# Thermophysical and Compositional Analyses of Dunes at Hargraves Crater, Mars

A. Emran AR Center for Space and Planetary Sciences, University of Arkansas,
Fayetteville, AR 72701, USA  Department of Geosciences, Auburn University,
Auburn, AL 36849, USA  L. J. Marzen Department of Geosciences, Auburn
University, Auburn, AL 36849, USA  D. T. King Jr. Department of Geosciences,
Auburn University, Auburn, AL 36849, USA  V. F. Chevrier AR Center for Space
and Planetary Sciences, University of Arkansas, Fayetteville, AR 72701, USA

(Received October 2, 2020; Accepted September 9, 2021)

###### Abstract

We analyze thermal emission spectra using the 2001 Mars Odyssey Thermal
Emission Imaging System (THEMIS) and the Mars Global Surveyor (MGS) Thermal
Emission Spectrometer (TES) to characterize grain-size and mineralogical
composition of dunes at Hargraves crater, Mars. Thermal inertia and bulk
composition of the dunes were compared to inferred provenances from the
thermal infrared response of surface constituent materials. We use a Markov
Chain Monte Carlo (MCMC) technique to estimate the bulk amount of mineralogy
contributed by each inferred provenance to the dune field composition. An
average thermal inertia value of 238$\mathrm{\pm}$17 Jm-2K-1s-0.5 was found
for the dunes corresponding to a surface composed of an average effective
grain-size of $\mathrm{\sim}$391$\mathrm{\pm}$172 $\mu$m. This effective
particle size suggests the presence of mostly medium sand-sized materials
mixed with fine and coarse grain sands. The dunes are likely comprised of a
weakly indurated surface mixed with unconsolidated materials. Compositional
analysis specifies that the dunes are comprised of a mixture of feldspar,
olivine, pyroxene, and relatively low bulk-silica content. Dune materials were
likely derived from physical weathering, especially eolian erosion,
predominantly from the crater ejecta unit at the crater, mixed with a small
amount from the crater floor and crater rim and wall lithologies- indicating
the dune materials were likely sourced locally.

Solar system planets (1260) — Solar system astronomy (1529) — Solar system
(1528) — Inner planets (797) — Mars (1007)

††journal: The Planetary Science Journal

##  1 Introduction

Despite a variety of environments involved, dune fields provide an important
insight into eolian transport regimes (both past and present) of terrestrial
planets (Greeley & Iversen, 1987). The surface of Mars is rife with eolian
dune fields in a variety of locations e.g., the circum-north polar region
(Edgett & Christensen, 1991). Dunes on the martian surface were first detected
during the early 1970s (Sagan et al., 1972). The advent of higher resolution
orbital images further confirmed the presence of dunes during the late 1990s
(Edgett & Malin, 2000) and showed similarities with terrestrial dunes
(Bandeira et al., 2010). Recently, a global martian dune database called the
Mars Global Digital Dune Database (MGD3) (Hayward et al., 2007a, 2010, 2012)
has been prepared by planetary scientists. The database contains spatial and
morphological characteristics of identified dune fields of $\mathrm{>}$1 km2
(Hayward et al., 2007b, 2014). Understanding of martian dune materials and
eolian processes opens a window for constraining the surface-atmospheric
interactions, weather, climate, and climatic evolution of the planet (Greeley
et al., 2001; Wilson & Zimbelman, 2004). Over the past decades, studies were
carried out to better understand martian surface processes and global scale
atmospheric dynamics from analysis of various eolian processes and wind-
related features (Ward et al., 1985; Fenton et al., 2005; Hayward et al.,
2009; Silvestro et al., 2010; Gardin et al., 2012; Sefton-Nash et al., 2014).

Throughout its geologic history, the surface of Mars has been shaped by eolian
processes. Thus, analyses of thermophysical, grain-size, and mineral
compositional distribution within dunes are regarded as important aspects for
helping to constrain the geology and climate of the planet (Greeley & Iversen,
1987; Greeley et al., 2001; Charles et al., 2017; Fenton et al., 2019b). Local
and regional scale analysis of grain-size distributions is important in
classifying sedimentary environments and understanding sediment transport
dynamics. Particle grain-size provides important information about shear
stress applied by the medium of transport to initiate and sustain particle
movement (Abuodha, 2003). Particle grain-size in dunes is actively influenced
by nature of source materials, surface topography, transport medium, and the
distance traveled from source to sink (Abuodha, 2003). On Mars, grain-size
distribution has a direct relationship with thermophysical characteristics of
constituent materials (Kieffer et al., 1977; Haberle & Jakosky, 1991). Eolian
dunes of Mars are likely comprised of particle-sizes of homogenous materials
within a dune field and, therefore, grain-size determination from thermal
inertia is considered a reliable method (Edwards et al., 2018).

The approximate bulk particle size of dunes on Mars have been determined
successfully using thermal inertia from the Thermal Emission Imaging System
(THEMIS) images (Fenton & Mellon, 2006; Fergason et al., 2006, 2012). Edwards
et al. (2018) estimated the average grain size for the Bagnold dune field to
be $\mathrm{\sim}$250$\mathrm{\pm}$85 $\mu$m with a minimum of 148 $\mu$m and
a maximum of 968 $\mu$m based on derived thermal inertia values. Using data
from Mars Science Laboratory (MSL) Curiosity, Ehlmann et al. (2017) reported
presence of very fine to medium-sized ($\mathrm{\sim}$45-500 $\mu$m) sand
particles in the active Bagnold dune field. THEMIS-derived thermal inertia of
$\mathrm{\sim}$250 - 410 Jm-2K-1s-0.5 was found across the majority of the
Gale crater landing site of MSL, which corresponds to dunes comprised of an
indurated surface, likely mixed with unconsolidated materials (Fergason et
al., 2012). In addition, Mawrth Vallis has an average thermal inertia of 310
Jm-2K-1s-0.5, indicating${}^{\ }$a mixture of bedrock, bedform, indurated
surface, and unconsolidated materials (Fergason et al., 2012).

Analysis of thermal inertia and local dune morphology variation provides
insight into near-surface composition and local to regional climate
characteristics (Courville et al., 2016), for instance, the presence of
subsurface ice or volatiles. Besides thermal inertia derived from THEMIS
nighttime thermal infrared (TIR) measurements, higher resolution images
compared to THEMIS, such as the Context Camera (CTX) (Malin et al., 2007) and
the High Resolution Imaging Science Experiment (HiRISE) (McEwen et al., 2007),
can be used as a visual survey tool for interpretation of thermal inertia
(Fergason et al., 2012) and analyzing local tonal variability of dune fields.
Compositional analysis of dune materials is key to better understanding
sediment source and transport history. Bulk composition of dunes can reveal
the extent of similarities (and dissimilarities) between eolian sands and
surrounding surface compositions, provenances (sources) of dune materials,
transport behavior of eolian medium, eolian activity level, and alteration (if
any) of eolian sands versus their source materials (Fenton et al., 2019a, b).

Fenton et al. (2019b) did a complimentary science publication to MGD3 which
describes mineralogy and morphologic stability of 79 large dune fields from
thermal infrared (TIR) spectra at nearly global scale (80.7∘S - 41.7∘N).
Gullikson et al. (2018) expanded MGD3 to include thermal inertia and
compositional information of dune fields in the equatorial and south polar
regions. However, the latest installments of MGD3 only included compositions
for dune fields $\mathrm{\geq}$300 km2 and, therefore, compositional analysis
for dune fields smaller than that threshold is warranted. Also, there is a
need to compare dune field composition with surrounding materials to determine
whether their potential provenance is at a local scale versus regional or
global. The outlines of dune fields in MGD3 are not always accurate and
overlooks the recognition of smaller dune forms (Emran et al., 2020) likely
due to the low-resolution THEMIS imagery used and the possibility of human
error when manually outlining the dune fields. Thus, extracting information
from an accurate dune outline is a challenge.

With increasing image resolution, the accuracy of outlining dune fields also
increases. Recently, Emran et al. (2020) accurately outlined an MGD3 dune
field at Hargraves crater from CTX data using a semi-automated method to
include small dune forms present within the crater that were overlooked by
MGD${}^{3\ }$defined outline. Their research provides an excellent opportunity
to study a small dune field (area of $\mathrm{<}$ 100 km2) and compare it with
potential provenances. The crater is located in the Nili Fossae region of the
northwest Isidis basin (Fig. 1). The region has received substantial attention
for planetary exploration missions and has been extensively studied over the
past decade (Salvatore et al. (2018), and references therein). Jezero crater,
the recently landed site of Mars 2020 Perseverance rover, is also located in
this region. Hargraves crater is considered to be part of the Jezero watershed
(Goudge et al., 2015), though relatively little attention has been paid to
this crater in comparison to Jezero crater and Nili Fossae.

Figure 1: a) Hargraves crater is located east of Nili Fossae and northwest of
Isidis Basin, and is considered to be part of the Jezero watershed (Goudge et
al., 2015). The background mosaic consists of MOLA colorized elevation data
overlain onto a THEMIS image mosaic. The enlarged location (a) is shown in
THEMIS daytime infrared images where the yellow star indicates the location of
the dune field within the crater. b) The dune field in CTX image resolution.
c) Dune polygon layer as derived by Emran et al. (2019a). The red polygons
highlight the dunes within the crater. The figure is recreated after Emran et
al. (2020). North is up.

We determine thermophysical and mineralogical characteristics of the dunes
within Hargraves. The dune field outline in Emran et al. (2020) was chosen as
the boundary of dune materials in this study (the dune database is found in
Emran et al. (2019a)). We analyze the surface grain-size distribution and bulk
compositional (mineral abundances) characteristics across the dune field.
Active dunes preclude the possibility of cementation and vertical variation of
constituent materials and, therefore, thermal inertia measurements can be used
for grain-size analysis (Edwards et al., 2018). Grain size analysis derived
from THEMIS thermal inertia is considered to be a reliable measurement in this
study because the dune field within Hargraves is both potentially active
(Emran et al., 2020) and covers a large area, i.e., its spatial extent spans
across numerous THEMIS pixels.

We use CTX ($\mathrm{\sim}$6 m/pixel) and HiRISE (25-60 cm/pixel) images as a
visual survey tool for analyzing tonal variability and presence of induration
morphology i.e., degradational characteristics, such as erosional features
(for example, mass wasting) or impact cratering as well as surface mantling
within the dune field. We determine morphologic stability of the dune field
that measures the degree of modification by non-eolian processes responsible
for erosion and stabilization of the dunes (Fenton et al., 2019b). Martian
dunes are classified into six stability index (SI) categories based on an
inferred scale from 1 to 6 constructed upon presence (or lack) of superposed
non-eolian features (such as gullies, small pits, mass wasting features, etc.)
and apparent level of degradation by non-eolian processes i.e., erosion (Banks
et al., 2018; Fenton et al., 2019a). Assigning a stability index is a
subjective procedure; dunes with no apparent stability features (i.e., an
active dune field) are assigned a SI of 1 whereas SI values from 2 to 6
indicate increasing non-eolian modification (Fenton et al., 2019a). Refer to
Fenton & Hayward (2010) for details of dune stability classes and their
characteristics.

We then compare the bulk mineral assemblage of the dune field to surrounding
geologic units inside the crater (Goudge et al., 2015), which have been
inferred as potential source materials. Martian dune sand is believed to have
not traveled far from their sources and reflect varying mineralogy of
surrounding local surface terrain (Fenton et al., 2019b). Thus, we hypothesize
that the dunes at Hargraves are also locally sourced within the crater.
Accordingly, we compare dune mineralogy to the surrounding geologic units at
the crater using spectral data from Thermal Emission Spectrometer (TES)
(Christensen et al., 2001) instrument and then subsequently modeled to
determine the compositions.

##  2 Hargraves crater

Hargraves crater is a 65 km diameter impact crater centered at
75.75${}^{{}^{\circ}}$ E, 20.75${}^{{}^{\circ}}$ N, east of Nili Fossae (Fig.
1a), and formed during the late Noachian or early Hesperian (Ivanov et al.,
2012). The impact event that created Hargraves crater involved an array of
different target bedrock lithologies, including a diverse set of mineralogy,
such as mafic minerals and phyllosilicates (Mangold et al., 2007). Based on
tone, texture, and morphological characteristics, Goudge et al. (2015)
identified a few major morphologic units inside the crater such as an alluvial
fan (AF), crater central peak (Ccp), crater floor materials (Cfm), crater
ejecta (Ce), and surficial debris cover (Ac) (Fig. 2). There is an outside-
crater ejecta unit, hereafter we refer as Ce (Outer) unit. A dune field,
visible at CTX and THEMIS resolutions, is located on the western side of the
crater floor (Fig. 1b). The dune field is labeled as surficial debris cover
(Ac) and is characterized by aggregates of dunes (Goudge et al., 2015). The
MGD3 and previous studies (Emran, 2019; Emran et al., 2019b, 2020) report that
the dune field has barchan (B) and barchanoidal (Bd) dune types (see Fig.
3b)(McKee, 1979). Northwest of the dune field is an alluvial fan that was
emplaced by fluvial activities along the crater rim (Mangold et al., 2007).

Figure 2: A geologic map of Hargraves crater and its surroundings; crater
ejecta (Ce), crater floor materials (Cfm), crater central peak (Ccp), rim and
wall materials (Crw), alluvial fans (AF), and dune materials (Du) units within
the crater and outside-crater ejecta (Ce (Outer)) unit. Inset (upper right) is
the reference location. The background mosaic consists of THEMIS daytime
infrared images. Note that the dune unit has been described as the surficial
debris cover (Ac) unit in Goudge et al. (2015). The figure is recreated after
Goudge et al. (2015). North is up.

##  3 Observation and methods

The bulk mineralogy was derived from TES data at spatial resolution roughly 3
x 6 km2. We use THEMIS ($\mathrm{\sim}$100 m/pixel) data for additional
compositional analysis. Though TES has lower spatial resolution compared to
THEMIS, it has many more spectral bands than THEMIS, which allows bulk mineral
assemblages to be modeled. THEMIS images were used to examine grain size
distribution using thermal inertia, as well as decorrelation stretch (DCS) as
an additional compositional component.

###  3.1 Thermal Emission Spectrometer (TES)

TES, onboard the Mars Global Surveyor (MGS) spacecraft, has an infrared (5.8
to 50 $\mu$m) interferometric spectrometer, broadband thermal (5.1 $\mu$m to
150 $\mu$m) radiometer, and a visible/near-infrared (0.3 $\mu$m to 2.9-$\mu$m)
radiometer (Christensen et al., 2001). We use TES data recorded between 1651
cm-1 and 201 cm-1 with a sampling interval of 10 cm-1 resulting in 143
channels. To ensure the best quality data used in this study, we extract TES
data following the method described in Rogers & Bandfield (2009) and Salvatore
et al. (2018). TES spectra were extracted from early in the mapping orbit
phase (orbits 1- 5317) that belong to high surface temperatures
($\mathrm{\geq}$260 K), low total ice ($\mathrm{\leq}$0.04), and low total
dust cover ($\mathrm{\leq}$0.15). This quality control practice has
successfully been applied in previous studies (Rogers et al., 2007; Rogers &
Christensen, 2007; Salvatore et al., 2014, 2016) and helps to ensure the
accuracy of the true spectral characteristics (Salvatore et al., 2018).

Individual spectra were collected only from TES footprints that fall over the
dune field, hereafter as dune unit (Du). For compositional comparison to
inferred provenances, spectra were also collected over the crater floor
materials (Cfm), crater ejecta (Ce), crater central peak (Ccp), and crater rim
and wall materials (Crw) units. To ensure the spectra represented individual
geologic units, data were only collected when TES footprints were fully within
the bounds of a unit. We limit our spectra collection to between orbit counter
keeper (OCK) 1583 and 7000 to account for instrumental error. Based on
measured slipface direction, Emran et al. (2020) reported that the prevailing
wind inside the crater flows to the west-northwest. Since the alluvial fan
(AF) is located downwind of the dune field, we did not consider the alluvial
fan unit (AF) as a potential source for the dunes. A list of TES spectra used
for each geologic unit is given in appendix section of this paper (A1).

####  3.1.1 Spectral data processing

TES spectral data were extracted using JMARS (Christensen et al., 2009). The
data were then processed and analyzed with DaVinci programming environment
(Edwards et al., 2015). Prior to averaging the spectra for each representative
morphologic unit, an atmospheric correction is applied to each orbital group
(OCKs) spectra using the deconvolution algorithm of Bandfield et al. (2000),
Smith et al. (2000), and Rogers & Aharonson (2008). We used a library of
atmospheric end-members (Bandfield et al., 2000) and treated each spectra
individually for atmospheric correction, which involves extracting surface
emissivity through subtracting atmospheric contributions from measured
emissivity spectra (Salvatore et al., 2018). Upon atmospheric removal, the
surface emissivity spectra for each representative morphologic unit were
averaged. The average surface composition and mineral assemblage were then
modeled using an unmixing algorithm (Ramsey & Christensen, 1998; Rogers &
Aharonson, 2008) with a selected mineral endmember library (see Table 1).

The algorithm uses a non-negative least square minimization routine (Rogers &
Aharonson, 2008) between the wavelength range of 230 cm-1 and 1305 cm-1. The
non-negative least square routine employs all spectra in design matrix and
remain available to the algorithm until a final non-negative solution is
reached (Rogers & Aharonson, 2008). As the solution of the routine converges
toward the best fit, a positive or zero spectral coefficient is maintained in
the algorithm. However, the algorithm allows atmospheric and blackbody end-
member coefficients to be negative. Details of the algorithm can be found in
(Rogers & Aharonson, 2008). Using the spectral unmixing routine, we generated
8 mineral groups (Rogers & Fergason, 2011) for each unit with a
$\mathrm{\sim}$10% accuracy and detection threshold for mineral abundance
(Feely & Christensen, 1999; Christensen et al., 2000; Bandfield, 2002; Rogers
& Christensen, 2007). The final outputs of spectral unmixing include modeled
abundance mineral groups (feldspar, high-silica phases, pyroxene, olivine,
hematite, sulfate, carbonate, and quartz), error estimation, and an individual
end-member assemblage (Rogers & Fergason, 2011).

Table 1: The list of thermal infrared spectral endmembers (both a library of
atmospheric end-members and a library of surface end-member spectra) used for
unmixing TES data in this study.

Mineral Group | Spectrum/endmember name | Mineral ID | Reference
---|---|---|---
Quartz | Quartz | BUR-4120 | [1]
Alkali feldspar | Microcline | BUR-3460 | [1]
Plagioclase | Albite | WAR-0235 | [1]
| Oligoclase | BUR-060D | [1]
| Andesine | WAR-0024 | [1]
| Labradorite | BUR-3080A | [1]
| Bytownite | WAR-1384 | [1]
| Anorthite | BUR-340 | [1]
| Shocked anorthosite | None | [2]
| at 17 GPa |  |
| at 21 GPa |  |
| at 25.5 GPa |  |
| at 27 GPa |  |
| at 38 GPa |  |
| at 56.3 GPa |  |
Orthopyroxene | Bronzite | NMNH-93527 | [1]
| Enstatite | HS-9.4B | [1]
| Hypersthene | NMNH-B18247 | [1]
Low-Ca clinopyroxene | Average Lindsley pigeonite | None | [3]
High-Ca clinopyroxene | Diopside | WAR-6474 | [1]
| Augite | NMNH-9780 | [1]
| Augite | NMHN-122302 | [1]
| Hedenbergite manganoan | DSM-HED01 | [1]
Olivine
| Forsterite | BUR-3720A | [1]
| Fayalite | WAR-RGFAY01 | [1]
| Olivine Fo60 | KI 3362 | [4]
| Olivine Fo68 | KI 3115 | [4]
| Olivine Fo35 | KI 3373 | [4]
| Olivine Fo10 | KI 3008 | [4]
Phyllosilicates | Illite Imt-1 $\mathrm{<}$ 0.2 $\mu$m (pellet) | Imt-1 | [5]
| Ca-montmorillonite solid | STx-1 | [1]
| Saponite (Eb-1) $\mathrm{<}$ 0.2 $\mu$m (pellet) | None | [6]
| SWy-1 $\mathrm{<}$ 0.2 microns (pellet) | None | [5]
Glass | K-rich glass | None | [3]
| SiO2 glass | None | [3]
Amorphous silica | Opal-A | 01–011 | [7]
| Al-Opal | None | [8]
Oxide | Average Meridiani and Aram Hematite (TT derived) | None | [9]
Sulfate | Anhydrite | ML-S9 | [1]
| Gypsum | ML-S6 | [1]
Carbonate | Kieserite | None | [10]
| Calcite | C40 | [1]
Zeolite | Dolomite | C20 | [1]
| Crystalline heulandite | None | [11]
| Crystalline stilbite | None | [11]
Atmosphere | Low-opacity dust | None | [12]
| High-opacity dust | None | [12]
| Water ice (small) | None | [12]
| Water ice (large) | None | [12]
| Synthetic CO2 | None | [12]
| Synthetic water vapor | None | [12]

Note. — [1] Christensen et al. (2000); [2] Johnson et al. (2002); [3] Wyatt et
al. (2001); [4] Koeppen & Hamilton (2008); [5] Michalski et al. (2006); [6]
Michalski et al. (2005); [7] Michalski et al. (2003); [8] provided by M. D.
Kraft/cited in Rogers & Fergason (2011); [9] Glotch et al. (2004); [10]
Baldridge (2008); [11] Ruff (2004); [12] Bandfield et al. (2000).

####  3.1.2 Regression models

We use linear regression to model the relationship between the bulk
composition of each surrounding geologic unit and dune field by fitting a
linear equation to the mineral abundance. Since spectral unmixing generates
abundances of 8 mineral groups i.e., endmember abundance with a mean ($\mu$)
and uncertainty (1-$\sigma$) value (Rogers & Fergason, 2011), we resample each
endmember assuming the bulk composition as a gaussian scatter. We draw a total
of 1,000 normal random samples (125 for each endmember) from each surrounding
geologic unit centered around $\mu$ within the limit imposed by the 1-$\sigma$
of the endmember abundance. In our linear regression model, we assume the
abundance of minerals in the dune field as the response (y) and each
surrounding geologic unit as an explanatory variable (x). For gaussian
scatter, the linear regression model uses the equation as:

$y=ax+b$ (1)

where the slope of the line is a, and b is the intercept or offset. Both a and
b are the parameters to estimate in the model. Using this model, we estimate
the posterior probability distribution of the model parameters considering the
uncertainty (1-$\sigma$ value) of the response (dune unit) using the Bayesian
inference approach. The posterior probability distribution of the parameters
of the linear model tells us the degree of similarity of the bulk mineralogy
between the composition of each surrounding geologic unit to the dune field.
Thus, the posterior probability distribution of the parameters indicates the
likelihood of contribution of the bulk mineralogy from each geologic unit to
the dune field’s bulk composition.

We employ a multiple linear regression model to estimate how much of each
surrounding unit mixing with other units could produce the dune field
composition. For that, we assume the volumetric (bulk) percent contribution of
mineral compositions from each of the surrounding units to the dune field. We
adopt this approach as it is the simplest and physically possible scenario for
the compositional mixing of geologic materials on Mars. In the case of
volumetric percent contribution, the bulk compositions of the surrounding
individual units (Xi) are summed with weights equal to the fractional
contributed by each unit (Fi), as given below:

$Y=\sum_{i}^{n}{F_{i}X_{i}}+\textit{Z${}_{0}$}$ (2)

where 0 $\leq$ Fi $\leq$ 1, Y is the bulk composition of the dune field, Z0 is
the additive offset, and i represents the surrounding individual unit. In our
model, the fractions contributed by each unit (Fi) and Z0 are the parameters
to estimate considering the uncertainty (1-$\sigma$) of Y. We estimate the
posterior probability distribution of these parameters of the multiple linear
model which tells us the fractional contribution of bulk mineralogy from each
surrounding unit to the dune field composition. The model uses a non-negative
linear combination (mixing) strategy i.e., Fi must render a positive value.

We use a Markov Chain Monte Carlo (MCMC) simulation technique (Hogg & Foreman-
Mackey, 2018) to estimate the parameters of our models. Inherently the MCMC
employs a Bayesian inference approach for sampling the probability density
function, performing probabilistic inferences, and fitting models to data
(Hogg & Foreman-Mackey, 2018). For detail of the mathematical foundation of
the MCMC technique refer to Goodman & Weare (2010); Hogg & Foreman-Mackey
(2018). We use emcee (Foreman-Mackey et al., 2013), a Python routine for MCMC
simulation proposed by Goodman & Weare (2010), for the implementation of our
models. With the MCMC routine, we use 1,000 iterations to estimate the model
parameters. We report the median, including 16% quantile as lower 1-$\sigma$
error and 84% quantile as an upper 1-$\sigma$ error, fraction contribution of
the bulk composition of each surrounding unit using a corner plot diagram.

###  3.2 Thermal Emission Imaging System (THEMIS)

THEMIS consists of two multispectral imagers onboard the 2001 Mars Odyssey
spacecraft, enabling a useful analysis of the thermophysical, composition, and
physical properties of the martian surface (Christensen et al., 2004). The
THEMIS sensor consists of visible (VIS), near-infrared (NIR), and thermal-
infrared (TIR) imagers. The thermal infrared sensor has 10 channels ranging
from 6.78 $\mu$m to 14.88 $\mu$m at a spatial resolution for daytime and
nighttime images at 100 m/pixel (Christensen et al., 2004). We used THEMIS
daytime IR to analyze surface composition and nighttime IR to analyze
thermophysical characteristics.

Surface compositional and thermal inertia data can be extracted from
representative surface units through overlapping THEMIS daytime and nighttime
IR images. This allows an assessment of the relationship between composition
and grain-size distribution (Bandfield et al., 2011). THEMIS daytime radiance
data are atmospherically corrected, calibrated, and converted to surface
emissivity using the methods described in Christensen et al. (2004), Bandfield
et al. (2004a), and Edwards et al. (2011). We used the THEMIS I02781003 image
acquired under an average surface temperature of 247${}^{\ }$K, water ice
opacity of 0.072, and low atmospheric dust opacity of 0.052 at 9 $\mu$m.
THEMIS images with a similar specification of average surface temperature and
low atmospheric dust and water ice opacity have successfully been used in
previous studies (Bandfield et al., 2004b).

####  3.2.1 Thermal inertia

Thermal inertia (TI) is considered as a primary physical property measuring
the resistance of materials to a temperature change and can be used as an
indicator of surface geological characteristics for the upper few centimeters
(Jakosky et al., 2000; Mellon et al., 2000; Putzig et al., 2005). Thermal
inertia is influenced by thermal conductivity and surface physical structure
such as particle grain-size, induration, porosity, rock abundance, bedrock
exposure, and compaction of surface materials (Kieffer et al., 1973; Putzig et
al., 2005; Fergason et al., 2006; Piqueux & Christensen, 2011; Putzig et al.,
2014). Thermal inertia of material, I (J m-2 K-1s-1/2) can be defined as:

$I=\sqrt{}(k\rho c)$ (3)

where k is the thermal conductivity (W m-1 K -1), $\rho$ is the density (kg
m-3), and c is the specific heat (J kg-1 K-1). On Mars, the value of $\rho$c
is assumed to be 1 x 106 Jm-3K-1 (Neugebauer et al., 1971; Fergason et al.,
2006).

Ideally, higher thermal inertia values are associated with compacted rock
and/or larger particle-size materials corresponding to a mechanically strong
surface, such as well-indurated (cemented) rock. In contrast, weakly lithified
rocks and/or unconsolidated (weakly indurated) materials are associated with
lower thermal inertia values (Williams et al., 2018). Thus, lower thermal
inertia ($\mathrm{<}$ $\mathrm{\sim}$350 Jm-2K-1s-0.5) represents loose and
fine-grained surface particles (Piqueux & Christensen, 2009a, b) whereas
higher thermal inertia ($\mathrm{>}$ 1,200 Jm-2K-1s-0.5) indicates igneous
bedrock (Edwards et al., 2009). We extracted quantitative thermal inertia from
the THEMIS global thermal inertia product (Christensen et al., 2013) produced
using the method of Fergason et al. (2006). The overall accuracy and precision
of THEMIS-derived thermal inertia is around $\mathrm{\sim}$ 20% (Fergason et
al., 2006).

Effective grain-size can be interpreted from thermal inertia values (Kieffer
et al., 1973). We calculated the effective particle size diameter of the dune
materials using the following equation:

$k=\left(C*P^{0.6}\right)*d^{-0.11.log\ (\frac{P}{K})}$ (4)

where C and K are constants, with values of 0.0015 and 8.1 x 104 torr,
respectively, P is the atmospheric pressure in torr, d is the average particle
diameter measured in $\mu$m, and k is the thermal conductivity in Wm-1K-1,
which was derived using equation 3 (Presley & Christensen, 1997a, 2010). Mars’
average surface pressure of 3.9 torr (5.2 mbar or 520 Pa) at L${}_{s\ }$= 0∘
(Smith & Zuber, 1998) was used as the reference atmospheric pressure in this
study. Using equation (4), the derivation of particle diameter is valid for
thermal inertia values less than $\mathrm{\sim}$350 and an expected precision
of the measurements in particle size is to be $\mathrm{\pm}$10-15% (Presley &
Christensen, 1997a; Fergason et al., 2006). Thermal inertia values higher than
350 are difficult to interpret and may be indicative of a solid rock outcrop
mixed with fine-grained material within the footprint of thermal data (Presley
& Christensen, 1997a).

####  3.2.2 Decorrelation stretch (DCS)

Decorrelation is a contrast stretch method that displays enhanced information
from multispectral thermal infrared images. Refer to Gillespie et al. (1986)
for details. We use DCS images (Edwards et al., 2011) derived as a standard
THEMIS IR radiance product (Bandfield et al., 2004b) to highlight surface
spectral and compositional variability at a spatial resolution much higher
than TES. DCS images are typically rendered with 3-band combinations such as
8-7-5, 9-6-4, or 6-4-2 displayed in red, green, and blue channels,
respectively. Yellow appearance in DCS images using bands 8-7-5 and 9-6-4, and
magenta using bands 6-4-2 tend to correspond with an elevated bulk-silica
content (Amador & Bandfield, 2016). Materials that appear purple in both 8-7-5
and 9-6-4 DCS images and cyan in 6-4-2 typically represent olivine-bearing
basalts (Edwards et al., 2011; Bandfield et al., 2011; Amador & Bandfield,
2016; Salvatore et al., 2016). We primarily focus on DCS images stretch using
bands 8-7-5- in red, green, and blue channels, respectively, to highlight
compositional variability present within the dune field and surrounding areas,
because this band combination exhibits most variability. The color variability
in DCS images is scene-specific and the spectral variability of DCS within a
particular scene should be interpreted for that specific scene only because of
its temperature dependence (Rogers & Fergason, 2011).

##  4 Results

The result of the grain-size distribution for the dune materials was presented
first followed by the results of dune morphology and stability, and bulk
mineralogy of the dune field and the potential provenances. Then the result of
bulk mineralogy from the TES analysis was revisited using THEMIS-derived
analysis of DCS image.

###  4.1 Grain-size distribution

THEMIS nighttime data are more reliable than daytime data when deriving
thermal inertia and estimating grain-size distribution because nighttime
observation minimizes the effects of slope and albedo (Edwards et al., 2018).
Quantitative thermal inertias were extracted using the THEMIS global thermal
inertia mosaic by Christensen et al. (2013) for the dune field. The estimated
average thermal inertia across the dune field is 238$\mathrm{\pm}$17 111Mean
$\mathrm{\pm}$ 1-$\mathrm{\sigma}$ standard deviation Jm-2K-1s-0.5, with a
minimum value of 200 Jm-2K-1s-0.5 and a maximum of 426 Jm-2K-1s-0.5. This
value is consistent with the average thermal inertia measured in the Bagnold
dune field using multiple THEMIS observations (Edwards et al., 2018). The
thermal inertia map (Fig. 3a) shows a concentration of relatively similar
values within the outline of the dune field. Visual inspection indicates a
comparatively lower thermal inertia across the dune field when compared to the
surrounding geologic units. For instance, the crater floor materials (Cfm) on
the east side of dunes have an average thermal inertia value of
$\mathrm{\sim}$400 Jm-2K-1s-0.5.

We calculate the average particle size across the dune field using the
equations (3 \- 4). We convert each THEMIS thermal inertia pixel to particle
size instead of converting average thermal inertia to average particle size.
We adopted this method because the conversion of thermal inertia to particle-
size is non-linear (Edwards et al., 2018). The average grain-size values were
calculated after converting each TI pixel to grain-size, which resulted in an
average particle size of $\mathrm{\sim}$391$\mathrm{\pm}$172 $\mu$m. This
particle size diameter corresponds to mostly medium sand-sized materials mixed
with fine and coarse grain sands (Presley & Christensen, 1997b).

Figure 3: a) Thermophysical characteristics of the dunes and surrounding
materials derived from nighttime THEMIS thermal infrared data overlain onto a
CTX mosaic. Warmer colors represent higher thermal inertia. Over the dunes and
the associated sand aprons, there is comparatively lower thermal inertia than
over the surrounding areas. b) Tonal variation of dunes from non-dune
materials in CTX image resolution where darker tone distinguishes dunes from
non-dune materials. c). Distinctly identifiable crests and slipface are
visible in high-resolution HiRISE image. North is up.

###  4.2 Morphology and stability

Like the typical dunes elsewhere on Mars (Hayward et al., 2007a, 2010, 2012;
Fenton et al., 2019b), the dune field on the floor of Hargraves crater is
darker in tone when compared to surrounding materials (Fig. 3b-c). A
consistent tone across the dune field suggests that dune surface materials are
relatively homogenous. Visual inspection of high-resolution morphology from
CTX and HiRISE images finds an absence of in-situ bedrock surface within the
dune field, except exposure of a light-toned feature in the south-eastern
fringe (Fig. 4a). The feature has a semi-conical shape and appears to be
layered. The layers dip away from the central point and have been eroded to
the extent that they show the layered nature of this feature. A central vent,
which is in line with some well-defined fractures, shows lighter shaded
materials residing below the eroded layers that enshroud the feature. The
feature can be interpreted to be a “possible” intrusion of igneous origin,
developed from small (isolated) volcanos (Richardson et al., 2021), supported
by the nearby presence of the Syrtis Major volcanic province (Hiesinger & Head
III, 2004). However, this interpretation is based on our visual inspection and
other possible interpretations of this feature may also be plausible. The
physical relationship of this feature with surrounding dunes does not,
unambiguously, reveal the relative age of the feature.

Figure 4: a) An exposed light-toned feature in the south-eastern fringe from
CTX image. The feature, interpreted to be a “possible” intrusion of igneous
origin, has a semi-conical shape, appears to be layered, and dips away from
the central point. b) Thin dark streaks, indicated by white arrows, emanating
from the dunes on the south-eastern boundary of the dune field. The streaks
are interpreted as the result of wind activities. North is up.

We investigate fine-scale induration morphologies, non-eolian modification (or
erosion), and degradation characteristics within the dune field, such as
erosional features or impact cratering from HiRISE images ESP_030302_2010 and
ESP_030091_2010 (Fig. 5). An investigation of these images reveals a sand
apron on the western boundary of the dune field. We also find potential
mantling on the boundary between the dunes and sand apron, as well as impact
cratering on the mantling material. The presence of potential mantling along
the boundary which appears to expand into the dune field by filling in space
between the dunes, small impact craters, and an average thermal inertia value
of $\mathrm{\sim}$250 Jm-2K-1s-0.5 in the dune field, may be an indication of
weak induration or lithification of the dune materials (Fergason et al.,
2012). We did not find other non-eolian or erosional features like gullies or
mass wasting features. However, the dunes on the south-eastern boundary of the
dune field show a different scenario. CTX (see Fig. 1b and 4b) image reveals
thin dark streaks emanating from the dunes on the south-eastern boundary of
the dune field.

There are features atop several dune crests and upper windward sides, such as
sets of straight or very nearly straight grooves, akin to eolian ripples, of
several different widths, orientations, and spacings. All the various sets of
grooves have in common that they are roughly perpendicular to the crest of the
dunes upon which they reside. There are also features upon the dunes including
curved and scalloped grooves that follow trellis and dendritic patterns (Fig.
5b). These curved and scalloped features have in common that they are closer
to the crests of the dunes upon which they reside than are the nearby straight
or nearly straight grooves, and in some instances persist down the leeward
(avalanche) face of the dune. Both of these groove features exhibit shallow
penetration into the dune structure and reveal lighter color sands. These
bright-toned grooves are interpreted to be martian eolian mega-ripples, often
refer as Transverse Aeolian Ridges-TARs (Silvestro et al., 2020).

We assign the dune field within Hargraves carter a stability index of 2, since
it has a partial apron that exists along the western side of the dune field
(Fenton & Hayward, 2010). Note that the dune field may overlap the
morphological characteristics between multiple stability index classes and
assignment to an appropriate class is a subjective procedure. Southern high-
latitudes dunes show increased stability and inactivity (Fenton et al., 2019a)
due to presence of subsurface (near-surface ground) water ice that could
stabilize dunes, and their thermal model generally fits to rock/ice thermal
signature (Gullikson et al., 2018). Unlike the southern-high latitude dune
fields (Fenton & Hayward, 2010; Gullikson et al., 2018; Fenton et al., 2019a),
the dunes within Hargraves crater may not be affected by subsurface ice or
volatiles (Emran et al., 2020). We presume this because the dunes at the
crater are potentially active and deprived of stability features and
morphologies- indicating the absence of influences from subsurface (near-
surface ground) water ice that would otherwise stabilize the dunes. Subsurface
ice extends as far equatorward as 45∘S between 40 and 140∘ E longitudes
(Fenton & Hayward, 2010). Martian water-equivalent hydrogen-rich deposits of
$\mathrm{\sim}$ $\mathrm{>}$20% (by mass) at poleward of $\mathrm{\pm}$50∘
latitudes and less rich deposits near-equatorial latitudes were reported from
Neutron data observed using the Neutron Spectrometer aboard 2001 Mars Odyssey
(Feldman et al., 2004). Thus, residing in the equatorial region
($\mathrm{\sim}$20${}^{{}^{\circ}}$ N), he dunes at Hargraves are less likely
to be affected by subsurface ice or volatiles.

Figure 5: a) Fine-scale morphology in the dune field from HiRISE
ESP_030302_2010 image. The white and black rectangles (polygons) indicate the
reference locations of b) and c), respectively. b) Transverse Aeolian Ridges
(TARs) along the dune faces, near the crests. TARs are light-toned eolian
mega-ripple features that appear to follow trellis and dendritic patterns. c)
Small impact craters (black arrows) on mantling surface. North is up.

###  4.3 Bulk composition

We model bulk mineralogy across the dune field (Du) and surrounding geologic
units within the crater. We then compare the dune composition with potential
provenances: crater floor materials (Cfm), crater ejecta (Ce), central peak
(Ccp), and crater rim and wall (Crw) units. The bulk composition analyzed here
consists of different mineral groups (Rogers & Fergason, 2011) based on the
spectral library used in the unmixing analysis from TES emissivity data.
Spectral unmixing generates eight mineral groups, such as feldspar (both
plagioclase and alkali feldspar), pyroxene (both high-Ca clinopyroxene [HCP]
and low-Ca clinopyroxene [LCP]), high-silica phase (HSP; including amorphous
silica and phyllosilicates), sulfate, olivine, hematite, carbonate, and
quartz. Our compositional modeling results are presented in Table 2.
Individual mineral endmember abundances are listed in appendix section of this
paper (A2). We report the unmixing result of model abundances to nearest
integer value and model error to hundredths decimal place as similarly
reported by previous studies (Rogers et al., 2007). Though the model produces
mineral abundances out to several decimal places, TES unmixing results are
prone to $\mathrm{\sim}$10% uncertainly (Rogers & Christensen, 2007)
regardless of the actual model error reported. Furthermore, the model error in
TES unmixing results is strongly tied to mineral endmember selection. The
precision of mineral abundances and model error we report in this study are
based on what the unmixing model has produced.

Table 2: Our spectral unmixing result for dune (Du), crater floor materials (Cfm), central peak (Cp), and crater rim and wall (Crw) unit within the crater as defined by Goudge et al. (2015). Reported here are the average areal abundances of each mineral group (%) along with the calculated model error (RMSE). All values were normalized to the blackbody (BB) percentage (%). Mineral Group | Dune(Du) | Floor Materials (Cfm) | Crater ejecta (Ce) | Central peak (Ccp) | Rim and wall (Crw)
---|---|---|---|---|---
Feldspar | 36$\mathrm{\pm}$7 | 24$\mathrm{\pm}$6 | 39$\mathrm{\pm}$4 | 27$\mathrm{\pm}$6 | 36$\mathrm{\pm}$7
Pyroxene | 22$\mathrm{\pm}$6 | 29$\mathrm{\pm}$4 | 29$\mathrm{\pm}$4 | 21$\mathrm{\pm}$5 | 21$\mathrm{\pm}$5
Olivine | 12$\mathrm{\pm}$5 | 7$\mathrm{\pm}$5 | 3$\mathrm{\pm}$2 | 8$\mathrm{\pm}$4 | 7$\mathrm{\pm}$3
High-silica phase | 16$\mathrm{\pm}$7 | 23$\mathrm{\pm}$5 | 15$\mathrm{\pm}$4 | 22$\mathrm{\pm}$6 | 21$\mathrm{\pm}$5
Carbonate | 5$\mathrm{\pm}$1 | 3$\mathrm{\pm}$1 | 4$\mathrm{\pm}$1 | 4$\mathrm{\pm}$1 | 3$\mathrm{\pm}$1
Sulfate | 3$\mathrm{\pm}$2 | 10$\mathrm{\pm}$1 | 7$\mathrm{\pm}$1 | 12$\mathrm{\pm}$3 | 9$\mathrm{\pm}$2
Hematite | 4$\mathrm{\pm}$1 | 4$\mathrm{\pm}$1 | 4$\mathrm{\pm}$1 | 6$\mathrm{\pm}$2 | 6$\mathrm{\pm}$1
Quartz | 1$\mathrm{\pm}$2 | 0$\mathrm{\pm}$0 | 0$\mathrm{\pm}$0 | 0$\mathrm{\pm}$0 | 1$\mathrm{\pm}$1
Blackbody | 0.4$\mathrm{\pm}$3 | 0.4$\mathrm{\pm}$2 | 0.2$\mathrm{\pm}$2 | 0.4$\mathrm{\pm}$3 | 0.5$\mathrm{\pm}$2
RMSE | 0.25 | 0.22 | 0.28 | 0.26 | 0.17

Our unmixing results reveal that the dune field is mainly comprised of a
mixture of feldspar, pyroxene, olivine, and other silicate minerals. The Du
unit has both the highest concentration of olivine (12$\mathrm{\pm}$5%) and
lower concentration of high-silica phase minerals (16$\mathrm{\pm}$7%)
compared to surrounding units. For example, the Ccp unit has the second-
highest olivine abundance, modeled at (8$\mathrm{\pm}$4%), but higher
concentration of high-silica phase minerals (22$\mathrm{\pm}$6%). The Ce unit
has the highest concentration of feldspar (39$\mathrm{\pm}$4%) but has the
lowest abundance of olivine (3$\mathrm{\pm}$2%). The Cfm unit has the highest
abundances of high-silica phases (23$\mathrm{\pm}$5%), but the lowest feldspar
concentration (24$\mathrm{\pm}$6%). The average surface emissivity spectra and
modeled spectra for the five geomorphic units within the crater are shown in
Fig. 6.

Figure 6: Average atmospherically corrected TES emissivity spectra (red) for
each of the investigated morphologic units in Hargraves crater, with the best-
fit model spectra (green) derived from the linear unmixing procedures. For
interpretation of the references to color in this figure legend, the reader is
referred to the web version of this article.

We model the relationship between the bulk mineralogic composition of the
geological units within the crater and the dune field. Our results from the
linear regression show the posterior probability distribution of slope and
offset (intercept) of the model fit for each geologic unit (Fig. 7). The
values for the posterior probability distribution of the slope parameter in
Fig. 7 indicate the degree of similarities, and thus the likelihood of
contribution by, each of the surrounding units has to the dune field. The Ce
unit has the highest median slope value of ${0.72}^{+0.01}_{-0.01}$, followed
by the Crw (${0.64}^{+0.02}_{-0.02}$), Cfm ${(0.60}^{+0.01}_{-0.01}$), and Ccp
${(0.54}^{+0.01}_{-0.01}$) units. Based on the median slope values from the
posterior probability distribution, we suggest that the likelihood of the bulk
mineralogy in the Ce unit to the bulk composition of the dune field is the
highest whereas the Ccp has the lowest likelihood within the crater.

Figure 7: Posterior probability distribution of the linear model parameters
slope and offset for each unit within the crater such as a) Cfm, b) Ce, c)
Ccp, and d) Crw. The number value represents the median, including their 16%
quantile as lower 1-$\sigma$ error bar and 84% quantile as an upper 1-$\sigma$
error bar, of linear model’s slope and offset for the respective geologic
units.

We model the bulk compositions of the geological units within the crater to
estimate the possible fraction amount contributed by each unit to the dune
field. We show the result of the posterior probability distribution of the
parameters of our multiple linear regression model in Fig. 8. That is the
values for the posterior probability distribution for each of the unit
indicates the fractional contribution from each of the surrounding units to
the dune field. The result of our modeled parameters show that the posterior
probability distribution of the Ce unit has dominantly the highest median
fraction of ${0.66}^{+0.02}_{-0.02}$, followed by a disproportionally lower
fraction contributed by the Crw (${0.07}^{+0.03}_{-0.03})$ and Cfm
(${0.01}^{+0.01}_{-0.00})$ units. Our model did not predict any fraction
contribution by the Ccp unit as reported in Fig. 8. This indicates that the
dune materials are, likely, predominantly sourced from the adjacent Ce unit
within the crater- consistent with the linear model result described above.
The Crw and Cfm units within the crater contribute to the dune composition
mixture at lower fractions.

To assess the possibility of potential contribution from the geological units
outside the crater, we further run MCMC simulation by including the outside-
crater ejecta, Ce (Outer), unit located on the eastern and south-eastern sides
of the crater (see Fig. 2). We consider the Ce (Outer) unit because it is the
largest geological unit, extents to around $\mathrm{\sim}$30 km from the
crater rim and wall (Crw), outside the crater. The selection of this unit is
also based on the dune field’s raw slipface direction, indicating wind
movement, and its upwind position in respect to the dune field. To minimize
possible spectra selection-induced bias, we used a maximum number (24
observations) of TES spectra from the Ce (Outer) unit. We adopt the same
spectral unmixing procedure to estimate the bulk composition of the Ce (Outer)
unit.

We first fit the linear model to the bulk mineralogy of the Ce (Outer) and
dune field units (please see Fig. 9). The result shows a median slope value of
$({0.52}^{+0.01}_{-0.01}$), the lowest likelihood unit to dune materials among
the units within and beyond the crater. Using the same multiple linear
regression model, we repeat the procedure of MCMC simulation using bulk
mineralogy of the geologic units within the crater along with the Ce (Outer)
unit. Our modeled results of the posterior probability distribution did not
confirm any contribution from the Ce (Outer) unit. The median fraction
contribution from Ce (Outer) unit reports no value and did not alter the
median fraction contribution by geological units within the crater as reported
above (please see Fig. 10). However, it is important to note that this
presumption is based on the modeled results using the MCMC technique adopted
here and may not be true.

Note that, even though the outside crater ejecta unit has the pyroxene and
high-silica phases that are very similar to the dune field, our model is based
on the overall bulk composition of the geologic units, and therefore, the
result of the linear models is not determined by individual mineral endmembers
(phases) alone or a combination of a few. Thus, albeit multiple mineral
endmember phases have similar abundances, the overall bulk mineralogy of the
geologic units determines the fractional contribution to the dune field owing
to the nature of the model used. The bulk mineralogy from TES spectra shows
compositional disparities between the ejecta on the floor of the crater (Ce)
and the outside-crater ejecta blanket, Ce (outer) units. An interpretation of
reasons of the differences between the bulk mineralogy of the ejecta on the
floor of the crater and the crater’s ejecta blanket is beyond the scope of the
current study. A further study can be carried out to understand the reasons
for these disparities by considering the mechanism of crater formation and
subsequent physical and chemical weathering processes, for instance.

For reference, the list of all TES spectra used for the Ce (Outer) unit and
its bulk mineralogy are reported in the appendix section of this paper (A3).
We adopt this strategy as our models predict that the composition of the Ce
(Outer) unit is less significant in interpreting the mineralogy of the dune
field at the crater and, thus, we restrict our subsequent analyses and
discussion to the geological units within the crater units only.

Figure 8: Posterior probability distribution of the model parameters as the
fractions contributed by each unit within the crater. The number value
represents the median, including their 16% quantile as lower 1-$\sigma$ error
bar and 84% quantile as an upper 1-$\sigma$ error bar, of fractions
contributed by respective individual unit. Figure 9: Posterior probability
distribution of the linear model parameters as slope and offset for the Ce
(outer) unit and dune field. The number value represents the median, including
their 16% quantile as lower 1-$\sigma$ error bar and 84% quantile as an upper
1-$\sigma$ error bar, of linear model’s slope and offset for the respective
geologic unit.  Figure 10: Posterior probability distribution of the modeled
parameters as the fractions contributed by each unit within the crater and
outside-crater Ce (Outer) unit. The number value represents the median,
including their 16% quantile as lower 1-$\sigma$ error bar and 84% quantile as
an upper 1-$\sigma$ error bar, of fractions contributed by respective
individual unit.

The composition of the dune field was qualitatively revisited using DCS images
at different band combinations, primarily of 8-7-5 bands, from THEMIS daytime
IR observations (Fig. 11). We used DCS image because it can be used as a proxy
tool for identifying olivine-bearing basalt and silica content (Edwards et
al., 2011; Amador & Bandfield, 2016). The dune field does not show dominance
of a single color or distinctive spectral feature in DCS combinations. The
8-7-5 DCS has a mixture of dark purple, bright magenta, and yellow colors
stretching across the dune field, suggesting a mixture of olivine, pyroxene,
and Fe/Mg-smectite, which is a typical mineral assemblage for the Nili Fossae
area (Goudge et al. (2015) and the references therein). The color variations
of the DCS band combinations of 9-6-4 and 6-4-2 are in agreement with the
interpretation of the band combination 8-7-5. For instance, the dark purple
appearance on crater floor materials in DCS 8-7-5 and 9-6-4 bands is
consistent with the appearance of cyan color in DCS 6-4-2 bands, representing
olivine-bearing basalts (Edwards et al., 2011; Bandfield et al., 2011; Amador
& Bandfield, 2016; Salvatore et al., 2016). The dune field also shows a
mixture of different colors in DCS band combinations of 9-6-4 and 6-4-2,
indicating a mixture of minerals.

Figure 11: 3-panel decorrelation stretch (DCS) images using bands 8-7-5
(left), 9-6-4 (middle), and 6-4-2 (right) for the THEMIS image I02781003
overlain on the radiance image of the same scene. The geomorphic units are
labeled on the DCS images: dune (Du), central peak (Ccp), floor materials
(Cfm), crater ejecta (Ce), and rim and wall materials (Crw) in the color
combination across all three stretches. A mixture of dark purple, bright
magenta, and yellow colors in DCS 8-7-5 bands stretching across the dune field
indicates a mixture of olivine, pyroxene, and Fe/Mg-smectite. The dark purple
appearance on crater floor materials in DCS 8-7-5 and 9-6-4 bands is
consistent with the appearance of cyan color in DCS 6-4-2 bands. The yellowish
appearance (left panel) in the alluvial fan corresponds to an elevated bulk-
silica content.

##  5  Discussion

The thermal inertia results suggest that the dunes are comprised of mostly
medium sand-sized materials mixed with fine and coarse grain sands. Martian
eolian dune fields are likely comprised of particle size of homogeneous
materials within a dune field that reduces the possibility of surface mixture-
induced anisothermality (Edwards et al., 2018). Thus, grain-size analysis from
THEMIS thermal inertia characteristics for this dune field is considered
ideal. We use the thermal inertia values across the dune field to estimate the
grain size distribution. However, thermal inertia can be an incredibly
ambiguous metric of physical properties of martian surface materials, and its
interpretation is challenging due to several features, such as cementation,
mixing of different particle sizes, presence of duricrust, ice exposure, sub-
pixel-scale slope, etc. (Fergason et al., 2006; Piqueux & Christensen, 2009a).
For instance, low thermal inertia ($\mathrm{<}$100 J m- 2 K-1 s-1/2) can
result from unconsolidated dust mantling the surface of the dune field
(Presley & Christensen, 1997a; Fergason et al., 2012). There are also
possibilities where both vertical (layering) and lateral (horizontal mixing)
mixtures (Presley & Christensen, 1997a; Fergason et al., 2006; Piqueux &
Christensen, 2009a) could also produce a low thermal inertia value. We did not
incorporate these alternative explanations for the interpretation of thermal
inertia. Examination of these alternative explanations needs a future study.

The presence of potential mantling, impact cratering on the mantling surface,
and thermal inertia values in the dune field can be an indication of material
induration (Fergason et al., 2012). Dunes within the crater are likely
comprised of weakly indurated surfaces where there might be a consolidation
that occurred in some degree to the surface and mixed with unconsolidated
materials. This interpretation is consistent with the fact that a thermal
inertia ($\mathrm{\sim}$200s) surface likely represents an eolian bedform
overlying on an indurated regolith surface (Fergason et al., 2012). However,
the presence of thin dark streaks on the south-eastern boundary suggests a
different scenario. Based on the relation between dark steaks, dunes, and
subjacent terrain, the dark streaks are interpreted as the result of wind
activities (Edgett, 2002). Gusts of wind can blow the materials out and result
in streaks of deposited sands (Thomas et al., 1981; Geissler et al., 2008)
toward the downwind direction (west-northwest) into the dune field. Thus, the
simplest explanation of these dark streaks can be the indication of the
prevalence of wind activities (Edgett, 2002) within the crater and
mobilization of the dune materials. Along the dune faces, near the crests on
the western boundary, appears to be mega-ripples (i.e., TARs) spaced several
meters. Bright-toned TARs in equatorial areas, for example, Syrtis Major
nearby Hargraves crater, suggest an active ripple migration and presence of
strong wind at the surface (Silvestro et al., 2020). Thus, we hypothesize that
the dune field at Hargraves is potentially active - consistent with the study
of (Emran et al., 2020).

Based on the unmixing results, regression model results, and proximity of
available sources of erodible materials (supported further by 4.2 Morphology
and Stability section), we suggest that the dune materials were likely derived
from physical weathering, especially eolian erosion, predominantly from the
crater ejecta unit at the crater, mixed with a small amount from the crater
floor and crater rim and wall lithologies. This is inferred due to their
similar compositions and proximity to the dune field, situated at upwind
locations in respect to the dune field. Our regression model infers a possible
combination of the fraction contributions (as shown in the posterior
probability distribution) from the geological units within the crater to the
dune field. Albeit the similarities of bulk compositions of surrounding
geological units and the dune materials gives the impression that the dunes
are likely locally sourced, the use of regression models quantitatively
strengthens that claim, because the regression models estimate the degree of
similarities and the combination of fractional contribution by the geologic
units to the dune field. However, we do not rule out the possibility that the
dunes may be sourced from either one location or a varying combination of
multiple provenances e.g., the mixing of two units versus input from all units
within the crater. Furthermore, there is also a possibility that the dune
field was sourced from only one unit that may have undergone alteration or
chemical weathering that could have altered the dune field’s composition.

Erosion and transport processes typically modify the relative modal abundances
of constituent grains, such as the local example here where olivine is
apparently enriched in the dunes. The higher concentration of olivine in the
dunes versus provenance sites cannot be attributed only to the density of the
olivine grains (2.9 gm/cm3), which is slightly less than the companion
pyroxene grains (3.2 to 3.5 gm/cm3) (Lapotre et al., 2017, 2018). Lapotre et
al. (2017) suggested grain size and/or grain shape as mitigating factors in
understanding compositional analysis of other martian dunes. One of these
factors likely causes olivine grains to be preferentially left behind as a lag
deposit, particularly on the windward side of dunes such as those in the study
area and the interdune areas(Greeley et al., 1999, 2002; Jerolmack et al.,
2006; Mangold et al., 2011; Hooper et al., 2012). Rather than being a
pervasive lag deposit, olivine may be sequestered specifically in small
coarse-grained ripples and other fine-scale bedforms of the windward sides and
interdunes, which are very common in dune areas of Mars (Hooper et al., 2012;
Lapotre et al., 2017, 2018).

Olivine grain shape cannot be assessed directly, but the lack of cleavage in
olivine and the pervasive presence of strong cleavage in pyroxenes may result
in more angular and more nearly equidimensional grains as fundamental
crystallographic characteristics of the olivine grain population (Klein &
Philpotts, 2016). Wind-drag across coarse-grained martian ripples has been
suggested to cause infiltration of finer, less equidimensional grains (e.g.,
pyroxenes) (Hooper et al., 2012). These factors, combined with the slight
tendency for olivine grains to be slightly larger than companion pyroxenes in
Mars eolian systems (Lapotre et al., 2017, 2018) suggest that olivine may
comprise a smaller amount of surficial grains in the dune-mantling ripple
deposits envisioned here. However, the presumption that the olivine-pyroxene
grain size and shape differences leading to pyroxene filling pore spaces is
based on our modeled results, and other possible explanations may also be
plausible.

Orbiter-based observations from spectroscopic measurements suggest that active
dune fields and sand sheets are largely comprised of mafic minerals, such as
plagioclase feldspar, high and low calcium pyroxene, and olivine (Achilles et
al., 2017; Cousin et al., 2017; Ehlmann et al., 2017; Lapotre et al., 2017;
Rampe et al., 2018). A strong olivine signature has also been reported
elsewhere on Mars in a barchanoidal type dunes along the upwind margins of
dune fields, for instance, in the Bagnold dune field as observed by high-
resolution VSWIR orbital data from the Compact Reconnaissance Imaging
Spectrometer for Mars (Seelos et al., 2014; Lapotre et al., 2017; Rampe et
al., 2018). The barchanoidal type dunes at Hargraves crater may be another
instance of a dune field with elevated olivine content. We model average
spectra extracted over the entire dune field (not separately from upwind and
downwind margins) from a comparatively lower resolution TES footprint and,
thus, we cannot confirm if there is olivine enrichment along the upwind
margins. However, this may be the scenario that enriched olivine content along
the upwind margins of the barchanoidal dunes at Hargraves contributes to an
enhanced olivine abundance in our modeled composition.

A lower abundance of high-silica phase is reported in the dune field. Martian
sands source from local to deeper craters typically contain a proportionally
lower abundance of high-silica phase (Fenton et al., 2019b). Dune fields with
a lower abundance of high-silica phase are typically found on deeper crater
floors whereas those with an elevated abundance ($\mathrm{>}$25%) are found on
very shallow crater floor or intercrater plains (Fenton et al., 2019b). This
statement is consistent with the geographical position of Hargraves crater and
the relatively low abundance of high-silica within the dune field. Abundance
of high-silica phase can be correlated with morphological characteristics such
as stabilization features and bedform activity. Dune fields with elevated
abundances of high-silica phase are found in southern high latitudes (60-70∘S)
where morphological characteristics of increasing stability are common (Fenton
et al., 2019b). With a stability index of 2, the dune field within Hargraves
shows a relatively lower abundance of high-silica content. The yellowish
appearance in DCS 8-7-5 for the alluvial fan unit represents a higher
concentration of bulk-silica content (Amador & Bandfield, 2016). We assume
that the differences in silica content between the alluvial fan and the dune
field are due to the different geologic processes that are responsible in
forming these features, as well as their source material. The alluvial fan is
likely sourced from the crater rim or outside the crater and deposited on the
crater floor through fluvial processes.

Having a paucity of high-resolution observations from the Compact
Reconnaissance Imaging Spectrometer for Mars (CRISM) (Murchie et al., 2007)
across the crater floor covering all geologic units, we could not analyze
reflectance spectra for the surface mineralogy. We did not use reflectance
data from The Observatoire pour la Minéralogie, l’Eau, les Glaces et
l’Activité (OMEGA) (Bibring et al., 2004) sensor because the instrument has a
spatial resolution of 300 m to 5 km/pixel which is comparatively lower spatial
resolution than both CRISM and THEMIS.

Bulk mineralogy in this area from TES data by Salvatore et al. (2018)
indicates that the crater floor unit (combining the Cfm and Ccp units) has a
roughly comparable phyllosilicate and amorphous component (PAC) to nearby
Syrtis basalt. Their study also reported a comparable amount of plagioclase,
pyroxene (both high and low calcium), and olivine to this study. There is a
slight discrepancy between our result and Salvatore et al. (2018) when
describing the bulk mineralogy of crater floor materials (for example, olivine
abundance of 7$\mathrm{\pm}$5% vs 7$\mathrm{\pm}$3%, respectively), which is
likely because they averaged spectra from the central peak and crater floor
units into a single unit, as well as using different unmixing algorithms. Due
to a scarcity of quality CRISM data across the crater floor, we cannot assess
about finer scale individual mineral distributions for the units
independently. However, the DCS image at different band combinations and the
result from bulk mineralogy of TES data provide substantial information about
the constituent materials of the dune field and its surrounding morphologic
units.

##  6 Conclusion

We analyzed the thermal infrared response of the dune materials to identify
the grain-size distribution and surface mineral composition of the dune field
at Hargraves crater. We hypothesize that the surrounding units within the
crater are the provenances for the dune field, and this paper provided modeled
compositions for these units. Accordingly, we compared the dune mineralogy
with the composition of surrounding geological units.

We found the average thermal inertia for the dune materials to be
238$\mathrm{\pm}$17 Jm-2K-1s-0.5, indicating a surface composed of an average
effective grain-size of $\mathrm{\sim}$391$\mathrm{\pm}$172 $\mu$m. This size
range indicates that the dune field is likely comprised of mostly medium sand-
sized materials mixed with fine and coarse grain sands. The dunes are likely
comprised of weakly indurated surfaces, mixed with unconsolidated materials.
In this study, the determination of particle size was based on the derived
thermal inertia of surface dune materials. A future study can be carried out
to investigate the possibility of vertical (layering) and lateral (horizontal
mixing) mixtures in the dune field.

The results from our TES compositional modeling and THEMIS DCS image analysis
suggest that the dune materials are comprised of a mixture of feldspar,
olivine, pyroxene, and with relatively lower bulk-silica content than the
surrounding geologic units. Compositional information of the dune materials
and the surrounding geologic units suggest that the dunes were likely sourced
locally within the crater, predominantly from the crater ejecta, mixed with a
small amount from the crater floor and crater rim and wall lithologies. The
erosion and transport process likely have modified the constituent grains to
some degree. We consider that the dune materials were sourced through physical
weathering of the inferred provenances, based on our TES unmixing results. It
is likely that through erosional and transport processes that constituent
grains were modified, however, a future study is needed to investigate the
possibility of alteration or chemical weathering of source materials.

Comparison of bulk mineralogy of dunes and surrounding geologic units provide
useful clues on local versus distant provenances of aeolian materials.
Moreover, the identification of the source provenances paves the way for a
better understanding of the climate dynamics in the area of interest. For
instance, the dunes at Hargraves crater are likely sourced from the geologic
units within the crater- indicating the dominance of local-scale wind movement
inside the crater and responsible for the deposition of sands in the dune
field. It also provides insight into the material transport mechanism and
erosion pattern within the crater. Since the dunes are likely locally sourced,
the influence of regional and global scale dust materials is minimal in the
dune field. This result coupled with the dune morphology and slipface
orientation of the dunes will help in future climatic studies and geologic
processes acting upon this region. On top of that, the understanding of the
transport mechanism of dune materials from source to sink may provide
information on recent climate changes in the region (Fenton, 2005).

This study expands our knowledge in understanding the structure, composition,
and eolian transport regime of the dune field at Hargraves crater through
investigating grain-size, induration, modification by non-eolian processes,
and dune field stability. In addition, our compositional modeling helped to
constrain likely provenances and whether the dune field was sourced locally,
regionally, or globally. We interpret an active and weakly indurated,
simultaneously, martian dune field and showed the avenues for analyzing dunes
that appear to exist on a spectrum exhibiting features of both. Our approach
provides a systematic guide for interpretation and helps in expanding our
understanding of both active and weakly indurated eolian environments from the
observations of sands on Mars.

The authors are grateful for all of those involved in the 2001 Mars Odyssey,
Mars Global Surveyor (MGS), and Mars Reconnaissance Orbiter (MRO) spacecraft
missions for targeting, collecting, and archiving THEMIS, TES, CTX, and HiRISE
datasets. We would like to thank Bethany Ehlmann and Amber Gullikson for
providing useful information on data processing. The authors would like to
thank two anonymous reviewers for their useful comments.

##  Appendix A Appendix information

List of all used TES observations (A1) and spectral unmixing results of
individual mineral endmembers (A2) for the geologic units within the crater,
and a list of all TES spectra used for the Ce (Outer) unit and its bulk
mineralogy (A3) are reported in appendix section of this paper.

### A.1

A list of all TES observations used in this investigation from the geologic
units within the crater. For each unit, all TES observations were averaged and
treated as a single thermal infrared spectrum throughout the entirety of the
study (see manuscript for more information regarding the analysis of TES
observations).

Table 3: List of all TES observations used in this investigation for the geologic units within the crater. OCK = Orbit Counter Keeper, ICK = Incremental Counter Keeper, and DET = Individual Detector. Unit |  | TES Observations |
---|---|---|---
| OCK | ICK | DET
Dune (Du) | 3094 | 1950 | 3
| 3094 | 1950 | 6
| 3094 | 1949 | 3

Crater ejecta (Ce) | 3094 | 1947 | 6
| 3094 | 1947 | 5
| 3094 | 1947 | 3

Crater central peak (Ccp) | 3421 | 1942 | 2
| 3421 | 1942 | 1
| 3421 | 1941 | 4
| 3421 | 1942 | 6

Rim and wall (Crw) | 3421 | 1937 | 5
| 3421 | 1938 | 1
| 3421 | 1938 | 2
| 3421 | 1938 | 3
| 3094 | 1946 | 1
| 3421 | 1937 | 6
| 3094 | 1946 | 2
| 3421 | 1938 | 6

Crater floor materials (Cfm) | 3421 | 1940 | 1
| 3421 | 1939 | 5
| 3421 | 1940 | 2
| 3421 | 1940 | 4
| 3421 | 1940 | 5
| 3421 | 1939 | 6

### A.2

Modeled spectral unmixing results of individual mineral endmembers for the
geologic units within the crater. Reported here are the average areal
abundances of endmembers (%) and normalized for blackbody (BB) % along with
model error (RMSE).

####  A.2.1 Dune (Du) unit

Table 4: Mineral endmembers for the Du unit within the crater. Endmember | Abundance (%) | Normalized for BB (%)
---|---|---
Bronzite NMNH-93527 | 10.20 +/- 3.79 | 16.99 +/- 6.31
Shocked An 25.5 GPa | 7.90 +/- 6.87 | 13.16 +/- 11.45
Bytownite WAR-1384 | 7.43 +/- 6.95 | 12.38 +/- 11.58
Swy-1 $\mathrm{<}$ 0.2 mic | 6.28 +/- 5.29 | 10.46 +/- 8.81
KI 3362 Fo60 | 5.45 +/- 9.73 | 9.08 +/- 16.20
Anorthite BUR-340 | 4.07 +/- 5.31 | 6.78 +/- 8.84
Dolomite C20 | 2.99 +/- 0.52 | 4.98 +/- 0.87
Avg. Lindsley pigeonite | 2.61 +/- 4.15 | 4.35 +/- 6.91
Average Martian Hematite | 2.55 +/- 0.86 | 4.24 +/- 1.44
Microcline BUR-3460 | 1.98 +/- 2.12 | 3.29 +/- 3.53
Kieserite Kieserite | 1.84 +/- 1.11 | 3.06 +/- 1.84
Crystalline heulandite (z | 1.77 +/- 5.49 | 2.94 +/- 9.14
02-011 Opal A | 1.70 +/- 1.50 | 2.83 +/- 2.50
Quartz BUR-4120 | 0.80 +/- 1.07 | 1.34 +/- 1.78
KI 3115 Fo68 | 0.75 +/- 6.68 | 1.24 +/- 11.13
Fayalite WAR-RGFAY01 | 0.66 +/- 2.62 | 1.11 +/- 4.36
KI 3373 Fo35 | 0.50 +/- 5.23 | 0.83 +/- 8.71
Augite NMHN-122302 | 0.32 +/- 3.78 | 0.54 +/- 6.30
Oligoclase BUR-060D | 0.15 +/- 5.11 | 0.25 +/- 8.51
Enstatite HS-9.4B | 0.08 +/- 3.04 | 0.14 +/- 5.06
| Sum (BB included) = 100.02 |
| Sum (BB normalized) = 100.00 |
| Blackbody Abundance = 39.99 +/- 3.21 |
| RMS Error (%) = 0.25 |

####  A.2.2 Crater ejecta (Ce) unit

Table 5: Mineral endmembers for the Ce unit within the crater. Endmember | Abundance (%) | Normalized for BB (%)
---|---|---
Labradorite EIBUR-3080A | 13.72 +/- 7.70 | 17.10 +/- 9.59
Avg. Lindsley pigeonite | 12.05 +/- 3.55 | 15.02 +/- 4.43
Bronzite NMNH-93527 | 11.28 +/- 1.85 | 14.06 +/- 2.30
Bytownite WAR-1384 | 10.91 +/- 7.98 | 13.59 +/- 9.94
Crystalline heulandite (z | 10.78 +/- 4.96 | 13.44 +/- 6.18
Anorthite BUR-340 | 6.34 +/- 3.79 | 7.91 +/- 4.72
Gypsum (Satin spar) IS6 | 3.29 +/- 1.21 | 4.10 +/- 1.50
Average Martian Hematite | 3.05 +/- 0.73 | 3.81 +/- 0.91
Dolomite C20 | 2.92 +/- 0.37 | 3.65 +/- 0.46
Kieserite Kieserite | 2.39 +/- 0.81 | 2.98 +/- 1.01
KI 3362 Fo60 | 1.50 +/- 2.69 | 1.87 +/- 3.35
Crystalline stilbite (zeo | 0.76 +/- 2.33 | 0.94 +/- 2.90
KI 3373 Fo35 | 0.62 +/- 2.86 | 0.77+/- 3.56
02-011 Opal A | 0.62 +/- 0.93 | 0.77 +/- 1.16
| Sum (BB included) = 99.99 |
| Sum (BB normalized) = 100.00 |
| Blackbody Abundance = 19.77 +/- 2.51 |
| RMS Error (%) = 0.28 |

####  A.2.3 Crater central peak (Ccp) unit

Table 6: Mineral endmembers for the Ccp unit within the crater. Endmember | Abundance (%) | Normalized for BB (%)
---|---|---
Anorthite BUR-340 | 9.83 +/- 4.53 | 17.15 +/- 7.91
Bronzite NMNH-93527 | 8.94 +/- 2.22 | 15.60 +/- 3.87
Crystalline heulandite (z | 7.82 +/- 7.39 | 13.63 +/- 12.90
Bytownite WAR-1384 | 4.62 +/- 7.01 | 8.06 +/- 12.22
Gypsum (Satin spar) IS6 | 3.43 +/- 1.60 | 5.98 +/- 2.79
Swy-1 $\mathrm{<}$ 0.2 mic | 3.25 +/- 5.19 | 5.67 +/- 9.05
Avg. Lindsley pigeonite | 3.21 +/- 3.77 | 5.60 +/- 6.58
Average Martian Hematite | 3.19 +/- 0.92 | 5.56 +/- 1.61
Kieserite Kieserite | 3.09 +/- 1.08 | 5.38 +/- 1.88
KI 3115 Fo68 | 3.00 +/- 1.74 | 5.24 +/- 3.04
Dolomite C20 | 2.36 +/- 0.42 | 4.11 +/- 0.73
KI 3373 Fo35 | 1.64 +/- 2.46 | 2.86 +/- 4.29
Microcline BUR-3460 | 1.05 +/- 2.34 | 1.84 +/- 4.08
Crystalline stilbite (zeo | 0.73 +/- 3.69 | 1.28 +/- 6.43
saponite $\mathrm{<}$0.2 mic | 0.66 +/- 1.04 | 1.14 +/- 1.81
02-011 Opal A | 0.39 +/- 1.08 | 0.68 +/- 1.89
Anhydrite S9 | 0.12 +/- 0.80 | 0.21 +/- 1.40
| Sum (BB included) = 99.88 |
| Sum (BB normalized) = 100.00 |
| Blackbody Abundance = 42.56 +/- 2.63 |
| RMS Error (%) = 0.26 |

####  A.2.4 Crater floor material (Cfm) unit

Table 7: Mineral endmembers for the Cfm unit within the crater. Endmember | Abundance (%) | Normalized for BB (%)
---|---|---
Bytownite WAR-1384 | 11.84 +/- 4.63 | 19.38 +/- 7.58
Bronzite NMNH-93527 | 11.23 +/- 1.84 | 18.39 +/- 3.02
Crystalline heulandite (z | 8.71 +/- 4.12 | 14.27 +/- 6.75
Augite NMHN-122302 | 3.75 +/- 2.64 | 6.15 +/- 4.32
Kieserite Kieserite | 3.35 +/- 0.66 | 5.48 +/- 1.09
KI 3115 Fo68 | 3.21 +/- 1.55 | 5.25 +/- 2.54
Swy-1 $\mathrm{<}$ 0.2 mic | 3.09 +/- 4.16 | 5.06 +/- 6.81
Anorthite BUR-340 | 3.02 +/- 3.83 | 4.95 +/- 6.26
Avg. Lindsley pigeonite | 3.02 +/- 3.36 | 4.94 +/- 5.51
Gypsum (Satin spar) IS6 | 2.69 +/- 0.97 | 4.40 +/- 1.59
Average Martian Hematite | 2.28 +/- 0.88 | 3.74 +/- 1.44
Crystalline stilbite (zeo | 1.67 +/- 2.45 | 2.74 +/- 4.00
Dolomite C20 | 1.56 +/- 0.40 | 2.55 +/- 0.66
Fayalite WAR-RGFAY01 | 0.92 +/- 4.25 | 1.51 +/- 6.97
K-rich Glass | 0.40 +/- 2.24 | 0.65 +/- 3.66
KI 3373 Fo35 | 0.25 +/- 3.64 | 0.41 +/- 5.97
KI 3008 Fo10 | 0.10 +/- 4.52 | 0.16 +/- 7.40
| Sum (BB included) = 100.00 |
| Sum (BB normalized) = 100.00 |
| Blackbody Abundance = 38.92 +/- 2.22 |
| RMS Error (%) = 0.22 |

####  A.2.5 Crater rim and wall (Crw) unit

Table 8: Mineral endmembers for the Crw unit within the crater. Endmember | Abundance (%) | Normalized for BB (%)
---|---|---
Bytownite WAR-1384 | 10.21 +/- 4.85 | 22.27 +/- 10.58
Crystalline heulandite (z | 6.54 +/- 4.10 | 14.26 +/- 8.93
Shocked An 21 GPa | 4.97 +/- 4.42 | 10.85 +/- 9.64
Bronzite NMNH-93527 | 4.79 +/- 2.66 | 10.45 +/- 5.81
Avg. Lindsley pigeonite | 4.33 +/- 2.34 | 9.45 +/- 5.11
Swy-1 $\mathrm{<}$ 0.2 mic | 3.32 +/- 3.81 | 7.23 +/- 8.30
Average Martian Hematite | 2.72+/- 0.60 | 5.94 +/- 1.31
Kieserite Kieserite | 2.27 +/- 0.71 | 4.95 +/- 1.55
Gypsum (Satin spar) IS6 | 1.68 +/- 1.01 | 3.67 +/- 2.20
Dolomite C20 | 1.40 +/- 0.28 | 3.05 +/- 0.61
Anorthite BUR-340 | 1.12 +/- 3.91 | 2.43 +/- 8.52
KI 3362 Fo60 | 0.91 +/- 3.39 | 1.99 +/- 7.40
Quartz BUR-4120 | 0.50 +/- 0.45 | 1.08 +/- 0.98
KI 3115 Fo68 | 0.47 +/- 2.78 | 1.03 +/- 6.07
Microcline BUR-3460 | 0.30 +/- 1.59 | 0.65+/- 3.47
Enstatite HS-9.4B | 0.29 +/- 1.94 | 0.62 +/- 4.24
Albite WAR-0235 | 0.02 +/- 1.38 | 0.04 +/- 3.01
Anhydrite S9 | 0.01 +/- 0.54 | 0.03 +/- 1.17
| Sum (BB included) = 99.97 |
| Sum (BB normalized) = 100.00 |
| Blackbody Abundance = 54.12 +/- 2.32 |
| RMS Error (%) = 0.17 |

### A.3

#### A.3.1

A list of all TES observations used in this investigation for the Ce (Outer)
unit. All TES observations were averaged and treated as a single thermal
infrared spectrum throughout the entirety of the study (see manuscript for
more information regarding the analysis of TES observations).

Table 9: List of all TES observations used in this investigation for the Ce (Outer) unit. OCK = Orbit Counter Keeper, ICK = Incremental Counter Keeper, and DET = Individual Detector. Unit |  |  |  | TES | Observations |  |  |  |
---|---|---|---|---|---|---|---|---|---
| OCK | ICK | DET | OCK | ICK | DET | OCK | ICK | DET
| 3836 | 1944 | 1 | 3836 | 1944 | 4 | 3836 | 1943 | 5
| 3836 | 1944 | 3 | 3836 | 1942 | 4 | 3836 | 1945 | 4
| 3836 | 1942 | 3 | 3836 | 1944 | 6 | 3836 | 1945 | 6
Ce (Outer) | 3836 | 1942 | 2 | 3836 | 1942 | 6 | 3836 | 1948 | 1
| 3836 | 1944 | 5 | 3836 | 1945 | 1 | 3836 | 1947 | 1
| 3836 | 1942 | 5 | 3836 | 1945 | 3 | 3836 | 1949 | 1
| 3836 | 1949 | 2 | 3836 | 1949 | 5 | 3836 | 1948 | 5
| 3836 | 1948 | 4 | 3836 | 1948 | 6 | 3836 | 1949 | 3

#### A.3.2

Table 10: Our spectral unmixing result for Ce (outer) unit. Reported here are
the average areal abundances of each mineral group (%) along with the
calculated model error (RMSE). All values were normalized to the blackbody
(BB) percentage (%).

Groups | Feldspar | Pyroxene | Olivine | HSP | Carbonate | Sulfate | Hematite | Quartz | BB | RMSE
---|---|---|---|---|---|---|---|---|---|---
% | 28$\mathrm{\pm}$4 | 25$\mathrm{\pm}$4 | 8$\mathrm{\pm}$2 | 17$\mathrm{\pm}$6 | 3$\mathrm{\pm}$1 | 9$\mathrm{\pm}$2 | 8$\mathrm{\pm}$2 | 2$\mathrm{\pm}$1 | 0.26$\mathrm{\pm}$3 | 0.21

Note. — HSP = High Silica Phase

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