A Mars orbital catalog of aqueous alteration signatures
(MOCAAS)

John Carter, Lucie Riu, François Poulet, Jean-Pierre Bibring, Yves Langevin,
Brigitte Gondet

► To cite this version:

John Carter, Lucie Riu, François Poulet, Jean-Pierre Bibring, Yves Langevin, et al. A Mars orbital catalog of aqueous alteration signatures (MOCAAS). Icarus, 2023, 389, pp.115164. 10.1016/j.icarus.2022.115164. hal-04300416

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# A Mars Orbital Catalog of Aqueous Alteration Signatures
(MOCAAS)

John Carter<fnref n="1" />,<fnref n="2" />, Lucie Riu<fnref n="3" />, François Poulet<fnref n="1" />, Jean-Pierre Bibring<fnref n="1" />, Yves Langevin<fnref n="1" />, Brigitte Gondet<fnref n="1" />

*This paper is dedicated to the memory of our dear colleague and friend, Brigitte Gondet.

## Abstract

We describe the completion of the MOCAAS project providing a global repository of secondary minerals formed through interaction with water on Mars. This work is based on the analysis of orbital imaging spectroscopy data from the OMEGA/Mars Express and CRISM/MRO near-infrared instruments. A database and a set of high-resolution global maps (200 m/pix) are produced which provide a large collection of these "aqueous" secondary mineral deposits, most of which were not previously reported. Several aqueous mineral classes are discriminated including hydrated silicates, hydrated silica, sulfate and carbonate salts. A preliminary statistical analysis on the database of aqueous mineral deposits is carried out, revealing significantly more widespread and diverse aqueous alteration on Noachian and Hesperian Mars than previously seen. Higher resolution local scale studies are also carried out over current and prospective rover landing sites on Mars, providing enhanced sensitivity to mineral detection and reachable science targets. Collectively, the data presented here at all scales is expected to foster synergy between orbital and landed missions, particularly for future missions and to pinpoint prospective resources for human exploration.

## 1. Introduction

### 1.1 Scope of the work

We discuss the global-scale aqueous mineralogy of Mars as seen from orbit. For the purpose of this work, aqueous minerals are defined here as secondary minerals formed through aqueous alteration, i.e. in the presence of water, often containing water or hydroxyl in their structure. In our instance these include: phyllosilicates, other hydroxylated and hydrated silicates, hydrated silica (of various crystallization states), hydroxides, sulfate and carbonate salts. Aqueous minerals are high-value scientific targets and potential resources for future exploration. In particular, they are key drivers in the selection of contemporary and upcoming landing sites at Mars. From orbit, aqueous minerals are identified and studied using infrared imaging spectrometers, and with a more diverse suite of in-situ instruments on landed platforms.

<footnote n="1">¹Institut d'Astrophysique Spatiale (IAS), CNRS/Paris-Saclay University, France.</footnote>

<footnote>john.carter@ias.u-psud.fr</footnote>

<footnote n="2">²Laboratoire d'Astrophysique de Marseille (LAM), CNRS/Aix-Marseille University, France.</footnote>

<footnote n="3">³ESA-ESAC, Madrid, Spain.</footnote>
This paper describes the "MOCAAS" (Mars Orbital Catalog of Aqueous Alteration Signatures) project, a 10-year endeavor which built a comprehensive high-resolution repository of aqueous minerals on Mars.

## 1.2 State of the art

Here we present a succinct review of the global scale analyses of aqueous minerals on Mars. The first significant global map or repository of a Mars mineral thought to have formed through interaction with water was provided by the infrared imaging spectrometer TES on MGS [Bandfield+02]. The abundance of hematite was mapped globally (polar regions excluded) and a few hematite-rich provinces were recognized, notably in Aram Chaos and Sinus Meridiani. Ferric oxides were also detected and mapped using near-infrared datasets as reported in e.g. [Bibring+07].

Aqueously formed hydrated or hydroxylated minerals (OH/H₂O bearing) were first detected from orbit with the OMEGA (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité) instrument on Mars Express. These are detected in the [1.1-2.65] µm near-infrared wavelength range. A first global repository of their distribution was provided in [Bibring+06] for non-polar regions, and further completed in [Langevin+05] to include the northern peri-polar gypsum sulfate deposit. That map was later updated in [Poulet+06] based on a global survey of the 1.9 µm absorption band common to most hydrated/hydroxylated minerals. These can be considered the initial results from the OMEGA global-scale investigation of hydrated minerals. Taken together, these made possible the identification at the kilometric scale of large expanses of sulfates and clays in the Valles Marineris, Mawrth Vallis, Nili Fossae and Sinus Meridiani regions. These have been extensively studied since then, and we will refer to them hereafter as "mineral provinces" on the basis of their large sizes. A few tens of additional sites with aqueous mineral deposits were pinpointed throughout the southern highlands of Mars.

With the advent of the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) instrument on MRO (Mars Reconnaissance Orbiter), refined studies of the mineralogy and context became possible, despite the initial limitations in terms of spatial coverage (each high resolution spectral cube is about 9x6 km in size). Using the same wavelength range as OMEGA, an updated global repository of hydrated minerals was provided by [Mustard+08] based on CRISM data, which was appended to the initial OMEGA repository in [Poulet+09]. Together with [Murchie+09] these constitute the initial CRISM global repositories of hydrated minerals. By then, aqueous minerals were known to be diverse in their mineralogy and geomorphic context. They were observed to be abundant in the aforementioned mineral provinces, with additionally nearly 100 scattered deposits elsewhere at Mars. To note that *a*A number of other works were published which provided more detailed mapping and analyses of a small subset of these sites.

The most complete global-scale analyses of aqueous minerals for Mars were published in [Ehlmann+11] and [Carter+13]. The former work provides a catalogue of 365 sites with clay mineral detections, pinpointed on a global (non-polar) map. The detection database originated from the aforementioned works with new additions from the authors. [Carter+13] provides a catalogue of 1230 mostly new sites of aqueous mineral detections outside of the known mineral
78 provinces, which are also pinpointed on a global (non-polar) map. The large mineral provinces
79 are also mapped at the kilometric scale.

80 **To note that in** our study, we did not include hematite and chloride salts. Both are anhydrous
81 minerals but which are inferred to have formed in the presence of water on Mars [e.g.
82 Osterloo+10; Christensen+00; Christensen+04], although hematite is also known to occur as a
83 primary mineral on Earth. The latter reason was why hematite was not initially included, although
84 it can exhibit absorption features covered by the OMEGA and CRISM instruments albeit in the
85 shorter, visible part of the spectrum (<1.1 µm). Anhydrous chloride salts are unambiguously
86 detected in the infrared using TES and THEMIS but beyond the spectral range of OMEGA and
87 CRISM, which was the practical reason why they are not included in the first version of this
88 database. On the other hand, carbonates are aqueously formed minerals that are usually
89 anhydrous but have diagnostic features in the [1.1-2.65] µm spectral range, and so they were
90 included in the database.

91 The state of the art at the inception of this project in 2011 was the following:

92 1) The hydrated mineral provinces of Valles Marineris, Mawrth Vallis, Sinus Meridiani and
93 Nili Fossae had been mapped nearly entirely at the kilometric scale, and many detailed
94 pin-point analyses had been carried out of their mineralogy, stratigraphy and geomorphic
95 context. This was made possible at select areas thanks to high-resolution CRISM
96 observations. For most of these provinces, global sub-kilometric scale mapping of the
97 spatial distribution and extent of their aqueous mineral units could be improved.

98 2) Over 1000 isolated locations of aqueous minerals had been identified and their detailed
99 aqueous mineralogy reported. However their detailed spatial distribution and the full
100 extent of the mineral deposits was not analyzed and/or reported, except at a few
101 locations.

102 3) It was recognized that many more occurrences of hydrated minerals must exist on Mars,
103 as hinted at in an internal OMEGA database of 1.9 µm spectral signatures and from
104 localized test-mapping of CRISM "multispectral/survey" products.

105 Since the 2011-2013 global studies, no new global scale mapping campaigns of aqueous minerals
106 were performed. This can be explained by the difficulty in building a comprehensive repository
107 of aqueous minerals that is not just an increment over previous work. Our project, formerly
108 introduced as "MOCHAS" in [Carter+16] is a follow-up of the global repository from [Carter+13].
109 It is aimed at significantly expanding the catalog of aqueous mineral occurrences identified at
110 Mars, while enriching the information provided on them. The only viable option that was found
111 was a blunt-force approach akin to geologic mapping: the entirety of the Martian non-polar
112 surface would be systematically studied for its aqueous mineral content at high resolution
113 (mostly at ~200 m/pix), so as to retrieve both the detailed mineral composition and spatial
114 distribution of the deposits. An analogy to illustrate the significant change in the mapping
115 approach would be to consider the difference between having ~1000 scattered pin points on
116 Earth indicating the local geologic units, as compared to providing a full geologic *map* of the
117 Earth.
118 **1.3 Project goals**

119 Our project was intended by its authors to serve multiple purposes as listed below. That list is
120 provided here because it could serve as a basis of discussion for future prospective studies within
121 the Mars community.

122 • Provide a statistically meaningful approach to aqueous minerals on Mars. The catalogue
123 provides a significant number of mineral detections with fewer biases compared to previous
124 work. Previous global scale studies were detrimentally affected by observational and
125 methodological biases, most notably: i) partial spatial coverage of the Mars surface at sub-
126 km resolution, ii) lower detection sensitivity, and iii) uniformity biases arising when collating
127 repositories built with different approaches and analyzed by different scientists. Global
128 trends in terms of composition, context, age and relationships with other surface features
129 can now be more accurately derived with this database. Early results are shown in section 4.

130 • Provide regional mineralogical context to better interpret analyses from local-scale studies
131 carried out in specific "regions of interest". For example, is the aqueous mineral makeup of
132 an intra-crater deposit specific to a given crater, or one occurrence among many in the same
133 region? If the latter, a regional phenomenon could be invoked.

134 • Identify previously unknown aqueous mineral deposits that should be subjected to in-depth
135 follow-up investigations from orbit. Examples of such occurrence could include detections
136 with well-preserved geologic contexts or uncommon mineral makeup as illustrated in
137 [Carter+18; Carter+19].

138 • An important corollary: identify high science merit candidate landing sites for future surface
139 missions (landers, rovers, drones, human exploration). We discovered and proposed the Oxia
140 Planum landing site for ExoMars 2022 using this database ([Carter+15]). Most current and
141 several candidate rover landing sites have been mapped in detail as part of this work, as
142 presented in section 5.

143 • Newly identified aqueous mineral deposits can be targeted for complementary observation,
144 in particular by context imagers such as HRSC, HiRISE and CaSSIS. We submitted a large
145 number of HiRISE imaging targets from on our database that were processed through the
146 University of Arizona “HiWish” portal (www.uahirise.org/hiwish). A more systematic
147 integration of the aqueous mineral deposits as context imaging targets is desirable.

148 • This database provides mineral detections, not mineral abundances. The latter is highly
149 desirable because the modal makeup allows to predict the rock type and better constrain the
150 environment there, as well as quantifying the amount of volatiles that are sequestered in
151 Martian rocks. One goal that is already partially achieved was to feed an abundance retrieval
152 model with mineralogical make-up and the location of deposits from our database. We could
153 hence both i) provide realistic inputs on the likely minerals to be used in the inversion model,
154 and ii) allow for several orders of reduction in computational time by focusing on known
155 mineral deposits compared to systematically mapping the entirety of the Mars surface at high
156 resolution. That approach was first implemented at the local scale on OMEGA data using
157 Shkuratov theory [Shkuratov+99] in [Poulet+08]. Now, a first-run global abundance mapping
158 of aqueous minerals was conducted in [Riu+21] based on the largest OMEGA detections of
159 aqueous mineral deposits from our database.
160 • A strong emphasis was placed on reliability when making detections of aqueous mineral
161 deposits. In a sense, this database can be used as starting point for more advanced future
162 orbital mapping efforts. As described in section 2, we used rather simple algorithms to detect
163 and identify minerals with OMEGA and CRISM. Supposedly improvements in the sensitivity
164 and efficiency of orbital mapping will materialize, in which case our work can be used as a
165 training/benchmarking dataset.

166 To further these goals, in the following sections we:

167 a) Describe the methodology that allows us to detect aqueous mineral deposits at the surface
168 of Mars starting from the raw data from orbital spectrometers (section 2)

169 b) Define the procedure to map out these aqueous mineral deposits as a function of their
170 mineralogy, at the global scale (section 3)

171 c) Present first global-scale analyses of our detection sample (section 4)

172 d) Benchmark our methodology on rover landing sites at the regional to local scale (section 5)

173 e) Discuss limitations, biases and perspectives (section 6).

174 Technical description on the database and mineral maps are provided in the supplementary
175 material.

## 2 Methodology

177

### 2.1 Data sample

179 We used the OMEGA/Mars Express and CRISM/MRO datasets [Bibring+04; Murchie+07] which
180 both allow retrieval of Mars surface reflectance in the [1.1-2.65] µm range used in this study.
181 That wavelength range enables us to detect aqueously formed minerals based on their diagnostic
182 spectral absorptions resulting from the presence of hydroxyl, water or carbonate groups.
183 Anhydrous minerals that were formed aqueously such as chlorides or some oxides are not
184 included in this database. An illustration of the range of aqueous minerals that are detectable on
185 Mars in this range can be found in [Carter+13]. While several aqueous minerals also have
186 absorptions in the visible portion of the spectrum (<1.1 µm), they often cannot be uniquely
187 assigned to a specific mineral phases. The infrared range > 2.7 µm acquired by both instruments
188 turned out to be of little help to retrieve additional constraints on most aqueous minerals due to
189 a combination of effects, most notably: instrument artefacts, thermal emission from the surface
190 and strong absorption by molecular H₂O or non-structurally bound OH- and H₂O. We used nadir
191 or near-nadir pointings only. No observation after MEx orbit #4800 was used because of
192 instrument degradation with time.

193 OMEGA spatial sampling ranges from ~4 km/pix to ~300 m/pix, although the highest resolution
194 observations are seldom used in this study because they are minor contributors to surface
195 coverage, so that most data used here is at the kilometric scale.

196 Regarding the CRISM dataset, we mostly used the CRISM multispectral/survey mode
197 (observation modes labelled “MSP” for Multi Spectral Survey, “MSW” for Multispectral Window,
198 and “HSP” for Hyperspectral survey) with spatial sampling 100-200 m/pix which efficiently cover
large surface areas of Mars. In this respect MSP observations are the most useful CRISM product although they provide a relatively low number of spectral channels (at least twice fewer than OMEGA within the same spectral range). We also included CRISM observations in "targeted" mode (observation modes "ATO" for Along-Track Oversamples, "ATU" for Along-Track Undersampled, "HRL" for Half Resolution Long, "HRS" for Half Resolution Short, "FRS" for Full Resolution Short, and "FRT" for Full Resolution) with an effective spatial sampling down to less than 15 m/pix at best and a spectral sampling twice better than OMEGA. These highest resolution products are not used in a systematic fashion as detailed in section 3.2.2.

An important consideration here is that we based our analysis on the calibrated radiance for OMEGA or I/F ratio for CRISM. We did not use higher level data products comprising global/regional projected and tiled cubes from OMEGA or CRISM. Working on individual observations and in the native instrument detector space with limited pre-processing allows us to implement more sensitive algorithms to detect the faint signatures of hydrated minerals and tackle observation-specific biases.

The starting point of this study was the database from [Carter+13] as it was the largest monolithic catalogue of aqueous minerals available. That catalogue was itself built based on confirmed OMEGA detections from [Poulet+06] as well as many more additional aqueous mineral deposits. It featured in majority CRISM targeted observation-based detections (969 out of 1230). Section 4.1 provides detailed statistics related to our sample datasets.

## 2.2 Data reduction

The data reduction pipeline used an improved set of algorithms derived from [Carter+13b]. The calibrated surface I/F data from OMEGA and CRISM is corrected for the incident illumination angle and CO₂ atmospheric transmission (see references therein). The resulting reflectance cube is de-spiked and de-biased, then the spectral continuum is removed. De-spiking consists in detecting outliers in the spectrum of each pixel and replacing them by their local average.

Continuum removal is achieved by removing low frequency information in the spectrum of each pixel then normalizing the spectrum to unity. This is done by means of a 1D starlet transform [Stark+11]. That same transform is also used to remove high frequency noise in the spectra when building the so-called "browse maps" in section 3.2.3.

Schematically, the way the spectral cubes are de-biased is that we define an area within the cube over which we compute the average spectrum (hereafter the "neutral" or "denominator" spectrum). We then divide each pixel spectrum of that cube by this neutral spectrum. That neutral denominator is hypothesized to exhibit the following properties:

i) Exhibit a mineral signature that is "neutral" in the sense that it is mostly anhydrous and of no interest to us here. This is by design.

ii) Within an aqueous mineral deposit, it is hypothesized that this neutral mineral (or mineral mixture) is intimately mixed to some degree with the aqueous minerals we are seeking;

iii) Contain multiplicative instrument artefacts or calibration residuals;
iv) Contain residual CO₂ atmospheric transmission signatures especially around 2 µm due to an imperfect atmospheric correction, with an atmospheric path similar to that of the aqueous mineral that is sought after.

It was demonstrated in [Carter+13b] that the weak OMEGA or CRISM signatures of aqueous minerals could be efficiently extracted with good SNR once this de-biasing (or "cube flattening") procedure is fine-tuned. Best results occur when optimizing the region over which we compute the neutral denominator spectrum which a) entirely lacks aqueous mineral signatures in it to avoid self-subtraction, and b) best matches the non-aqueous spectral component in our aqueous mineral deposit of interest. CRISM data additionally suffers from vertical striping at each wavelength channel so the neutral denominator is in fact computed for each column in detector space then divided to all pixel spectra of that same column.

The specific data processing steps implemented in this work are as follows:

- To avoid self-subtraction (point "a") above), we use an iterative algorithm where we: i) make a first guess of the location of aqueous minerals in the cube using spectral parameter maps tuned to their absorption bands (defined in Table 1 in section 2.4), ii) remove those areas when averaging the neutral denominator spectrum, iii) de-bias the whole cube by dividing by the latter spectrum, iv) repeat step "i" but on the de-biased spectral cube, which will yield a more reliable map of aqueous minerals. Repeat steps ii)-iii) for up to 3 iterations max.

- Improvements also focused on better de-biasing OMEGA and CRISM multispectral observations since the pipeline was initially optimized for the nearly square and much smaller field of view of CRISM targeted observations. With the former data products, one cannot expect the non-aqueous mineralogy that dominates most of Mars by areal extent to be homogeneous within the observation FOV. That hypothesis could hold for CRISM targeted observations because the scale was a few kms, not 100s of kms. We hence implemented a rolling average with a large apodization window size (100s of pixels in the along-track direction of the S/C) to compute the local neutral denominator spectrum. Now the neutral mineralogy is hence re-assessed along track so that it is most suitable at each pixel of the spectral cube.

The routines are optimized so that full processing of an OMEGA or CRISM I/F data is entirely automated and takes no more than 10s of seconds, which yields the optimized de-biased cube with a set of spectral parameter maps (see next section).

## 2.3 Detecting aqueous mineral deposits

At this point it becomes necessary to distinguish between 1) the aqueous mineral deposit itself, which refers to the true physical state and extent of a concentration of aqueous mineral which has a physical boundary at the surface of Mars; and 2) our detections of clusters of aqueous mineral signatures from orbit, which can only provide a rough idea of what the actual deposit is for lack of ground truth. Hereafter we refer to these as an aqueous mineral detection (AMD). The latter is a data-oriented definition: an AMD is a cluster of contiguous (or nearly contiguous) pixels which we ascertained to contain similar spectral rationale for one or several aqueous minerals, to the sensibility limit of orbital near-infrared spectroscopy and observation spatial sampling. We
define AMDs irrespective of native instrument pixel scale: these may apply to high resolution < 277 20 m/pix CRISM data, to kilometric scale pixels in OMEGA data. An important consideration here 278 is that within an AMD, the true extent of the aqueous mineral deposit is not known accurately. 279 For example, within an AMD comprising several CRISM pixels of 200x200 m² each, it may well be 280 that the entire surface of each pixel is covered with aqueous minerals. Most likely however, only 281 a fraction of each pixel may contain aqueous minerals. This uncertainty is an intrinsic limitation 282 of orbital datasets. 283

Here we describe the generic process by which CRISM or OMEGA observations were analyzed 284 over an area of interest. We perform a series of operations so that in the end, only confirmed 285 AMDs remain, which are further classified (interpreted) into "spectral-mineral" classes of 286 aqueous mineral. The procedure is described step by step below and illustrated in figure 1. 287 Further information on the following steps may be found in the Supplementary Material. 288

- Step 1. The calibrated OMEGA radiance or CRISM I/F data cube is reduced according to the 289 strategy described in section 2.2. 290

- Step 2. We tune in to the diagnostic spectral features of aqueous minerals using the usual 291 approach to OMEGA and CRISM data, by measuring the depth of absorption bands or 292 shoulders. This is amply described in the literature (e.g. [Pelkey+07; Carter+13b; Viviano- 293 Beck+14]). An illustration is shown in figure 1 (panels C and D). Typical absorption bands are 294 from H₂O, Si-O, Si-OH, Metal-OH and CO₃. We build one parameter map for each band by 295 doing simple band math: a ratio between the local spectral average of the de-biased and 296 normalized reflectance within the foreseen band center position to that of its band 297 shoulder(s) position(s). Further description on band definitions is provided below. 298

- Step 3. Once we have built a set of individual spectral parameter maps tuned to the most 299 common aqueous mineral absorption bands, we build a map that is tuned to the most 300 common spectral contaminants. These have been observed to be due to instrument 301 artefacts, photometric effects, ices/frosts, and rarely anhydrous minerals. The resulting 302 « contaminant map » is used to mask out potential false positives in our aqueous mineral 303 maps. An example is shown in figure 1B with further information provided in the 304 Supplementary Material. 305

- Step 4. We combine the maps from steps 2 and 3. 306

o Step 4a. We combine together the spectral parameter maps of step 2. By combining 307 several absorption bands, we are able to provide maps that are more specific, 308 therefore a) less likely to be a false positive due to a non-aqueous mineral spectral 309 signature, and b) more likely to indicate a specific mineral or mineral class which share 310 diagnostic combination of bands. Depending on the class of mineral that is sought out, 311 we use different combinations. The combinations are simple: we add or multiply the 312 individual parameter maps together. The specific bands that are combined are 313 discussed in the following section and summarized in Table 1. 314

o Step 4b. The contaminant map of step 3 is subtracted from the combined spectral 315 parameter maps from step 4a. The resulting map is shown in figure 1E. Weights that 316
317 are experimentally determined are applied to both set of maps, they can be adjusted
318 as required per observation (see Supplementary Material for detailed discussion).

319 - Step 5. That final map typically has clusters of contiguous pixels and isolated pixels elsewhere
320 that are randomly distributed. We consider that the isolated pixels outside of the clusters are
321 not aqueous minerals, and we remove them (see discussion in Supplementary Material). We
322 use a cut-off criterion on the cluster size starting at 3 pixels minimum regardless of the data
323 product and its resolution, so that single pixels or 2-pixel clusters isolated from larger clusters
324 are removed. The result can be seen in figure 1F.

325 - Step 6. With the final map now "cleaned" of isolated pixels or clusters of 2 pixels, we now run
326 a cluster detection algorithm which we developed for this purpose. It which extracts the pixel
327 positions and size of each identified cluster (figure 1G).

328 - Step 7. We determine both the median spectrum and a weighted mean spectrum of each
329 identified cluster. Reliability is further improved by generating median or mean spectra with
330 or without further spectral continuum normalization. This normalization consists in dividing
331 the spectra by a mean "neutral" spectrum computed over an annulus outside the cluster. A
332 set of 4 spectra are hence generated. The annulus is 1 pixel wide with a radius starting 2 pixels
333 from the boundary of the studied clusters.

334 - Step 8. At the outcome of the previous step, the final cleaned map likely indicates the
335 presence of an AMD of the target mineralogy. However, false positives are still possible, as
336 well as mis-classifications between aqueous mineral classes (see Table 1 listing the occasional
337 cross-contaminations between spectral-mineral classes). We therefore systematically
338 implemented a manual visual inspection by a human operator as a validation step. The
339 operator investigated the mean and median spectra with or without normalization resulting
340 from step 7. This is the most time-consuming part of the process, but it is critical for the
341 reliability of detections: for each candidate AMD cluster in all OMEGA and CRISM
342 observations, we determine on the basis of the spectral features observed in these four
343 spectra whether this cluster is indeed an AMD or not. To be conclusive, the following
344 conditions must be fulfilled:

345 o A) the most diagnostic spectral band depth of the targeted aqueous mineral must
346 exceed 5 sigma as assessed on the nearby spectral continuum. For example, the 2.3
347 µm band for Fe/Mg-rich phyllosilicates with respect to a continuum in the 2.08-2.25
348 µm region.

349 o B) In line with the way the spectral band maps are combined in step 4 (see Table 1
350 and also Supplementary Material), we require the spectral rationale to show at least
351 one other band at 3-sigma depth to confirm the detection. For example the 1.9x µm
352 band for those same phyllosilicates.

353 o C) No obvious spectrally dominant artefacts must be present in the same spectral
354 regions as the aqueous mineral signatures. Artefact or contaminants are those
355 mapped in step 3 (see Supplementary Material).

356 o D) The shape of the cluster as mapped in step 5 must not show an obvious artificial
357 shape that would indicate it is an instrument artefact. These include along track
358 (vertical in detector space) stripes, large (>2x2 pix) squares or rectangles with dubious
359 spectral content, across track (horizontal) clusters.
It can happen that the four cluster spectra generated in step 7 do not resemble each other, especially when the clusters are small (small statistics effects over a few pixels) and the spectral variance high in the area surrounding the cluster (e.g. mixtures of bright dusty and dark volcanic terrains). If at least one of the four cluster spectra shows convincing spectral rationale and abides by conditions A-D above, we validate that detection. While these conditions allow for objective attribution of AMDs, it should be noted that the trained eye of the workers glancing at the spectra is often tantamount to these deterministic decision criteria.

If the cluster is identified as an AMD, we further classify it according to our mineral classes described in the next section.

## 2.4 Identifying the mineral composition

At the end of step 8, only confirmed AMDs are retained which have been visually verified by a human worker. They are further classified into one of 5 “spectral-mineral” class which are summarized in **table 1**. This classification is done visually each time. We do not discriminate exact mineral species but rather we sort aqueous minerals by spectral resemblance. Doing this relies on a large number of theoretical and laboratory work on analogue samples, yielding spectral libraries, that provide us good confidence on what makes certain spectral features diagnostic of this or that aqueous mineral. Fortunately, minerals with similar chemistry and structure have spectral signatures in the near-infrared that are also similar, which provides a degree of coherence for some species within a given class. Unfortunately, exceptions exist where minerals of different families may have very similar spectral features in the near infrared, especially with limited SNR or spectral sampling. This is for example the case of zeolites versus hydrated sulfate salts. This is why in our AMD database we provide confirmed detections of aqueous minerals, but we do not provide the exact mineral makeup of the AMD.

We have extensively surveyed the fine spectral content within each class, using CRISM targeted observations with its superior spectral resolution to validate our mineral discrimination capability. This allows us to make a list of what minerals are most often detected within our spectral-mineral class: those that are more rarely detected, and those that may be occasional contaminants across classes. This is summarized in **table 1**. The user of the present database is therefore warned that there is no absolute guaranty of the exact mineralogy provided for any given AMD: it is most often the dominant specie(s) as per **table 1**, or more rarely another aqueous mineral “contaminant”. For each class we give the spectral range where we test the existence and measure the depth of the absorption features or spectral shoulders. As described above and in the cited literature, we use standard band ratios between a band center and its side(s).

<table><thead><tr><td>Spectral-Mineral class</td><td>Dominant species</td><td>Minor species</td><td>Occasional contaminants between classes</td><td>Color code in maps</td><td>Diagnostic spectral bands tested</td><td>Range of the band's associated wings</td></tr></thead></table>
<table><caption>Table 1. Spectral-Mineral classes applied to each AMD identified.</caption><tr><td>1</td><td>Fe/Mg phyllosilicate clay (2:1 and 2:1:1) of smectite, vermiculite and chlorite groups</td><td>Prehnite Fe/Mg-Mica Talc group</td><td>Class 5</td><td>Red</td><td>2.28-2.35 µm, 1.9 µm</td><td>2.08-2.24 µm, 1.73-2.16 µm</td></tr><tr><td>2</td><td>Hydrous aluminosilicates (1:1 and 2:1): Al-smectites, kaolins. Hydrated silica (HySi), all forms</td><td>Al-mica Al-Fe smectites, allophane</td><td>Class 3 (Jarosite)</td><td>Cyan</td><td>2.17-2.25 µm, 1.9 µm, 2.4-2.5 µm</td><td>2.05-2.17 µm, 1.73-2.16 µm, 2.15-2.30 µm</td></tr><tr><td>3</td><td>Poly/Semi-hydrated sulfate (PHS), H₂O or OH bearing</td><td>Zeolite</td><td>Class 2+4</td><td>Green</td><td>2.4-2.5 µm, 1.9 µm</td><td>2.15-2.30 µm, 1.73-2.16 µm</td></tr><tr><td>4</td><td>Mono-hydrated sulfate (MHS)</td><td>None</td><td>Class 3</td><td>Blue</td><td>2.1 µm, 2.4-2.5 µm</td><td>1.85-2.24 µm, 2.15-2.30 µm</td></tr><tr><td>5</td><td>Carbonates</td><td>Serpentine</td><td>Class 1</td><td>Orange</td><td>2.3 µm, 2.5-2.6 µm</td><td>2.08-2.24 µm, 2.37-2.63 µm</td></tr></table>

To note that a sixth spectral-mineral class layer was identified corresponding to the iron hydroxide akaganéite. However, because of the rare occurrence of this class, it was not included in the database presented here. A sole exception to this is found in section 5 where we included this mineral when mapping the Nili/Jezero landing site region because akaganéite is a common occurrence there.

We are at times able to provide more precise identification of the mineral species within a given deposit than the spectral-mineral class of the database. Even using MSP data, when the signal-to-noise ratio is very high and devoid of spectral artefacts specific minerals are resolvable such as prehnite, or Al-mica. This is more so the case with CRISM HSP or OMEGA which have a better spectral sampling. We chose not to provide this finer level of analysis and stuck with the spectral-mineral classes so that the database is coherent.

The bulk of our detections originate from OMEGA and CRISM multispectral "MSP" datasets the latter having both the lowest spectral resolution and most often the lowest SNR. This hinders precise mineral identification efforts for AMDs, so we may not be seeing certain mixtures because we do not resolve the mixture spectra correctly. The user of the database should be warned that an AMD attributed to a single spectral-mineral class does not preclude mixtures with aqueous mineral pertaining to another class, even when the spectral evidence for such an admixture is lacking. Conversely, one should not expect mixtures to always occur and not be observable. In some cases, mixtures can be identified from the data, most notably Al-rich with Fe/Mg rich clays, or Fe/Mg rich clays with sulfates. In this event, the AMD is attributed several spectral-mineral classes.

## 2.5 Study dataset

To date, 26464 CRISM multispectral observations (16460 MSP, 1879 MSW, 8125 HSP) have been used in this study and processed automatically to build the browse maps. A subset of this sample was processed in detail, that is individually, by-hand with the thorough visual inspection
process described in section 2.3. At least 13798 CRISM observations were processed
individually, including 6991 multispectral observations (2761 HSP, 3944 MPS, 286 MSW) and at
least 6807 targeted observations (ATO, ATU, FRS, FRT, HRL and HRS). At least 1987 OMEGA
observations were used for the detailed investigation and 5076 pre-processed level-3
observations from [Riu+19] were used for the automatic production of browse maps.

428

429 3. Mapping procedure

430

## 3.1 General considerations to mapping aqueous minerals from orbit

431 Here we describe the procedure that allows us to build a coherence database and set of global
432 maps of aqueous minerals. Our mapping approach had to be a compromise between several
433 things: spatial coverage, spatial resolution, detection sensitivity and mineral identification
434 specificity. An additional practical constraint is the amount of resources (computational, labor)
435 that could be invested in the project.

436 The overall strategy can be summarized as the following: we map as much of the planet as
437 possible, at a moderately high resolution that is useful for geomorphic context evaluation (~200
438 m/pix), focusing on detection sensitivity to detect as many deposits as possible, without requiring
439 the exact determination of the aqueous mineral composition. That relaxed constraint is mostly
440 due to limitation in the CRISM multispectral dataset which provides the bulk of the moderately
441 high resolution coverage but with low spectral sampling. It is also our view that the hard part of
442 the work is to identify AMDs. Once that information is at hand, subsequent, more detailed
443 analyses are possible on individual deposits to better constrain their exact mineral makeup.

444 3.2 Mapping strategy for each data type

445 It was not possible to settle on a single mapping strategy to build our global repository of AMDs,
446 because the datasets have such differences in spatial resolution and coverage, and because of
447 the sheer volume of data to process. Below is a breakdown of the mapping strategy as a function
448 of the data product used to make the detections. These are not implemented sequentially in a
449 pipeline, but were conducted in parallel. As is described, part of the observation dataset was
450 systematically analyzed, other parts were first pre-screened for AMDs using automated
451 classification algorithms, while more regional spot analyses were also conducted. Whatever the
452 strategy below, we always apply the 8-step process of section 2.3 to provide robust, validated
453 AMDs.

454 3.2.1 OMEGA Low-resolution observations

455 We process and study manually all available, good quality nadir data of OMEGA from orbits up
456 to ~4800 which have a spatial sampling > 2 km/pix (so-called "mode 128" products) because they
457 provide near global coverage at that scale. By good quality data we mean observations with no
458 obvious significant data gaps or corruptions, observations which our "contaminant map" (step 3
459 from above procedure) shows not to be almost entirely covered in frost/ice, and observations
460 which surface features are clearly distinguishable at near-infrared wavelengths attesting to low
atmospheric opacity. No strict numerical cutoff values are defined here, we rely on visual
inspection of the above criteria, having developed the experience to quickly appraise
observations which will provide no useful surface mineral information. Because of the intrinsic
high sensitivity of the OMEGA C channel, this dataset is efficient at identifying and mapping the
largest deposits throughout Mars insuring that no large AMD (10s km) is missed out. This low-
resolution dataset allows to fill in the coverage gap in areas where higher resolution data is
lacking. For each of these OMEGA observation, we build the mineral maps as defined in Table 1
and go through the 8-step process to confirm the AMDs.

3.2.2 OMEGA and CRISM at moderate resolution: using "browse" maps

It is not feasible to take the entirety of the higher resolution OMEGA and CRISM multispectral/survey datasets and to study each observation systematically through the time-consuming 8 steps of section 2.3. For a large part of our observation database, some measure of automation was taken to pre-screen the dataset and provide hints as to where the AMDs may be. We generated so-called "browse" maps which provide the locations of candidate AMDs globally at Mars as clusters of pixels with coherent spectral content. Based on these locations, we then processed individual OMEGA or CRISM data cubes overlapping these clusters, and confirmed or not the AMDs through the 8-step process of section 2.3. Below we describe how we find these candidate AMDs in an automatized fashion.

The browse maps are generated following steps 1-5, without human intervention, as an
automated unsupervised algorithm. We applied this procedure on a large fraction of the
instrument datasets to pre-screen candidate AMDs. The output of this approach is a "browse
map" in raster format, providing near global coverage of Mars at 200 m/pix sampling. The map
is built much like the spectral parameter maps described in step 4 and shown in Figure 1F. For
the automatically generated browse maps, each spectral feature so-tested is only retained if its
signature exceeds a minimum threshold and the color code is defined on an arbitrary scale but
that is proportional to the absorption depth (see Supplementary Material for details on the
method). All observations in the data sample repository are processed identically, with the same
threshold and color scale. The browse maps are in RGB format, examples for both OMEGA and
CRISM are shown in figure 2. Each color codes for one combination of spectral parameter maps
(as per step 4 of section 2.3). These browse maps are generated with a low detection threshold
so as to retain a level of sensitivity that is useful but consequently suffer from high noise level
and biases. Part of that "noise" is visible as spatially uncorrelated pixels above the detection
threshold. These maps are also commonly detrimentally affected by false positives (resulting e.g.
from surface frost, non-aqueous mineral contaminants, photometric effects). As such they
cannot be used as-is for mapping aqueous minerals be it locally or globally. An example is shown
in figure 3 where the browse map and final AMD map are extracted for a given region of Mars.
The zoom inserts provide 3 cases of candidate AMDs. Case a) is the sought out case where a
candidate cluster in the browse map turns out to correspond to an AMD. The spatial extent of
the confirmed cluster is somewhat reduced compared to a seemingly larger size in the browse
map. We consider it likely that we are somewhat underestimating the true extent of that deposit
because our detection criteria reject weak spectral absorptions which are more likely to occur at
the deposit boundary where mixing with anhydrous minerals is systematic. We prefer to
somewhat degrade the size and boundary of a deposit but to be certain that it is indeed an aqueous mineral. This will again be illustrated in section 5. It is always possible in turn to remap an individual AMD to improve its boundaries when the need arises. Case b) involves a false negative where the final mineral map shows more areas with aqueous mineral than initially reported in the browse map. This is routinely the case. Case c) is the most problematic and the most common with browse maps: contiguous pixels suggest a potential AMD that is not confirmed at the outcome of a detailed investigation (step 8 in our method). In the vast majority of cases, the mean or median spectra from such cluster shows obvious spectral artefacts that induced the false positive detection, and it is not an issue of low signal-to-noise.

Where these maps yield clusters of positive AMDs, individual OMEGA and CRISM observation overlapping that cluster are analyzed manually, and the search area can be expanded when required.

### 3.2.3 The special case of CRISM targeted observations

CRISM targeted observations contribute negligible surface coverage (<1%) but the precise mineralogical information they provide was useful to the project as accurate - albeit localized - probes into AMDs otherwise mapped at the coarser spectral & spatial resolution with the other data products. We built an ancillary database which is a continuation of our CRISM high-resolution database from [Carter+13]. It is a table entry which, for each targeted observation detecting aqueous minerals, provides the results of the detailed analysis of its mineral diversity and a first-assessment of the morphologic context. This is illustrated in section 4.3 and the repository provided in table S1. It takes about 10 times longer to analyze and map a CRISM targeted observation than a CRISM multispectral observation. That and the limited spatial coverage which they provide made it undesirable to systematically map out their mineral content.

## 3.3 Data reconciliation

While highly sensitive to detection, OMEGA products have a limited spatial resolution (typically > 500 m/pix) that make them useful to identify a deposit but less so to map out its detailed distribution - and hence its geomorphic context. For OMEGA based detections this issue is reconciled by mapping with overlapping CRISM multispectral observations to refine the spatial distribution of the deposit. This reconciliation is not systematically done: it can be overlooked in particular when the boundaries of the unit as mapped with OMEGA are observed to trivially relate to a geomorphic feature observable with THEMIS daytime IR data (100 m/pix).

It should be mentioned also that not all AMDs reported in this work were initially candidate AMDs identified from the browse map-based approach of section 3.2.2. In parallel to this pre-screening approach, certain regions of Mars have been processed in detail as part of this project. This was done by manually investigating thousands of observations to provide good coverage of these regions. These include some well-known mineral provinces, but also poorly-known new mineral provinces at Mars and scattered locations throughout. For lack of a better option, no specific reconciliation was carried out: these detections are an integral part of the database. Since a manual systematic investigation of the mineralogy is more sensitive than one based on the
browse maps, it results that some regions of Mars were more scrutinized than others. This will
induce a small bias which cannot be quantified.

## 3.4 Global map generation

The way our final global mineral maps are generated is that we collate individual mineral maps
generated for each observation which has AMD detections in it as detailed in section 2.3 and
illustrated in figure 1. We chose to project each individual map onto a common, global mineral
map which grid has a 200 m/pix sampling at the equator in equi-rectangular projection. Such grid
sampling therefore matches or exceeds the native sampling of the OMEGA and CRISM HSP/MSP
datasets. As the OMEGA and the CRISM multispectral products used here have one order of
magnitude difference in spatial resolution, they should not be integrated seamlessly in the same
map and database. We chose to separate the mineral maps according to whether they are
OMEGA-based or CRISM-based. The user may hence display both layers concurrently or not,
depending on the need.

It should be noted that the global mineral maps so-generated are in fact Boolean maps: for each
200x200m² (at equator) pixel of the grid, we indicate if an AMD was detected within it or not
(and also provide the spectral-mineral class(es)). As discussed in section 2.3, this doesn't address
the fill-factor of the AMD within that grid pixel which may at times be low. In addition, for the
OMEGA dataset which spatial sampling is coarser than our global mineral grid, we may be
oversampling mineral detections some of the time. For example, a 4x4 km² OMEGA pixel that
contributes a confirmed AMD will be projected onto our common mineral grid as 20x20 pixels
(sized 200 m each). Although we assess that a non-negligible fraction of these 400 grid pixels will
contain aqueous minerals, we cannot guarantee that all will if we lack overlapping higher
resolution data.

Such global maps are convenient for quick visualization of the database, and we use these for the
first-order analyses that are carried out in the following section. We retain however the individual
OMEGA et CRISM observation AMDs in their native resolution which are available in vectorial
format and may be used for more detailed statistical studies

## 4. Results

### 4.1 Coverage

We computed the coverage maps for the OMEGA and CRISM datasets used for this study as
summarized in section 2.5, separately and then combined. These can be seen in figure S1. Any
region not covered in this study is left in greyscale. Green denotes the coverage of OMEGA and
CRISM observations processed automatically to produce the browse maps that are subsequently
analyzed in detail wherever candidate AMDs are found. Blue denotes observations that are
further analyzed as part of the detailed investigation.

The combined coverage of the OMEGA and CRISM observations that were analyzed in detail
(manually) is shown in figure 4. The CRISM coverage (orange) is not homogenous for two reasons:
1) There are intrinsically more AMD in selected regions of Mars. Browse products show increased
candidates there, and so naturally more observations will have been processed in detail over these regions. 2) A few other regions were processed in a more systematic fashion despite the absence of increased AMDs. Contrarily to CRISM, the fully analyzed OMEGA observations sample (purple, figure 4) provides near-continuous coverage over the southern highlands and dichotomy, further mitigating any representativity bias. Dust-coated/surficially oxidized and younger regions were mostly excluded from the latter sample based on previous global mapping works.

The coverage maps can be further refined by down-selecting a subset of the surface of Mars. While the 70° latitude cut-off removes terrains affected by perennial ice and seasonal frost to a large extent, other areas of Mars are obscured to VISNIR investigation by dust coatings/surficial oxidation. Dust/oxidation coatings distribution at a global scale is well documented, having been mapped independently with OMEGA [Ody+12] and TES [Ruff+02]. We combine these two maps setting a hard threshold between dusty and non-dusty regions. For the TES "Dust Cover Index", we threshold at 0.945 (average emissivity at 1350-1400 cm⁻¹). For OMEGA, we threshold the "Ferric Nanophase index" at 1.03. The resulting map is shown in figure S2 (top, blue), it broadly masks out dust-coated or icy regions of Mars where we consider that we do not access the bulk rock mineralogy (representing >30% of the surface of Mars). We make the assumption that older "dusty" regions of Mars likely exhibited similar aqueous alteration compared to "non dusty" regions of similar age, and that they were not dust coated or oxidized over their entire history. The coating by dust would be a subsequent surficial deposition or oxidation process which volumetrically is a minor modification of the top surface, but which blocks our analysis.

Another way of refining the statistical analysis of our detection sample is to account for terrain type. It is well established that the younger terrains of Mars have undergone very limited aqueous alteration, and often constitute mantling/capping units to older, altered terrains. We can therefore focus on the more ancient terrains of Mars. To achieve this, we use priors on the terrain types from the following sources: the "Geologic map of the northern plains of Mars" (USGS map #2888 from [Tanaka+05]) and the "Geologic map of Mars" (USGS map #3292 from [Tanaka+14]). We chose to retain all terrains from geologic units that are older than or include the Middle Hesperian, as dated in these publications. The precise age cut-off is somewhat arbitrary but had to account for the large error bar and/or age span of some geologic units. Few geologic units younger than the middle Hesperian are of relevance here, both because we already know that little aqueous alteration took place from the mineral record, but also because the independent geological interpretations of these units do not indicate long-lasting aqueous environments (polar terrains, volcanic terrains). We chose to merge both geological datasets for two reasons: 1) map #2888 may be older than map #3292 but the mapping is more detailed over the young northern plains and the older dichotomy boundary regions. We therefore prioritize map #2888 wherever it has covered Mars. 2) One unit is difficult to deal with: AHi, "Amazonian and Hesperian impact unit". While the impact event may be younger than our middle Hesperian age cut-off, the impact may exhume material that is older than that. This may not be the case in the young northern plains, as it depends on the crater exhumation depth and location within the northern plains. We chose not to retain crater units (interior or ejecta) in the northern plains for that reason. In the southern highlands where most terrains are older than the middle Hesperian,
statistically speaking the crater unit will exhume material that is also on average older than that age. We therefore chose to retain the crater units over the southern highlands (i.e. wherever map #2888 has no coverage). The resulting filtered map is shown in figure S2 (middle panel, in yellow). Terrains in yellow are those that are included, the rest of Mars is rejected. We restate that this approach has a degree of arbitrary and by no means is guaranteed to be exact at the local scale, it is merely a means to do first-order global scale statistics.

Both selection criteria can be combined so as to produce a map of both older and dust-free terrains, which are the most suitable for orbital investigations of aqueous minerals. This is shown in figure S2 (bottom, orange).

The quantified surface fraction covered by both instruments in this study is summarized in table 2 and broken down according to the mapping area. The first line corresponds to all of Mars equatorward of 70°. The following lines further filter down areas according to dust coatings and terrain ages shown in figure S2. We can see that the surface coverage ranges from 19.7% to 99.0% depending on how we select our mapping area and our level of investigation.

For example, considering the entirety of Mars at non-polar latitudes, less than 20% of its surface area has been mapped through the detailed approach with CRISM at 200 m/pix. Conversely, if we down-select the areas we are most interested in which are dust-free and older or equal to the middle Hesperian, then that fraction increases to 31%. If we also consider OMEGA observations at kilometric pixel scale, that fraction is drastically increased to over 90% of the surface area.

<table><caption>Table 2. Surface fraction (%) of Mars covered by OMEGA and CRISM in this study.</caption><thead><tr><th rowspan="2">Mapping area</th><th colspan="5">Percent of the surface of Mars studied</th></tr><tr><th>OMEGA Browse mapping</th><th>OMEGA detailed studies</th><th>CRISM Browse mapping</th><th>CRISM detailed studies</th><th>OMEGA+CRISM Browse mapping</th><th>OMEGA+CRISM detailed studies</th></tr></thead><tbody><tr><td>&lt; ±70° latitude</td><td>90.4</td><td>61.0</td><td>62.0</td><td>19.7</td><td>92.3</td><td>63.9</td></tr><tr><td>&lt; ±70° latitude &amp; Dust-free</td><td>94.9</td><td>78.2</td><td>64.7</td><td>26.9</td><td>98.2</td><td>81.6</td></tr><tr><td>&lt; ±70° latitude &amp; Old</td><td>96.6</td><td>81.9</td><td>62.5</td><td>27.2</td><td>98.4</td><td>84.9</td></tr><tr><td>&lt; ±70° latitude &amp; Dust-free &amp; Old</td><td>97.6</td><td>88.7</td><td>63.2</td><td>31.0</td><td>99.0</td><td>91.8</td></tr></tbody></table>

## 4.2 Overall distribution of aqueous minerals

Here we present the initial results from this global mapping. We look at the number of AMDs, their spatial extent and their surface density. We do this globally, then apply the above down-selection criteria for the mapping areas. We present in figure 5 the global distribution of aqueous minerals as detected using the CRISM (bottom) and OMEGA (top) datasets. The map is generated at the common grid sampling of 200 m/pix, so that each pixel of that map that is colored in light blue is considered to have an AMD in it.

Note that regardless of the grid sampling chosen, orbital sensitivity assuredly limits our detection capability, so that more AMDs exist at Mars than could be mapped here. Conversely and as discussed above, we may over-estimate the size of some aqueous mineral deposits. We refer here to AMDs as a data-oriented indicator of aqueous mineral deposit, we do not have the
ground truth to provide the precise spatial extent of the deposit. Specifically here, because 1) the lower native sampling of OMEGA can lead to oversampling at the 200 m grid size, and 2) sub-OMEGA and sub-CRISM pixel mixtures between aqueous and non-aqueous minerals certainly exist: we cannot guarantee that each 200 meter-pixel that is part of an AMD indeed has the aqueous mineral in it, although a majority will by construction. In order words, there is some degree of uncertainty not on the existence of individual AMDs, but on their precise geographic shape and extent.

Previous investigations had reported on the order of 1000 AMDs [Carter+13], the bulk originating from targeted CRISM observations. It is not possible to compare directly the size of the new AMD sample from this study because the spatial extent was not available in previous studies. However, an approximation can be made that conveys a key observation: the surface of Mars is very much altered. We can count AMDs as isolated clusters (also called ROIs) of pixels that are found in the maps of figure 5. After merging the OMEGA & CRISM detection maps, we report 94083 unique AMDs.

We can use a slightly different approach to counting ROIs that reduces to total number of AMDs to a more manageable size. Instead of counting AMDs as strictly isolated clusters of detections, we can put a more relaxed constraint. To achieve this we apply a morphological dilation operator with a 9x9 pixel disk-shaped kernel on the stacked OMEGA+CRISM (200 m/pix) map. What this does is that it seeks clusters of mineral detections that are not only strictly connected (with pixels which are in contact), but may have a distance of up to 4 pixels between them. Using this method we end up with 36,607 independent AMDs of aqueous minerals. While we do not manually (i.e. visually) re-validate the spectra of each so-counted AMD, each of these is based on validated detections (through the 8-step process) which were carried out on individual OMEGA and CRISM observations, and which guarantee that there are no false positives. There are 46,335,514 pixels ~200x200m² in size exhibiting aqueous mineral detections. The cumulative surface area that exhibit AMDs (based on our definition) is 1.32x10⁶ km² which translates into ~0.9% of the surface of Mars.

As seen in figure 7 and table 3 below, most AMDs are of small size per our mapping approach. Half of the AMDs are smaller than 1.6 km² and about 99% of them are smaller than 390 km². The cumulative surface area of the 99th percentile is ~30% of the total aqueous mineral surface extent. So very few, very large AMDs contribute the bulk of the surface coverage.

<table><caption>Table 3. Statistics on the AMDs (also called ROIs)</caption><thead><tr><td>AMD statistics on combined OMEGA+CRISM dataset</td><td>Value</td></tr></thead><tbody><tr><td>Total AMD count (using dilation factor on adjoining clusters)</td><td>36,607</td></tr><tr><td>Cumulative surface area</td><td>1.32x10<sup>6</sup> km<sup>2</sup></td></tr><tr><td>Total surface fraction of Mars</td><td>0.9%</td></tr><tr><td>Largest AMD surface area</td><td>1.1x10<sup>5</sup> km<sup>2</sup></td></tr><tr><td>Median AMD surface area</td><td>1.6 km<sup>2</sup></td></tr><tr><td>99th Percentile AMD surface area</td><td>390 km<sup>2</sup></td></tr><tr><td>99th Percentile cumulative surface area</td><td>3.9x10<sup>5</sup> km<sup>2</sup></td></tr></tbody></table>
Looking at the global maps of figure 5 at its grid resolution of 200 m/pix, one may be under the impression that aqueous minerals are not so common on Mars except for the well-known mineral provinces of Mawrth Vallis, Sinus Meridiani, Nili Fossae and Valles Marineris. It is particularly true for the CRISM-based map. This is because, while pervasive, the deposit size is typically too small to be visible at this scale. A map generated at 10 km/pix gives a better idea of how widespread the deposits are (see figure 7, top). This coarse binning was chosen because it is close in size to the landing ellipse of current NASA rover missions. Note that in some instances, nearly 100% of a 10x10km² pixel may be covered in aqueous minerals, but in many cases, only a small fraction is.

A striking feature in the distribution of aqueous minerals already highlighted in [Carter+13] is that there is very good anti-correlation between young, low dust-coatings and non-frosty terrains and terrains with AMDs (figure 7, bottom). The latter two properties are likely observational biases, but the former not. It was found that 93.9% by extent of the AMDs detected fall within the low dust-coating and older terrains below 70° latitude. That figure increases to 97.6% for terrains that are also below 50° latitude. This confirms quantitatively the excellent correlation between these terrains and aqueous alteration as observable today at the surface. That said, some Noachian-aged terrains with good observational conditions have fewer AMDs than others, so their global distribution is not only governed by the dust, frost and age biases. This is the case of Terra Cimmeria for example.

The surface fraction of aqueous minerals presented above can be refined by looking at the different subset of mapping areas. These results are shown in the table 4 below. Compared to the bulk estimate of 0.9% surface fraction of AMD, we see that selecting sub-regions per the prior rationale increases that fraction by up to a factor of 2.

<table><caption>Table 4. Surface extent and surface fraction of AMDs</caption><thead><tr><td>Mapping area</td><td>Total surface extent of AMDs (m²)</td><td>Surface fraction of AMDs (indiscriminate)</td></tr></thead><tbody><tr><td>&lt; ±70° latitude</td><td>1.3x10<sup>14</sup></td><td>1.0%</td></tr><tr><td>&lt; ±70° latitude &amp; Dust-free</td><td>9.4 x10<sup>13</sup></td><td>1.4%</td></tr><tr><td>&lt; ±70° latitude &amp; Older terrains</td><td>8.5 x10<sup>13</sup></td><td>1.6%</td></tr><tr><td>&lt; ±70° latitude &amp; Dust-free &amp; Older terrains</td><td>6.6 x10<sup>13</sup></td><td>1.9%</td></tr></tbody></table>

Instead of looking at global surface fraction of mineral detections, we can study regional differences. To do this we compute the density map of aqueous mineral detections based on the global 200 m/pix map. We define a rolling search area with radius 100 km then derive the fractional area of pixels that have detections in them to the total area of the disk. The result is shown as the density map of figure 8. This map highlights again the preferential occurrence of aqueous minerals within terrains that are low dust-coated and ancient. There are however regional differences within them. Of particular interest is that while the well-known mineral provinces are visible, there are other regions on Mars where the detection density is high (10s %) over large areas, in par with Mawrth Vallis, Sinus Meridiani or Nili Fossae. These include regions in Thaumasia Planum (-50°E; -20°N) and northwest of the Argyre basin (-60°N; -35°N). Therefore we can report new "mineral provinces" at Mars, which warrant further study. Other
regions with lower density yet pervasive aqueous mineral deposits include W. Hellas Planitia (S. Terra Sabaea, E. Noachis Terra), regions of Terra Cimmeria and Terra Sirenum.

## 4.3 Aqueous mineral diversity

The maps of figures 9 show the same global maps as above but this time color-coding for the aqueous mineralogy. For visual clarity, the maps shown here are generated at 2 km/pix. Statistics can be derived based on these maps and are summarized in the histogram of figure 10 and table 5 below. In the former we present the relative surface coverage of each class of mineral relative to class #1. We see that poly-hydrated sulfates are about half as widespread as Fe/Mg phyllosilicates, while aluminosilicates + hydrated silica are about one-fifth as frequent (a smaller but still significant prevalence). Mono-hydrated sulfates and carbonates are very minor contributors to surface coverage.

<table><thead><tr><th>Spectral-mineral class:</th><th>Class #1<br>(FeMg)</th><th>Class #2<br>(Al-Opal)</th><th>Class #3<br>(PHS)</th><th>Class #4<br>(MHS)</th><th>Class #5<br>(Carb.)</th></tr></thead><tbody><tr><td>Surface fraction of AMD, &lt; ± 70° latitude</td><td>0.67%</td><td>0.13%</td><td>0.34%</td><td>0.02%</td><td>0.01%</td></tr><tr><td>Surface fraction of AMD, &lt; ±70° latitude &amp; Dust-free &amp; Old</td><td>1.31%</td><td>0.26%</td><td>0.63%</td><td>0.04%</td><td>0.03%</td></tr></tbody></table>

Table 5. Surface fraction of AMDs globally as a function of spectral-mineral class

We complete this approach with a more refined investigation using the ancillary detection database providing 3267 spot AMDs based on CRISM targeted observations. While the bulk of these AMDs are not mapped out because they are negligible contributors to surface coverage, they do provide more detailed local-scale insight into the mineral makeup. The location of these sites is shown in figure 11 (top). Here, the detailed mineralogical knowledge of these sites was degraded so as to fit within the 5 spectral-mineral classes of the main database. When more than one aqueous mineral type is found within these spots, we do not show mixtures on the map, instead for clarity we superimpose detections in the following order starting from the rear: Fe/Mg phyllosilicates (class #1) < Poly/Semi hydrated sulfate (class #3) < Hydrous aluminosilicates and hydrated silica (class #2) < Mono hydrated sulfate (class #4) < Carbonates (class #5). The statistic on the composition within these sites is summarized in table 6. Its numbers should be read in relative term from one spectral-mineral class to another, not in absolute value of spots detected. This is because there is here a strong selection bias in the targeted data sample (some 50% of our sample has AMDs in them). A comparison is made using the same superimposition order of the global maps which is shown with the same color code in figure 11 (bottom).

<table><thead><tr><th>Spectral-mineral class:</th><th>Class #1<br>(FeMg)</th><th>Class #2<br>(Al-Opal)</th><th>Class #3<br>(PHS)</th><th>Class #4<br>(MHS)</th><th>Class #5<br>(Carb.)</th></tr></thead><tbody><tr><td>Number of CRISM targeted sites with aqueous minerals:</td><td>2681</td><td>1379</td><td>950</td><td>195</td><td>230</td></tr></tbody></table>

Table 6. Number of pinpoint detections with CRISM targeted data as a function of spectral-mineral class

A striking feature shown by these two tables and maps is that while Fe/Mg phyllosilicates dominate there is obvious diversity throughout the altered regions of Mars. In particular, the
755 hydrous aluminosilicates & hydrated silica class #2 cannot be overlooked. The concentration of
756 mineral types in specific areas such as carbonates (orange) in Nili Fossae or sulfates (blue + green)
757 in Valles Marineris and Sinus Meridiani are highlighted here. The diversity of composition shown
758 here is time-integrated and spatially-integrated over all units for Mars, so that it should not be
759 interpreted as resulting from one episode of aqueous alteration which yielded the above
760 minerals in their relative abundance.

761 It is also interesting to note that the relative surface fraction of all mineral classes compared to
762 class #1 is somewhat different between the 2 datasets (the global mapping versus the high-
763 resolution CRISM targeted observations). This is likely due in part by representativity bias with
764 the latter database. However, it has also been seen experimentally that most deposits from class
765 #2 are of much smaller size compared to class #1 from that high-resolution database. This may
766 explain their lower value in the global lower-resolution database.

767 Another important result is that when looking at the CRISM spot detection dataset, a majority of
768 these sites have more than one spectral-mineral class in them. In fact, only 49% of CRISM
769 targeted observations with AMD detections exhibit a single spectral-mineral type (which in 75%
770 of cases is the Fe/Mg phyllosilicates class #1). Complex mixtures become more rare, with 33.5%
771 having two spectral-mineral classes, and the remainder having 3 to 5 classes mixed. This should
772 be considered a firm lower limit to the diversity because: a) spectral-mineral classes represent
773 more than one aqueous mineral type so we do not account for the finer variation of the
774 mineralogy here; b) orbital remote sensing has strong sensitivity limitations and biases including
775 some aqueous minerals which are intrinsically easier to detect than others; and c) we do not
776 efficiently detect complex sub-pixel mixtures. Because the lower resolution global dataset
777 doesn't provide the same level of detail as CRISM targeted observations, this is all the more true
778 there and in conclusion the user is warned again that the diversity reported globally is a strong
779 lower limit, and mixtures of several aqueous minerals within a given AMD are more often likely
780 than not.

## 4.4 Geography of aqueous mineral deposits

782 In this section we provide more detailed correlation studies between AMDs and broad
783 geographic features of Mars. A simple way of viewing the dataset is to look at latitudinal
784 variations. In figure 12 we compute the surface fraction covered by AMDs as a function of
785 latitude, and do so for each spectral-mineral class. High latitude regions in excess of +/- 50° are
786 excluded here as they are discussed later. The signal of Nili Fossae (orange bump near 20°N) is
787 visible, so is the signal of Valles Marineris and Sinus Meridiani (blue, green and cyan bumps
788 around -15°N to 5°N). Although there are high frequency variations in the surface fraction of class
789 #1 (red), there is an overall constant trend where AMDs with this mineral class are roughly found
790 over 1.5% of the surface throughout the southern highlands. By contrast to the other minerals,
791 class #1 seems less connected to regional contexts and is a more constant signal.

792 We can do the same with altitude using global MOLA altimetry data. We compute the surface
793 fraction of each type of aqueous mineral as a function of MOLA altimetry for 200 elevation bins
794 between -8000 m and +8000 m. Results are shown in figure 13. A remarkable property of these
minerals is that they cover a wide elevation range, from approximately -8000 m to +4000 m. While the bulk are found above -4000 m, there is a distinct feature below that altitude where sulfates (green) and aluminosilicates/hydrated silica (cyan) are found to be present in relatively high fraction (1-2%) and to be the near-exclusive species. This is almost entirely contributed to by the AMDs within Hellas Planitia. At higher elevation, there is a distinct spike (x3 local average) around [-1500,-500] m which concerns all aqueous mineral species and is mostly contributed to by Sinus Meridiani. The skewed sulfate bump (green) in range [600,-2500] m is for a large part contributed by southern high latitude sulfate deposits (>55°S).

A preliminary conclusion from looking at how aqueous mineral distribute as a function of latitude and altitude is that a few mineral provinces dominate those distributions. A more subtle approach is required to understand any potential underlying signal that could yield global trends unhindered by particular regional exceptions.

## 4.5 Geology and age of aqueous mineral deposits

Finally, we can make correlations between aqueous minerals and geologic units as well as their estimated age range. Here we use again the global geological map of Mars from [Tanaka+14] which discriminates the surface according to 43 geologic units, each with an age range estimate. We compare our aqueous mineral detection map with these units, extracting what absolute surface extent of each mineral is present within each unit, and then normalizing by each unit's areal extent. The latter provides surface fraction of aqueous minerals per geologic unit. The complete results are provided in table S2 and illustrated with the histogram of figure 14. The units within which there is the most aqueous alteration are 1) mNh, 2) eNh, 3) HNhu, 4) INh and 5) AHi. These are respectively: 1) The Middle Noachian highland unit, 2) The Early Noachian highland unit, 3) The layered Hesperian & Noachian highland undivided unit, 4) The Late Noachian highland unit, and 5) the Amazonian and Hesperian impact unit. When studying unit ages, the latter unit (AHi,) should not be taken into account as it shows the obvious pattern of excavation of older, buried aqueous material (as discussed in section 4.2). After excluding this unit, our analysis provides a robust quantification for a well-known fact of Mars aqueous minerals: most are of Noachian age. These results are also consistent with the preliminary assessment carried out in [Carter+13].

When looking at the surface fraction mapped as AMDs within each unit, the aforementioned units show broadly similar values (~1%) with two exceptions: HNHu (Hesperian and Noachian highland undivided) and Hp (Hesperian polar), which are heavily enhanced (>10%). These correspond to Sinus Meridiani and parts of the high latitude southern sulfate deposits (>55°S). A potential source of bias when looking at such coverage fractions is that a significant portion of certain units may be obscured by dust coatings or peri-polar processes. To address this, we re-do the computations excluding all areas above +/-55° latitude as well as those that are flagged as dust-coated from our previous masking. The results are shown in figure S3, they do not significantly differ: aqueous minerals are concentrated in the Noachian terrains at the ~1%
coverage level, with the same two outlier units (HNHu, Hp) as before. Another striking feature is that out of the 43 geologic units, only 12 have meaningful fractions of aqueous minerals.

A grouping of the 43 units exists in [Tanaka+14] which sorts them into 8 categories per the authors' rationale. We show in figure 15 (top & center) the statistics on these categories, both in terms of total surface extent of aqueous minerals, and total surface coverage. We see that the Highland Units category has both a disproportionate amount and fraction of aqueous minerals compared to other categories. This feature was already visible in the geographic correlations of above when looking at the southern high-altitude terrains, which comprise the Highland Units to a large extent.

Absolute dating of geologic units is an arduous task, and only approximate unit age ranges can be derived in most cases, although stratigraphic relationships do allow for relative dating with a better degree of confidence. This applies to the proposed age ranges for all geologic units in [Tanaka+14]. Based on these inputs we can propose an approximate timeline of AMDs (but not a timeline of alteration) from the early Noachian (eN) to the late Amazonian (IA) which is shown in Figure 16. We discarded the AHi impact unit as well as the polar units which are both known not to be reliable for our study. The way the timeline is built is that for each geologic era division from [Tanaka+14], we sum the surface fraction of aqueous minerals, and also ratio that sum to the total surface fraction of units within that geologic age range. Because many units range over more than one epoch, we repeat the calculation on these units on each epoch they pertain to. This may exaggerate the contribution of certain units as their contribution may be counted more than once in our timeline. This was deemed a better approach than attributing a single, arbitrary age to a unit that can span eons. The trend that is found in the bottom panel of Figure 16 is striking: we see a slow then sharp decrease in the fraction of aqueous minerals as a function of time, with the inflexion point somewhere between the early Hesperian (eH) and the late Hesperian (IH). This is consistent with previous studies on smaller samples. It is worth noting that the earliest terrains on Mars (eH) exhibit the most AMDs by surface fraction.

We can also make basic comparisons between aqueous mineral types. For instance, in Figure 14 (bottom) we can see that the relative coverage between minerals is not the same from one unit to the other. Unit Htu for example is significantly enhanced in sulfates and hydrated silica, and corresponds geographically to the well-known deposits of Valles Marineris. To explore this in a more systematic fashion, we introduce the ratio between the surface extent of spectral-mineral class #1 (Fe/Mg phyllosilicates) to the total surface extent of all aqueous minerals. This quantity is a measure of how diverse the aqueous mineralogy is, as compared to the dominant class. We compute this ratio for the 6 geologic unit categories (excluding polar and impact units) and as a function of unit ages, same as above. Results are shown in Figure 15 (bottom) and 17. The histogram of figure 15 tells us that significant diversity exists for all 5 categories, with varying proportion ranging 42-72% for all but one category. As observable today, Fe/Mg phyllosilicates are not always the dominant species or are so by a short majority. This may not reflect their original extent however. The histograms of figure 17 show that this is also the case through the geologic eons: Fe/Mg phyllosilicates are often the most abundance mineralogical group but by a short margin. Striking features also include i) the restricted occurrence of MHS minerals within the IH/eH-aged unit ii) an overabundance of PHS minerals in the eH. While systematic age
association is not as accurate as doing crater counting on specific areas that have the aqueous mineral signatures, our analysis does suggest that carbonates and PHS may have been occurring at earlier times than the IN/eH. As for the last class, because we do not disentangle between Al-rich phyllosilicates and hydrated silica, it is hazardous to interpret the relatively similar fraction of that spectral-mineral class from the eN to the IH.

It should however be noted here that in our mapping campaign, AMDs that have minerals from other classes than class #1 may in fact include mixtures with class #1 Fe/Mg phyllosilicates, but these may not always be reported. This would cause an underestimation of the extent of class #1 minerals and therefore artificially decrease our ratios here. Nonetheless, this shows that there is significant diversity for all unit categories and unit ages. There is however no reason to assert that this diversity testifies to similar alteration environments, or their spatial extent or intensity through time. Different processes can produce similar aqueous minerals.

Interpreting all of the above observations requires great care as there are multiple sources of biases and large uncertainties. The global trends which are reported here trace the geologic and hydrologic evolution of Mars, but more subtle trends should not be seen as reliable enough to be interpreted straightforwardly. This is discussed further in section 6.

## 5. Application to local scale studies: high-resolution mineral mapping of rover landing sites

The dataset allows generating mineral maps at various spatial sampling as shown above. When the need arises, it is possible to generate mineral maps at very high resolution, 10 m/pix, so as to match the best available data resolution of CRISM targeted observations. In this section are shown minerals maps of select regions chosen because they are candidate or current landing sites for rover mission. For these maps, we have integrated all available CRISM targeted observations, not as pinpoints but also mapping their mineral content. Other than that, there is no difference in the mineral identification and mapping procedure compared to the 200 m/pix global scale study. To note that in the maps below are only shown confirmed detection clusters, i.e. there is no false positive “noise”. This section also serves as a benchmark by comparing with pre-existing maps of some of these regions. Even at this resolution, one should note that the orbital approach remains much coarser than in-situ measurements of the mineralogy. Both MSL and Mars 2020 have found a richer diversity of minerals than has been found from orbit.

### 5.1 ExoMars 2022 Landing Sites

During the process of landing site selection we built aqueous mineral maps of the finalist sites for ExoMars 2022, the Mawrth Vallis Plateaus and Oxia Planum. For these sites, they are no peer-reviewed maps to compare to because the sites were both proposed and mapped by our group. Because mapping accuracy is paramount here, we use CRISM multispectral and targeted observations as our principal source of data, and fill-in coverage gaps with OMEGA lower resolution data.

The case of Oxia Planum is relatively straightforward to map because the aqueous mineralogy is remarkably homogeneous and consists in a type of Fe/Mg phyllosilicate akin to vermiculite and/or Fe-bearing saponite smectite. These are mapped in red in figure 18. Only small patches
of confirmed other aqueous minerals have been found on the southeast part of the landing ellipse, mainly overlapping with the delta-fan and on the fluvial valley walls. There, we mainly see hydrated silica (spectrally pure), and there is only one outcrop of kaolin clay. Both are mapped in cyan here.

Mawrth Vallis exhibits more diverse mineralogy which make our broad spectral-mineral classification less suitable here. There is a large fraction of Fe-rich nontronite smectite over the Mawrth Vallis Plateaus as mapped in red. Smaller outcrops on the north-facing walls of the Mawrth channel exhibit a different spectral signature more akin to the Oxia Palnum Fe/Mg phyllosilicates, but they are also mapped in red here. The cyan layer encompasses a large diversity of aqueous minerals which has been abundantly documented in the past (e.g. [Loizeau+07; Bishop+20]) and includes allophane, kaolin clays, Al-rich smectite (with a likely continuum between the Al and Fe endmembers) as well as hydrated silica. Partially mixed within this mineralogical unit are sulfates including jarosite and alunite. Bassanite sulfate mainly occurs towards the channel floor to the north, as previously documented (e.g. [Wray+10]). More detailed mapping and a study of the stratigraphic relationships between the two sites mineral units will be the topic of dedicated work.

## 5.2 MSL Landing Site

We present here the continuation of work initially focused on identifying the closest clay-bearing terrains to MSL as the rover was traversing the lower Murray formation, presented in [Carter+16]. We now expand that detailed CRISM mapping work to the entirety of Mt Sharp.

It is useful here to generate a map using only the high-resolution CRISM products (< 40m/pix) and then another with all CRISM observations (18-200 m/pix). The former provides more limited coverage but is more adequate to map the mineral stratigraphy that is known at Gale. The maps are shown in figures 19-20. At Gale crater, we see common mixtures between a) Fe-Al smectite and poly-hydrated sulfates, b) between poly-hydrated sulfates and hydrated silica, and c) to a lesser extent between mono and poly-hydrated sulfates. For the sake of clarity, we adapt our color-coding so that the first mixture (a) is shown in yellow, the second (b) in green and the third (c) in blue. To note that the Fe-Al smectite bearing unit has varying amount of Al-smectite in it, which is not represented here. Finally in cyan we show deposits with hydrated silica in them.

We then provide in figure 21 a very local map centered around the current location of MSL (as of August 2021). The original color code is used here and specifically maps the following minerals: Fe-Al smectite (red), poly-hydrated sulfate (green) and a mixture of hydrated silica and the sulfate jarosite (cyan). Only CRISM targeted observations with resolution better than 20 m/pix were used for this particular close-up.

Our mapping work here shows a sensitivity to the detection and mineral diversity that compares favorably to previous work in the area. While the main mineral units belonging to specific Mt Sharp strata were already reported in previous work (e.g. [Milliken+10; Fraeman+16; Fox+20; Fox+21]), we show increased spatial extent of some AMDs and smaller ROIs that are detected unambiguously. The clay-rich unit on which MSL is currently located is clearly mapped, as well as
the sulfates in the layered unit above that, and the hydrated silica unit opposite the Greenheugh fan/pediment. Spectral rationale for the latter mineral is provided in Figure S6. We also show a small unit enriched in mono-hydrated sulfates. To note that contrarily to most other works our maps are not spectral parameter maps that may indicate certain minerals but actually confirmed mineral detections. This means that the spectral rationale was verified over the entire area/ extent for all the mineral maps which are built, which is not the standard approach wherein only spot checks are performed. This additional step we perform is mostly relevant for detailed mapping e.g. of landing sites, less so when one is only interested in the coarse distribution of aqueous minerals. The step going from the former to the latter does cause a small decrease in our detection sensitivity threshold.

The most refined mineral mapping of Gale crater which was available so far is arguably from [Sheppard+21]. This study implements advanced CRISM processing methods to de-noise and debias the CRISM targeted datasets on the flanks of Mt Sharp. To detect aqueous minerals, standard spectral parameter maps are computed and then used in combination to increase mineral specificity. The final step involves manual verification of the combined parameter maps to confirm the presence of each type of aqueous mineral. That part of the mapping procedure is hence quite similar to the one we have been implementing, although we use much simpler pre-processing of the CRISM data. Where the procedure diverges significantly is that they then map the mineral unit manually as a polygon in GIS software, akin to mapping of a geologic unit. Our assessment of the difference between approaches is that we provide mineral maps closer to the data and somewhat more robust and conservative, but which are fuzzier and likely less convenient for geologic interpretations. Combining both approaches is preferable.

A comparison can be made at the scale of Mt Sharp between our figures 19-20 and figure 9 from [Sheppard+21]. Focusing only on the overlapping CRISM observations between our work and the latter, we see that there is an excellent agreement in the mineral mapping. A few small scattered areas (typically < 1 km in size) are mapped in our work that are not detected in theirs and vice versa. We note that their interpreted maps show the same PHS units as ours but are less noisy and slightly more extended than ours. This is to be expected given the difference in mapping procedure: our initial spectral parameter maps are also more extended than our confirmed mineral clusters maps. This is due to our rejection, filtering and clustering algorithms which map mineral units with contours that are somewhat fuzzier but more robust than interpreted units. That is because our algorithm which suppresses spatially uncorrelated (un-clustered) pixels has the side-effect of also being a morphological erosion operator, detrimentally affecting small ROIs and ROI borders. To note that our mapping project was not designed to provide meter-scale accuracy, nor does it strive to pinpoint the location of very small (a few pixels) clusters.

We produce a more detailed comparison benchmark in figure S4 which shows the same close-up view as the figure 11(b) of [Sheppard+21]. The mineral maps are on the left, and associated spectral rationale extracted from some of these units per outlines are on the right. Here are present Fe/Mg phyllosilicates, poly-hydrated sulfate (PHS) and mono-hydrated sulfate, with various degrees of mixing between the 3. The spectral parameter maps in their work (b, top-
right) are then spatially interpreted (b, bottom-left). We see that the 3 mineral units are mapped
in both works with broadly similar extent, although the contour of our mineral units appear again
fuzzier than the interpreted units, as is expected. Our mapping of PHS minerals in CRISM targeted
data may be slightly less sensitive when comparing to their final mineral map. However, because
we only show confirmed mineral signatures, our detection threshold in the map is higher, which
accounts for at least part of the relative loss in sensitivity. Our mapping also shows the localized
presence of hydrated silica (in pink here), which is not sought out in the latter work. The limitation
of using spectral parameter maps alone is illustrated on this figure as they show pervasive false
positives in green tones which are caused by pyroxene signatures. The authors alleviate this issue
by combining parameter maps, conducting spectral spot checks and then interpreting the
mineral unit extent. We do so by combining the maps and conducting systematic spectral checks
on clusters.

In conclusion, although the final products are not of the same nature between the 2 works, we
can conclude that both provide a similar degree of sensitivity when studying CRISM targeted
data. No comparison is possible for multispectral or OMEGA data which is not used in the other
works. An interesting perspective would be to merge the pre-processing steps of
[Itoh&Parente+21] used in their work and the mineral detection and mapping procedure in our
work, which may provide enhanced sensitivity.

The intent of this type of map remains to provide reconnaissance mapping for rovers such as
MSL, and provide them with high science potential targets such as aqueous mineral units west of
the Greenheugh pediment.

## 5.3 Mars 2020 Landing Sites

Here we revisit work we originally reported in [Carter+19] in which we provided the locations of
carbonate, Fe/Mg clays, kaolins, sulfate and akaganéite. Because akaganéite is detected reported
here, we include this mineral layer which is not otherwise available as the other global-scale
classes.

We first build a regional map which encompasses final landing sites for Mars 2020 (Jezero,
Midway and NE Syrtis). As for Gale crater, we build a version with limited coverage using only
CRISM targeted observations, and another with all CRISM observations. The grid is again 10
m/pix. No OMEGA observations are used here although they provide good mineral coverage of
the area but at a coarser spatial resolution. The mapping results are shown in figure 22. The
mineral categories we define and map are not defined exactly the same way between the two
maps. The first map includes the lower signal-to-noise and lower spectral resolution multispectral
dataset of CRISM (MSP, HSP, MSW) which do not systematically allow discriminating between
minerals with 2.15-2.25 µm absorptions. Hence we group together the Al phyllosilicate (kaolins)
and hydrated silica layer. Al-smectite and jarosite are minor contributors to this layer. In a similar
fashion, carbonate-Fe/Mg phyllosilicate mixtures are harder to evaluate with this dataset, so that
while the Fe/Mg phyllosilicate component is readily identified, the carbonate is harder to detect.
As a result, one can expect that the carbonate layer as displayed in orange corresponds to a lower
limit in extent. Carbonates are also identified within the Fe/Mg phyllosilicate layer (red). These degeneracies do not apply to the second map which includes only high resolution targeted data.

We also provide a set of higher resolution maps of the interior of Jezero crater and the immediate surroundings of the Mars 2020 rover. For the latter, our automatic projection pipeline is inadequate because there are small residual errors in the CRISM geometry which hinder the precise localization of small mineral ROIs. Instead, we proceeded to manually correct the geometry of the CRISM targeted observations within the field of view (FRT00005850_07, FRS00031442_01, FRT0001FB74_07, FRT000047A3_07, FRT00005C5E_07, HRL000040FF_07) so that there is good co-registration between the CRISM observations and basemaps. The residual error is estimated to be within 2 CRISM pixels (<36 m). Other than that our mapping procedure is the same as before. The results are shown in Figures 23-24. We notice that there are confirmed aqueous mineral clusters all around Perseverance, including the units directly ahead in Séitah and at the Octavia. E. Butler landing site. The aqueous mineralogy here is dominated by Fe/Mg phyllosilicates (saponite-vermiculite, in red), but we also see pervasive signature of carbonates (orange, with or without an additional phase mapped in pink) and hydrated silica (cyan). The signatures concentrate mostly in bright, lower dune-rich unit, and to the north on the scarps of the delta. Spectral rationale is provided in Figure S5 (see ROIs #1-3 and associated spectra). Although akaganéite is mapped regionally in Figure 22, Here we do not show the akaganéite layer inside Jezero. Most akaganéite detections are seen to occur south of Jezero, in the Nilo-Syrtis area. What's more, as we do not consider our detections confirmed in Jezero to be as reliable. More work is needed on its systematic mapping. Finally, the igneous mineral o. To note that olivine signatures are pervasive within this area, but *is* not mapped here.

In the remainder of this section, we discuss the regional scale composition and compare to recent work for benchmarking purposes. The variability of the spectral shape of the carbonate units has been reported before (e.g. [Horgan+19]) which we also see in our dataset. Although we do not routinely map such subtle variability within a mineral class in this project, an exception was made here because a substantial part of the carbonate deposits within Jezero crater is seen to be mixed with another aqueous phase. It exhibits a clear inflexion point (spectral shoulder) or band in the 2.20-2.25 µm spectral region. While it is possible that a particular carbonate composition that is not an endmember Mg, Fe or Ca species could contribute that band (due to chemical substitution), we did not find satisfactory matches in our spectral analogue libraries. Instead, that band could be contributed to by Al-smectite, hydrated silica, or the Mg-hydroxide brucite. The latter would not be unexpected in this geological context, while the others are already found elsewhere within Jezero crater. That carbonate spectral sub-unit is mapped in pink in figures 23 (top) and 24 while indiscriminate carbonates are mapped in orange. A detailed investigation of that sub-unit is out of scope of this paper, but simple comparisons within the ROIs which have or lack that additional band tend to favor the hypothesis of mixture with an Al+Fe/Mg smectite at least in some cases.

Hydrated silica (cyan in map) is widely present within Jezero in the distal deltaic unit including at close proximity to Perseverance, but rarely seen to be spectrally pure. Examples are given in the spectral rationale of figure S5 (see ROI #2). Its presence is consistent with what has been observed that hydrated silica is associated to most deltas and alluvial on Mars [Carter+12;
1075 **Pan+20**]. It likely speaks to late stage alteration in water-limited environments, however older
1076 sedimentary material possibly as sediment are not implausible in the ROIs associated to the delta
1077 itself. There is a plausible concentration of hydrated silica at the head of the delta that is seen to
1078 be spectrally pure with stronger absorption signatures (see ROI #6, figure S5). Finally, there is
1079 another patch of relatively pure hydrated silica about 4 km due West of the rover (see ROI #5,
1080 figure S5). Elsewhere, mixtures of hydrated silica with Al/Fe smectite and/or jarosite are common
1081 within Jezero, and we do not attempt to distinguish between them here.

1082 Kaolin (blue in map) is detected at several locations in Jezero, including along the possible route
1083 between Perseverance touchdown site and Midway. It is always associated with the degraded
1084 crater rim of Jezero, and it is not found in the lower units. The closest occurrence is seen mixed
1085 with Fe/Mg phyllosilicate and may be on the rover path (see ROI #9 and associated spectra in
1086 figure S5).

1087 Within class #1 layer (red map) there is spectral variability that indicates different species within
1088 the Fe/Mg phyllosilicate group. The dominant specie is a type of intermediate Fe-Mg
1089 smectite/vermiculite, while instances of serpentine and chlorite exist locally. We do not attempt
1090 to map this diversity.

1091 Akaganéite is reported in Figure 22 (purple) mainly in the SW part of the mapped area, near the
1092 N. Syrtis lava flows. Its distribution is consistent with our initial mapping [Carter+19], but now
1093 more extended and visible from both CRISM targeted and multispectral data. It correlates very
1094 well with local topographic lows, consistent with late-stage ponding and the gradual drying up of
1095 ponds/lakes of increasing salinity, as also proposed for a similar occurrence in nearby Robert
1096 Sharp crater [Carter+15b; Broissier+21].

1097 It should be noted that all recent work we are comparing to do not actually provide mineral maps,
1098 but RGB composite map of spectral parameters or interpreted maps which highlight the possible
1099 presence of specific absorption bands and their combinations. While these maps are broadly
1100 efficient for strong signatures as are present in Jezero, they are also prone to false positives, and
1101 are not qualitative: they hint at the presence of a mineral but offer no guarantee that it is indeed
1102 there.

1103 We conducted a review of available mineral maps of that site which are based on high resolution
1104 CRISM data [Ehlmann+08; Goudge+15; Tarnas+19; Tarnas+21; Zastrow+21] and in particular of
1105 [Parente+18, 4th Mars 2020 landing site Workshop,
1106 https://marsnext.jpl.nasa.gov/workshops/wkshp_2018_10.cfm] where these authors use the
1107 same advanced processing method as for Gale crater [Itoh&Parente+21] so as to provide
1108 interpreted aqueous mineral maps. Visually comparing the mineral maps of the touchdown site
1109 and southern fan area for Mars 2020, we can see that our maps compare favorably, providing
1110 equal to higher sensitivity to the detection of Fe/Mg phyllosilicates, carbonates, hydrated silica,
1111 Al smectite, and kaolins.

1112 Regarding akaganéite, in our original map of [Carter+19] we did not report any inside Jezero
1113 crater. That mineral was since reported in [Dundar+19] at several observations including less than
1114 5 km from Perseverance touchdown point. The reason we did not report this particular
occurrence is that because of the pervasiveness of aqueous minerals in Jezero, it is difficult to
find a clean spectral denominator representative of a neutral mineralogy. Failing this, our spectral
ratio will include residuals of minerals such as carbonates and opal that create inverse features
that can erroneously be attributed to the diagnostic absorption features of akaganéite at 2.45
µm. Nonetheless, we build an akaganéite map within Jezero (in pink in figure 22) with the caveat
that there is a risk of false positive. Interestingly, our akaganéite map while more extended does
not overlap with the small patches of akaganéite reported in [Dundar+19]. More work would be
required to disentangle potential akaganéite signatures from contaminants in the spectra.

1123 6. Discussion and perspectives

1124 6.1 On the extent of aqueous minerals

1125 Near-infrared orbital spectroscopy at the 100s meter pixel scale is an efficient albeit coarse
1126 approach to detecting aqueous minerals. Sub-pixel mixing with anhydrous minerals, thin coatings
1127 by dust or late stage oxidation or frost, intimate mixtures of minerals and texture effects are all
1128 known to subdue spectral signatures of interest. What's more, as demonstrated above, our
1129 procedure provides only confirmed detections but at the expense of somewhat degrading the
1130 deposit original shape and extent. What we are detecting is therefore a firm lower limit in terms
1131 of number of AMDs. Regarding their areal extent, our method cannot guarantee that each pixel
1132 of our global 200 m/pix grid that is part of an AMD indeed has 100% or even >0% of the aqueous
1133 mineral. A large fraction will, but exceptions will occur notably due to the oversampling of coarse
1134 resolution OMEGA data. It is important to recall however that sensitivity to detection broadly
1135 scales linearly with the pixel filling factor of the aqueous mineral sought out (i.e. how much of
1136 that pixel area is indeed covered in aqueous mineral) and to the aqueous mineral surficial
1137 abundance within that deposit. These parameters are independent of scale, so that a CRISM
1138 targeted 20x20 m² pixel or a 4x4 km² low-resolution OMEGA pixel with an aqueous mineral
1139 detection both are required to have a non-negligible physical area that is covered in aqueous
1140 mineral (our first guess is on the order of >25% - >50%). Overall, we do not expect this effect to
1141 have a significant impact on the first-order statistics that are provided throughout section 4.

1142 Secondly, it has long been observed that aqueous minerals concentrate in the older terrains, and
1143 widely interpreted that these minerals were formed before the late Hesperian, and not because
1144 older terrains were preferentially altered at a later time (e.g. [Bibring+06]). Noachian-aged
1145 terrains are not the most abundant on Mars (<50% surface) and they are also the most disrupted
1146 by > 3.5 Gyrs of endogeneous and exogeneous processes. These include mantling by lava or
1147 aeolian deposits, mass wasting, erosion which may have removed 100s of meters or more in
1148 some regions, impact gardening, glacial erosion or mantling, and late-stage fluvial activity. In
1149 conclusion, what we can state qualitatively is that 1) not only does our orbital approach provide
1150 a conservative lower limit of number of AMDs, but also 2) most of those AMD themselves were
1151 heavily degraded over time. This begs the question as to how extensive could the Noachian
1152 aqueous minerals have been originally?

1153 6.2 On the age of aqueous minerals
1154 While it is tempting to provide a timeline of aqueous alteration of Mars, there are too many
1155 unknowns and unknown unknowns to do this in a straightforward way. Our timeline of figure 17
1156 does show trends that are somewhat different from the bulk of the literature, notably the large
1157 time scale on which carbonates, poly hydrated sulfates and Al-phyllosilicates are found.
1158 Subsequent analysis should focus on the terrains they pertain to, redo crater statistics on them
1159 for more accurate dating, and when accessible study stratigraphic relationships. What can be said
1160 for now is that the summary timelines of aqueous alteration that are documented in the
1161 literature should be not be taken as fact, as they are based on small statistics and suffer from
1162 sampling bias. Caution is therefore advised.

1163 **6.3 On the composition of aqueous minerals**

1164 As discussed above, the diversity reported here is a firm lower estimate both in the number of
1165 aqueous mineral species, and in their respective occurrence frequency at Mars. The effect of
1166 increased spatial and spectral resolution when using CRISM targeted observation is obvious, with
1167 more diversity and mixtures reported than at the coarse scale. This is all the more true with in-
1168 situ mineralogical studies by rovers which not only have shown more minerals than seen from
1169 orbit, but also mixtures at the sub meter scale unreachable by orbital instruments.

1170 Laboratory work on synthetic mixtures of aqueous minerals have shown that the diagnostic
1171 spectral absorption features of a given mineral can be subdued when it is mixed even with minor
1172 amount of another aqueous mineral (e.g. sulfates and clays, e.g. [Sheppard+21b]). This will not
1173 be easily detectable from orbit. Some aqueous minerals which have fewer diagnostic bands
1174 and/or bands which overlap with spectral contaminants will also be harder to detect. This is for
1175 instance the case of sulfate salts when mixed with other aqueous minerals or in the presence of
1176 frost. Al-phyllosilicates have a 2.2 µm band which is detrimentally affected by atmospheric CO2
1177 residuals on the shorter wavelength side, also reducing our capability to detect them. Finally, our
1178 procedure to detect aqueous minerals require at least 2 diagnostic bands, so this will affect
1179 minerals which have intrinsically subdued bands and fewer transitions in the near infrared.

1180 **6.4 Future Enhancements**

1181 Several enhancements are foreseen once new characterization methods are validated, and
1182 perhaps based on feedback from the user community and from Mars rover exploration.

1183 To reduce uncertainties on the geographic extent of AMDs, it will be useful to more systematically
1184 complement low-resolution, OMEGA-based AMDs with overlapping coverage from CRISM
1185 multispectral/survey observations. This will not always be possible however due to incomplete
1186 coverage of the Mars surface with CRISM, and what we have noticed to be an inherently higher
1187 detection sensitivity of OMEGA for resolvable AMDs.

1188 The first major improvement foreseen regards the spectral characterization of each AMD,
1189 whether it is provided as a single median spectrum or a multi-pixel cluster of spectra. We can
1190 mine the AMD database and run automated spectral inversion methods which may yield more
1191 systematic identifications unencumbered by potential bias when a human worker assigns the
1192 mineral class. The most urgent application will be to split the "cyan" class #2 AMDs into 2 or 3
1193 new mineral classes which discriminate between hydrated silica and Al-rich phyllosilicates
1194 (further discriminating between kaolins and smectites). This will not always be possible especially
1195 when based on low SNR, CRISM MSP data.

1196 Lacking in this database are the reported detections of anhydrous chloride salts and some iron
1197 oxides which geologic context argue for an aqueous origin. While associated databases or mineral
1198 maps exist for some of these phases, a revisit would be warranted.

1199 For a large fraction of our database, we have recorded the median spectra of confirmed AMD
1200 clusters. This provides a large (>10000 spectra all over Mars) training dataset for more advanced
1201 datamining algorithms such as those based on neural networks. It will hence be possible to
1202 reanalyze automatically the bulk of the OMEGA and CRISM database and search for similarities
1203 between single or cluster spectra and our training database. It is likely that this will increase
1204 significantly our detection sensibility, but the number of AMDs will likely not increase by an order
1205 of magnitude.

1206 The first-run mineral abundance retrieval study on our reported AMDs is available in [Riu+21].
1207 Follow-up work will involve improving our modelling and applying it indiscriminately to all AMDs,
1208 of all sizes and instrument type (only kilometer-scale OMEGA data have been processed so far).
1209 These will discuss the effect of sub-pixel mixing between aqueous and non-aqueous minerals, as
1210 well as surficial abundance within a mineral deposit.

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1373

1375
1376 Acknowledgements

1377 This work received funding from NASA/JPL under grant agreement KM-2691-947266 for Mapping

1378 of Water Deposits to Support NASA Mars Exploration Program Studies. The authors also

1379 acknowledge funding from the CNES postdoctoral fellowship program. We are very grateful to

1380 the dedicated technical and operations teams of the OMEGA and CRISM instruments.