# The M3 project: 3 – Global abundance distribution of hydrated silicates at Mars

Lucie Riu, John Carter, François Poulet

► To cite this version:

Lucie Riu, John Carter, François Poulet. The M3 project: 3 – Global abundance distribution of hydrated silicates at Mars. Icarus, 2022, 374 (4), pp.114809. 10.1016/j.icarus.2021.114809. hal-04500249

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# The M3 project: 3 – Global abundance distribution of hydrated silicates at

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## Mars

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Lucie Riu¹,², John Carter²,³, François Poulet²

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### Affiliations

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¹Institut of Space and Astronautical Science (ISAS), Japanese Aerospace eXploration Agency

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(JAXA), Sagamihara, Japan

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²Institut d'Astrophysique Spatiale (IAS), Université Paris-Saclay, Orsay, France

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³Laboratoire d'Astrophysique de Marseille (LAM), Marseille, France

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### Abstract

This paper is the third paper of a series that provides the modal mineralogy of the Martian surface (M³ project) at the global scale using near-infrared hyperspectral imagery. Numerous locations at the surface of Mars have previously been identified to harbor hydrated minerals which offer unique insights on the past water activity at the red planet. A radiative transfer model has been used to reproduce the spectra of these locations, based on the OMEGA instrument (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité). Here we present the methodology applied to derive the hydrated quantitative composition and the first global compositional maps of hydrated minerals at Mars. Millions of spectra have been modelled to extract the modal composition of the hydrated locations, excluding sulfate-rich units. The lithology is summarized with 11 compositional maps of hydrated minerals at global scale at a sub-kilometer resolution. The hydrated mineralogy is dominated by an end-member of Fe-hydroxide, Fe- and Al-phyllosilicates and Fe/Mg micas which have on average an abundance > 6vol% and are spread globally on the identified regions. Locally, spots with high abundance (>20vol%) of Al-smectite and Chlorite are also identified. The abundance of
24 hydrated minerals is highest in Marwth Vallis, Nili Fossae and Meridiani Planum. However, 25 the primary minerals almost always account for more than 50% of the composition. The 26 modelling offers an opportunity to do local analysis of prospective landing sites and prepare 27 for the upcoming landed missions. In the landing site for the ExoMars2022 and Mars2020 28 rovers, the obtained composition is in agreement with the expected detected mineralogy 29 which demonstrates the robustness of the model and also offers a representation of the 30 compositional variability. These global maps open the way to follow-up studies that will 31 provide an in-depth characterization of the compositional gradients in local settings. 32 Additionally, these compositional maps can 1) be used to calculate the water content namely 33 potential amount of water stored within the quantified minerals and 2) provide information 34 about the ISRU potential.

35 **1- Introduction**

36 Mars has been one of the key targets in our Solar System to study the evolution of planetary 37 surfaces. During the last decades, numerous orbital and *in situ* missions have greatly 38 contributed to our understanding of the climatic and geological evolution of the planet. 39 Studying its mineralogy has been an important asset to build the history of Mars. A prime 40 focus of past, ongoing and future investigations has been set on the aqueous mineralogy. In 41 addition to providing information on the past liquid water environments on and near the 42 surface, altered phases have an exobiological potential. As such, they play an important role 43 in driving the selection of landing sites for potential future roving and human missions to 44 Mars. Orbital studies of aqueous minerals (e.g., Bibring et al., 2006; Carter et al., 2013; 45 Murchie et al., 2009; Ehlmann et al., 2011; Poulet et al., 2005 amongst others) have shown 46 that globally the Martian surface displays various aqueous mineral assemblages dominated by 47 hydrous clays, hydroxides, hydrated silica, salts and other minor altered phases. Although 48 globally the surface of Mars is basalt-dominated (Bandfield et al., 2000; Christensen et al.,
2000; Mustard et al., 2005; Rogers et al., 2007; Koeppen and Hamilton, 2009; Poulet et al., 2009b), the great diversity of composition and geologic context have helped trace the water history on Mars (Bishop et al., 2013; Carter et al., 2013; Ehlmann et al., 2009 amongst others).

Those past mineral mapping campaigns have already provided valuable information on the history of water at Mars, from both orbital and *in situ* approaches. Orbital mapping of the aqueous mineralogy is limited by its spatial resolution and sensitivity, but provides broader sampling of the surface of Mars, therefore representing all types of terrains and aqueous environments when compared to the in-depth but local view provided by landed missions.

The strengths of orbital mapping have yet to be fully exploited however, in particular the potential to provide the precise mineral composition globally at Mars. In the thermal infrared, global maps of anhydrous mineral abundances have been produced at a scale of ~3km/px with the TES instrument (Bandfield et al., 2000; Rogers and Christensen, 2007; Rogers and Hamilton, 2015). Although these modal mineralogical maps provide contextual studies of the mafic history of Mars, the hydrated mineral abundances are poorly discussed. Moreover, the lower TIR resolution is significantly larger than those of the analysis presented here.

Therefore, a new catalogue of the Martian chemically altered aqueous mineralogy is being built to overcome some of these limitations and provide a more complete overview of the Martian altered mineralogy: the MOCCAS project (a Mars Orbital Catalogue of Chemical Alteration Signatures, Carter et al. 2020; in prep). This catalogue, built from the OMEGA/Mars Express and CRISM/MRO orbital datasets, provides a more robust and statistical approach to mapping aqueous minerals on Mars. The results from MOCCAS are used as the starting point for this study. Here, we do not aim at identifying new hydrated mineral exposures (as we rely on the MOCCAS dataset for that), but rather quantifying those already identified.
Based on these latest *qualitative* maps of mineral detections and their classification provided by MOCCAS, the next challenges are now to have a more *quantitative* view of the altered mineralogy. Previous work showed that orbital near-infrared spectroscopy could be used not only to detect but also to quantify the mineralogy of hydrated deposits of phyllosilicates and sulfates on Mars (Poulet et al., 2014; Poulet et al., 2008; Liu et al., 2016) by using radiative transfer modelling (e.g., Shkuratov et al., 1999) to invert the surface reflectance spectra of Mars. Providing access to the modal composition allows deriving individual mineral and chemical abundances at the surface of Mars, including potential resources such as water. That additional information has the potential to yield additional insight on the past water-bearing environments and the coupled geological-aqueous evolution of Mars by better constraining the formation processes of the various mineralogical assemblages observed in their geological context.

So far, local-scale abundance studies of phyllosilicate-bearing terrains have enabled the characterization of potential landing sites for landed missions (including of the Curiosity/MSL and Perseverance/Mars2020 rovers) such as at Gale crater, Marwth Vallis and Nili Fossae. The modelling predicted, for the richest regions, up to 30 vol% of phyllosilicates on the surface and abundant hydroxides (10s vol%) (Poulet et al., 2014; Poulet et al., 2018). This quantitative approach has the added value of providing an estimation of the resources available at the site for future In-Situ Resource Utilization (ISRU). In particular, the water content at the top surface of Mars can be derived by converting the mineralogical composition to elemental composition and thus to retrieve H₂O content (in wt%; Audouard et al., 2014; Milliken et al., 2007).

This work is a follow-up of those previous local scale quantitative analyses, which we now expand to all of Mars, thus encompassing more diverse aqueous settings. It also benefits from previous global quantitative analyses of the Martian mafic mineralogy (Riu et al., 2019) from
which the radiative transfer model was adapted to meet the requirements for an application to
hydrated minerals and large data sets. This study is restricted to those hydrated minerals for
which optical constants (OC or OCs hereafter) are available in the literature, it is thus for now
not applied to hydrated sulfate salt detections or anhydrous chloride salt detections. While the
detection database uses both OMEGA and CRISM datasets, only detections made with the
OMEGA datasets are modelled for their abundances at this point. For this first global
analysis, we excluded CRISM data due to time constraints on the modelling (e.g., limitation
on the number of spectra to be modelled). Before applying the model systematically at the
global scale and at the OMEGA pixel scale, a series of tests to select the end-members to be
modelled and evaluations of the model parameters sensibility was performed (Sections 2 and
3). The results on the compositional maps of hydrated minerals are presented then discussed
in Sections 4 and 5.

2- Methodology

112 2.1- Data selection

113 We use calibrated hyperspectral image-cubes of the C-channel of the OMEGA instrument,
114 ranging from 1 to 2.5 µm. The calibration pipeline and following updates for 13-level
115 OMEGA data can be found in Audouard et al., 2014. The spectral resolution is fixed by the
116 OMEGA instrument to 13 nm, the optical indexes of the end-members are resampled to the
117 same resolution for the modelling. The spatial resolution used is the raw OMEGA resolution
118 that varies from one hyperspectral cube to the next from ~300m/px to 4km/px depending on
119 the spacecraft altitude.

120 Figure 1 describes the process set up to derive the global maps of abundances from processed
121 OMEGA images-cubes (13-level data). The different steps will be discussed and described in
122 the following subsections.
<figure><img src="image_2.png" /><figcaption>Figure 1 - Chart describing the methodology to obtain global hydrated mineral abundance maps and a global map of the content of water stored in the minerals. Reads from top to bottom.</figcaption></figure>

## 2.2- Prior selection

Instead of assessing the mineral content of each OMEGA pixel and then selecting the endmembers for the model at that same pixel, we adopt a less time-consuming and arguably more reliable approach: mineral priors. The selection of the aqueous mineral end-members to be modelled is hence based on a preceding estimate of the mineralogical content of the surface, hereafter called our mineral “priors” (“PRIORS” step in Figure 1). This estimate is qualitative and not quantitative: it provides the model with what are assumed to be the most likely hydrous mineral endmembers to be present, based on the steps described in this section.
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Here we summarize the detection, mapping and mineral assignment procedure of the MOCCAS database which is then used as mineral priors for this work. A companion paper will describe the MOCCAS project at length (Carter et al., in prep).

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- Step 1. The calibrated radiance or I/F data-cube is reduced according to the strategy described in Carter et al., 2013. The output are spectral cubes of ratioed reflectance, which provides the reflectance of each pixel normalized to a local average.

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- Step 2. We build a series of spectral index maps that are tuned to i) the most common aqueous mineral bands and ii) the most common mineral contaminants or other sources of spectral artefacts. That second set of map is used algebraically against the first set, so as to limit the amount of false positives. We build a final single map that combines the two sets of spectral index maps. That combination is done by applying calibrated coefficients to each spectral index map, which are sometimes adjusted empirically when the need arises (e.g. dusty surface or frost-rich region).

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- Step 3. That final map typically has clusters of contiguous pixels and isolated pixels elsewhere that are randomly distributed. We consider that the isolated pixels outside of the clusters are not aqueous minerals. We set a cut-off on the cluster size starting at 3 pixels minimum, regardless of the data product and its resolution.

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- Step 4. With the final map "cleaned" of isolated pixels or clusters of 2 pixels, we now run a cluster detection algorithm which extracts the pixel positions and size of each identified cluster.

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- Step 5. For each identified cluster, we compute the median spectrum of that cluster.

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- Step 6. The median spectrum is visually inspected. We determine whether this cluster is indeed an aqueous mineral deposit or not.

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- Step 7. If the cluster is confirmed, we further classify it according to the board spectral-mineral classes. This is done manually by comparing the spectral shape and absorptions to known spectral analogues. The classification is kept broad to avoid interpretation biases and spectral degeneracies between minerals which spectral features are similar.

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It is important to emphasize that none of the mineral detections from the MOCCAS starting database fall within a single OMEGA pixel, instead these are required to be found as clusters of contiguous (>1) OMEGA pixels which share similar spectral properties to an extent that they were mapped as a cluster of aqueous minerals. These clusters are called ROIs (Regions of Interests) hereafter. The first source of information on the mineral priors comes from the mineral classification of the MOCCAS database itself, which attributes to each ROI one or several broad classes of aqueous minerals. Here, 3 broad classes are distinguished and summarized in Table 1: a “red”, “cyan” and “orange” class. All the mineral candidates listed within each class is thus added to the list of potential priors according to each ROI’s class.

Because these broad classes may not always encompass the full diversity of aqueous minerals present within a ROI, and because occasional classification errors may incorrectly assess the prior mineralogy, we needed refined estimates of the endmembers. We hence implement a second step to the prior selection, which aims at adding additional realistic candidate priors which may have been overlooked with the first approach. To do so, the following steps are implemented:

1) We re-process OMEGA data, deriving the median OMEGA surface reflectance spectrum of each ROI. This “ROI-median” spectrum is then ratioed to a median spectrum averaged over nearby pixels which do not exhibit aqueous mineral signatures. The denominator spectrum is assessed using an iterative algorithm: our set of spectral parameter maps, that are kept as broad as possible, are built so as to encompass as many aqueous deposits as possible. Only then the pixels which are below their detection threshold are selected. The denominator spectrum is applied to all pixels, and the process is repeated so that the final denominator spectrum is as devoid as possible of aqueous mineral contaminants.
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This removes calibration residuals and other systematic noise, further highlighting the subtle
spectral features of the aqueous minerals. This is a standard approach for processing OMEGA
or CRISM data as described in the previous paragraphs.

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2) The resulting ratio spectrum is further processed to remove its spectral continuum in order
to highlight the presence of absorption features diagnostic of specific aqueous minerals. We
use a 1D starlet transform to remove the low-frequency spectral continuum.

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3) A spectral linear unmixing algorithm is implemented. We use a pool of laboratory analogue
reflectance spectra chosen from an exhaustive library of spectral analogues. The list of
potential initial spectra used for the linear unmixing is provided in supplementary material
(Section S1). These typically originate from the RELAB, ASTER, USGS libraries as well as
internally acquired spectra. We retain the analogue spectra which we know best fit the
Martian counterparts based on knowledge from detailed studies using CRISM at high
spatial/spectral resolution. Each laboratory spectrum is resampled to the CRISM spectral
sampling, and we similarly apply the continuum-removal algorithm on them.

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3a) As a first step we compare the continuum-removed ROI-median spectrum from Mars
to a linear combination of the laboratory analogue spectra, also continuum-removed. This is
done through a fitting procedure based on a Levenberg-Marquardt least-squares minimization
algorithm. Weighting is applied to the fit, retaining wavelengths in the range [1.1-2.6] µm and
only those where the laboratory spectra exhibit absorption features. The weight is highly
dependent on the absorption depth of said lab spectra (to the power of 6). This is justified by
the fact that we want to detect and match as many absorption bands as possible, while
discarding the effect of the spectral continuum shape. A set of 5 best-fit mineral candidates is
down-selected from this fit.

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3b) The above approach selected only aqueous minerals which absorption bands fit the position of the absorption bands of the OMEGA data. The drawback is that some degeneracies can occur, as some minerals have similar absorption band positions but their spectral shape and band depths vary greatly. Generally speaking, the most likely match to a Mars mineral should have both its spectral band positions and overall shapes which will match. To address this, we re-run the spectral fitting procedure on the ROI-median OMEGA spectrum but for which the continuum was not removed. If the spectral shape is not consistent with one or several candidate mineral priors, it is removed from the list of best fit prior candidates. This time the weight is adjusted so that it is unit between 1.1 and 2.6 µm, but higher (up to seven times higher) at specific spectral intervals in the [1.85-2.58] µm range. Figure S1 (supplementary material, Section S2) indicates the weight applied for each wavelength. From this step, if the final linear abundance of some of the 5 remaining candidate priors is less than 5%, it is removed. Whereas we set a minimum lower limit threshold for all linear abundances of 5%, we also account for the fact that our model is intrinsically more sensitive to some minerals compared to others. For example, the raw output of the linear unmixing model yields carbonates and sulfate salts which are detected with a linear abundance which is often too high, i.e. that is not corroborated by visual inspection of the associated spectra. We handle these exceptions by artificially lowering the linear abundance by the means of a scaling factor. The scaling factor was determined experimentally by comparing with known areas or suspected areas of carbonates and salts. While there is some degree of arbitrary here, the linear model in itself is quite arbitrary when dealing with abundances and not just confirming the existence of a given mineral. That is because the linear model does not account for non-linear mixtures, the effect of grain sizes, and our laboratory analogue spectra may have absorption bands that do not reflect the absorption band strengths of the actual minerals at Mars. In summary, we adjust the parameters of our linear
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model so that 1) when it detects a mineral, we consider it a plausible phase and thus an input
for the subsequent abundance retrieval model, 2) if it does not detect a mineral (below the
corrected detection threshold) then that mineral is not included in the next step.

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Once the set of best prior candidates is set for each ROI, we consider that all pixels falling
within that ROI have the same priors. The abundance modelling will then be implemented per
pixel based on the ROI priors, and a comparison of the modelling per pixel or per ROI is
discussed later. Figure S2 (displayed in the supplementary material) shows examples of the
prior maps color-coded for their relative linear abundance (from 0 to 100%, per ROI).

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Implementing this approach per ROI instead of per pixel substantially decreases
computational time, which is a first advantage. While there is the risk of leaving out a minor
aqueous mineral phase by the spectral dilution effect of averaging many pixels together, this
is largely outweighed by the fact that median-averaging spectra within a ROI substantially
increases signal-to-noise and cancels out residual systematic artefacts that would be
misinterpreted as minerals in individual OMEGA pixels.

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The ROIs can be composed of as little as 2 pixels up to 175000 pixels depending on the
OMEGA observation considered. On average, they are composed of 562 pixels. For each
ROI, a median spectrum is provided and is used to select the end-members for all the pixels
within the ROI (Figure 1). The complete dataset corresponds to a total of ~12000000 of pixels
clustered into ~20000 ROIs. The comparison between ROI-scale and individual pixel-scale
resulting abundances will be discussed in the following section.

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## 2.3- End-members’ selection

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Based on the priors identified and selected with the process described in the previous section,
we selected end-members to include in the modelling based on a trade-off between initial

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257 guesses (priors), previous studies using Shkuratov radiative transfer model applied to 258 OMEGA data for primary minerals (Poulet et al., 2009, Riu et al., 2019), availability of the 259 optical constants (OCs) and limit on the number of phases to include per spectra (to prevent 260 from potential degeneracy of the results and for computing time reduction). The maximum 261 number of phases to be included to model a spectrum is set to 10. For the computing of the 262 global maps by merging all OMEGA observations after modelling of all spectra, if several 263 OMEGA observations overlap with ROIs belonging to different classes, the corresponding 264 pixel of the final map will always be masked if for one of the overlapping observation 265 belonged to one of the two excluded classes. Table 1 summarizes for those three remaining 266 classes the expected end-members to be modelled. For each ROI, we check the corresponding 267 class and add the corresponding end-members to the mixture (Table 1). The priors correspond 268 to the *a priori* phases to be included in the model based on the linear un-mixing performed on 269 the MOCCAS catalogue detections. The end-members correspond to the phases ultimately 270 included in the model and remaining at the end of the fitting procedure based on the 271 availability of OCs.

Phases ultimately tested with linear unmixing:

(Fe/Mg/Al)-Phyllosilicates, Al-smectite, Opal, AlSiOH, Carbonates, Serpentine, Chlorite

in addition to systematically included phases:

<table><thead><tr><th colspan="2">Class 1 - RED</th><th colspan="2">Class 2 - CYAN</th><th colspan="2">Class 3 - ORANGE</th></tr><tr><th>Phase</th><th>End-member</th><th>Phase</th><th>End-member</th><th>Phase</th><th>End-member</th></tr></thead><tbody><tr><td><strong>Fe-Phyllosilicate</strong></td><td>Nontronite</td><td><strong>Al-Phyllosilicate</strong></td><td>Kaolinite</td><td><strong>Fe-Phyllosilicate</strong></td><td>Nontronite</td></tr><tr><td><strong>Mg-Phyllosilicate</strong></td><td>Saponite</td><td><strong>Al-Smectite</strong></td><td>Beidellite</td><td><strong>Mg-Phyllosilicate</strong></td><td>Saponite</td></tr><tr><td><strong>Fe/Mg Micas</strong></td><td>Celadonite</td><td><strong>Hydrated silica</strong></td><td>Opal</td><td><strong>Carbonates</strong></td><td>Magnesite</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>AlSiOH</td><td>Serpentine</td><td>Lizardite</td></tr></table>

Table 1 - Summary of the phases added to the mixture based on the classification of Carter et al., 2018.

The selection of end-members through the MOCCAS database provides the following initial dataset, separated into three classes to be modelled: Class 1 (Fe/Mg-phylosilicates) is composed of the majority of pixels (~ 10.5 millions), class 2 (Al-phylosilicates/Al-smectites) is composed of a smaller fraction of pixels (~ 1.5 million) and class 3 (serpentines et carbonates) comprises only 30000 pixels.

For the mafic minerals, for each spectrum to be modelled, the presence of olivine (both Fe-rich and Mg-rich) is tested beforehand using the criteria developed by Poulet et al., (2007) and adapted by Ody et al., 2012), referred to as OSP1 and OSP2. If the criteria for OSP1 is positive, indicating the presence of low-Fe olivine and/or low abundance and/or small grains (Ody et al., 2012), then forsterite (Mg-rich olivine) will be added to the mixture. If the criteria for OSP2 is positive, indicating the presence of high-Fe olivine and/or high abundance and/or large grains, then fayalite (Fe-rich olivine). If both criteria predict the presence of olivine both end-members (forsterite and fayalite) will be selected as end-members. The same process is applied for pyroxenes. The presence of this mineral is tested using the pyroxene index mapped in (Ody et al., 2012). If the pyroxene index is positive (band depth > 2%), then both high-calcium (HCP) and low-calcium (LCP) pyroxenes will be added to the mixture. Plagioclase with magnetite inclusion is always selected as an end-member (Poulet et al., 2009; Riu et al., 2019). The inclusion of magnetite in plagioclase has been proven necessary to lower the average reflectance and reduce spectral contrast as demonstrated in Poulet and Erard (2004). Previous works have also demonstrated that Fe-hydroxide (Ferrihydrite end-member) have to be systematically included in the mixture to adjust the intensity of the 1.9
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µm band and account for the asymmetry of the 2.3 µm feature (see Poulet et al., 2008, Poulet et al. 2014).

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We present on Figure 2 the extinction coefficients k of all final used end-members, depending on the class they are attributed to (Table 1).

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<figure><img src="image_3.png" /></figure>

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Figure 2 - Extinction coefficient k of the different end-members used in the modelling. The coefficients are offset for clarity in each graph. We provide the absolute value of k[1 µm] of all end-members: Fe-Phyllosilicate (7.64e-5), Mg-Phyllosilicates (8.66e-6), Fe/Mg Micas (8.58e-4), Al-Phyllosilicates (5.14e-8), Al-smectite (3.03e-5), Opal (1.21e-5), AlSiOH (4.94e-4), Carbonates (9.65e-6), Serpentine (6.74e-6), Fe-hydroxide (1.08e-3), Chlorite (1.69e-4),
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HCP (5.19e-4), LCP (3.26e-4), Plagioclase (5.76e-5), Fayalite (2.86e-4), Forsterite (1.13e-4).

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The optical constants were computed using the Shkuratov radiative transfer theory. The
sources for each end-members to obtain the OCs are referenced in Poulet et al., 2008; Poulet
et al. 2014.

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The main issue regarding this methodology to select end-members is that the number of
minerals added in the mixture can easily reach 10. Firstly, as the modelling is very time-
consuming it is important to restrict the number of end-members to restrict computing time,
we thus put a threshold on the maximum hydrated end-members to be added to the mixture to
10. Secondly, as the number of end-members will increase the model may be more subject to
degenerated results which makes the interpretations of the retrieved abundances more
difficult. A filtering process was emplaced to provide more consistent results and reduce the
number of end-members for each pixel, it will be discussed in Section 3.2.

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2.4- Model

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For this study, we use Shkuratov radiative transfer theory (Shkuratov et al., 1999) that mimics
the light/matter interactions in an intimate mixture. This model has been validated and largely
used to retrieve modal composition using near-infrared spectroscopy for both primary and
altered minerals of planetary surface materials (Li & Milliken, 2015; Poulet et al., 2009;
Poulet et al., 2014; Poulet & Erard, 2004; Poulet et al., 2009; Poulet et al., 2018; Riu et al.,
2019; Robertson et al., 2015). This model simulates the reflectance of a particulate surface
from the complex refractive indices (n,k) of each component added in the mixture (Figure 2
for the k imaginary refractive index of the selected end-members). Both the abundance and
grain size of each end-member are used as parameters in the model to find the best-fit to the
Martian spectra.

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The initial conditions for each end-members are fixed in terms of grain sizes and the abundances are adapted depending on the number of end-members ultimately added in the mixture. Table 2 summarizes the initial conditions applied. The plagioclase end-member is used as a complementary end-member for initial condition on the abundance as its initial abundance is always set to be 1 – Σ(initial abundance of all other end-members), so that the total initial abundance is set to 1. If the total initial abundance is already greater than 0 without plagioclase, the initial abundance of plagioclase will be set to 0 and the rest of the mineral abundance will be normalized to 1 prior to the modelling.

<table><thead><tr><td></td><td>Grain size (μm)</td><td>Abundance (vol%)</td></tr></thead><tbody><tr><td>Fe-hydroxide</td><td>40</td><td>10</td></tr><tr><td>Fe-Phyllosilicate</td><td>40</td><td>30</td></tr><tr><td>Mg-Phyllosilicate</td><td>40</td><td>20</td></tr><tr><td>Al-Phyllosilicate</td><td>40</td><td>10</td></tr><tr><td>Al-Smectite</td><td>40</td><td>10</td></tr><tr><td>Fe/Mg Micas</td><td>40</td><td>10</td></tr><tr><td>Chlorite</td><td>40</td><td>10</td></tr><tr><td>Opal</td><td>40</td><td>10</td></tr><tr><td>AlSiOH</td><td>40</td><td>10</td></tr><tr><td>Serpentine</td><td>40</td><td>10</td></tr><tr><td>Carbonates</td><td>40</td><td>10</td></tr><tr><td>HCP</td><td>100</td><td>10</td></tr><tr><td>LCP</td><td>100</td><td>10</td></tr></tbody></table>
<table><tr><td>Fe-olivine</td><td>150</td><td>10</td></tr><tr><td>Mg-olivine</td><td>150</td><td>10</td></tr><tr><td>Plagioclase</td><td>150</td><td>1-Σ(total initial abundances)</td></tr></table>

Table 2 - Starting conditions of the modelling for the grain size and abundance of each end-member. The starting conditions are based on past studies of abundance quantification with this model for both mafic and hydrated minerals (e.g., Poulet et al., 2008, Poulet et al., 2014, Poulet et al., 2018, Riu et al., 2019)

We use a downhill simplex method for multidimensional minimization that aims at minimizing the root-mean square (RMS) between the OMEGA spectrum and the modelled spectrum. To evaluate the RMS, we put a larger weight on the spectral channels where hydration features are found: between 1.87 and 1.95 µm and between 2.15 and 2.44 µm in order to improve the fits (Figure 3). The RMS is then used as a criterion on the quality of the fit, the modelling result of a fit will be deemed acceptable if the RMS is < 0.003. This threshold was tested and validated by Poulet et al., (2009) and Riu et al., (2019). This value is used as a threshold to accept or reject a fit, however the minimization routine is not stopped when this value is reached but will continue until an actual minimum is reached. As a result, the final RMS of a fit can be below this threshold value of 0.003. The model is then applied as is to all ROI median spectra first, and then to all individual spectra.
<figure><img src="image_4.png" /><figcaption>Figure 3 - OMEGA spectra of one ROI. In red is represented the ROI median spectra. In green are shown the spectral ranges where a larger weight is applied in the model. In black are shown the wavelengths that are excluded from the modelling (weight = 0).</figcaption></figure>

## 3- Tests and validation

Based on this methodology, a series of tests were also performed to estimate uncertainties on the resulting abundances and on the robustness of the selected priors based on the prior selection method and their representativeness of using a ROI-scale versus the individual pixel scale. These tests are presented in the following sections. All tests demanding large amount of simulations were performed on ROI-median spectra to reduce the number of spectra and thus the computing time. The total number of ROIs is close to 20000 which still represent a large sample that can be used for statistical analyses. In the following sections, we discuss tests to evaluate the uncertainties on the resulting abundances based on the methodology used (as described in Figure 1). We do not discuss the uncertainty based on the Shkuratov radiative transfer theory that are covered in Poulet et al. (2009).

### 3.1- Tests on the presence of minor phases
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For this first test, we wished to understand the following issue: what happens to our
abundance estimate if we add or retract additional end-member in the model that have been
rarely observed at Mars? Will we improve significantly the resulting fits or shall we focus on
the more frequent aqueous minerals?

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Before converging towards the final set of end-members (Figure 2 and Table 2), other rare
phases were investigated (pumpellyite, prehnite and serpentine). These were either detected at
the surface of Mars from detailed local-scale studies (Carter et al., 2010; Carter et al., 2013;
Ehlmann et al., 2008; McSween et al., 2015; Michalski & Niles, 2010), or suggested as a
result of linear unmixing when determining the potential priors (Poulet et al., 2008).

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Therefore, some preliminary tests had to be carried out to narrow down the set of end-members to be used for the final global mapping. Limiting the number of end-members is important to reduce the computing time and also for avoiding degeneration of the results. For these tests, we used the exact same methodology as described in the previous section and illustrated in Figure 1. The rare mineral phases that were tested are prehnite, pumpellyite and serpentine (Figure 4). Prehnite and serpentine were previously detected on Mars but are found to be scattered in select few regions of the ancient crust (e.g., Ehlmann et al., 2009; Carter et al., 2013). The presence of pumpellyite has been suggested by previous local studies of the modal composition of hydrated terrains, and its presence appeared to be correlated with the presence of prehnite and chlorite (Poulet et al., 2008). However, at the OMEGA spectral resolution, the spectral characteristics of these phases (chlorite and pumpellyite) are very similar and can therefore be hard to distinguish even with non-linear unmixing. Serpentine was only tested on spectra from class 3 (Table 1) or if suggested by the linear unmixing.

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Pumpellyite and prehnite were tested if suggested by the linear unmixing. These two phases
were tested individually and also in parallel with the addition of chlorite when either phase
was put a priori.

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<figure><img src="image_5.png" /><figcaption>Figure 4 - Reflectance spectra of the rare phases tested as end-members (Prehnite, Pumpellyite and Serpentine). The grain size is 40 µm.</figcaption></figure>

We recall that the study presented in this paper focuses on a global approach to quantify the hydrated mineralogy on the Martian surface at the km-scale, the exclusion or inclusion of these minor (because rare) phases may thus be biased by the methodology (the global approach) and dataset (low spatial and spectral resolutions of the OMEGA dataset). In the future, the final set of end-members may be refined for local scale analyses of regions of interests (e.g., landing sites candidates). We based our analyses on the RMS, so that if the presence of one of these rare phases statistically degrades the RMS, they will be ultimately excluded from the set of end-members.
<figure><img src="image_6.png" /><figcaption>Figure 5 - Evolution of the RMS for simulations with or without the tested rare phases. (a) Pumpellyite and prehnite. (b) Lizardite (serpentine end-member).</figcaption></figure>

We found that the locations where both pumpellyite, prehnite and chlorite were tested, the RMS is improved (decreased) for the simulations without pumpellyite and prehnite (Figure 5a) and that the number of spectra with good RMS < 0.003 is increased. Based on these observations, even if those minor phases have previously been detected locally in certain regions, we ultimately decided to only keep chlorite into the model for our global approach, as a means to lower the number of end-members.

For the case of lizardite (serpentine end-member), we found that globally the RMS is unchanged for a majority of spectra when this end-member is included (Figure 5b). As only a minor portion of the ROIs, as defined by MOCCAS (Carter et al., 2018; Carter et al., in prep), belong to class 3 and hence potentially serpentine-bearing (corresponding to a total of 31231 spectra), as a result this will only impact a few ROIs on the overall surface. In addition, as discussed in the following section, a threshold on abundance was applied to exclude, for each spectrum, minor phases from the model. This additional step will play the role of an additional sorting step to make sure that each mineral included in the mixture contributes
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spectrally, and this is relevant especially for minor phases with a low expected abundance at
the OMEGA pixel scale.

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Based on those tests, we evaluated an uncertainty on all abundances based on the presence or
not of these minor phases in the mixture. To do so, we compared the computed abundances
for each spectrum, with then without the minor phases added in the mixture. The resulting
uncertainties are given as an average of the difference on all tested spectra where either
prehnite, pumpellyite or serpentine were included or not. The resulting uncertainties are
summarized in Table 5 and will contribute along with the other evaluated uncertainties
(Sections 3.2, 3.4, 2.5) as global uncertainties on the final global maps (Section 4).

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## 3.2- Tests on abundance detection threshold

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Here, we wished to test the abundance detection threshold. In other words, for minerals that
are found in low abundance compared to the others, are they useful to the fit or should we
focus on the more abundant minerals detected in the mixture?

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While modelling the hydrated minerals, we found that their abundances may often be very
close to 0 vol%. This observation questions the contribution of some end-members to the
modelled spectrum based on their spectral characteristics and/or the accuracy of the selection
of a priori end-members. The number of end-members included in the mixture is large thus
the model may or may not be sensitive to the exclusion or addition of a single end-member to
the mixture. It is important to evaluate the emplacement of an abundance threshold to
constrain these observations, and from which we might say unambiguously that a mineral is
present or not. The threshold for the exclusion of an end-member was tested between 0.5 up
to 5 vol%. As it can be expected to find abundance below 5 vol% for minor phases, no higher
thresholds were tested. This additional step will permit the reduction of the number of end-
members for each pixel, therefore prevent possible degeneration of the results. It implies more

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computing times because it increases the number of modelling rounds but ultimately it will
provide more robust results. For each spectrum, a first run of modelling is performed with the
complete set of selected end-members and at the end of the fitting procedure each abundance
is checked: if one end-member or several end-members have abundances lower than the tested
threshold (from 0.5 to 5 vol%), they are excluded and the model is launched again until it
reaches a solution where all end-members have abundances higher than that threshold. The
improvement in the RMS error as end-members were increased in abundance, relative to the
remaining abundances, was closely monitored. To do so, simulations were launched on all
ROI spectra, in order to cover varying mineralogical assemblages and thus take into account
various initial mixtures.

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We found that the RMS doesn't follow a trend of improvement with the value of the detection threshold: it is neither improved nor degraded significantly. For each ROI, the RMS variations remain relatively small when varying the detection threshold (RMS difference < 1%). As a result, because the improvement (or degradation) of the RMS is not significant between a 0.5 vol% and 5 vol% exclusion thresholds, we ultimately chose to fix the exclusion threshold for the building of the final global distribution maps to 1 vol%. We interpret this lack of impact on the RMS as a combination of several factors: i) the stochastic instrument noise which render small variations in the spectral features not significant, ii) inherent spectral degeneracies between minerals (aqueous or other) for which the spectral shape and/or absorption bands are not significantly different from one another, resulting in that % level variations have no meaningful impact on the final modelled spectrum.

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This value of 1 vol% is a trade-off to limit the number of end-members for the modelling of
each spectrum, to limit the very low and potentially not representative abundances (therefore
simplifying the interpretation of the results), but at the same time to avoid completely
excluding minor phases from the modelling (mainly carbonates, that are known to be present

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469 in some of the terrains modelled and are thus necessary in the mixture). Amongst the different
470 hydrated minerals modelled, some are more sensitive to this exclusion threshold of 1 vol%
471 than others (Table 3), which can be both due to inexact selection of priors and/or smaller
472 spectral contribution to the spectrum and/or consistently smaller abundance.

<table><thead><tr><th>End-member</th><th>Fe-hydroxide</th><th>Fe-Phyllosilicate</th><th>Mg-Phyllosilicate</th><th>Al-Phyllosilicate</th><th>Al-Smectite</th><th>Fe/Mg Micas</th><th>Chlorite</th><th>Opal</th><th>AlSiOH</th><th>Serpentine</th><th>Carbonates</th></tr></thead><tbody><tr><td>% of pixels where the end-member is excluded from the modelling with 1% threshold exclusion</td><td>15</td><td>48</td><td>67</td><td>19</td><td>44</td><td>73</td><td>77</td><td>69</td><td>55</td><td>69</td><td>81</td></tr></tbody></table>

473 Table 3 – Percentage of pixels affected by the 1% exclusion threshold for each of the hydrated
474 phases.

475 From these tests, we computed uncertainties on the resulting abundances based on the
476 emplacement of a detection threshold. The uncertainty is the result of the average standard
477 deviation on all ROIs. The corresponding uncertainties based on this extra computing step are
478 reported in Table 5 (Section 3.6) and will be used to evaluate global uncertainties on the
479 global maps abundances (Section 4).

480 3.3- Influence of the initial conditions

481 Here, we wanted to evaluate the impact on initial conditions: as the number of parameters is
482 large (abundance and grain size of each end-member), how many different pathways lead to
483 an acceptable fit? Will the resulting fits provide different results in terms of final abundances?
484 The rationale is two-fold. First, because of the large number of end-members and thus free
485 parameters, the pathway of the minimization may be subject to variations depending on the
486 initial conditions with which the minimization routine starts. Local minima in the parameter
487 space may arise that are not the global minimum sought out for the modelled spectra. Second,
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we have observed that there is not one unique solution for each spectrum where the RMS < 0.003. Therefore, the dependency on initial conditions can be treated as an uncertainty on the final abundances resulting from various fits on the same spectrum which yield a good RMS < threshold (0.003).

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This test requires launching the modelling on the same spectrum for a number of times with varying initial conditions. The modelling is time consuming, thus in order to limit the calculation time allocated to this test, we decided for each spectrum to launch 500 simulations with different, randomly varying initial conditions for the abundance. The initial conditions on the grain size are fixed (Table 2). This test was performed on all ROI median spectra, but not on the individual pixel spectra, in order to limit the number of spectra to be modelled 500 times each. For each modelled spectrum, we extracted the abundances of all fits where the RMS reaches values ≤ 0.003 and evaluated (1) the dispersion on the resulting abundances, and (2) the possible correlation between initial conditions and resulting abundances.

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For a number of selected spectra with various mineralogy, we present in Figure 6 the results of the simulations and corresponding abundances for selected ROIs. A large number of simulations yield a good fit for the same OMEGA spectrum.
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<figure><img src="image_7.png" /></figure>

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Figure 6 - Example of simulated spectra by varying the initial conditions on the hydrated minerals and corresponding abundances, for three different ROIs. All spectra that provide an acceptable fit (RMS < 0.003) are plotted in light purple and the OMEGA spectrum is in black. The abundances on the right panel are displayed for each end-member based on the obtained abundances from the accepted fits. The horizontal bars represent the statistical analysis of the range of abundances computed for each end-member. Their full extent (between the tick boundaries) represent minimum and maximum values obtained within the accepted fits. The box represents 50% of the light purple spectra and is delimited by the 25- and 75-quartiles (in total spectra with good fit). The blue bar represents the median abundance and the red bar the average abundance calculated. Similar graphs are available for all ROIs.

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We find that on average, we have ~380 spectra that provide acceptable fits (RMS < 0.003) out of the 500 launched when varying the initial conditions. The abundances can vary quite a lot (from < ±1vol% to ±10vol%, see the abundance dispersion boxes on Figure 6 were 50% of the acceptable fits are found) amongst the acceptable fits which increases the uncertainties on the resulting abundances. However, we do not find correlation between initial conditions and final abundances which shows that the resulting abundances are not dependent on the initial conditions (Figure 7). For the global modelling, we will thus be using the initial conditions displayed in Table 2.

<figure><img src="image_8.png" /></figure>

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Figure 7 – Final abundance for all modelled spectra with RMS < 0.003 with respect to the initial condition on abundance tested. Graph is shown for the Fe-phyllosilicate end-member.

Same trends are observed for all end-members. The red line corresponds to the 1:1 line.

The uncertainties are finally computed as an average on the standard deviation of the resulting abundance on all ROIs and for each end-member. This calculation was performed on all ROIs and can therefore be accessed as an individual uncertainty per mineral and for each ROI. The corresponding average uncertainties obtained on the hydrated minerals are summarized in Section 3.6 (Table 5).
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### 3.4- Evaluation of the uncertainties based on possible degeneration of the resulting abundances

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Here, we wished to quantify the possible degeneration of the resulting abundance: how many combinations can lead to an acceptable fit that can reproduce the Martian spectra? What will be the impact on the resulting abundances? We showed in the previous section that we can find, for each spectrum, a large number of solutions that fit this requirement which depend on the initial conditions provided to the model. Those uncertainties may further depend on the minimization procedure that is used. To verify the effect of the minimization procedure, we performed similar tests but this time to estimate the degeneracy of the results putting aside the minimization routine: we manually explored the parameter space around the best-fit optimization values.

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To do so, for each ROI median spectrum, as a starting point we derive the abundances and grain sizes obtained from the “nominal” fit, as launched with initial conditions provided in Table 2. This yields a model best-fit spectrum. We then created synthetic variations around that best-fit spectrum, by keeping the grain sizes identical to the best-fit values but then artificially varying the abundances from ± 1 vol% to ± 5 vol% around the best fit abundances.

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We did not perform any additional sensitivity test on the grain size in this study, as it was shown in Riu et al., 2019 that this parameter had a minor impact on the resulting abundances, thus the grain size was fixed to the optimal best-fit value for this sensitivity test. We only applied those deviations on the abundances of the hydrated minerals detected in the three mineralogical classes (Table 1) while keeping fixed the abundances of the mafic minerals. Their abundance uncertainty was already discussed in Riu et al., (2019). This allowed reducing drastically the number of spectra to be simulated and hence the computing time.

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With these test conditions, we observed that a variation of more than 5 vol% on the nominal abundance of any of the hydrated minerals never resulted in an acceptable fit, this is why the
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simulated spectra were built by exploring abundance at most ± 5 vol% from the best fit
abundance. This observation shows that the hydrated mineral abundances are relatively well-
constrained, in specific ranges of values provided by the best-fit, and for spectra with fixed
grain sizes and fixed mafic minerals abundances.

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Thousands of synthetic spectra (close to 50000) were thus simulated for each ROI median
OMEGA spectrum. The RMS was then calculated between the OMEGA spectrum, used as
the reference, and the thousands of simulated spectra with equivalent grain sizes, equivalent
abundances for anhydrous minerals and deviations of abundances for hydrated minerals.
Figure 8 illustrates an example of simulated spectra with varying abundances (± 1 vol% to ± 5
vol%, with steps of 1 vol%, around the best fit abundance) for ROI median spectra extracted
from the three different mineralogical classes (red, cyan and orange). The spectra that exhibit
a final RMS ≤ 0.003 are overlaid in light purple. We can see that with the methodology only a
small percentage of the simulated spectra actually fits OMEGA initial spectrum.
<figure><img src="image_9.png" /><figcaption>Figure 8 - Example of simulated spectra (color) by varying the abundances of hydrated minerals (± 1 vol% to ± 5 vol%) around the best fit abundance. (a) Red class, (b) Cyan class, (c) Orange class. The spectra with final RMS ≤ 0.003 are displayed in light purple. The initial OMEGA ROI median spectra are displayed in black.</figcaption></figure>

We performed this evaluation for all of the ROI median spectra. We found that for half of the ROIs, we have more than 1 spectrum amongst the simulated spectra that will fit the ROI median OMEGA spectrum. And on average ~120 simulated spectra will provide acceptable fit with RMS ≤ 0.003 (as represented in light purple on Figure 8), which represents less than 1% of the 50000 simulated spectra. At maximum, we have an OMEGA spectrum that will be correctly fitted with 1453 spectra and with 1 at minimum. Uncertainties on the resulting

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582 hydrated mineral abundances based on this test were computed, however because of the
583 constraint on the ± 5 vol% around the best fit abundance, the uncertainty provided here will
584 always be smaller than 5 vol%. For each ROI and for each end-member, we extracted the
585 standard deviation on abundance and average abundance resulting from the accepted fits
586 when several simulations amongst the 50000 launches provided an alternative acceptable fit.

587 In the graph below (Figure 9), we grouped together all the alternative good fit abundance
588 values of each mineral as an average value and compared to its corresponding standard
589 deviation amongst the same set of spectra.
<figure><img src="image_10.png" /><figcaption>Figure 9 – Average individual mineral abundances of the accepted fits with RMS ≤ threshold (cf spectra in light purple on Figure 8) with respect to the standard deviation on the abundance on the acceptable fits. For each ROI, we used the same colour code to display the mineralogical class (red (1), cyan (2) and orange (3)).</figcaption></figure>

For the end-members that are found in several classes (such as Fe-hydroxide and Fe/Mg phyllosilicates), we do not find correlations between the standard deviations and the mineralogical class (e.g., mineralogical assemblage). Also, we observe an increase of the standard deviation with the average abundance, which means that the resulting uncertainties
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do depend on the overall abundance. This means that when a mineral is found in high
abundance, the solution tends to be less unique, with relatively more alternate acceptable fits
within +/- 5% of that mineral's abundance.

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The uncertainties that will be used and propagated to the global final maps (Section 4) are
computed for each mineral from the average standard deviation (that we define as
uncertainties) on all ROIs (based on Figure 9). Although, because the computation is carried
out for each of the ROIs, the individual uncertainties can also be accessed – if necessary.

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### 3.5- Application at the pixel-scale and comparison with ROI-scale

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In this section, we wanted to evaluate the representativeness of the ROI median spectrum
compared to the spectra within the ROI: is the mineralogy at the ROI scale, at which the
priors are selected, representative of the mineralogy at the pixel scale in the same ROI?

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Here we describe a set of tests that are designed to evaluate both 1) the representativeness of
the ROI median spectrum versus that of individual OMEGA pixel and 2) the applicability of
the model at the pixel scale despite intrinsically lower SNR. This test is also useful to 3)
evaluate the selection of initial conditions for a fit, because we use the same initial conditions
based on the mineralogical class and prior linear unmixing on the ROI median spectrum. As
the initial conditions may impact the resulting abundances (see Section 3.3), it is important to
make sure that they are correctly chosen and representative of the expected mineralogy.

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We compared for selected areas the resulting abundances between the ROI median spectrum
and all individual spectrum within the ROI. We extracted for each ROI containing 150 spectra
or more (which corresponds to a total of 4703 ROIs) the abundance of the median spectrum
and all individual spectra's abundances within the ROI, where RMS ≤ 0.003. An average
abundance was then evaluated from all individual pixel abundance within the ROI to compare

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the abundance resulting from the ROI median spectrum and the average abundance on the
overall ROI resulting from individual spectrum modelling (Figure 10).

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<figure><img src="image_11.png" /></figure>

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Figure 10 – Average ROI abundance calculated over all pixels resulting abundances (x-axis) with respect to the median ROI spectrum’s abundance (y-axis) for all hydrated end-members.

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The graph reads as: for each ROI, we compare the abundance value from the ROI spectrum (vertical) to the computed abundance that is averaged over all that ROI's pixels abundances.

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The black dotted line corresponds to the 1:1 line. The color code corresponds to the standard variation in terms of abundance within the ROI.

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We find correlation between the abundance obtained from the ROI median spectrum and the average abundance on the overall ROI for some of the end-members (mostly common phases: Fe-hydroxide, Fe-Phyllosilicates, Al-phyllosilicates, and Al-smectites). We do not observe that the abundance obtained from the ROI median spectrum and the average ROI abundance are closer if the standard deviation within the ROI is smaller. The standard deviation within the ROIs in terms of abundances can reach high values at maximum (close to 15 vol% for most end-members and a maximum of 34 vol% for the Al-smectite end-member) but are mostly contained below 7/8 vol%, which shows some variability within the ROIs. We find that the median ROI abundance is, for all end-members, most of the time (> 50% of the cases) larger than the average abundance on the ROI. This would mean that the ROI median spectra present stronger characteristic features associated to the class of the ROI and is therefore more likely to result into higher abundances. Because we emplaced a detection threshold in terms of abundance (Section 3.2), it seems more robust to have higher abundance at the ROI median spectrum scale (stronger absorption features) that might be less representative of the pixel-scale spectra. Although, from the lack of evident correlation for some minerals, to check if the end-members are well representative of the mineralogy of the entire ROI we evaluated the number of pixels within each ROI where the end-member was excluded from the ROI median spectrum modelling based on the exclusion protocol described in Section 3.2 and was kept at the pixel scale. Conversely, we evaluated the number of pixels where an end-member was kept at the ROI median pixel scale but excluded at the ROI pixel scale. The results are summarized in Table 4.

<table><thead><tr><th>% of pixels where:</th><th>Fe-hydroxide</th><th>Fe-Phyllosilicate</th><th>Mg-Phyllosilicate</th><th>Al-Phyllosilicate</th><th>Al-Smectite</th><th>Fe/Mg Micas</th><th>Chlorite</th><th>Opal</th><th>AlSiOH</th><th>Serpentine</th><th>Carbonates</th></tr></thead><tbody><tr><td>- The end-member is kept at the pixel scale but</td><td>15</td><td>57</td><td>86</td><td>21</td><td>44</td><td>74</td><td>80</td><td>70</td><td>52</td><td>70</td><td>90</td></tr></tbody></table>
<table><caption>Table 4 – Summary of the comparison between ROI median spectrum's abundances and the average abundances on the overall ROI. The results are displayed for each hydrated end-member.</caption><tr><td>excluded from ROI median spectrum's modelling</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- The end-member is excluded at the pixel scale (see Section 3.2) but kept for ROI median spectrum's modelling</td><td>0</td><td>&lt; 1</td><td>1</td><td>&lt; 1</td><td>0</td><td>1</td><td>&lt; 1</td><td>1</td><td>1</td><td>&lt; 1</td><td>3</td></tr></table>

We find that for all end-members the cases where the end-member will be kept from the modelling of ROI median spectrum and excluded at the pixel scale are very low (last row of Table 4). This shows that the end-members that are selected based on the ROI classification are well-chosen for the overall ROI pixels and that an additional end-member may have been selected based from the ROI classification and/or prior linear unmixing (Table 1) but is actually not fit for the ROI spectra. On the other hand, when the end-member is excluded based on the ROI median spectrum's modelling there are a majority of cases where it is kept at the pixel scale, except for Fe-hydroxide and Al-phyllosilicate end-members where the occurrences are lower (< 30% of cases). The slight differences can be explained by higher signal over noise ratio of the ROI median spectra. Indeed, the quality of the spectra for the ROI median is higher and enable a better fit ultimately. Although, the averaging process to obtain the median ROI spectra can mask the presence of minor/rare phases (spatially and quantitatively). Minor phases might miss from the selected priors, as they might not be detected at the median ROI-scale. However, as shown in Section 3.1, minor phases do not play a major role in the resulting final abundances. In the future, a systematic estimation of the priors at the pixel scale can be envisioned for selected ROI for refined local analysis. For this global approach, the methodology emplaced thus enables to tune the mixture to be used at the pixel scale with a conservative first *a priori* on the mineralogy selected from the ROI classification and prior linear unmixing based on the ROI median spectrum.
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3.6-

Wrap-up of the uncertainties sources and estimations

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We presented in the previous sections a series of tests to evaluate the uncertainties on the abundances provided by the modelling and the methodology emplaced. Several levels of tests were performed to cover various potential errors sources. These tests were performed at the ROI-median spectrum scale, thus some of the computed uncertainties depend on the ROI (see Section 3.5 for example where the uncertainties only affect ROI where minor phases are tested). These errors are propagated globally and combined onto global average uncertainties for the discussion on the final global map abundances (Section 4).

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Firstly, we evaluated the influence on the abundances of the presence of several minor phases that have been suggested or detected on Mars (pumpellyite, prehnite and serpentine). We conclude that to have the best overall RMS and to limit the number of end-members included in the mixture, only the serpentine end-member had to be kept in the modelling (Section 3.1).

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We computed the errors by comparing the resulting abundances when including or not those minor phases. We estimated for each spectrum and each end-member, the absolute value of the abundance's difference, the overall uncertainty is calculated as an average of this difference. Uncertainties based on a detection threshold were also estimated. This detection threshold was emplaced as a parameter to estimate for which minimal abundance an end-member is necessary to the mixture in order to improve the fit. The results show that if an end-member is present in abundance below 1 vol%, its contribution to the spectra do not help improving the fits (Section 3.2). The improvement on the RMS was not significant enough to evaluate the best exclusion threshold to apply to improve the overall RMS. As a trade-off between keeping minor phases in the mixture and to avoid having to deal with potentially insignificant abundances (very close to 0 vol%), we fixed the exclusion threshold at 1 vol%.

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The RMS is not worsened, and the resulting abundances are not much impacted (≤ 4vol% of uncertainties for all phases except for Fe-hydroxide were the uncertainties based on the
exclusion threshold is quite higher, see Table 2). From this test, we estimated the variation of all abundances depending on the threshold applied (see Table 5). A third test was applied to estimate the uncertainties resulting from the initial conditions provided to the model, hence the minimization routine (Section 3.3). This test showed that with the set of end-members used there is no unique solution to fit one OMEGA spectrum. To investigate further, we performed a third test to evaluate the possible degeneracy of the results but without taking into account the minimization routine (Section 3.4). Based on Shkuratov's model, we simulated tens of thousands of spectra with various hydrated minerals' abundances and fixing the rest of the parameters (grain sizes and mafic minerals' abundances) for each OMEGA ROI median spectrum (Section 3.5). For each spectrum, the abundance of the hydrated minerals was investigated from ± 1% to ± 5% around the best-fit abundance provided by the initial modelling. Without varying the other parameters (grain sizes and mafic minerals' abundances), no spectra will provide a RMS ≤ acceptance threshold if we vary hydrated minerals' abundance with more than ± 5 vol% difference from the best-fit abundance.

We summarize in Table 5 the results obtained. We also computed global uncertainties using the square-root of quadratic sum of the individual uncertainties. Those uncertainties will be used as global errors on the final global maps (Section 4).

<table><thead><tr><th>Uncertainties in vol%</th><th>Fe-Phyllosilicate</th><th>Mg-Phyllosilicate</th><th>Al-Phyllosilicate</th><th>Al-Smectite</th><th>Fe/Mg Micas</th><th>Chlorite</th><th>Opal</th><th>AlSiOH</th><th>Serpentine</th><th>Carbonates</th></tr></thead><tbody><tr><td>Uncertainty based on the presence of minor phases (Section 3.1)</td><td>6.0</td><td>7.0</td><td>7.8</td><td>3.1</td><td>3.5</td><td>8.2</td><td>&lt; 1</td><td>&lt; 1</td><td>/</td><td>8.6</td></tr><tr><td>Uncertainty based on the detection threshold (Section 3.2)</td><td>7.2</td><td>3.5</td><td>2.4</td><td>3.4</td><td>4.0</td><td>2.8</td><td>3.5</td><td>3.4</td><td>4.0</td><td>3.5</td></tr><tr><td>Uncertainty based on the initial</td><td>7.6</td><td>1.9</td><td>1.5</td><td>3.2</td><td>3.7</td><td>2.5</td><td>1.3</td><td>1.0</td><td>&lt; 1</td><td>&lt; 1</td></tr></tbody></table>
<table><tr><td>conditions (Section 3.3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncertainty based on possible degeneration of the fits (Section 3.4)</td><td>2.4</td><td>1.7</td><td>1.6</td><td>2.2</td><td>2.7</td><td>1.7</td><td>/*</td><td>1.9</td><td>2.1</td><td>1.8</td><td>1.5</td></tr><tr><td>Variability within the ROIs based on the representativeness of the ROI median spectrum (Section 3.5)</td><td>6.2</td><td>3.8</td><td>1.9</td><td>4.9</td><td>9.5</td><td>2.7</td><td>2.6</td><td>3.2</td><td>4.9</td><td>2.9</td><td>1.7</td></tr><tr><td>Total uncertainty (vol%)</td><td>12.3</td><td>8.2</td><td>8.5</td><td>6.0</td><td>7.0</td><td>9.2</td><td>4.0</td><td>4.0</td><td>3.9</td><td>4.4</td><td>9.4</td></tr></table>

Table 5 – Summary of the uncertainties provided by the model and methodology. The uncertainties are displayed for each test described in the previous sections in vol% and the total uncertainties are computed from a square-root of the quadratic sum of all sources of uncertainties. *The “variability” based on the representativeness of the ROI is not a source of uncertainties but is calculated from the average STD on the abundances of all pixels within each ROI. It is not used in the summation of the uncertainties. **No uncertainty for the chlorite end-member was computed from Section 3.4 because this end-member is not associated to one of the 3 mineralogical classes (Table 1) used for this test.

## 4- Mineralogical abundances and global distributions

### 4.1- Merging of the data

A total of 900 individual OMEGA image-cubes were modelled at the pixel scale. For each individual image-cube, the modelling was applied at the raw OMEGA resolution, which means that all observations do not have the same spatial resolution hence all resulting mineralogical maps also do not have the same spatial resolution. In order to be able to merge all individual mineralogical maps into global maps, we projected them one by one onto a fixed equirectangular grid of 256 pixels per degree (px/° hereafter), which corresponds to < 500 m/px at the equator. This resolution corresponds to a highest OMEGA resolution, this means that one raw OMEGA pixel with rougher resolution will be projected onto several pixels on the 256px/° grid. For each observation, we only keep and project the OMEGA
pixels that reached a fit with RMS ≤ 0.003. As a result, there may be areas at Mars where aqueous minerals are detected in the MOCCAS database but for which there is no abundance estimate for lack of a good fit. The median value of the RMS is 0.0022 (Figure 11). A total of 8,209,352 spectra where modelled with RMS < 0.003. As one raw OMEGA pixel can overlap several pixels when projected on the 256 px/° grid, this results in ~13.5 millions of pixels projected observation per observation and ~9 millions of pixels after merging the different observations on the global maps. The resulting number of pixels with modelled data is smaller than the number of pixels projected due to overlapping observations. Ultimately, 14% of the projected spectra were not kept in the global maps based on the RMS criterion. The majority of these pixels have a low signal over noise ratio which explains the high resulting RMS.

<figure><img src="image_12.png" /><figcaption>Figure 11 - Histogram distribution of the RMS value for all pixels before projection. The vertical dotted line indicates the RMS threshold of 0.003 used for exclusion of bad fits.</figcaption></figure>

The RMS is also projected and kept as an indicator of the fit quality and is used as a weight when merging overlapping observations. For numerous locations at the surface there are
several OMEGA observations that were modelled which overlap. Hence, for the final average
abundance value of the global map of all merged observations, it has been decided to assign a
weight to each individual abundance depending on the RMS value of the fit (equation
1). This method also enables the computation of a standard deviation (STD) map for (1)
each abundance when observations overlap.

$$
\overline{\text{abundance}[x, y]} = \frac{\sum_{i=0}^{\text{\#obs}} \text{abundance}_i[x, y] \times \frac{1}{\sqrt{\text{RMS}_i[x, y]}}}{\sum_{i=0}^{\text{\#obs}} \frac{1}{\sqrt{\text{RMS}_i[x, y]}}}
$$

The projected final abundances are then normalized, so that after the weighting and averaging
steps we have for each pixel the sum of all end-members' abundance equal to 1. Because of
the projection and merging procedures, on the final abundance maps, we can find individual
abundance value lower than the threshold of 1 vol% discussed in Section 3.2 as a lower limit
on abundances. For each pixel of the final maps, we also keep additional information: name of
OMEGA observation, number of end-members, STD on the abundance where several
observations are overlapping and final RMS as calculated using equation 1. Additionally, less
than 1% of the projected pixels do not necessitate the addition of hydrated minerals in order to
be successfully modelled. Those pixels where ultimately also excluded from the global maps.
They are mostly located in the southern highlands (as are the majority of the detections of
hydrated spots) and found in isolated detections as "clusters" of non-hydrated locations.

Finally, we exclude all locations that are known (based on the MOCCAS detections) to
possibly harbour sulfates, due to the lack of available constants for this mineralogical class,
regardless of the modelling results. This additional mask, projected onto the global grid, led to
the exclusion of 17% pixels on the global maps, mainly located in Terra Meridiani. This
additional exclusion step can only be done after projection on the global grid of 256px/°. It is
772 complementary to the exclusion of sulfates based on the classification scheme of MOCCAS
773 as explained in Section 2.

774 4.2- Global minerals abundances

775 The final abundance maps are discussed in the following sections and displayed in the
776 Appendix. They represent the final products for the global modelling of phyllosilicates at the
777 surface of Mars form -60° to +60° using the MOCCAS dataset and by performing Shkuratov
778 radiative transfer model to the OMEGA spectra. The uncertainties based on the model and the
779 methodology can be quite significant (Table 5). We find uncertainties on the abundances
780 ranging from 3.9 to 12.3 vol% depending on the mineralogical phase. For each mineral phase
781 the number of corresponding pixels presenting abundance > 0 vol% can vary depending on
782 the class considered. The corresponding coverage for all end-members (for the pixels where
783 RMS > 0.003) is summarized in Table 6. In this table, we provide information about the
784 standard deviation on the abundance that illustrates the spatial variations across the surface
785 rather than uncertainties that are discussed in the previous sections. We also provide
786 information on the average standard deviation of each phases where several observations are
787 overlapping. At maximum, we find that 15 observations overlap on the same location. The
788 most observed regions are Nili Fossae and Marwth Vallis. This value can be seen as an
789 additional uncertainty on the final abundances and may also represent variability of the
790 detection amongst the different OMEGA observations (that could be due to spatial resolution
791 for example).

<table><tr><td></td><td>Average abundance (vol%)</td><td>STD on abundance (vol%)</td><td>% of coverage with respect to the total number of pixels modelled</td><td>Average STD on overlapping observations (vol%)</td><td>% of pixels with several overlapping observations</td></tr></table>
<table><caption>Table 6 - Summary of the average abundance, standard deviation and spatial coverage.</caption><tr><td>Fe-hydroxide</td><td><b>16</b></td><td>10</td><td>95.6%</td><td>4.5</td><td>29.7%</td></tr><tr><td>Fe-Phyllosilicate</td><td><b>6</b></td><td>5</td><td>63.8%</td><td>3.1</td><td>26.0%</td></tr><tr><td>Mg-Phyllosilicate</td><td><b>3</b></td><td>4</td><td>30.5%</td><td>2.2</td><td>22.1%</td></tr><tr><td>Fe/Mg Micas</td><td><b>6</b></td><td>6</td><td>58.6%</td><td>2.3</td><td>25.4%</td></tr><tr><td>Al-Phyllosilicate</td><td><b>7</b></td><td>6</td><td>44.9%</td><td>3.2</td><td>16.6%</td></tr><tr><td>Al-Smectite</td><td><b>14</b></td><td>11</td><td>30.1%</td><td>6.0</td><td>22.2%</td></tr><tr><td>Opal</td><td><b>5</b></td><td>7</td><td>14.5%</td><td>3.1</td><td>24.6%</td></tr><tr><td>AlSiOH</td><td><b>6</b></td><td>8</td><td>6.8%</td><td>3.8</td><td>16.7%</td></tr><tr><td>Chlorite</td><td><b>10</b></td><td>12</td><td>21.2%</td><td>3.1</td><td>17.0%</td></tr><tr><td>Serpentine</td><td><b>6</b></td><td>10</td><td>11.6%</td><td>2.6</td><td>16.2%</td></tr><tr><td>Carbonates</td><td><b>1.5</b></td><td>3</td><td>&lt;1%</td><td>1.4</td><td>18.0%</td></tr><tr><td>HCP</td><td><b>14</b></td><td>6</td><td>100%</td><td>3.1</td><td>28.3%</td></tr><tr><td>LCP</td><td><b>8</b></td><td>4</td><td>93.8%</td><td>1.9</td><td>27.7%</td></tr><tr><td>Fe-olivine</td><td><b>6</b></td><td>8</td><td>3.4%</td><td>3.2</td><td>22.5%</td></tr><tr><td>Mg-olivine</td><td><b>4</b></td><td>4</td><td>14.0%</td><td>2.0</td><td>17.9%</td></tr><tr><td>Plagioclase</td><td><b>47</b></td><td>14</td><td>100%</td><td>8.2</td><td>29.5%</td></tr></table>

Additional information is gathered to account for the location where several OMEGA observations are overlapping.

Between overlapping observations, we observe small standard deviation between 1.4 and 6.0vol% for hydrated minerals indicating the methods and dataset have proved to be robust and consistent, and allowing the production of individual modal compositional map for each end-member (Section 4.2-1 and Appendix). Each individual map presented in Appendix will also
799 be made available to the community on the PSUP portal (https://psup.ias.u-psud.fr, Poulet et al.,
800 2018).

801 4.2-1. Global trends

802 We evaluated the abundance of the total of hydrated minerals at all locations. To do so, we
803 summed up the individual abundances of hydrated minerals for each pixel. Figure 12 (a)
804 shows the spatial distribution of the total hydrated minerals abundance. In the global map, the
805 detections are enhanced 10 times in size to be visible, this is why we still see large hydrated
806 deposits close to Meridiani Planum that is in the real maps (without enhanced detections)
807 almost completely masked due to the presence of sulfates (Section 2). We find that the
808 majority of locations exhibits abundance > 20 vol% (Figure 12b). This indicates that the
809 mafic or dusty components (plagioclase and pyroxenes) are dominant in most regions
810 identified as hydrated. Nili Fossae, Meridiani Planum and Mawrth Vallis present the largest
811 abundances of hydrated minerals. More local detections of high abundances are also present
812 in the southern highlands but are less spatially extended.
<figure><img src="image_13.png" /><figcaption>Figure 12 - (a) Abundance map of total hydrated minerals in vol%. The detections are enhanced 10 times in size to be visible. At each pixel, the displayed abundance represents the sum of all hydrated minerals modelled. (b) Histogram of total hydrated minerals in vol%.</figcaption></figure>

The number of hydrated end-members used to model a spectrum gives information onto the complexity of the assemblage. In Figure 13, we show the number of hydrated end-members based on the final abundance maps (see Appendix). Apart from a few small locations in Mawrth Vallis and Meridiani Planum, there is no correlation with the total hydrated abundance and the number of hydrated end-member necessary to model a spectrum.
<figure><img src="image_14.png" /><figcaption>Figure 13 - Number of hydrated minerals used in the modelling for each pixel. The values are found between 1 and 8 hydrated end-members. The detections are enhanced 10 times in size to be visible.</figcaption></figure>

4.2-2. Individual end-members' abundances

The individual maps of abundances are included in the Appendix, from Figures S1 to S16. We display in Figure 14 and Figure 15 the histogram of abundances for all hydrated minerals, at the pixel scale. We separated the minerals between “common” and “rare” phases. A phase is indicated as common (resp. rare) if its abundance is positive for more (resp. less) than 30% of the modelled pixels (Table 6). This means that they are either frequently or infrequently found at the surface, but does not take into account their mean abundance in the sites where they are found.
<figure><img src="image_15.png" /><figcaption>Figure 14 - Histogram of abundances for the common phases at the pixel scale: Fe-hydroxide, Fe-phyllosilicate, Mg-phyllosilicate, Al-phyllosilicate, Al-smectite and Fe/Mg Micas.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Figure 15 - Histogram of abundances for the rare phases at the pixel scale: Chlorite, Opal, AlSiOH, Serpentine and Carbonates.</figcaption></figure>

Individually, based on the global maps of the Appendix and the summary Table 6, we find that amongst our end-members set, Fe-hydroxide and Fe-phyllosilicates are the most widespread hydrated minerals found at the surface (Table 6, and see also Figure A1 in the Appendix). Fe-hydroxide is also the one that presents the largest abundances (Table 6, see also Figure A1). Marwth Vallis is slightly enriched in Fe-hydroxide compared to the rest of the locations with average abundance in this area > 25vol%. Fe-phyllosilicate is found to be quite homogeneous on the overall surface with no clear trend with the latitude or longitude, which is consistent with a low standard deviation on the overall surface (~5vol%, see Table 6). The highest abundances of Fe-phyllosilicates are found in Marwth Vallis. The Mg-phyllosilicate end-member is often excluded from the modelling and presents a low average abundance of 3vol%. The largest abundance is located in the southern highlands at latitude around -40° and quite dispersed across the surface. Nili Fossae is the region with the most
widespread deposits of Mg-phylosilicates but with most pixel with abundance < 3vol%. Al-
phylosilicates are found at all latitudes and longitudes and do not cluster in specific areas
(Figure A5). Al-smectite and chlorite present an average abundance ≥ 10vol%, which is larger
than for the majority of hydrated end-members. Al-smectite is found with greater abundance
in the highlands and especially in Nili Fossae and Marwth Vallis. Chlorite is also detected in
most regions with no clear trends with longitude or latitude; however, its abundance is slightly
increased in craters' floor located around 90° longitude and -5° latitude, with abundance
reaching 25/30vol% locally (Figure A9). Fe/Mg micas are quite widespread on the surface
and often kept in the modelling but their average abundance remains quite low with 6vol%.
High values (> 10vol%) of this phase seem to be correlated with the craters' floor where
chlorite is most abundant (Figure A4). Serpentine, opal and AlSiOH end-members are also
relatively not widespread with spatial coverage < 15% of the modelled pixels. Opal and
AlSiOH seem to be spatially correlated with increasing occurrences in Mawrth Vallis and lack
of occurrences in the southern highlands. They are often excluded from the modelling,
indicating that they are not found with abundance > 1vol% in a large portion of the class 2
spectra (see Section 3.2, Table 1). Serpentine is also found in a couple of craters' floor on the
equator with chlorite and Fe/Mg micas. Serpentine presents a large dispersion in terms of
abundance on the surface compared to its average abundance (Table 6). Carbonates are the
least abundant both in terms of absolute average abundance and spatial coverage. They are
almost only modelled in Nili Fossae. As for the non-hydrated minerals, plagioclase is found to
be the most abundant on average with 47vol%, which is similar to the average plagioclase
abundance found on mafic regions at Mars which is of ~49vol% (Riu et al., 2019). The
average abundances of pyroxenes in the modelled regions are smaller than on mafic-rich
regions but the ratio between HCP and LCP abundance is the same with a LCP/(LCP + HCP)
ratio ~37vol%. Mg-olivine is more widespread than Fe-olivine but its average abundance is
877

smaller. The olivine abundances are slightly larger than the ones obtained on the pyroxene-rich regions from Riu et al., 2019. These values are also in good agreement with past global studies in the TIR with the TES instrument (see Figure 25 in Riu et al., 2019 for extensive comparison of OMEGA and TES derived abundances for mafic minerals).

878

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881

### 4.2-3. Potential abundance correlation and different mineral assemblages

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891

In detail, we looked at the potential correlation between the different end-members' occurrences and abundances (Figure 16a) to quantify whether specific assemblages were favoured in distinct locations in addition to the global trends described in the previous sections. The occurrence correlation is derived by evaluating the percentage of pixels where a pair of end-members are both required in the modeling. Therefore, for each pair of end-members (end-member A and end-member B), this results in two numbers: (1) percentage of pixels where end-member B is modelled amongst the pixel where end-member A is modelled; (2) percentage of pixels where end-member A is modelled amongst the pixel where end-member B is modelled.

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For the abundance correlation, we calculated the Pearson Correlation Coefficient, *r* (Härdel & Simar, 2007) for each pair of end-members: abundance of end-member A with respect to abundance of end-member B in location where both end-members are present. If *r* > 0, it indicates that there is a positive correlation between the two datasets, if *r* < 0 it indicates anti-correlation and if *r* = 0 there is no correlation. We evaluated the Pearson Correlation Coefficient for each pair of end-members on modelled pixels only where both end-members (A & B) are present at the surface. The results are displayed on Figure 16. We find that most pair of end-members, regardless of the class they are modelled in favourably, are not very correlated in terms of abundance (white or light colours on Figure 16b). Although, few pairs
901

show significant (|r| > 0.5) correlation. In the following paragraphs, we detailed the
observations for all hydrated minerals.

902

<figure><img src="image_17.png" /></figure>

903

904

Figure 16 - (a) Percentage of pixels where both end-members in the pair are positive. The lines indicate the percentage of pixels where the end-member indicated in the column is found with the end-member of the line. The columns indicate the percentage of pixels where the end-member indicated in the line is found in association with the end-member of the column. Red indicates the low percentage and blue indicates the high percentage. This plot is biased by the number of occurrences of a given phase. For instance, the carbonate cells are red, which is the result of the very small number of detection in comparison to the other phases.

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(b) Pearson Correlation Coefficient *r* for each pair of end-members, red indicates anti-correlation, blue indicates correlation and white indicates no correlation, only for pixels where both end-members in the pair are positive are used to calculate *r*. The dotted lines delimitate the hydrated minerals from the non-hydrated mineral. Because this graph is symmetrical we only show one half.

916

a. Fe-Hydroxide
Fe-Hydroxide is always included in the mixture and is present at more than 90% of the modelled pixels in the final maps. It is associated at more than 80% of pixels for all end-members (see Fe-Hydroxide column in Figure 16a). As for its abundance, there is no apparent strong correlation between the Fe-Hydroxide and other hydrated end-members. We observe that it is slightly anti-correlated with the abundance of HCP. This end-member is needed in the modelling with abundance ~16vol% (Figure 14) for all pixels thus for all mineralogical mixtures observed.

## b. Fe-Phyllosilicates

Fe-Phyllosilicates are included in the mixture for class 1 and class 3. They systematically are included with Mg-Phyllosilicates and Fe-Mg Micas for class 1 and with Serpentine and Carbonates for class 3 (Table 1). We explained in Section 2.1, that other end-members could be mixed with Fe-Phyllosilicates based on prior linear fits. The results for this end-member are as follows: in the majority of pixels where Fe-Phyllosilicates are modelled with an abundance > 1vol% we also find Fe-Hydroxide, and Fe-Mg Micas (red on Figure 16a for Fe-Phyllosilicate line), which spatially seems to be the most common assemblage. There is only a minor portion of pixels where Fe-Phyllosilicates are modelled with Mg-Phyllosilicates, Al-Phyllosilicates, Al-Smectite, Olivine, Chlorite, AlSiOH, Opal, Carbonates and Serpentine (blue on Figure 16a for Fe-Phyllosilicate line). Because Fe-Phyllosilicate is spatially prominent, the fact that the rare phases do not correlate in terms of occurrences can come from the fact that they are included in less pixels. This is verified on Figure 16a for the Fe-Phyllosilicate column, where we can observe that for all end-members, except AlSiOH, we find more than 50% of pixels (blue) where Fe-Phyllosilicate is combined with each end-member. AlSiOH is the only exception where the majority of pixels where it is modelled do not require the presence of Fe-Phyllosilicate. In terms of abundance, the only apparent correlation is between Fe-Phyllosilicate abundance and Fe-Olivine abundance (blue on
942

Figure 16b), however the occurrences of Fe-Phyllosilicate and Fe-Olivine are so few that this
correlation is not statistically significant.

943

944

c. Mg-Phyllosilicates

945

Mg-Phyllosilicates are included as often as Fe-Phyllosilicates based on the classification
scheme (Section 2.1). However, they are not very abundant spatially compared to Fe-
Phyllosilicates and do not display a high average abundance. They are often excluded from
the mixture, except for when carbonates are included. Carbonates are the only end-member
for which more than 50% of the modelled pixels required the inclusion of Mg-Phyllosilicates.

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947

948

949

950

In terms of abundance these two end-members show very slight correlation too. There is no
other peculiar combination observed with Mg-Phyllosilicates and other end-members.

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d. Fe-Mg Micas

953

Fe-Mg Micas are included in the mixture in class 1 with Fe- and Mg-Phyllosilicates. They are
an abundant end-member at the surface in terms of spatial coverage but present a relatively
low average abundance. They follow similar trends as Fe-Phyllosilicates and Fe-Hydroxide in
terms of occurrences with other minerals. They are found in combination with all the other
end-members with a match for more than 50% of the pixels (see Fe-Mg Micas column all in
blue on Figure 16a). Also, more than 75 % of the locations where Carbonates and Chlorite
are modelled are also associated with the presence of Fe-Mg Micas. Additionally, we observe
that not a significant portion of pixels show occurrences of AlSiOH in combination with Fe-
Mg Micas their abundances appear to be slightly correlated for the locations where both
minerals are found which corresponds to Marwth Vallis.

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e. Al-Phyllosilicates

964

Al-Phyllosilicates are included in the mixture in class 2 with AlSiOH, Opal and Al-Smectite
and they are less systematically included based on prior linear fits. They are found to be quite

965
spatially spread as they cover more than 40% of the modelled locations. Opal and AlSiOH are found predominantly in combination with Al-Phyllosilicates with more than 50% of positive abundance in association with this phase. Their abundances are also slightly correlated. Al-Smectite is not preferentially found with Al-Phyllosilicate (kaolinite), indicating that even if class 2 assumes that their presence is correlated, it does not appear in the final maps. Especially Al-smectite is almost never present in the southern highlands, where Al-phyllosilicate is indicated. But the locations where both end-members are found (in Marwth Vallis predominantly and in Nili Fossae) seem to show slight abundance correlation.

### f. Al-Smectite

Al-Smectite are also a class 2 mineral. They are found to be a common phase but with just over 30% of modelled pixels with positive abundance. Their average abundance is the second largest after Fe-Hydroxide. AlSiOH is found predominantly in combination with Al-Smectite with more than 50% of positive abundance in association with this phase. Moreover, Al-smectite is not preferentially found with Al-Phyllosilicate, as indicated in the previous paragraph. This spatial distinction is mostly associated to northern lowlands and southern highlands, where Al-Phyllosilicate is found without Al-Smectite in the southern highlands. Its abundance is slightly correlated with Fe-Olivine, as observed for AlSiOH. However, the occurrences of combination of these two phases are quite low and thus this correlation is not statistically significant. The abundance of Al-Smectite also appear to be slightly correlated with Opal.

### g. Opal

Opal is also a mineral phase that is included in class 2. It is a rare phase. It is associated with the majority of AlSiOH detections, and is preferentially found with Al-Phyllosilicates. Also, we observe some occurrences with Fe-Phyllosilicates, Fe-Mg Micas and Al-smectite. In terms
990

of abundances, Opal appears to be slightly correlated with Fe-Mg Micas and Serpentine abundances but the occurrences of the latter pair of minerals being modelled together are very small.

991

992

993

### h. AlSiOH

994

AlSiOH is included in the mixture for pixels of class 2. It is always included with Al-Phyllosilicates, Al-smectite and Opal. This phase is nevertheless rare compared to class 2 Al-smectite and Al-Phyllosilicates both in terms of abundance and spatial coverage (10% of the final maps pixels exhibiting AlSiOH abundance > 1vol%). The co-occurrence of AlSiOH with Al-Phyllosilicates, Opal and Al-smectite supports that the *a priori* assemblage for class 2 is representative of what is found on the Martian surface. AlSiOH does not seem to be favoured with Fe- or Mg-Phyllosilicates, which also indicates that the hypothesis for this end-member are well estimated. Surprisingly, the abundance of AlSiOH is correlated to both Mg- and Fe-olivine. But the abundance correlation corresponds to a very small fraction of pixels where both AlSiOH and olivine are detected simultaneously (only ~5000 px located in majority around 80°W longitude and 20°N latitude, east of Nili Fossae).

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1003

1004

1005

### i. Chlorite

1006

Chlorite is included in the mixture based on the prior linear fits (Section 2.1). It is found to be one of the rare phases with less than 30% of hydrated pixels presenting positive chlorite abundance (Table 6). The abundance of chlorite is correlated to Fe-Mg Micas and anti-correlated to plagioclase and HCP. Also, chlorite and Fe-Mg Micas are correlated in terms of occurrences (dark-blue on Figure 16a), this correlation is probably indicating the enrichment of chlorite and Fe-Mg micas in the cratered terrains of Terra Tyrrhena located around 90° longitude and -10° latitude. However, if we look at the Fe-Mg Micas that are more widespread than chlorite on the surface (Table 6), they do not seem to be preferentially found

1007

1008

1009

1010

1011

1012

1013
1014 with chlorite in terms of occurrences (red on Figure 16a). Its occurrences with Fe-
1015 Phyllosilicates are just above 50% of pixels and a slight correlation is observed in terms of
1016 abundance. Chlorite is barely detected with Al-smectite or Al-phyllosilicate. Chlorite and
1017 AlSiOH have almost no spatial occurrence at similar locations, indicating that this mineral
1018 assemblage is not favour at the Martian surface. Chlorite apparently is not preferentially
1019 found with Al-rich hydrated minerals.

1020 j. Serpentine

1021 Serpentine is one of the mineral phases included in class 3 with Fe- and Mg-Phyllosilicates
1022 and Carbonates. It is found to be a rare phase. It is well correlated in terms of occurrences
1023 with the minerals included in class 3, however more than half of the positive abundance
1024 detections do not include Carbonates. Its occurrences with Al-rich hydrated minerals are quite
1025 low (always < 50%). However, for the few locations where Serpentine is found in association
1026 with class 2 minerals the abundances seem to be correlated. These can be observed in Nili
1027 Fossae and in the cratered terrains where Chlorite and Fe/Mg Micas are most abundant.
1028 Conversely, the abundance of Serpentine is anti-correlated with Fe-Phyllosilicates.

1029 k. Carbonates

1030 Carbonates are included in the mixture in class 3 in association with Fe- and Mg-
1031 Phyllosilicates and Serpentine. It is the minority hydrated phase. The positive abundances of
1032 Carbonates are found with Fe- and Mg-Phyllosilicates and Fe-Mg Micas. It is the only
1033 mineral that have more than 50% of detections in association with Mg-Phyllosilicates (see
1034 Carbonates line on Figure 16a). It is also the only mineral that have more than 50% of
1035 positive detections in association with Serpentine and with Fe-Olivine. Abundances of
1036 Carbonates and Serpentine appear to be correlated too in the eastern part of Nili Fossae.
1037 Carbonates are almost never found in association with Al-rich hydrated minerals. The phase
1038 that has the most positive abundance detection in association with Carbonates is Fe-Olivine
1039 that seem to be preferentially found with this phase, as observed in Nili Fossae (Poulet et al.
1040 2019). Their abundances appear to be slightly correlated (blue on Figure 16b).

1041 **5. Discussion**

1042 We discuss in this section the observed correlations of minerals as described in the previous
1043 section.

1044 5.1- Carbonates and serpentines

1045 Both are correlated which corresponds to the reality of mixture spectra reported by many
1046 regional scale analyses of the carbonates and/or serpentine rich areas at Mars: prominently at
1047 Nili Fossae, but also on the Claritas Rise (Ehlmann et al., 2010), on the Lybia Montes (Tirsh
1048 et al., 2018), as well as other sites peppered through the southern highlands including
1049 Huygens crater and McLaughin crater (Michalski et al., 2013, Wray et al., 2016, Ehlmann et
1050 al., 2010, Bishop et al., 2013 ; Carter et al., 2013). There, at the resolution and SNR of orbital
1051 instruments, spectra are at times indiscriminate between the two phases, which may also be
1052 reflected by model degeneracies. At several locations, both have been confidently identified
1053 and modelled however. These mineral also commonly co-occur on Earth in a number of high
1054 temperature contexts (e.g., Grozeva et al., 2017). It is therefore interpreted that their
1055 correlation not only reflects the occasional degeneracies in the spectra but also their
1056 preferential co-occurrence at Mars.

1057 5.2- Olivine and carbonates

1058 Olivine being the primary protolith for carbonates, their correlation is not surprising. To note
1059 that in the VISNIR, a carbonate spectrum may be mistaken for olivine, especially the Fe-rich
1060 (siderite) specie, but the reverse is not true. If the model detects carbonate, given the weight
1061 set on the diagnostic spectral bands of carbonate, then the co-occurrence of olivine is likely
1062 real. To note however that not all end-members of carbonates are provided as OC in the model, so that the spectral shape < 2 µm characteristic of carbonate could be due to a carbonate species not represented by the model. This warrants future study in the cases where the correlation is the strongest (located in Nili Fossae).
1063
1064
1065

1066 5.3- Pyroxenes and hydrated minerals

1067 The lack of obvious selective correlation at global scale between hydrated minerals and HCP
1068 or LCP or HCP+LCP is puzzling. Although as observed in previous modelling of highly
1069 altered regions (Poulet et al., 2008 and Poulet et al., 2014), in locations where Nontronite is
1070 found in abundance (typically > 20vol%) we start to observe an anti-correlation between
1071 Nontronite abundance and global pyroxene (HCP+LCP) abundance with a Pearson coefficient
1072 of ~-0.35. One typical product of pyroxene alteration is Nontronite which can contain a
1073 variable amount of Ca. The weathering rates and products of both pyroxenes are somewhat
1074 different, so that we expect different secondary mineral to co-occur between Ca-rich and Ca-
1075 poor environments. The variation in nontronite composition is not discernable in VISNIR
1076 spectroscopy, which could be one explanation to this lack of selective correlation. More
1077 generally, alteration of pyroxene to smectite would liberate various amounts of Ca, Mg, Fe
1078 and Na. The fine smectite composition should reflect this to some degree but is likely not
1079 discernable from orbital spectroscopy. This is considered likely also given the fact that it has
1080 been demonstrated that HCP formed later in the Martian history (Poulet, et al., 2009b), in
1081 which the average aqueous settings were likely different to the early era where LCP
1082 dominated the pyroxene content of the basaltic crust.
1083

1083 5.4- Opal and AlSiOH

1084 These phases have spectra and OCs that are very similar (see Figure 2), it is therefore not
1085 surprising that they are strongly correlated in occurrence and abundance (Section 4.2.3). We
1086 interpret this as model degeneracies more than the possible co-occurrence of both phases. The
1087 latter (co-occurrence of Opal and AlSiOH) is not unexpected as precursor phases to
1088 weathering. High spectral resolution data from CRISM would be useful here to better
1089 discriminate phases.

1090 5.5- Al-smectite (Beidellite), Al-phyllosilicate (Kaolin), Opal and AlSiOH

1091 A weak level of correlation is found between the 4 phases, which is partly explained by the
1092 presence of the one diagnostic absorption band in the 2.17-2.25 µm region for each of them.
1093 At the OMEGA spectral resolution and SNR, it is likely that some degree of model
1094 degeneracy exists. To note that kaolins have their spectral band that is most different from the
1095 other three end-members, being a sharper ‘doublet’ band at 2.17-2.21 µm, compared to the
1096 smoother, larger bands that are typically in the 2.20-2.25 µm regions for the other three.
1097 Although, that species is not less correlated to the others, which may suggest that the model
1098 degeneracy is not the only explanation. The Arabia Terra region of Mars (which includes the
1099 Mawrth Vallis plateaus) exhibits laterally extensive leached horizons akin to laterites (e.g.,
1100 Gaudin et al., 2011 and reference therein). Detailed analyses at high resolution in the region
1101 shows that these four phases are co-occuring, sometimes mixed at what would be a sub-pixel
1102 scale in the OMEGA dataset (Bishop et al., 2013, 2020; Poulet et al., 2014). These are typical
1103 leaching products on Earth. Their co-occurrence is therefore not only attributable to model
1104 degeneracies but also reflect their generic co-occurrence, at least in leaching profiles at Mars,
1105 and perhaps at other types of paleo-environments.

1106 5.6- Fe-hydroxide

1107 Fe-hydroxide is modelled to be present and abundant almost everywhere when hydrated
1108 minerals are detected. This hydroxide is not unexpected for Mars, while it is notable that its
1109 presence was not required in the mafic terrains (Riu et al. 2019). However, its lack of
1110 correlation with any hydrated phase is puzzling. Below we present an argument suggesting
1111 that the modelled Fe-hydroxide is necessary in our modelling given our set of end-members
1112 optical constants but perhaps not the only one hydroxides phase.

1113 First of all, its spectral features are non-diagnostic but the model requires its OC in order to
1114 provide a good fit in most cases. This leaves the possibility for other Fe-oxides or perhaps
1115 other phases. The prevalence of this (or another) hydroxide at aqueous mineral deposits on
1116 Mars is demonstrated, and these phases are readily formed on an iron-rich, oxidizing surface
1117 and subsurface.

1118 Other oxides such as hematite have been confidently discriminated in large areas of Mars
1119 from orbit (e.g., TES/MGS at Sinus Meridiani (Christensen et al., 2001), MSL at Gale crater
1120 (Rampe et al., 2017)), as well as poorly crystalline nanophase Fe-oxides (Bell et al., 2000).
1121 Therefore, the presence of abundant oxides is likely, but there is no strong case for
1122 specifically ferrihydrite. In addition to oxides, other phases may be present as suggested by
1123 analyses of the northern plain from VISNIR spectroscopy (Horgan et al., 2014) or by MSL
1124 (e.g., Dehouck et al., 2014). These suggest the presence of glassy, poorly crystalline phases,
1125 that can make up the bulk of the hydrated material as seen *in situ* (e.g., by CheMin, Bristow et
1126 al., 2018). Thus, we posit that this dominant Ferrihydrite phase may well be a mixture of
1127 oxides including Ferrihydrite and poorly crystalline to amorphous phases that are likely also
1128 Fe-bearing. Ferrihydrite is a placeholder pending the acquisitions of OCs for these other
1129 candidates. A more detailed effort to discriminate between hydroxides and their specific but
1130 subtle spectral features is warranted, including them as OCs to the model.

1131 5.7- Phyllosilicates and Olivine

1132 There is a weak positive correlation between Fe-phyllosilicate and Fe-Olivine on one side,
1133 and Mg-phyllosilicate with Mg-Olivine on the other. To note that the spectrum of olivine is
1134 somewhat degenerate because the diagnostic Fe²⁺ electronic transition band is affected by
1135 grain size, so that the model may detect Mg-Olivine with a certain grain size instead of Fe-
1136 Olivine with a smaller grain size. This issue is well documented in Ody et al., 2013. On the
1137 other hand, while grain size may affect band depth of phyllosilicates, it does not affect its
1138 shape or position. Our interpretation of this weak correlation is that the Fe vs Mg
1139 phyllosilicates' occurrences are reflected by the protolith, i.e., Fe vs Mg-rich olivine. In a
1140 mafic to ultramafic protolith such as is that of Mars, olivine will likely be the most susceptible
1141 primary mineral to weathering, followed by pyroxene. We are sensitive to both minerals in
1142 the OMEGA spectra, so that in the spectra where olivine is detected, we expect that the
1143 corresponding rock will be weathered first through the transformation of olivine. On Earth,
1144 olivine is readily replaced or destroyed, so that water-limited alteration would leave detectable
1145 amounts of olivine in the spectra whereas high water:rock alteration would likely not. Only
1146 limited water activity and/or a closed system would therefore have allowed olivine to survive
1147 in part. Those alteration settings also tend to limit inherence of dissolved cations from other
1148 sources, so that the secondary mineral ion composition is close to the primary rock ion
1149 composition: Mg-dominated olivine rock would preferentially weather into Mg-rich smectite,
1150 versus Fe-dominated olivine rock would into Fe-rich smectite. This proposed scenario
1151 corresponds to water-limited alteration where the protolith is still largely in place.

1152 5.8- Chlorite and Fe-Mg Micas

1153 This correlation was wholly expected for two reasons. Firstly, both species present a
1154 diagnostic narrow spectral band at ~2.25 µm. The similarity originates in similar structure:
1155 chlorites are 2:1:1 clays, the :1 begin a mica sheet in between two 2:1 smectite (T-O-T)
1156 layers. This triggers some degree of model degeneracy, which occurrence frequency is not
1157 determined. To note that the depth of this band is variable, so that the model may fit a
1158 spectrum with Chlorite and Fe-Mg Micas to correctly represent the OMEGA data. The second
reason is linked to the formation of Chlorite, which in a diagenetic or pedogenic sequence, originates from mica. Intermediate, interstratified (smectite-mica or vermiculitic) phases are common on Earth where a smectite and/or mica rich rock undergoes chloritization (e.g. Chamley 1994; Tolpeshta et al., 2018), where the 2:1 structure gradually transforms into 2:1:1 as diagenesis follows its course. A good example are the large amounts of chlorite/mica found in the Terra Tyrrhena region, which is dominated by deep crustal exhumation of aqueous minerals by impact craters (e.g., Loizeau et al., 2012, Carter et al., 2013, Viviano et al., 2013).

The presence of Fe-Mg Micas is a lingering puzzle for Mars, as this phase is expected to be extremely common, but has seldom been identified on its own. It rather occurs as mixtures with smectites, chlorites, and perhaps talc and prehnite. One prospective study of high importance would be to look for sites where Fe-Mg Micas are detected with high abundance by the model, but where chlorite is not (mainly in locations in Nili Fossae and in some crater floors in the Southern highlands). This would help pinpoint specifically Fe-Mg Micas deposits on Mars. In contrast, Al-K Micas have more discernable absorption features which are expected to be less common on a basaltic protolith, but are nevertheless confidently found at several locations of Mars.

## 6- Perspectives for in situ characterization

We presented the global distribution of minerals and the different implications in terms of assemblages at the global scale. These maps enable a local characterization of dedicated regions at the surface of Mars. In particular, they can be used for in-depth studies of prospective landing sites for future landed missions and serve as prediction to what will be observed there, even though the OMEGA spatial resolution is quite low. Indeed, the ExoMars2022 rover will carry the MicrOmega instrument which will perform NIR
hyperspectral imaging (Bibring et al., 2017) of the Martian surface for the first time. These
measurements will be the first microscopic measurements of the Martian surface using the
same technique as the one used from orbit with OMEGA. These ground-truth measurements
will enable to verify and constrain what is observed from orbit and presented in this paper.
Additionally, the Perseverance Mars2020 rover carries on its board the SuperCam instrument
that will also allow NIR measurement of the Martian surface. We present in the following
sub-sections the local distribution of hydrated minerals in Oxia Planum selected as
ExoMars2022 landing site and Jezero Crater, landing site of the Perseverance rover. We recall
that the abundances displayed are based on spectral detections of the different end-member
prior the modelling, hence if a mineral is modelled with an abundance > 0vol% it means that
it was previously identified in the MOCCAS catalogue (Section 2.2). The modal mineralogy
of these two sites is in good agreement with what is expected from qualitative mineralogical
detections as described in the following sub-sections (Quantin-Nataf et al., 2021, Horgan et
al., 2020).

6.1) Oxia Planum – landing site of the ExoMars2022 Rosalind Franklin rover

Oxia Planum ellipse is dominated by the presence of Fe-hydroxyde and Fe-phyllosilicate.
Small spots of Mg-phyllosilicate within the Fe-phyllosilicate deposits are also identified with
abundance reaching 20vol%. These locations also correspond to location where Fe/Mg micas
are modelled. This assemblage is common at the global scale (Section 3) when Mg-
phyllosilicate is modelled (representative of class 1, see Section 2). In different locations, we
also find high abundance (>20vol%) of Al-phyllosilicates very locally.

Of particular interest, the kaolin and Al-smectite detections in the eastern outbound of the landing
ellipse correlates with the distal delta-fan unit of Oxia Planum (Quantin-Nataf et al., 2021) where most
mineral diversity has been observed. The latter include kaolin clays and hydrated silica, but were only
resolved at the CRISM pixel scale (Carter et al., 2016). The modelling here may be detecting low
abundance and diffuse signatures of these minerals. At this resolution, the presence of other species with high abundance is not confirmed while other recent studies of this site at higher resolution (Mandon et al., 2021) suggested the potential presence of carbonates in association with strong detections of Fe/Mg-phyllosilicates. This particular discrepancy on the presence of minor phases may be due to the low resolution of OMEGA compared to CRISM, although the presence of carbonates has not been confirmed. The upcoming *in situ* analysis of Oxia Planum soils, in particular with the MicrOmega instrument onboard ExoMars rover, that will perform NIR hyperspectral imaging of the samples, should help confirm (or infirm) the presences of such minor phases.

The absence of several end-members, if verified, also tends to show that the mineral diversity is as complex in Oxia Planum as other well-studied landing site regions (Marwth Vallis or Jezero crater, see Figure 20).

<figure><img src="image_18.png" /><figcaption>Figure 17 - Abundance maps of hydrated minerals at Oxia Planum. The landing ellipse (1220) and butterfly (1221) is shown in black line. The end-member name is indicated on top of each map and</figcaption></figure>
1222

the color bar corresponding to the range of abundance values is located at the bottom of each map. The local maps are generated at a resolution of 64 pixels per degree. The background is HRSC mosaic MC-11W24 (Gwinner et al., 2016). Longitude [-25.5°W; -23°W] / Latitude

1223

1224

1225

[17°; 19°].

1226

## 6.2) Jezero Crater – landing site of the Mars2020 Perseverance rover

1227

1228

1229

1230

1231

1232

1233

1234

1235

1236

1237

1238

1239

1240

1241

1242

1243

1244

1245

1246

The Jezero Crater appears quite depleted in hydrated minerals compared to its vicinity. At this scale, only few scattered detections appear positive in the landing ellipse of Perseverance. Fe-hydroxide is the most abundant hydrated mineral detected in this region with average abundance around 20vol%. As stated before, this end-member has to be systematically included in our model (Section 2.3) to account for the overall shape of the OMEGA spectra and to fit the overall albedo as the spectral signatures are quite weak, however it might not be the most representative oxide to be present at the surface of Mars (Section 5.6). This might explain why in the latest analyses using the CRISM instrument (Zastrow & Glotch, 2021), this end-member is not detected. Further analysis will have to be carried out at local scale to decipher these differences but they fall out of the scope of this paper that primary aims at discussing the global distributions at planetary scale. We observe, that the highest Mg-Carbonates abundances are gathered at the northwestern outer region of Jezero Crater with abundance > 10vol% in a few spots. These observations are consistent with the detection of high abundance of Magnesite in these spots (Tarnas, et al., 2019; Zastrow & Glotch, 2021) called the Marginal Carbonates Unit (Horgan et al., 2020). The majority of Mg-Carbonates spots are correlated with Fe/Mg phyllosilicates and anti-correlated with Al-rich phases, as previously observed in Horgan et al., 2020. This is also consistent with the detection of Opal outside of the carbonates unit that we observe, and that was also made by CRISM (Zastrow & Glotch, 2021). These positive abundances of hydrated minerals are correlated with the presence of Olivine, with mainly Mg-rich olivine (Figure S3). It has been previously observed
that the hydrated mineralogy of Jezero crater is correlated to the presence of olivine (Poulet et al., 2018; Horgan et al., 2020; Zastrow & Glotch, 2021). We show in Figure 18 an example of ROI median spectrum modelled (Figure 18a) with 6vol% of Mg-carbonates; the detections are emphasized on the ratioed spectrum displayed in Figure 18b. The carbonates abundance is lower than the one obtained in Zastrow & Glotch 2021, which is around 20vol%, this is most likely due to the large spatial resolution difference between OMEGA and CRISM.

<figure><img src="image_19.png" /><figcaption>Figure 18 – (a) ROI median spectra modelled in Jezero crater. The modelling predicts a content of 6vol% of Mg-carbonates associated with 3vol% of Mg-phyllosilicates. (b) Ratioed spectrum of the ROI median spectrum. The ROI is located around 77.2°E in longitude and 20.6° in latitude.</figcaption></figure>

We also highlight the presence of Serpentine with abundance ~15vol% on the northeastern rim of Jezero Crater. At this resolution, we observe positive correlation of Serpentine and Carbonates in the Marginal Carbonates unit. At the resolution of CRISM, these detections are distinct (Zastrow & Glotch, 2021). These detections or relatively high abundance of Serpentine are found with Mg-phyllosilicates. Locations with high content of Fe/Mg-micas in combination with Fe/Mg-phyllosilicate are also commonly find around the Jezero crater. We show in Figure 19 an example of ROI median spectrum with high content of Fe/Mg-micas (20vol%) to illustrate the spectral characteristics of these settings.
<figure><img src="image_20.png" /><figcaption>Figure 19 – (a) ROI median spectra modelled in Jezero crater. The modelling predicts a content of 20vol% of Fe/Mg-micas associated with 6vol% of Fe-phyllosilicates. (b) Ratioed spectrum of the ROI median spectrum. The ROI is located around 76.8°E in longitude and 18.9° in latitude.</figcaption></figure>

We newly detected positive abundance of Al-rich phases in the southern part of the crater. These detections may suggest other Al-rich alteration processes, distinctively of the Fe/Mg-rich alteration products detected in the north. The local abundance maps that we produced through the modelling thus increase the diversity observed in Jezero crater from orbit. Although OMEGA has much lower spatial resolution when compared to CRISM and this is not adapted for a direct comparison to CRISM modelling (Zastrow & Glotch, 2021) or specific morphogical unit analysis (Goudge et al., 2015; Horgan et al., 2020), globally our results are consistent with the mineralogical diversity and distribution by these previous workers. Therefore, these recent results also suggest that new findings (e.g., detection of positive abundance of Al-rich phases in new locations within the crater) should be investigated by CRISM to verify in follow up studies.
<figure><img src="image_21.png" /></figure>

1282

Figure 20 - Abundance maps of hydrated minerals in Jezero Crater. The touchdown site of Perseverance is indicated by the green dot in the center. The end-member name is indicated on top of each map and the color bar corresponding to the range of abundance values is located at the bottom of each map. The local maps are generated at a resolution of 64 pixels per degree. The background is HRSC mosaic MC-13E10 (Gwinner et al., 2016). Longitude [76.7°E; 78.0°E] / Latitude [17.8°; 19.2°].

1283

1284

1285

1286

1287

1288

1289
1292 **7- Conclusion and future work**

1293 We present the first global abundance maps of hydrated minerals at the surface of Mars. The
1294 abundances are evaluated using a radiative transfer model that was applied to ~13 millions of
1295 spectra previously clustered into ~20000 regions of interest (ROIs) of similar mineralogy, as
1296 identified with the OMEGA instrument. The 11 abundance maps provide the distribution of:
1297 Fe-, Mg-, Al-phyllosilicates, Fe-hydroxide, Al-smectite, Opal, AlSiOH, Chlorite, Serpentine
1298 and Magnesite at a sub-kilometric resolution. All pixels that included signatures of sulfates
1299 were excluded of the modelling. The hydrated minerals are modelled in combination with a
1300 set of primary minerals (high- and low-calcium pyroxene, Fe- and Mg-olivine and
1301 plagioclase) that are tracers of the broadly basaltic nature of the surface. The modelling has
1302 proven efficient as the global maps are able to highlight the expected variations of altered
1303 mineralogy at the Martian surface. We observe that the most abundant mineral is represented
1304 by the end-member of Fe-hydroxide with an average abundance of 16vol%. This mineral is
1305 present in more that 95% of the modelled spectra, thus it is the most abundant spatially and
1306 compositionally. The most frequent phases are: Fe-phyllosilicates (6vol% on average); Fe/Mg
1307 Micas (6vol% on average); Al-phyllosilicates (7vol% on average); Al-smectite (14vol% on
1308 average) and Chlorite (10vol% on average). The remaining end-members are the rare phases
1309 with low average abundance and/or low spatial coverage: Mg-phyllosilicates (3vol% on
1310 average); Opal (5vol% on average); AlSiOH (6vol% on average); Serpentine (6vol% on
1311 average) and Magnesite (1.5vol% on average). While the global distributions presented here
1312 are representative of what is expected at Mars, with the quantitative approach we are able to
1313 highlight the variability of the abundance of hydrated mineral between the southern highlands
1314 and northern lowlands with an increase of the total hydrated minerals content up to ~50vol%
1315 in specific spots. However, the output of the model indicates that in these hydrated locations,
1316 the primary minerals almost always account for more than 50% of the mineralogy.
At a more local scale, we show that the mineralogical distribution of the ExoMars2022 and Mars2020 rovers landing sites is in good agreement with what is predicted with detection methods and also suggests new findings which shows that the modelling is promising for further local analyses. In depth analysis of the abundance variations will be carried out in the future, with combination with CRISM observations to greatly increase the spatial resolution that constitute a drawback of the OMEGA dataset for in-depth local analysis. This is envisioned in order to prepare for the upcoming *in situ* analyses. The new global composition maps will pave the way for subsequent local studies.

1325

## Acknowledgements:

This project has received some funding from NASA-JPL under grant agreement n° KM-2691-947266 and from the CNES space agency. We would like to warmly thank the two reviewers Mikki Osterloo and Tim Goudge for their very helpful and constructive comments and reviews that contributed to improve this manuscript.

1331

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1556

1557

1558

1559
1560

# Appendix

1561

We display in the appendix all the global abundance maps detailed in the manuscript. For the non-hydrated minerals. We also provide here the histograms of the abundance's distribution.

1562

1563

1) Abundance maps of hydrated minerals

1564

1565

1.1) Fe-hydroxide (Ferrihydrite)

1566

1567

<figure><img src="image_22.png" /></figure>

1568

Figure A1 -Abundance map of Fe-hydroxide overlaid on THEMIS background. The abundance values are between 1vol% and 50vol%.

1569

1570

1571

1572

1573
1574 1.2) Fe-phyllosilicate (Nontronite)

<figure><img src="image_23.png" /></figure>

1575

1576 Figure A2 - Abundance map of Fe-phyllosilicate overlaid on THEMIS background. The

1577 abundance values are between 1vol% and 35vol%.

1578 1.3) Mg-phyllosilicate (Saponite)

<figure><img src="image_24.png" /></figure>

1579

1580 Figure A3 - Abundance map of Mg-phyllosilicate overlaid on THEMIS background. The

1581 abundance values are between 1vol% and 20vol%.
1582 1.4) Fe/Mg-micas (Celadonite)

<figure><img src="image_25.png" /></figure>

Figure A4 - Abundance map of Fe/Mg micas overlaid on THEMIS background. The abundance values are between 1vol% and 20vol%.

1583

1584

1585

1586 1.5) Al-phyllosilicates (Kaolins)

<figure><img src="image_26.png" /></figure>

Figure A5 - Abundance map of Al-phyllosilicate overlaid on THEMIS background. The abundance values are between 1vol% and 25vol%.

1587

1588

1589
1590 1.6) Al-smectite (Beidellite)

<figure><img src="image_27.png" /></figure>

1591

1592 Figure A 6 - Abundance map of Al-smectite overlaid on THEMIS background. The

1593 abundance values are between 1vol% and 35vol%.

1594 1.7) Opal

<figure><img src="image_28.png" /></figure>

1595

1596 Figure A 7 - Abundance map of Opal overlaid on THEMIS background. The abundance

1597 values are between 1vol% and 20vol%.
1598 1.8) AlSiOH

<figure><img src="image_29.png" /></figure>

1599

1600 Figure A8 - Abundance map of AlSiOH overlaid on THEMIS background. The abundance

1601

values are between 1vol% and 15vol%.

1602 1.9) Chlorite
<figure><img src="image_30.png" /></figure>

Figure A9 - Abundance map of Chlorite overlaid on THEMIS background. The abundance values are between 1vol% and 20vol%.

<figure><img src="image_31.png" /><figcaption>1.10) Serpentine (Lizardite)</figcaption></figure>

Figure A10 - Abundance map of Serpentine overlaid on THEMIS background. The abundance values are between 1vol% and 15vol%.
1610 1.11) Carbonates (Magnesite)

<figure><img src="image_32.png" /><figcaption>Figure A11 - Abundance map of Carbonates overlaid on THEMIS background. The abundance values are between 1vol% and 5vol%.</figcaption></figure>

1611

1612

1613

1614

1615

1616

1617

1618

1619

1620

1621

1622

1623

1624

1625

1626

1627

1628
1629

2) Abundance maps of non-hydrated minerals

1630

2.1) High-calcium Pyroxene (Diopside)

<figure><img src="image_33.png" /></figure>

1631

1632

Figure A12 - Abundance map of HCP overlaid on THEMIS background. The abundance values are between 1vol% and 30vol%.

1633

<figure><img src="image_34.png" /></figure>

1634

1635

Figure A13 - Histogram of HCP abundance (vol%).
1636 2.2) Low-calcium Pyroxene (Pigeonite)

<figure><img src="image_35.png" /></figure>

Figure A14 - Abundance map of LCP overlaid on THEMIS background. The abundance values are between 1vol% and 20vol%.

<figure><img src="image_36.png" /></figure>

Figure A15 - Histogram of LCP abundance (vol%).

1637

1638

1639

1640

1641

1642
1643 2.3) Mg-olivine (Forsterite)

<figure><img src="image_37.png" /><figcaption>Figure A16 - Abundance map of Mg-olivine overlaid on THEMIS background. The abundance values are between 1vol% and 15vol%.</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>Figure A17 - Histogram of Mg-olivine abundance (vol%).</figcaption></figure>

1644

1645

1646

1647

1648

1649
1650 2.4) Fe-olivine (Fayalite)

<figure><img src="image_39.png" /></figure>

1651

1652 Figure A18 - Abundance map of Fe-olivine overlaid on THEMIS background. The abundance values are between 1vol% and 15vol%.

1653

<figure><img src="image_40.png" /></figure>

1654

1655

<caption>Figure A19 - Histogram of Fe-olivine abundance (vol%).</caption>

1656
1657 2.5) Plagioclase (Labradorite)

<figure><img src="image_41.png" /></figure>

Figure A20 - Abundance map of Plagioclase overlaid on THEMIS background. The abundance values are between 1vol% and 60vol%.

1658

1659

1660

1661

<figure><img src="image_42.png" /></figure>

Figure A21 - Histogram of Plagioclase abundance (vol%).

1662

1663