1000

200

ED

200

400

600 800 Wavlength (nm)

1000

Intercrater Material

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A4. This graphs display the Hapke parameter values (left), for the combined intercrater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

67

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The following graphs display the Kaasalainen-Shkuratov model results for the intercrater materials, with the corresponding error estimates. The parameter associated with surface roughness displays the largest errors. Both this parameter and the partition parameter show larger scatter as a function of wavelength than the normal albedo. Polynomial fits to the parameter values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit.

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Normal Albedo Errors: 1540 0.3

0.25

0.25

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0.2

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0 200

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600 800 Wavelength (nm)

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1000

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0.2

0.15

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0

0 400

600 800 Wavelength (nm)

1545

0.25

Normal Albedo Errors: 1545

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0.15 0.1 0.05 0

0

400

1000

0.2

0.05

200

600 800 Wavelength (nm)

0.25

CE

0.2

400

0.3

PT

0.3

200

1000

ED

200

Normal Albedo

0.15

0.1

0.1

1000

AN US

0.25

Error

0.3

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1541

0.3

Error

Normal Albedo

1541

400

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Error

Normal Albedo

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600 800 Wavelength (nm)

1000

200

69

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Normal Albedo Errors: 1546 0.3

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0.2

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0.1

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1000

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0.2

0.15

0.05

0.05

M

0.1

0

0 400

600 800 Wavelength (nm)

1552

0.25

Normal Albedo Errors: 1552

AC

0.15 0.1 0.05 0

0

400

1000

0.2

0.05

200

600 800 Wavelength (nm)

0.25

CE

0.2

400

0.3

PT

0.3

200

1000

ED

200

Normal Albedo

0.15

0.1

0.1

1000

AN US

0.25

Error

0.3

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1550

0.3

Error

Normal Albedo

1550

400

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Error

Normal Albedo

1546 0.3

600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A5. Displayed are (left) Kaasalainen-Shkuratov model normal albedo values for the intercrater material units, separated by region, with the corresponding error estimates (right).

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0.7 0.6 0.5 0.4 0.3

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Par

on Parameter

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1541 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1545 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3

AC

Par

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M

Par

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1000

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Par

on Parameter

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1550 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1552 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3

AC

Par

1000

ED

200

M

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A6. Displayed are (left) Kaasalainen-Shkuratov model partition parameter values for the intercrater material units, separated by region, with the corresponding error estimates (right).

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Surface Roughness Errors: 1540 1

0.9

0.9

0.8

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0.6

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0 200

400

600 800 Wavelength (nm)

200

1000

1000

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

0.9

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1545 1

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface Roughness Errors: 1545

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

200

1000

ED

200

Surface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1541

1

Error

Surface Roughness (m)

1541

400

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Error

Surface Roughness (m)

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600 800 Wavelength (nm)

1000

200

73

400

600 800 Wavelength (nm)

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Surface Roughness Errors: 1546 1

0.9

0.9

0.8

0.8

0.7

0.7

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0.6

0.5

0.5

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0.4

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0.3

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0.1 0

0 200

400

600 800 Wavelength (nm)

200

1000

1000

0.9

0.8

0.8

0.7

0.7

0.6

0.6

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AN US

0.9

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1552 1

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface Roughness Errors: 1552

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

200

1000

ED

200

Surface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1550

1

Error

Surface Roughness (m)

1550

400

CR IP T

Error

Surface Roughness (m)

1546 1

600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A7. Displayed are (left) Kaasalainen-Shkuratov model surface roughness parameter values for the intercrater material units, separated by region, with the corresponding error estimates (right).

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Intercrater Material

Normal Albedo Errors: Intercrater Material

0.3

0.3 0.25

0.2

0.1

0.1

0.05

0.05 0

0 200

400

600 800 Wavelength (nm)

200

1000

Intercrater Material

400

600 800 Wavelength (nm)

1000

Surface Roughness Errors: Intercrater Material

1

AN US

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6 0.5

Error

0.4

0.6 0.5 0.4

0.3

0.3

0.2

0.2

0.1

M

Surface Roughness (m)

0.15

CR IP T

0.15

Error

Normal Albedo

0.25 0.2

0.1 0

0 400

600 800 Wavelength (nm)

1000

200

ED

200

400

600 800 Wavelength (nm)

1000

Intercrater Material 1

PT

0.9

0.7

CE

0.6 0.5 0.4 0.3

AC

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A8. This graphs display the Kaasalainen-Shkuratov parameter values (left), for the combined intercrater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

75

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CE

PT

ED

M

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CR IP T

Smooth Material. The Hapke model solutions, along with the associated error estimates, are displayed in the following graphs for all nine regions which contained smooth material units. Similar to the intercrater material units, the Hapke model results for the smooth material units can often be segregated between a set of parameters corresponding to a lower value of w and those corresponding with a higher value of w. Polynomial fits to the parameter sets with the lower w values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit.

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1540 0.8

0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.7

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1542

AN US

0.8

0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1544 0.8

PT

0.6

CE

0.5 0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

0.7

1000

ED

200

M

Single Sca ering Albedo (w)

0.7

0.1 0

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600 800 Wavelength (nm)

1000

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Single Sca ering Albedo (w)

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0.2 0.1 0 200

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600 800 Wavelength (nm)

1000

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600 800 Wavelength (nm)

1548 0.8

PT

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CE

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AC

Single Sca ering Albedo (w)

0.7

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Single Sca ering Albedo (w)

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0.1 0

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1000

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Single Sca ering Albedo (w)

0.7

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1551

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0.8

0.6 0.5 0.4 0.3 0.2 0.1 0 200

400 600 800 Wavelength (nm)

1553 0.8

1200

PT

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CE

0.5 0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

0.7

1000

ED

0

M

Single Sca ering Albedo (w)

0.7

0.1 0

0

200

400 600 800 Wavelength (nm)

1000

1200

Figure A9. Displayed are (left) the Hapke model single scattering albedo values for the smooth material units, separated by region, with the corresponding error estimates (right).

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CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1542

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1544

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

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Sca ering Func on Amplitude (b)

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Sca ering Func on Amplitude (b)

1

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600 800 Wavelength (nm)

1000

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0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1548

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

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M

Sca ering Func on Amplitude (b)

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Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

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600 800 Wavelength (nm)

1000

1551

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0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1553

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

200

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600 800 Wavelength (nm)

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Figure A10. Displayed are (left) the Hapke model single particle scattering function amplitude values for the smooth material units, separated by region, with the corresponding error estimates (right).

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35

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400

600 800 Wavelength (nm)

1544 40

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PT

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Surface Roughness Errors: 1544 40 35

20 15 10 5

0

400

600 800 Wavelegnth (nm)

25

5

200

400

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CE

25

AC

Surface Roughness (q)

600 800 Wavelegnth (nm)

Surface Roughness Errors: 1542

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Error

Surface Roughness (q)

1542

400

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Error

Surface Roughness (q)

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0 600 800 Wavelength (nm)

1000

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83

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600 800 Wavelength (nm)

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Error

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M

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600 800 Wavelength (nm)

1548 40

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20 15

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Surface Roughness Errors: 1548 35

20 15 10 5

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600 800 Wavelegnth (nm)

30

CE

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600 800 Wavelegnth (nm)

Surface Roughness Errors: 1547

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Error

Surface Roughness (q)

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Error

Surface Roughness (q)

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0 600 800 Wavelength (nm)

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84

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ACCEPTED MANUSCRIPT

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25

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20

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15

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10

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5

5

0

0 200

400

600 800 Wavelength (nm)

1000

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1000

35

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AN US

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Error

40

20 15

10

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M

15

5

5

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400

600 800 Wavelength (nm)

1553 40

200

30

20 15

AC

10

Surface Roughness Errors: 1553 35

20 15 10 5

0

400

1000

25

5

200

600 800 Wavelegnth (nm)

30

CE

25

400

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PT

35

1000

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200

Surface Roughness (q)

600 800 Wavelegnth (nm)

Surface Roughness Errors: 1551

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Error

Surface Roughness (q)

1551

400

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Error

Surface Roughness (q)

1549 40

0 600 800 Wavelength (nm)

1000

200

400

600 800 Wavelegnth (nm)

1000

Figure A11. Displayed are (left) the Hapke model surface roughness values for the smooth material units, separated by region, with the corresponding error estimates (right).

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Smooth Material 0.8

0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.7

0.2 0.1 0 0

200

400 600 800 Wavelength (nm)

1000

1200

Smooth Material

Surface Roughness Errors: Smooth Material

40

AN US

40

35

30 25

25

20

Error

15

20 15

10

10

5

M

Surface Roughness (q)

35 30

5

0

0

400

600 800 Wavelength (nm)

1000

200

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600 800 Wavelength (nm)

1000

Smooth Material

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

200

400

600 800 Wavelength (nm)

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Figure A12. This graphs display the Hapke parameter values (left), for the combined smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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The following graphs display the Kaasalainen-Shkuratov model results for the smooth plains, with the corresponding error estimates. The parameter associated with surface roughness displays the largest errors. Polynomial fits to the parameter values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit.

87

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Normal Albedo Errors: 1540 0.3

0.25

0.25

0.2

0.2

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0.15

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0.1

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0.05

0

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600 800 Wavelength (nm)

1000

200

0.25

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0.2

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0.05

M

0.1

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400

600 800 Wavelength (nm)

1544

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PT

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1000

ED

200

0.25

1000

Normal Albedo Errors: 1544 0.3

0.15 0.1 0.05

0

400

600 800 Wavelength (nm)

0.2

0.05

200

400

0.25

CE

0.2

AC

Nornal Albedo

0.15

0.1

0.1

1000

AN US

0.25

Error

0.3

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1542

0.3

Error

Nornal Albedo

1542

400

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Error

Nornal Albedo

1540 0.3

0 600 800 Wavelength (nm)

1000

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88

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1000

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Normal Albedo Errors: 1546 0.3

0.25

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0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

0.25

0.2

0.2

0.15

0.05

0.05

M

0.1

0

0

400

600 800 Wavelength (nm)

1548

200

0.25

Normal Albedo Errors: 1548

AC

0.15 0.1 0.05

0

400

1000

0.2

0.05

200

600 800 Wavelength (nm)

0.25

CE

0.2

400

0.3

PT

0.3

1000

ED

200

Nornal Albedo

0.15

0.1

0.1

1000

AN US

0.25

Error

0.3

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1547

0.3

Error

Nornal Albedo

1547

400

CR IP T

Error

Nornal Albedo

1546 0.3

0 600 800 Wavelength (nm)

1000

200

89

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Normal Albedo Errors: 1549 0.3

0.25

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

0.25

0.2

0.2

0.15

0.05

0.05

M

0.1

0

0

400

600 800 Wavelength (nm)

1553

200

0.25

Normal Albedo Errors: 1553

AC

0.15 0.1 0.05

0

400

1000

0.2

0.05

200

600 800 Wavelength (nm)

0.25

CE

0.2

400

0.3

PT

0.3

1000

ED

200

Nornal Albedo

0.15

0.1

0.1

1000

AN US

0.25

Error

0.3

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1551

0.3

Error

Nornal Albedo

1551

400

CR IP T

Error

Nornal Albedo

1549 0.3

0 600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A13. Displayed are (left) the Kaasalainen-Shkuratov model normal albedo values for the smooth material units, separated by region, with the corresponding error estimates (right).

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ACCEPTED MANUSCRIPT

1540 1 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Par

on Parameter

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1542 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1544 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3

AC

Par

1000

ED

200

M

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1546 1 0.9

0.7 0.6 0.5 0.4 0.3

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Par

on Parameter

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1547 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1548 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3 0.2

AC

Par

1000

ED

200

M

Par

on Parameter

0.8

0.1 0

200

400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1549 1 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Par

on Parameter

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1551 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1553 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3

AC

Par

1000

ED

200

M

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure 14. Displayed are (left) the Kaasalainen-Shkuratov model partition parameter values for the smooth material units, separated by region, with the corresponding error estimates (right).

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Surface Roughness Errors: 1540 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

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0.4

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0

0 200

400

600 800 Wavelength (nm)

1000

200

1000

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

0.9

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0

400

600 800 Wavelength (nm

1544 1

200

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1

0

400

1000

Surface Roughness Errors: 1544

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

1000

ED

200

Sureface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1542

1

Error

Sureface Roughness (m)

1542

400

CR IP T

Error

Sureface Roughness (m)

1540 1

0 600 800 Wavelength (nm

1000

200

94

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1546 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

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0

0 200

400

600 800 Wavelength (nm

1000

200

1000

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

0.9

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0

400

600 800 Wavelength (nm

1548 1

200

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1

0

400

1000

Surface Roughness Errors: 1548

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

1000

ED

200

Sureface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1547

1

Error

Sureface Roughness (m)

1547

400

CR IP T

Error

Sureface Roughness (m)

1546 1

0 600 800 Wavelength (nm

1000

200

95

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1549 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0 200

400

600 800 Wavelength (nm

1000

200

1000

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0

400

600 800 Wavelength (nm

1553 1

1200

200

PT

0.9

1000

ED

200

0.8 0.7

0.4 0.3

Surface Roughness Errors: 1553 0.9

0.6 0.5 0.4 0.3 0.2

0.1

0.1

0

400

1000

1

0.2

200

600 800 Wavelength (nm)

0.7 Error

0.5

400

0.8

CE

0.6

AC

Sureface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1551

1

Error

Sureface Roughness (m)

1551

400

CR IP T

Error

Sureface Roughness (m)

1549 1

0 600 800 Wavelength (nm

1000

1200

200

400

600 800 Wavelength (nm)

1000

Figure 15. Displayed are (left) the Kaasalainen-Shkuratov model surface roughness values for the smooth material units, separated by region, with the corresponding error estimates (right).

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Normal Albedo Errors: Smooth Material 0.3

0.25

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

Smooth Material

600 800 Wavelength (nm)

1000

Surface Roughness Errors: Smooth Material

1

AN US

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

Error

0.4

0.5 0.4

0.3

0.3

0.2

0.2

0.1

M

Sureface Roughness (m)

400

CR IP T

Error

Nornal Albedo

Smooth Material 0.3

0.1

0

0

400

600 800 Wavelength (nm

1000

200

ED

200

400

600 800 Wavelength (nm)

1000

Smooth Material 1

PT

0.9

0.7

CE

0.6 0.5 0.4 0.3

AC

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A16. This graphs display the Kaasalainen-Shkuratov parameter values (left), for the combined smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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Dark Smooth Material. The Hapke model solutions, along with the associated error estimates, are displayed in the following graphs. There were only two regions that contained sufficient coverage in the photometric angles to warrant modeling. One region (1553) showed results with a higher w value than the other region (1552). This geomorphological unit displayed variations between regions outside of the error estimates.

CR IP T

1552 0.9

0.7 0.6 0.5 0.4

AN US

Single Sca ering Albedo (w)

0.8

0.3 0.2 0.1 0 400

600 800 wavelength (nm)

1553

ED

0.9

0.7

PT

0.6 0.5 0.4

0.2

AC

0.1

CE

Single Sca ering Albedo (w)

0.8

0.3

1000

M

200

0

200

400

600 800 wavelength (nm)

1000

Figure A17. Displayed are (left) the Hapke model single scattering albedo values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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ACCEPTED MANUSCRIPT

1552 1

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1000

AN US

200

1553 1

0.8 0.7

M

0.5 0.4

ED

Par cle Func on Amplitude (b)

0.9

0.6

CR IP T

Par cle Func on Amplitude (b)

0.9

0.3 0.2

0

400

600 800 Wavelength (nm)

1000

CE

200

PT

0.1

AC

Figure A18. Displayed are (left) the Hapke model single particle scattering function amplitude values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1552

40

40

35

35

30

30

25

25

20 15

15

10

10

5

5

0

0 400

600 800 wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

1200

AN US

200

Surface Roughness Errors: 1553

1553

40

40

35

35

30

M

25 20 15

25

Error

30

10

0

400

600 800 Wavelength (nm)

10 5 0 1000

CE

200

PT

5

20 15

ED

Surface Roughness (q)

20

CR IP T

Error

Surface Roughness (q)

1552

200

400

600 800 Wavelength (nm)

1000

1200

AC

Figure A19. Displayed are (left) the Hapke model surface roughness values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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Dark Smooth Material 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.8

0.2 0.1 0 200

400

600 800 wavelength (nm)

1000

Surface Roughness Errors: Dark Smooth Material

Dark Smooth Material 40

AN US

40

35

30

25

25 Error

20

20

15

15

10

10

5

M

Surface Roughness (q)

35 30

5 0

0 400

600 800 wavelength (nm)

1000

200

ED

200

400

600 800 Wavelength (nm)

1000

1200

Dark Smooth Material 1

PT

0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Par cle Func on Amplitude (b)

0.9

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A20. This graphs display the Hapke single particle scattering function amplitude parameter values (left), for the combined dark smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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The Kaasalainen-Shkuratov model solutions, along with the associated error estimates, are displayed in the following graphs. Similarly to the Hapke model solutions, the KaasalainenShkuratov model parameter values indicate significant structural differences in the regolith and regolith particle properties between these two regions for this geomorphological unit.

0.2

0.15

0.15 Error

0.2

0.1

0.05

0

0

400

600 800 wavelength (nm)

1553

ED

0.25

0.2

AC 200

400

600 800 wavelength (nm)

400

600 800 Wavelength (nm)

1000

1200

Normal Albedo Errors: 1553

0.25

0.15

0.1

CE

0.1

200

0.2

PT

0.15

0

1000

M

200

0.05

AN US

0.1

0.05

Normal Albedo

CR IP T

Normal Albedo Errors: 1552 0.25

Error

Normal Albedo

1552 0.25

0.05

0 1000

200

400

600 800 Wavelength (nm)

1000

1200

Figure A21. Displayed are (left) the Kaasalainen-Shkuratov model normal albedo values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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1552 1 0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1000

AN US

200

1553 1 0.9

0.7

M

0.5 0.4

ED

Par

on Parameter

0.8

0.6

CR IP T

Par

on Parameter

0.8

0.3 0.2

0

400

600 800 Wavelength (nm)

1000

CE

200

PT

0.1

AC

Figure A22. Displayed are (left) the Kaasalainen-Shkuratov model partition parameter values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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Surface Roughness Errors: 1552

1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5 0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

1200

AN US

200

Surface Roughness Errors: 1553

1553

1

1

0.9

0.9

0.8

0.8

0.7

M

0.6 0.5 0.4

0.6

Error

0.7

ED

Surface Roughness (m)

0.5

CR IP T

Error

Surface roughness (m)

1552

0.3 0.2

0

400

600 800 Wavelength (nm)

0.4 0.3 0.2 0.1 0

1000

CE

200

PT

0.1

0.5

200

400

600 800 Wavelength (nm)

1000

1200

AC

Figure A23. Displayed are (left) the Kaasalainen-Shkuratov model surface rouhgness values for the dark smooth material units, separated by region, with the corresponding error estimates (right).

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Normal Albedo Errors: Dark Smooth Material

Dark Smooth Material 0.25

0.25 0.2

0.15

0.15 Error

0.1

0.05

0.05 0

0 200

400

600 800 Wavelength (nm)

200

1000

600 800 Wavelength (nm)

1000

1200

Surface Roughness Errors: Dark Smooth Material

Dark Smooth Material 1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6 Error

0.5 0.4

0.5 0.4

0.3

0.3

0.2

0.2

0.1

M

Surfae Roughness (m)

400

CR IP T

0.1

AN US

Normal Albedo

0.2

0.1 0

0 400

600 800 Wavelength (nm)

1000

200

ED

200

400

600 800 Wavelength (nm)

1000

1200

Dark Smooth Material 1

PT

0.9

0.7

CE

0.6 0.5 0.4 0.3

AC

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A24. This graphs display the Kaasalainen-Shkuratov parameter values (left), for the combined dark smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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AC

CE

PT

ED

M

AN US

CR IP T

Distal Ejecta. The Hapke model solutions, along with the associated error estimates, are displayed in the following graphs. Many regions have error estimates that indicate the parameter values are unconstrained. Polynomial fits to the parameter sets with the lower w values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit, where error values allow.

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1540 0.7

0.5 0.4 0.3 0.2

CR IP T

Single Sca ering Albedo (w)

0.6

0.1 0 200

400

600 800 Wavelength (nm)

1000

1542

AN US

0.7

0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1543

PT

0.7 0.6 0.5

CE

0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

1000

ED

200

M

Single Sca ering Albedo (w)

0.6

0.1 0

200

400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1544 0.7

0.5 0.4 0.3 0.2

CR IP T

Single Sca ering Albedo (w)

0.6

0.1 0 200

400

600 800 Wavelength (nm)

1000

1545

AN US

0.7

0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1546 0.7

PT

0.5

CE

0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

0.6

1000

ED

200

M

Single Sca ering Albedo (w)

0.6

0.1 0

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1000

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ACCEPTED MANUSCRIPT

1547 0.7

0.5 0.4 0.3 0.2

CR IP T

Single Sca ering Albedo (w)

0.6

0.1 0 200

400

600 800 Wavelength (nm)

1000

1548

AN US

0.7

0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1551

PT

0.7 0.6 0.5

CE

0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

1000

ED

200

M

Single Sca ering Albedo (w)

0.6

0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A25. Displayed are (left) the Hapke model single scattering albedo values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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1540 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1542 1

AN US

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1543

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

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0.2 0.1 0

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400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1544 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1545

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1546

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1547 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1548

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1551

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A26. Displayed are (left) the Hapke model single particle scattering function amplitude values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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Surface Roughness Errors: 1540 40

35

35

30

30

25

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20

20

15

15

10

10

5

5

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0 200

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600 800 Wavelength (nm)

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1000

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35

30

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25

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20

AN US

40

Error

40

20 15

10

10

M

15

5

5

0

0 400

600 800 Wavelength (nm)

1543 40

30

20 15

AC

10

Surface Roughness Errors: 1543 35

20 15 10 5

0

400

1000

25

5

200

600 800 Wavelength (nm)

30

CE

25

400

40

PT

35

200

1000

ED

200

Error

Surface Roughness (q)

600 800 Wavelength (nm)

Surface Roughness Errors: 1542

1542

Surface Roughness (q)

400

CR IP T

Error

Surface Roughness (q)

1540 40

0 600 800 Wavelength (nm)

1000

200

113

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness: 1544 40

35

35

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Error

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M

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600 800 Wavelength (nm)

1546 40

30

20 15

AC

10

Surface Roughness Errors: 1546 35

20 15 10 5 0

0

400

1000

25

5

200

600 800 Wavelength (nm)

30

CE

25

400

40

PT

35

200

1000

ED

200

Error

Surface Roughness (q)

600 800 Wavelength (nm)

Surface Roughness Errors: 1545

1545

Surface Roughness (q)

400

CR IP T

Error

Surface Roughness (q)

1544 40

600 800 Wavelength (nm)

1000

200

114

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1547 40

35

35

30

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15

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5

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AN US

35

Error

40

20 15

10

10

M

15

5

5

0

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400

600 800 Wavelength (nm)

1551 40

200

30

20 15

AC

10

Surface Roughness Errors: 1551 35

20 15 10 5

0

400

1000

25

5

200

600 800 Wavelength (nm)

30

CE

25

400

40

PT

35

1000

ED

200

Surface Roughness (q)

600 800 Wavelength (nm)

Surface Roughness Errors: 1548

40

Error

Surface Roughness (q)

1548

400

CR IP T

Error

Surface Roughness (q)

1547 40

0 600 800 AWavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A27. Displayed are (left) the Hapke model surface roughness parameter values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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Distal Ejecta 0.7

0.5 0.4 0.3 0.2

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Single Sca ering Albedo (w)

0.6

0.1 0 200

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600 800 Wavelength (nm)

1000

Surface Roughness Errors: Distal Ejecta

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20

20 15

10

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M

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Distal Ejecta 1

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600 800 Wavelength (nm)

1000

PT

0.9

1000

ED

200

0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

AN US

40

Error

Surface Roughness (q)

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400

600 800 Wavelength (nm)

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Figure A28. This graphs display the Hapke model parameter values (left), for the combined dark smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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The Kaasalainen-Shkuratov model solutions, unlike the Hapke model solutions, imply that the distal ejecta units are quite variable from region to region. The parameter values, and the associated error estimates, are displayed in the following graphs.

0.3

0.3

0.25

0.25

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0.2

0.15 0.1

0.1

0.05

0.05

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600 800 Wavelength (nm)

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200

1542

0.3

0.3

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600 800 Wavelength (nm)

PT

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0.15 0.1

ED

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0.05 0

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200

1543

400

600 800 Wavelength (nm)

1000

Normal Albedo Errors: 1543

CE

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AC

Normal Albedo

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1000

M

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600 800 Wavelength (nm)

Normal Albedo Errors: 1542

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AN US

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Normal Albedo

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Normal Albedo Errors: 1540 0.35

Error

Normal Albedo

1540 0.35

Error

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117

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Normal Albedo Errors: 1544 0.35

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0.25

0.25

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0.15

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0 200

400

600 800 Wavelength (nm)

1000

200

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0.25

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0.2 0.15

0.1

0.1 0.05

0.05

0

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600 800 Wavelength (nm)

1546

0.3

Normal Albedo Errors: 1546

AC

0.2 0.15 0.1 0.05

0

400

1000

0.25

0.05

200

600 800 Wavelength (nm)

0.3

CE

0.25

400

0.35

PT

0.35

200

1000

ED

200

Normal Albedo

M

0.15

0.1

1000

AN US

0.3

Error

0.3

Error

Normal Albedo

0.35

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1545

1545 0.35

0.2

400

CR IP T

Error

Normal Albedo

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118

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Normal Albedo Errors: 1547 0.35

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0.3

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600 800 Wavelength (nm)

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200

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0.25

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0.2 0.15

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0.05

0

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600 800 Wavelength (nm)

1551

PT

0.35

200

1000

ED

200

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AC

1000

Normal Albedo Errors: 1551 0.35

0.2 0.15 0.1 0.05

0

400

600 800 Wavelength (nm)

0.25

0.05

200

400

0.3

CE

0.25 Normal Albedo

M

0.15

0.1

1000

AN US

0.3

Error

0.3

Error

Normal Albedo

0.35

0.15

600 800 Wavelength (nm)

Normal Albedo Errors: 1548

1548 0.35

0.2

400

CR IP T

Error

Normal Albedo

1547 0.35

0 600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A29. Displayed are (left) the Kaasalainen-Shkuratov model normal albedo values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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1540 1 0.9

0.7 0.6 0.5 0.4 0.3

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Par

on Parameter

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0.2 0.1 0 200

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600 800 Wavelength (nm)

1000

1542 1

AN US

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0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1543 1

PT

0.9

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on Parameter

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0.5 0.4 0.3

AC

Par

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M

Par

on Parameter

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1544 1 0.9

0.7 0.6 0.5 0.4 0.3

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Par

on Parameter

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0.2 0.1 0 200

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600 800 Wavelength (nm)

1000

1545 1

AN US

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0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1546 0.8

PT

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CE

0.5 0.4 0.3 0.2

AC

Par

on Parameter

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on Parameter

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1547 1 0.9

0.7 0.6 0.5 0.4 0.3

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Par

on Parameter

0.8

0.2 0.1 0 200

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600 800 Wavelength (nm)

1000

1548 1

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1551 1

PT

0.9

0.7 0.6

CE

on Parameter

0.8

0.5 0.4 0.3

AC

Par

1000

ED

200

M

Par

on Parameter

0.8

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A30. Displayed are (left) the Kaasalainen-Shkuratov model partition parameter values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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Surface roughness Errors: 1540 1

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600 800 Wavelength (nm)

200

1000

1000

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AN US

1

Error

1

0.5 0.4

0.3

0.3

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0.2

M

0.4

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1543 0.9

PT

0.8

200

1000

ED

200

0.7

0.5 0.4 0.3

AC

0.2

Surface roughness Errors: 1543 1 0.9

0.5 0.4 0.3 0.2 0.1

0

400

1000

0.6

0.1

200

600 800 Wavelength (nm)

0.7

CE

0.6

400

0.8

Error

Surface roughness (m)

600 800 Wavelength (nm)

Surface roughness Errors: 1542

1542

Surface roughness (m)

400

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Error

Surface roughness (m)

1540 1

0 600 800 Wavelength (nm)

1000

200

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ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1544 1

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600 800 Wavelength (nm)

1000

200

1000

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AN US

1

Error

1

0.5 0.4

0.3

0.3

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0.2

M

0.4

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1546 1

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface roughness Errors: 1546

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

200

1000

ED

200

Error

Surface roughness (m)

600 800 Wavelength (nm)

Surface roughness Errors: 1545

1545

Surface roughness (m)

400

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Error

Surface roughness (m)

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600 800 Wavelength (nm)

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200

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ACCEPTED MANUSCRIPT

Surface roughness Errors: 1547 1

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400

600 800 Wavelength (nm)

1000

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1000

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AN US

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Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0

400

600 800 Wavelength (nm)

1551 1

200

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface roughness Errors: 1551

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

1000

ED

200

Surface roughness (m)

600 800 Wavelength (nm)

Surface roughness Errors: 1548

1

Error

Surface roughness (m)

1548

400

CR IP T

Error

Surface roughness (m)

1547 1

600 800 Wavelength (nm)

1000

200

400

600 800 Wavelength (nm)

1000

Figure A31. Displayed are (left) the Kaasalainen-Shkuratov model surface roughness values for the distal ejecta units, separated by region, with the corresponding error estimates (right).

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Normal Albedo Errors: Distal Ejecta 0.35

0.3

0.3

0.25

0.25

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0.2

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0.05

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600 800 Wavelength (nm)

1000

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600 800 Wavelength (nm)

1000

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0.9

AN US

1

0.8

0.6

0.7

0.5

0.6

0.4

0.5 0.4

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M

0.2

0.1

0.1

0

0

400

600 800 Wavelength (nm)

Distal Ejecta

200

400

600 800 Wavelength (nm)

1000

PT

0.7

1000

ED

200

0.6 0.5 0.4

CE

on Parameter

400

Surface roughness Errors: Distal Ejecta

0.8

Error

Surface roughness (m)

Distal Ejecta

0.3 0.2

AC

Par

0.15

CR IP T

Error

Normal Albedo

Distal Ejecta 0.35

0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A32. This graphs display the Kaasalainen-Shkuratov model parameter values (left), for the combined dark smooth material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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AC

CE

PT

ED

M

AN US

CR IP T

Crater Material. The Hapke model solutions, along with the associated error estimates, are displayed in the following graphs for the crater material. Of the seven regions containing crater material units, one unit (1553) has parameter values that are either unconstrained or poorly constrained. Regions 1549 and 1548 show larger error estimates below ~450nm, compared to the error estimates at longer wavelengths. Polynomial fits to the parameter sets with the lower w values are shown and discussed in the main text to examine variability between regions for the same geomorphological unit.

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1542 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1545

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1548 0.8

PT

0.6

CE

0.5 0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

0.7

1000

ED

200

M

Single Sca ering Albedo (w)

0.8

0.1 0

200

400

600 800 Wavelength (nm)

1000

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ACCEPTED MANUSCRIPT

1549 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1551

AN US

0.9

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1552 0.9

PT

0.7 0.6

CE

0.5 0.4 0.3 0.2

AC

Single Sca ering Albedo (w)

0.8

1000

ED

200

M

ASingle Sca ering Albedo (w)

0.8

0.1 0

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400

600 800 Wavelength (nm)

1000

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1553 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

AC

CE

PT

ED

M

AN US

Figure A33. This graphs display the Hapke single scattering albedo values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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1542 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1200

1545

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1548

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

ED

200

M

Sca ering Func on Amplitude (b)

1

0.2 0.1 0

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ACCEPTED MANUSCRIPT

1549 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1551

AN US

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 400

600 800 Wavelength (nm)

1552

PT

0.9 0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

1

1000

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M

Sca ering Func on Amplitude (b)

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1000

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1553 0.9 0.8 0.7 0.6 0.5 0.4 0.3

CR IP T

Sca ering Func on Amplitude (b)

1

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

AC

CE

PT

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Figure A34. This graphs display the Hapke single particle scattering function amplitude parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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30

30

25

25

20

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Error

Surface Roughness (q)

35

15

15

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5 0

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200

1000

1000

30

30

25

25

20

20

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AN US

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Error

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15 10

M

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5

5

0

0 400

600 800 Wavelength (nm)

1548

PT

35

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200

30 25

15

AC

10

Surface Roughness Errors: 1548 35

20 15

5 0

0

400

1000

10

5

200

600 800 Wavelength (nm)

25

CE

20

400

30

Error

Surface Roughness (q)

600 800 Wavelength (nm)

Surface Roughness Errors: 1545

1545

Surface Roughness (q)

400

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Surface Roughness Errors: 1542

1542 35

600 800 Wavelength (nm)

1000

200

134

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Surface Roughness Errors: 1549 35

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5 0

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1000

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30

25

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AN US

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Error

35

15

M

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400

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1552

200

30 25

15

AC

10

Surface Roughness Errors: 1552

20 15 10 5 0

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25

5

200

600 800 Wavelength (nm)

30

CE

20

400

35

PT

35

1000

ED

200

Surface Roughness (q)

600 800 Wavelength (nm)

Surface Roughness Errors: 1551

35

Error

Surface Roughness (q)

1551

400

CR IP T

Error

Surface Roughness (q)

1549 35

600 800 Wavelength (nm)

1000

200

135

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1553 35

30

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25

25

20

20

15

15

10

10

5

5

0

0 200

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600 800 Wavelength (nm)

1000

200

400

CR IP T

Error

Surface Roughness (q)

1553 35

600 800 Wavelength (nm)

1000

AC

CE

PT

ED

M

AN US

Figure A35. This graphs display the Hapke surface roughness parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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Crater Material 0.9

0.7 0.6 0.5 0.4 0.3

CR IP T

Single Sca ering Albedo (w)

0.8

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

Crater Material

Surface Roughness Errors: Crater Material

35

AN US

35 30

25 20 Error

15 10

15 10

5

5

0

0

400

600 800 Wavelength (nm)

Crater Material 1

200

400

600 800 Wavelength (nm)

1000

PT

0.9

1000

ED

200

0.8 0.7

CE

0.6 0.5 0.4 0.3

AC

Sca ering Func on Amplitude (b)

20

M

Surface Roughness (q)

30 25

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A36. This graphs display the Hapke model parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

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AC

CE

PT

ED

M

AN US

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The Kaasalainen-Shkuratov model results, displayed in the graphs below with the associated error estimates, show little variation in the crater material units from region to region in the values of normal albedo and the parameter associated with surface roughness. The partition parameter, however, indicates three groups of surface types. Polynomial fits to the parameter values are shown and discussed in the main text to further examine the variability between regions for this geomorphological unit.

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Normal Albedo Errors: 1542 0.35

0.3

0.3

0.25

0.25

0.2

0.2

0.15

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0.05 0

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400

600 800 Wavelength (nm)

200

1000

0.3

0.3

0.25

0.25

0.2

0.2

0.15

0.1

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0.05

0

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600 800 Wavelength (nm)

1548

0.3

AC

1000

Normal Albedo Errors: 1548 0.35

Error

0.2 0.15 0.1 0.05

0

400

600 800 Wavelength (nm)

0.25

0.05

200

400

0.3

CE

0.25 Normal Albedo

200

PT

0.35

1000

ED

200

0.1

0.15

M

0.1

0.15

1000

AN US

0.35

0.2

600 800 Wavelength (nm)

Normal Albedo Errors: 1545

0.35

Error

Normal Albedo

1545

400

CR IP T

Error

Normal Albedo

1542 0.35

0 600 800 Wavelength (nm)

1000

200

139

400

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ACCEPTED MANUSCRIPT

Normal Albedo Errors: 1549 0.35

0.3

0.3

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0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

0.3

0.3

0.25

0.25

0.2

0.2

0.15

0.1

0.05

0.05

0

0

400

600 800 Wavelength (nm)

1552

0.3

AC

1000

Normal Albedo Errors: 1552 0.35

Error

0.2 0.15 0.1 0.05 0

0

400

600 800 Wavelength (nm)

0.25

0.05

200

400

0.3

CE

0.25 Normal Albedo

200

PT

0.35

1000

ED

200

0.1

0.15

M

0.1

0.15

1000

AN US

0.35

0.2

600 800 Wavelength (nm)

Normal Albedo Errors: 15551

0.35

Error

Normal Albedo

1551

400

CR IP T

Error

Normal Albedo

1549 0.35

600 800 Wavelength (nm)

1000

200

140

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Normal Albedo Errors: 1553 0.35

0.3

0.3

0.25

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

400

CR IP T

Error

Normal Albedo

1553 0.35

600 800 Wavelength (nm)

1000

AC

CE

PT

ED

M

AN US

Figure A37. This graphs display the Kaasalainen-Shkuratov model normal albedo values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

141

ACCEPTED MANUSCRIPT

1542 1 0.9

on Parameter

0.7

Par

0.8

0.3

0.6 0.5

CR IP T

0.4

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1545 1

AN US

0.9

on Parameter

0.7

Par

0.8

0.3

0.6 0.5

0.2 0.1 0 400

600 800 Wavelength (nm)

1548 1

PT

0.9

on Parameter Par

0.8 0.7

0.3

1000

ED

200

M

0.4

CE

0.6 0.5

AC

0.4

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

142

ACCEPTED MANUSCRIPT

1549 1 0.9

on Parameter

0.7

Par

0.8

0.3

0.6 0.5

CR IP T

0.4

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

1551 1

AN US

0.9

on Parameter

0.7

Par

0.8

0.3

0.6 0.5

0.2 0.1 0 400

600 800 Wavelength (nm)

1552 1

PT

0.9

on Parameter Par

0.8 0.7

0.3

1000

ED

200

M

0.4

CE

0.6 0.5

AC

0.4

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

143

ACCEPTED MANUSCRIPT

1553 1 0.9

on Parameter

0.7

Par

0.8

0.3

0.6 0.5

CR IP T

0.4

0.2 0.1 0 200

400

600 800 Wavelength (nm)

1000

AC

CE

PT

ED

M

AN US

Figure A38. This graphs display the Kaasalainen-Shkuratov model partition parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

144

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1542 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1 0

0 200

400

600 800 Wavelength (nm)

200

1000

1000

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

1

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0 400

600 800 Wavelength (nm)

1548 1

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface Roughness Errors: 1548

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

200

1000

ED

200

Error

Surface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1545

1545

Surface Roughness (m)

400

CR IP T

Error

Surface Roughness (m)

1542 1

600 800 Wavelength (nm)

1000

200

145

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1549 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0 200

400

600 800 Wavelength (nm)

1000

200

1000

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

AN US

0.9

Error

1

0.5 0.4

0.3

0.3

0.2

0.2

M

0.4

0.1

0.1

0

0

400

600 800 Wavelength (nm)

1552 1

200

0.8 0.7

0.5 0.4

AC

0.3

0.9

0.7 0.6 0.5 0.4 0.3 0.2

0.1

0.1 0

0

400

1000

Surface Roughness Errors: 1552

0.2

200

600 800 Wavelength (nm)

0.8

CE

0.6

400

1

PT

0.9

1000

ED

200

Surface Roughness (m)

600 800 Wavelength (nm)

Surface Roughness Errors: 1551

1

Error

Surface Roughness (m)

1551

400

CR IP T

Error

Surface Roughness (m)

1549 1

600 800 Wavelength (nm)

1000

200

146

400

600 800 Wavelength (nm)

1000

ACCEPTED MANUSCRIPT

Surface Roughness Errors: 1553 1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0 200

400

600 800 Wavelength (nm)

1000

200

400

CR IP T

Error

Surface Roughness (m)

1553 1

600 800 Wavelength (nm)

1000

AC

CE

PT

ED

M

AN US

Figure A39. This graphs display the Kaasalainen-Shkuratov model roughness parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

147

ACCEPTED MANUSCRIPT

Normal Albedo Errors: Crater Material 0.35

0.3

0.3

0.25

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05

0

0 200

400

600 800 Wavelength (nm)

1000

200

Crater Material

0.9 0.8

0.7

0.7

0.6

0.6

0.5

Error

0.4

0.5 0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0

400

600 800 Wavelength (nm)

Crater Material 1

200

400

600 800 Wavelength (nm)

1000

PT

0.9

1000

ED

200

0.8 on Parameter

M

Surface Roughness (m)

0.8

Par

1000

AN US

1

0.9

0.3

600 800 Wavelength (nm)

Surface Roughness Errors: Crater Material

1

0.7

400

CR IP T

Error

Normal Albedo

Crater Material 0.35

CE

0.6 0.5

AC

0.4

0.2 0.1 0

200

400

600 800 Wavelength (nm)

1000

Figure A40. This graphs display the Kaasalainen-Shkuratov model parameter values (left), for the combined crater material unit. This includes data from all the regions. The associated error estimates are shown on the right.

148