Icarus 261 (2015) 66–79

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Icarusjournal homepage: www.elsevier.com/locate/icarus

Latitudinal variation in spectral properties of the lunar maria
and implications for space weathering
Douglas J. Hemingway a,⇑, Ian Garrick-Bethell a,b, Mikhail A. Kreslavsky aab

Department of Earth & Planetary Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA
School of Space Research, Kyung Hee University, Yongin-si, Gyeonggi-do 446-701, Republic of Korea

a r t i c l e

i n f oArticle history:
Received 6 May 2015
Revised 24 July 2015
Accepted 5 August 2015
Available online 7 August 2015
Keywords:
Moon
Solar wind
Magnetic fields
Infrared observations
Moon, surface

a b s t r a c t
Space weathering alters the optical properties of exposed surfaces over time, complicating theinterpretation of spectroscopic observations of airless bodies like asteroids, Mercury, and the Moon.
Solar wind and micrometeoroids are likely the dominant agents of space weathering, but their relative
contributions are not yet well understood. Based primarily on Clementine mosaics, we report apreviously unrecognized systematic latitudinal variation in the near-infrared spectral properties of thelunar maria and show that the characteristics of this latitudinal trend match those observed at ‘lunar
swirls’, where magnetic fields alter local solar wind flux without affecting the flux of micrometeoroids.
We show that the observed latitudinal color variations are not artifacts of phase angle effects and cannot
be accounted for by compositional variation alone. We propose that reduced solar wind flux, whichshould occur both at swirls and toward higher latitudes, is the common mechanism behind these color
variations. This model helps us quantify the distinct effects of solar wind and micrometeoroid weatheringand could aid in interpreting the spectra of airless bodies throughout the Solar System.
! 2015 Elsevier Inc. All rights reserved.

1. Introduction‘Space weathering’ refers to the processes by which the opticalproperties of airless bodies change due to exposure to solar windand micrometeoroid impacts. However, the difficulties of reproducing space-weathering conditions in the laboratory, or returning
weathered samples to Earth, make it challenging to determine precisely how space weathering operates (Pieters et al., 2000, 2012;
Hapke, 2001; Vernazza et al., 2009; Domingue et al., 2014). Remotesensing measurements, studies of lunar samples, and laboratory
experiments have established that solar wind ion and micrometeoroid bombardment weaken spectral absorption features andcause the lunar surface to darken and redden (increase in spectral
continuum slope in the visible and near-infrared) with time. Thesechanges appear to be due to some combination of the formation of
impact glasses and agglutinates (Adams and McCord, 1971), theregolith’s disintegration into increasingly finer soils (Pieters et al.,1993), and the accumulation of nanophase iron (Hapke, 2001;
Sasaki et al., 2001; Noble et al., 2007). Larger impacts also expose
fresh material, which then gradually matures until the reflectance⇑ Corresponding author.

E-mail address: djheming@ucsc.edu (D.J. Hemingway).

http://dx.doi.org/10.1016/j.icarus.2015.08.004
0019-1035/! 2015 Elsevier Inc. All rights reserved.

spectrum reaches a steady state, which we call ‘equilibrium color’for simplicity.
The equilibrium color varies considerably across the lunar
surface, due primarily to differences in mineralogy. This is mostobvious in the dichotomy between the bright, anorthositic highlands and the darker basaltic maria. However, as we will argue,
the presence of ‘lunar swirls’ suggests that equilibrium color may
also be influenced by the flux of weathering agents, rather than justtheir total accumulation (see Sections 3.1 and 4). If this is the case,then equilibrium color may also vary with latitude. Both solar windand micrometeoroids originate primarily from within the ecliptic
plane, which is inclined from the Moon’s equator by just 1.5".
Hence maximum flux of these weathering agents occurs near theequator, with flux decreasing as incidence angle increases toward
the poles.
This paper’s central observation is that, when we examine imagery from across the lunar surface, we find that the equilibriumcolor does vary systematically with latitude. In Section 3.2, weshow that this latitudinal color trend persists across a range of distinct compositions and that it is not an artifact of phase angle
biases in the Clementine mosaics. Interestingly, the spectral properties of the latitudinal color trend match the characteristic color
variation found at lunar swirls. In Section 3.1, we quantify thecharacteristics of the swirl-related color variation and, in
Section 3.2, we show that it is statistically equivalent to the67

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

observed latitudinal color trends, with a transition toward higher
latitudes being attended by the same color change that occurs
toward brighter parts of swirls. Finally, in Section 4, we argue thatthe best candidate for a common mechanism behind these color
variations is altered solar wind flux. We present a qualitativemodel illustrating how this hypothesis comports with the observations and we discuss the possible implications with respect to theinterpretation of spectral data, particularly at high latitudes.
2. Data sources
In this study, we use mosaics based on imagery from the
750 nm and 950 nm channels of the Clementine UVVIS(ultraviolet–visible) experiment (Nozette et al., 1994; Eliason
et al., 1999), available from the USGS (www.mapaplanet.org). Asa point of comparison, we also examine 1064 nm reflectance fromthe Lunar Orbiter Laser Altimeter (LOLA) experiment on board
the Lunar Reconnaissance Orbiter (LRO) (Lucey et al., 2014). In
discussing the observed trends in the Moon’s spectral properties,
we may use the word ‘color’ in a general sense to refer tocombinations of albedo and the ratio between 950 nm and
750 nm reflectance (e.g., as a proxy for continuum slope).
Parts of our analysis require isolating portions of the lunar surface according to composition and/or topographic roughness. For
composition, we use results from the Lunar Prospector Gamma
Ray Spectrometer (Lawrence et al., 2002; Prettyman et al., 2006),
specifically in order to identify FeO and TiO2 content in the regolith. The topographic roughness metric we use is the interquartile
range of the along-profile second derivative of elevation, at 1.8-km
baseline (Kreslavsky et al., 2013), derived from Lunar Orbiter Laser
Altimeter (LOLA) data. The latter is used to distinguish between thesmooth maria and the rougher highlands.
3. Analysis
Before discussing the observed latitudinal color variation, we
revisit the characteristic color signature observed at swirls, developing a new parameterization that will allow for a quantitative
comparison between swirls and the newly observed latitudinal
trends.
3.1. Color variation at lunar swirls
Lunar swirls are enigmatic collections of sinuous bright markings, often interposed with narrow dark lanes, that are co-locatedwith many of the Moon’s crustal magnetic anomalies (Fig. 1A).
The bright parts of swirls superficially resemble optically immature surfaces such as fresh impact craters (Lucey et al., 2000b;
Wilcox et al., 2005; Blewett et al., 2011). However, it has beenshown (Garrick-Bethell et al., 2011) that swirls exhibit spectral
trends that are distinct from those associated with impactrelated brightening (Lucey et al., 2000b). The two trends can be
distinguished from one another, using Clementine UVVIS(ultraviolet–visible) mosaics (Nozette et al., 1994; Eliason et al.,1999), by plotting 750 nm reflectance against the 950 nm/750 nm
reflectance ratio (Blewett et al., 2011; Garrick-Bethell et al., 2011),
the former representing albedo and the latter serving as a proxy forboth the near-infrared continuum slope and the 1 lm absorption
feature found in iron-bearing silicate minerals. Both theswirl- and impact-related color variations involve changes in bothalbedo and the 950 nm/750 nm band ratio, but the impact-related
variation is accompanied by a proportionally greater change inthe 950 nm/750 nm band ratio (Fig. 1B), as originally reported byGarrick-Bethell et al. (2011).

In order to establish a quantitative basis for comparison withthe latitudinal trends we discuss in Section 3.2, we parameterize
the color variations that are characteristic of swirls, averaging overthree different mare swirl areas: Reiner Gamma in western Oceanus Procellarum, Mare Ingenii on the farside, and Mare Marginison the eastern limb. In each case, in the albedo versus band ratio
diagrams, we found similar steep trends associated with the transition between impact craters and background soils, and shallower
trends associated with the transition between dark and brightparts of swirls (Figs. 1B, 2B and 3B), in accord with GarrickBethell et al. (2011). The distinct color variations associated with
impacts and swirls allow us to define parameters that clearly separate the two trends (Fig. 1C and D). The impact-related progression from bright craters to the more mature background soils canbe characterized by an impact maturity parametera ¼ R750 "

!

R950
R750

"#
m1

ð1Þ

where R750 and R950 are the Clementine 750 nm and 950 nm reflectances, respectively, and wherem1 ¼ "1:6 % 0:2
is the slope of the swirl-related trends (±1r), averaged from thethree separate mare swirl areas. The swirl-related trend slope isused in Eq. (1) so that impact maturity (a) is not affected byswirl-related color variations. Eq. (1) resembles previously developed optical maturity parameters (Lucey et al., 2000b; Wilcox
et al., 2005) except that here, the goal is explicitly to isolate theimpact-related color variation from that associated with swirls,
and so the constants are different. Similarly, we can represent the
swirl-related color variation, which we regard as distinct from optical maturity, as

b ¼ R750 "

!
"#
R950
m2
R750

ð2Þ

where

m2 ¼ "5:7 % 0:5
is the typical slope of the impact-related trends (±1r). The impactrelated trend slope is used in Eq. (2) so that b is not affected by
impact-related color variations.
Although the a and b values vary according to local composition, the slopes of the impact- and swirl-related trends do not varysignificantly across different mare regions. The values given here
for m1 and m2 are therefore largely composition independent, atleast within the maria. The a parameter is designed to have constant values along the swirl-related trends such that swirl features
do not influence the value of a and so maps generated for the aparameter show impact features but not swirl features (Fig. 1C).
Conversely, the b parameter is designed to have constant values
along the impact-related trends such that maps of the b parameter
highlight swirl features while muting impact features (Fig. 1D).
3.2. Latitudinal color variation
When we examine imagery from across the lunar surface, wefind that the reflectance spectra vary systematically with latitude.
The effect is not obvious when we examine the Moon as a whole,
likely because the spectra are so strongly affected by composition,
which varies considerably across the surface. However, when we
account for variations in composition, the latitudinal trends
emerge. As we will show, the latitudinal trends are especially pronounced within the maria, and may help to account for the unusually high albedo of Mare Frigoris—the highest latitude mare region.

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D.J. Hemingway et al. / Icarus 261 (2015) 66–79
9

0.15

0.9

swi
rl

0.13

301.5

302

302.5

303

0.75

0.7
0.09

0.09

0.1

0.11

0.12

Longitude (degrees E)

0.13

0.14

0.15

9

0.17

0.18

0.3

9

0.76

C

D
0.75

8.5

8

0.74

8

7.5

0.73

7

0.72

6.5

0.71

Latitude (degrees N)

8.5

Alpha

Latitude (degrees N)

0.16

750nm Reflectance

0.295
0.29
0.285
0.28
0.275

7.5

Beta

301

e

300.5

0.8

p
slo

0.1

10km

d, s
lop
e

g
si n
rea

6.5

swirl
equilibrium
color

tren

,
nd

0.11

0.85

ing

re

7

eas

t
ct

0.12

B

pa
im

7.5

950nm−to−750nm Reflectance Ratio

8

Clementine V1 750nm Reflectance

0.14

inc

Latitude (degrees N)

8.5

6
300

0.95

backgroundequilbirium color
incr

1

A

0.27
7

0.265
0.26

6.5

0.255
6
300

300.5

301

301.5

302

302.5

303

0.7

Longitude (degrees E)

6
300

300.5

301

301.5

302

302.5

303

0.25

Longitude (degrees E)

Fig. 1. (A) 750 nm reflectance (albedo) at the Reiner Gamma swirl. (B) Spectral characteristics (albedo versus 950 nm/750 nm band ratio) of pixels sampled from the fiverectangular boxes, with corresponding colors, in (A). The red point cloud corresponds to a representative background region whereas the dark blue point cloud corresponds tothe brightest part of the swirl. Each of the point clouds displays a steep trend that follows the progression from fresh impact craters (lower right part of each point cloud) todarker background soils (upper left part of each point cloud); the dashed black line is a best fit through the red point cloud and, averaging over three separate mare swirl
areas, has a slope of m2 = "5.7 ± 0.5. The black circles represent the steady state (equilibrium) colors reached with maximum optical maturity in each rectangle (since themajority of pixels are likely near saturation maturity, we take the median point in each cluster to represent the equilibrium color). The solid black line is a best fit through themedians (black circles) of each cluster and has a slope of m1 = "1.6 ± 0.2, averaged over the three separate mare swirl areas. (C) and (D) are maps of the a and b parameters,
defined according to Eqs. (1) and (2), respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Color anomalies have been identified previously in polar
regions (Yokota et al., 2011; Zuber et al., 2012) and latitudinal variation in space weathering has been considered previously (Yokota
et al., 2011; Hendrix et al., 2012), however, this is the first observation of a broad systematic latitudinal color trend that is visible
across the lunar maria. Moreover, the spectral characteristics ofthis trend match those found at lunar swirls (Section 3.1), suggesting a link between the two phenomena that may be helpful forunderstanding space weathering (see Section 4).
In addition to controlling for composition, below we examine
the possible contributing effects of highland contamination in themaria and phase angle biases in the Clementine mosaics; we showthat the observed latitudinal trends cannot be artifacts of such
effects.
3.2.1. Effect of composition
Because surface color is strongly affected by composition, we
separate the data into bins according to TiO2 and FeO content (asmeasured by the Lunar Prospector Gamma Ray Spectrometer[Lawrence et al., 2002; Prettyman et al., 2006]), and examine thespectral characteristics separately for each compositional bin.
Fig. 4A shows, in the same parameter space we used to characterize swirls, that pixels sampled from higher latitudes tend to havehigher albedo (750 nm reflectance) and lower 950 nm/750 nm
band ratios, than those sampled from lower latitudes.
While Fig. 4A shows just one illustrative example, we examined
all combinations of FeO and TiO2 content using bins of 2 wt% width
in FeO and 1 wt% width in TiO2, set at half bin width increments(see Fig. 5 for additional examples). Because the latitudinal trend
can only emerge if pixels are sampled from a wide range of latitudes, we discard compositional bins whose pixels do not reachlatitudes of at least ±50" or do not span at least 50 total degrees
of latitude. To ensure that slopes are estimated from robust samples only, we also discard bins whose pixels cover less than
0.25% of the lunar surface. Finally, to avoid artifacts in the Clementine mosaics that are related to severe illumination conditions athigh latitudes, we also discard data within 20" of the poles. After
applying these selection criteria, 59 compositional bins were69

D.J. Hemingway et al. / Icarus 261 (2015) 66–79
−32.5

0.2

A

−33

1.1

B

0.19
0.18

−34

0.17

−34.5

0.16

−35

0.15

−35.5

0.14

−36

0.13

−36.5

0.12

−37.5
162

15km
163

165

166

167

0.95
0.9
0.85
0.8
0.75

0.11

164

1

0.1

0.7

0.1

0.12

−32.5

0.16

0.18

0.2

0.35

−32.5

0.84

C

D

0.34

−33.5

0.82

−33.5

0.33

−34

0.81

−34

0.32

−34.5

0.8

−34.5

0.31

−35

0.79

−35

0.3

−35.5

0.78

−35.5

0.29

Latitude (degrees N)

−33

Alpha

0.83

−33

Latitude (degrees N)

0.14

750nm ReflectanceLongitude (degrees E)

−36

0.77

−36

0.28

−36.5

0.76

−36.5

0.27

−37

0.75

−37

0.26

0.74

−37.5
162

−37.5
162

163

164

165

166

167

Longitude (degrees E)

163

164

165

166

167

Beta

−37

950nm−to−750nm Reflectance Ratio−33.5

Clementine 750nm Reflectance

Latitude (degrees N)

1.05

0.25

Longitude (degrees E)

Fig. 2. (A) 750 nm reflectance at the Ingenii swirl. (B) Spectral characteristics (albedo versus 950 nm/750 nm band ratio) of pixels sampled from the five rectangular boxes,
with corresponding colors, in (A). For further details, refer to Fig. 1 legend. (C) and (D) are maps of the a and b parameters, defined according to Eqs. (1) and (2), respectively.
(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)retained for analysis. Fig. 4B shows a summary of all 59 latitudinal
trend lines (gray lines) along with the swirl- and impact-related
trends discussed in Section 3.1.
The effect of iron content is apparent when the trend lines arecolor-coded by wt% FeO, as determined by the Lunar Prospector
Gamma Ray Spectrometer (Lawrence et al., 2002) (Fig. 6). Lower
iron regions (pale orange lines) tend to plot farther up and to theright in Fig. 6, in accord with Wilcox et al. (2005), whereas highiron regions (dark lines) plot farther down and to the left. Thismeans that both a and b tend to be larger for low iron regions(in Section 3.2.5, we discuss the implications of this observation
with respect to highland contamination in the lunar maria).
Although the latitudinal trend slopes are largely similar acrossdifferent compositional bins, they become less consistent wheniron content is very low, such as in the highlands. This is not surprising given that several studies suggest that the production of
nanophase iron plays an important role in space weathering(Hapke, 2001; Sasaki et al., 2001; Noble et al., 2007). A paucity ofiron in the highlands may prevent a clear appearance of the typeof latitudinal trends we observe in the maria. In order to obtainas clear a signal as possible, we therefore focus our subsequent
analysis on the maria.

3.2.2. Isolation of the maria
Because the latitudinal trends are most consistent within themaria, and because we are interested in comparing these trends
with the spectral characteristics we analyzed for three marebased swirls (Section 3.1), we restrict the remainder of our analysis
to the lunar maria.
As previously discussed, we use Lunar Prospector Gamma Ray
Spectrometer (GRS) data to determine both iron and titanium content in the soils we examine. Unfortunately, in comparison to the
Clementine data, the relatively coarse resolution of the GRS data(the effective footprint is roughly 45 km wide [Lawrence et al.,2002]) means that we cannot use GRS-based iron estimates to isolate the maria—choosing a conservatively high threshold for ironcontent would result in the exclusion of significant portions of
the maria, while choosing a low threshold would result in the
inclusion of highland pixels. Inclusion of low-iron highland pixels
is undesired as it could artificially skew the spectral characteristics
toward higher 750 nm reflectance and higher 950 nm/750 nm
reflectance ratios (see Fig. 6 and Section 3.2.5).
We cannot isolate the maria based on iron estimates obtained
from Clementine-based spectral techniques (e.g., Lucey et al.,2000a; Wilcox et al., 2005) because these techniques rely on the

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D.J. Hemingway et al. / Icarus 261 (2015) 66–79
0.17

16

1.1

A
0.16

0.14

0.13

14.5

0.12
14
0.11

10km
13.5
82

82.5

83

83.5

84

84.5

950nm−to−750nm Reflectance Ratio

Latitude (degrees N)

15

Clementine 750nm Reflectance

15.5
0.15

B

1.05

1
0.95
0.9
0.85
0.8
0.75

0.1

0.7

0.1

0.12

Longitude (degrees E)

0.14

0.16

0.18

0.2

750nm Reflectance
16

0.82

16

0.33

C

D

0.325

0.81
15.5

15.5

0.32

0.78
14.5

0.77

0.315

15

0.31
0.305

14.5

Beta

15

Latitude (degrees N)

0.79

Alpha

Latitude (degrees N)

0.8

0.3
0.295

0.76
14

14

0.29

0.75
13.5
82

82.5

83

83.5

84

84.5

0.74

Longitude (degrees E)

0.285

13.5
82

82.5

83

83.5

84

84.5

Longitude (degrees E)

Fig. 3. (A) 750 nm reflectance at the Marginis swirl. (B) Spectral characteristics (albedo versus 950 nm/750 nm band ratio) of pixels sampled from the five rectangular boxes,
with corresponding colors, in (A). For further details, refer to Fig. 1 legend. (C) and (D) are maps of the a and b parameters, defined according to Eqs. (1) and (2), respectively.
(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)same parameter space we use in our own spectral analysis, namely
750 nm reflectance versus the 950 nm/750 nm reflectance ratio.
Attempting to isolate the maria according to such spectral-basediron estimates would necessarily bias our results, artificially altering the slope of the best-fit latitudinal trends shown in Fig. 4.
To distinguish mare from highland terrain, we instead adopt an
independent metric that characterizes topographic roughness at
1.8-km baseline (Kreslavsky et al., 2013) (see Section 2). A histogram of the topographic roughness (Fig. 7) shows a bimodal distribution, reflecting the dichotomy of kilometer-scale roughness
between smoother maria and rougher highlands, with maria peaking at a roughness value of 2.3 m/km2 and highland terrain peaking
at a roughness value of approximately 19 m/km2. We identify mareterrain as areas exhibiting roughness values below 5 m/km2. After
applying a de-speckling filter, designed to remove isolated featuresless than approximately 1" in diameter, we obtain a mask that we
use to exclude non-mare pixels from our subsequent analysis.
Fig. 8 illustrates the boundaries of our mare mask.
After excluding data from the lunar highlands, a clear latitudinal trend emerges in the Clementine 750 nm reflectance profile(Fig. 9A): mare surfaces are darkest near the equator and become

increasingly bright toward higher latitudes, with the profile being
approximately symmetric about the equator. This is a straightforward visualization of the latitudinal trend and allows for simple
comparison with different datasets (e.g., reflectance from the Lunar
Orbiter Laser Altimeter; see Section 3.2.6 and Fig. 9B), but in orderto facilitate comparison with the swirl-related spectral trends, we
now return to plots of 750 nm reflectance versus the
950 nm/750 nm reflectance ratio.
3.2.3. Comparison with swirl trends
After excluding the highlands, 28 of the original 59 compositional bins survive the selection criteria we described in Section 3.2.1. Fig. 10 illustrates not only that the latitudinal color
trends persist across a range of different compositions, but alsothat these trends are remarkably similar to those associated withlunar swirls. That is, low latitude regions tend to have lower
albedo and higher 950 nm/750 nm band ratios when comparedwith high latitude regions, and these changes occur in the ratio
m3 = –1.5 ± 0.3, a slope that is indistinguishable from theswirl-related slope, m1 = –1.6 ± 0.2, at the one-sigma level(Fig. 11). Fig. 12 further demonstrates this result by showing that

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D.J. Hemingway et al. / Icarus 261 (2015) 66–79
1.15

0.95

1.1

0.9

1.05

0.85

1

0.8

0.95

0.75

0.9

0.7

0.85

0.65

0.8

0.6

0.7

15−17 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

1.1

0.55
0.14

0.16

0.18

0.2

0.22

0.5

750nm Reflectance

950nm−to−750nm Reflectance Ratio

1.15

0.75

B

1

Cosine Latitude

950nm−to−750nm Reflectance Ratio

A

1.05

1

0.95
0.9
0.85
0.8
0.75
0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.26

0.28

750nm ReflectanceFig. 4. Color variation as a function of latitude for the full lunar surface, up to ±70" latitude (cf. Fig. 10, which is limited to the maria). (A) Spectral characteristics of pixels
sampled from regions with between 15 and 17 wt% FeO and between 1 and 2 wt% TiO2. Points are color-coded according to cosine latitude, illustrating that the opticalcharacteristics of the surface vary systematically with latitude. Equatorial regions (red points) tend to have lower albedo and higher 950 nm/750 nm reflectance ratios thanregions farther from the equator (blue points). The black trend line represents a least squares best fit through the data points. The solid black circle corresponds to the color
predicted by the best-fit line at the equator and the white circle represents the color predicted at latitudes of ±70". (B) Compilation of the 59 latitudinal trends we examined(gray lines), one for each compositional bin, compared with the trends discussed in Section 3.1: the solid red, green and blue lines represent the swirl-related color trends
observed at Reiner Gamma, Ingenii, and Marginis, respectively; the dashed lines represent the impact-related color trends observed at each of those swirls. (For interpretationof the references to color in this figure legend, the reader is referred to the web version of this article.)a transition to higher latitudes is attended by the same type ofcolor change that occurs toward brighter parts of swirls(Fig. 1D)—that is, increasing b.
3.2.4. Mare Frigoris
Mare Frigoris, spanning much of the near side at roughly 60"N,is the highest latitude and visibly brightest mare region, meaningthat it makes a significant contribution to the latitudinal trend
we observe (Fig. 12). The high b (high albedo and low
950 nm/750 nm band ratio) of Mare Frigoris could be attributedin part to its low FeO and TiO2 content (Lucchitta, 1978; Pieters,
1978; Staid and Pieters, 2000). However, the FeO and TiO2 abundances typical of Mare Frigoris (14 ± 2 wt% FeO; 0.9 ± 0.4 wt%
TiO2, see crosshairs in Fig. 13A) are also found in lower latitude
mare regions, which, by comparison, appear darker and havehigher 950 nm/750 nm band ratios (and therefore lower b). The
albedo and band ratio of Mare Frigoris differ significantly fromthose of low-latitude maria of equal composition (Fig. 13B, see also
lower left panel in Fig. 5).
Given that Mare Frigoris is apparently compositionally unexceptional, its spectral characteristics cannot be attributed merely
to low FeO and TiO2 content. While it remains possible that they
are the result of anomalous surface contamination (see Section 3.2.5), our results suggest that the high albedo and low
950 nm/750 nm band ratio of Mare Frigoris are better understoodas part of the broad latitudinal trend we observe across the lunar
maria. Although Mare Frigoris accounts for an important component of the observed latitudinal trend, the analysis is not significantly affected by its removal (Fig. 14). After manually removing
those mare pixels belonging to Mare Frigoris, five of the original
28 compositional bins must be excluded from analysis due to their
reduced area and latitudinal span, leaving a total of 23 bins. After
Mare Frigoris is removed, fewer high latitude pixels are included.
Nevertheless, the latitudinal trends remain, and the estimatedlatitudinal trend slope is unaffected (m3 = –1.5 ± 0.3 with or without Mare Frigoris). Hence, anomalous spectral characteristics ofMare Frigoris alone cannot account for the latitudinal color
trend, but the latitudinal color trend can account for the spectral
characteristics observed at Mare Frigoris.

3.2.5. Highlands contamination
Diffusive contamination near mare/highland boundaries andcontamination from major crater rays could be partly responsible
for the latitudinal trends we observe if, by chance, such contamination occurs preferentially at higher latitudes, and if it produces thesame relative changes in albedo and the 950 nm/750 nm band
ratio. Here, we examine these factors and conclude that their contributions to the observed latitudinal trend are not significant.
Impact-induced diffusive mixing of highland and mare materials occurs near the mare/highland boundaries, and may besignificant within 10–20 km of the boundary (Mustard et al.,1998). The edges of the maria may therefore be relatively brightcompared with other mare regions of similar underlying composition. If the morphology of high latitude mare regions is such that
their perimeter length to surface area ratios tend to be larger(e.g., Mare Frigoris is narrow compared with Mare Imbrium), theamount of highland contamination may be relatively greater athigh latitudes, potentially contributing to the latitudinal color
trends we observe. If true, this would imply that the color variation
we observe for any given compositional bin (e.g., Fig. 4A) is, tosome extent, a function of each pixel’s distance from the nearest
highlands, with pixels close to the highlands being brighter thanpixels that are farther from the highlands, where contamination
is weaker.
We test this hypothesis in two ways. First, we restrict our latitudinal color trend analysis to mare regions that are at least 50 km
from the nearest highlands. Mustard et al. (1998) show that diffusive mare–highland mixing is significant only within <10 km of themare–highland boundary and they find no evidence for mixing
beyond 40 km from the boundary. We use the great-circle distance(in kilometers) from the center of each mare pixel to the center of
its nearest highland neighbor pixel, where mare/highland surfaces
are identified according to our topographic roughness metric (seeSection 3.2.2). After excluding mare regions within 50 km of thenearest highlands, and again applying the selection criteria
described in Section 3.2.1, only 13 compositional bins remain.
The surface area coverage is considerably more limited than in
our full-mare analysis (Fig. 10), but the resulting latitudinal trend
slopes (–1.2 ± 0.3) are not significantly different (see

72

1.1
1.05
1
0.95
0.9
0.85

0.1

0.12

0.14

0.16

0.18

0.2

1
0.95
0.9
0.85
0.8
0.75
0.7

0.22

16−18 wt% FeO
2.0−3.0 wt% TiO2
0.08

0.1

1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
0.75
0.7

14−16 wt% FeO
1.5−2.5 wt% TiO2
0.08

0.1

0.12

0.14

0.16

0.18

0.2

950nm−to−750nm Reflectance Ratio

950nm−to−750nm Reflectance Ratio

1.05
1
0.95
0.9
0.85

13−15 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

0.14

0.16

0.18

0.2

950nm−to−750nm Reflectance Ratio

950nm−to−750nm Reflectance Ratio

1.05
1
0.95
0.9
0.85

13−15 wt% FeO
0.5−1.5 wt% TiO2
0.08

0.1

0.12

0.14

0.16

0.9
0.85
0.8
0.75

18−20 wt% FeO
2.0−3.0 wt% TiO2
0.08

0.1

1

0.9
0.85
0.8
0.75

16−18 wt% FeO
1.5−2.5 wt% TiO2
0.08

0.1

0.12

0.14

0.16

0.18

0.2

1.05
1
0.95
0.9
0.85

15−17 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.18

0.2

0.22

1.05
1
0.95
0.9
0.85

0.7

15−17 wt% FeO
0.5−1.5 wt% TiO2
0.08

750nm Reflectance

0.1

0.12

0.14

0.16

±60

0.6

0.7
±50

0.18

0.2

0.22

0.18

0.2

0.22

0.18

0.2

0.22

0.18

0.2

0.22

1
0.95
0.9
0.85
0.8
0.75

18−20 wt% FeO
1.5−2.5 wt% TiO2
0.08

0.1

0.12

0.14

0.16

1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
0.75

17−19 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

0.14

0.16

1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
0.75
0.7

17−19 wt% FeO
0.5−1.5 wt% TiO2
0.08

0.1

0.12

0.14

0.16

750nm Reflectance

0.8
±40

0.22

1.1

750nm Reflectance
Cosine Latitude
0.5

0.2

750nm Reflectance

1.1

0.8

0.18

1.05

0.7

0.22

1.15

0.75

0.16

750nm Reflectance

1.1

0.8

0.14

1.15

0.7

0.22

1.15

0.75

0.12

750nm Reflectance

750nm Reflectance

1.1

0.7

1
0.95

0.7

0.22

0.95

0.7

0.22

1.15

0.8

0.2

1.05

750nm Reflectance

0.75

0.18

1.1
1.05

750nm Reflectance

1.1

0.7

0.16

1.1

0.7

0.22

1.15

0.8

0.14

1.15

750nm Reflectance

0.75

0.12

1.15

750nm Reflectance

950nm−to−750nm Reflectance Ratio

950nm−to−750nm Reflectance Ratio

750nm Reflectance

950nm−to−750nm Reflectance Ratio

0.08

1.05

950nm−to−750nm Reflectance Ratio

0.7

14−16 wt% FeO
2.0−3.0 wt% TiO2

1.1

950nm−to−750nm Reflectance Ratio

0.8
0.75

1.15

950nm−to−750nm Reflectance Ratio

1.15

950nm−to−750nm Reflectance Ratio

950nm−to−750nm Reflectance Ratio

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

0.9
±30

1
0

Latitude (°)

Fig. 5. Latitudinal color variation across 12 distinct compositional bins. Equatorial regions (red points) tend to have lower albedo and higher 950 nm/750 nm reflectanceratios than regions farther from the equator (blue points). Each of the black trend lines represents the best-fit color variation between the equator (solid black circle) andlatitudes of ±70" (white circle). Although only 12 are shown here, we examined 59 compositional bins in total (see Fig. 4B). (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of this article.)

Fig. 15A and B and compare with Fig. 4). Note that the 50-km
threshold we use here is also larger than the Lunar Prospector
Gamma Ray Spectrometer footprint (&45 km), potentially helping
to improve the compositional accuracy of the bins.
As a further test, we once again plot 750 nm reflectance versus
the 950 nm/750 nm reflectance ratio, except that in this case, wecolor-code points not by cosine latitude, but instead by each pixel’s

distance from the highlands, from 50 to 100 km (Fig. 15C). No significant color trend is obvious in Fig. 15C and a least squares best
fit line through the data (black line) reveals only a slight tendency
for pixels 50 km from the mare/highland boundary (white circle) tobe brighter than pixels 100 km from the boundary (black circle).
This is not to say that mare pixels are not typically brighter nearmare/highland boundaries, only that (within a given compositional

25

1.1
20
1.05
1

15

0.95
10

0.9

Latitude (degrees N)

90

FeO wt%950nm−to−750nm Reflectance Ratio

1.15

0.35

60

0.3

30

0.25

0

0.2

−30
0.15
−60
−90
−180

0.1
−135

−90

−45

0

45

90

135

180

Clementine 750nm Reflectance73

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

Longitude (degrees E)

0.85

Fig. 8. Clementine 750 nm reflectance with red lines indicting the boundaries of themare regions as identified by our topographic roughness criterion (regions with lessthan 5 m/km2 roughness at 1.8-km baseline). (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of thisarticle.)

5
0.8
0.75
0.08

0.1

0.12 0.14 0.16 0.18

0.2

0.22 0.24 0.26 0.28

0750nm Reflectance

4

8

x 10

7

90

A

B

60

Latitude (degrees N)

Fig. 6. Compilation of latitudinal trend lines for all 59 compositional bins, as inFig. 4B but color-coded by iron content (Lawrence et al., 2002). Dark trend lines
correspond to high iron bins while pale trend lines correspond to low iron bins. For
comparison, the swirl- and impact-related trends are also shown: the solid red,green and blue lines represent the swirl-related color trends observed at Reiner
Gamma, Ingenii, and Marginis, respectively; the dashed lines represent the impactrelated color trends observed at each of those swirls. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version ofthis article.)

30

0

−30

−60

Number of Pixels

6
−90
5

0.1

0.15

0.2

Clementine 750nm Reflectance
4

0.2

0.25

LOLA 1064nm ReflectanceFig. 9. (A) Clementine 750 nm reflectance profile across the lunar maria (asidentified by surface roughness, see Fig. 8). (B) Profile of LOLA 1064 nm reflectancefor the same region. In both profiles, reflectance tends to be lowest near the equatorand higher toward higher northern and southern latitudes. The black lines and graybands represent the mean and standard deviation at each latitude.

3
2
1
0

0.15

0

10

20

30

40

50

LOLA 1.8−km Roughness (m/km2)
Fig. 7. Histogram of topographic roughness with red line indicating the threshold
below which we consider pixels to belong to the lunar maria; pixels above thethreshold are discarded in our subsequent analysis. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version ofthis article.)bin) this effect is relatively small compared to the effect of varying
latitudes (Fig. 15A). While Fig. 15C shows just one illustrative
example, we repeated this analysis for each of the 13 compositional bins described above. The resulting highland-distance trends
do not resemble the latitudinal trends and, in some cases, evenoppose them (Fig. 15D). Across the 13 bins, the highland-distancecolor trends are often poorly determined (large uncertainties),inconsistent (slopes are highly variable, with standard deviation±3.9), and generally weak (most of the trend lines are relatively
short). For example, the average change in 750 nm reflectance
between 50 km and 100 km away from the mare/highland
boundary is 0.001 ± 0.004, whereas the average change in
750 nm reflectance between the equator and latitudes of ±70" is

0.05 ± 0.01 (ranges are 1r). Although the effects of highland
contamination cannot be said to make no contribution
whatsoever, we conclude that they cannot be responsible for thestronger and more consistent latitudinal trends we observe(Figs. 4, 10, 14 and 15A and B).
Ray systems from major young impacts could also contribute to
the high albedo of Mare Frigoris and other high latitude maria.
However, ray systems similarly cross the low latitude mare regions
and should have similar effects there. For example, rays from
Copernicus, Aristarcus, and Kepler all extend across low latitude
mare regions. The young crater Anaxagoras (&50 km in diameter)is the main contributor to contamination of Mare Frigoris but lies
more than 300 km from its edge, at the closest point. Significant
brightening associated with the similarly young, but larger,
Copernicus crater (&100 km in diameter) is limited to within&300 km, suggesting that Anaxagoras contamination is unlikely
to dominate the spectra of Mare Frigoris. Moreover, although contamination from crater ray systems may affect the spectra of certain parts of the maria, there is no reason to expect suchcontamination to vary systematically with latitude in such a way
as to create the latitudinal trends we report here. Werner andMedvedev (2010) found no concentration of rayed craters at highlatitudes and, due to the present impact rate inhomogeneity(Le Feuvre and Wieczorek, 2011), we would actually expect less

74

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

the right compared with high-iron regions, such as the maria,
meaning that spectral trends due to highland contamination
should have a positive slope, unlike the negative latitudinal trend
slopes we observe (m1 = "1.5).

increa

sing

1.1
1.05

latitud

inal tr

ends,
s

1

lope m

3

0.95
0.9

inc

swir

l tre

nds

ct

pa

0.85

im

ing
as
re

tre

950nm−to−750nm Reflectance Ratio

1.15

s
nd

0.8
0.75
0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

750nm ReflectanceFig. 10. Summary of latitudinal trend lines (gray lines) for the 28 compositional
bins that correspond to the maria. The solid red, green and blue lines represent the
swirl-related color trends observed at Reiner Gamma, Ingenii, and Marginis,
respectively. The dashed lines represent the impact-related color trends observed ateach of those swirls. The latitudinal trends have slopes of "1.5 ± 0.3, whereas the
swirl-related trends have slopes of "1.6 ± 0.2. (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of thisarticle.)contamination from young crater ejecta at high latitudes. Crater
ray contamination could be substantially responsible for the trends
we observe only if such contamination favors high latitudes bychance. Although we cannot rule out this possibility, we regard it
as unlikely, especially given the symmetry of the latitudinal trends(Fig. 9) and the consistency of their slopes across a range of different compositions (Figs. 10, 14 and 15B).
Finally, even if highland contamination were to have a significant effect on the observed spectral characteristics, the effectwould more likely involve an increase (rather than a decrease) in
the 950 nm/750 nm reflectance ratio. As illustrated in Fig. 6, lowiron regions, such as the highlands, tend to plot farther up and to

2

A

1

1

0

0

−1

−1

Spectral Trend Slope

Spectral Trend Slope2

3.2.6. Phase angle biases and LOLA reflectance
The Clementine spacecraft imaged the lunar surface over arange of phase angles (Sun-surface-spacecraft angle), from typically less than 25" at the equator, to as high as &100" at high latitudes. The Clementine mosaics are photometrically normalized
and calibrated such that they provide an estimate of reflectance
reduced to standard illumination conditions (Eliason et al., 1999),
but the imperfect calibration and imperfect knowledge of photometric behavior, leave small but systematic errors in the mosaics.
In particular, the 950 nm reflectance mosaics exhibit artificially
low reflectance values when based on imagery collected at relatively high phase angles (imperfections in the 750 nm reflectancemosaics are insignificant by comparison). This can be seen by comparing adjacent parts of the mosaic that are based on imagery collected at differing phase angles (Fig. 16). From Eq. (2), and bearing
in mind that m2 is negative, it follows that this has the effect ofmaking relatively higher phase angle areas artificially low in b(and therefore appearing less like the bright parts and more likethe dark parts of swirls). Because the high latitude Clementine data
correspond to systematically higher phase angles, b therefore tends
to be underestimated at high latitudes. This is the opposite of thetrend we observe, indicating that the reported latitudinal effecton color must be even stronger than it appears in Figs. 4, 10 and 12.
The conclusion that phase angle biases are not the source of theobserved latitudinal color variation is further supported by examination of an independent dataset obtained from the Lunar Orbiter
Laser Altimeter (LOLA). LOLA was recently used to measure lunar
surface reflectance at 1064 nm (Lucey et al., 2014). Because LOLA
uses its own laser light source to illuminate the surface, reflectanceestimates are obtained at a constant phase angle of nearly zero.
Fig. 9B shows LOLA 1064 nm reflectance across the lunar maria,
confirming that, independent of phase angle, higher latitude maria
appear to exhibit higher reflectance, as seen in the Clementine
data.

−2
−3
−4
−5

−2
−3
−4
−5

−6

−6

−7

−7

−8

11

12

13

14

15

16

FeO wt%

17

18

19

20

B

−8

0

0.5

1

1.5

2

2.5

3

TiO2 wt%

Fig. 11. Latitudinal trend slopes (crosshairs) compared with swirl trend (pale red band) and impact trend (pale green band) slopes (with ±1r variability illustrated by thewidth of each band). All 28 mare-based slope estimates (the same trends illustrated in Fig. 10) are shown versus the FeO bins (A) and the TiO2 bins (B). In each case, thecrosshairs show the width of the FeO or TiO2 bin and the 1r uncertainty on the estimated slope. (For interpretation of the references to color in this figure legend, the reader isreferred to the web version of this article.)

75

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

Mare Frigoris

0.31

Mare
Crisium

30

0.3
0.29

Oceanus
Procellarum

0

0.28

−30
0.27
−60

−90
−180

−135

−90

−45

0

45

90

135

180

B

60

Latitude (degrees N)

60

Latitude (degrees N)

90

0.32

A

Beta

90

30

0

−30

0.26

−60

0.25

−90

0.25

0.3

Longitude (degrees E)

0.35

Beta

6

Clementine 950nm−to−750nm Reflectance Ratio

Fig. 12. Color variation as a function of latitude. (A) Map of b, computed according to Eq. (2), over the lunar maria (as identified by surface roughness, see Section 3.2.2). (B)Latitudinal profile of b across the lunar maria. The black line and gray band represent the mean and standard deviation at each latitude, averaged over all longitudes.

A

5

TiO wt%

4

2

3

2

1

0
0

5

10

15

20

25

30

B
1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
0.75
0.7

0.08

FeO wt%

0.1

0.12

0.14

0.16

0.18

0.2

0.22

Clementine 750nm ReflectanceFig. 13. (A) FeO and TiO2 content (based on Lunar Prospector Gamma Ray Spectrometer data [Lawrence et al., 2002; Prettyman et al., 2006]) of Mare Frigoris (blue points)
compared to other mare regions (red points). The black crosshairs illustrate the mean and standard deviation FeO and TiO2 abundances for Mare Frigoris (14 ± 2 wt% FeO;
0.9 ± 0.4 wt% TiO2). (B) Pixels from Mare Frigoris (blue points) have higher albedo and lower band ratio than low latitude mare regions (red points, restricted to within ±15" ofthe equator) of equal composition (12–16 wt% FeO and 0.5–1.3 wt% TiO2). The mean and standard deviation of each cluster is marked with crosshairs. (For interpretation of
the references to color in this figure legend, the reader is referred to the web version of this article.)
1.15

1.15

A

1.05
1
0.95
0.9
0.85
0.8
0.75
0.08

B

1.1

950nm−to−750nm Reflectance Ratio

950nm−to−750nm Reflectance Ratio

1.1

1.05
1
0.95
0.9
0.85
0.8

0.09

0.1

0.11

0.12

0.13

0.14

750nm Reflectance

0.15

0.16

0.17

0.18

0.75
0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

750nm ReflectanceFig. 14. Comparison of latitudinal trends with and without pixels from Mare Frigoris. (A) Summary of all 28 mare-based latitudinal trend lines, including data from Mare
Frigoris (same as Fig. 10). (B) Summary of the 23 mare-based latitudinal trend lines that remain after Mare Frigoris pixels are excluded. The mean slope differs by less than
0.04 between the two cases (much less than 1r = 0.3). In both cases, the swirl- and impact-related trends are shown for comparison as solid and dashed lines, respectively.

76

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

1.1

0.9

1.05

0.85

1

0.8

0.95

0.75

0.9

0.7

0.85

0.65

0.8

0.6

0.7

15−17 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

B
1.1

950nm−to−750nm Reflectance Ratio

0.95

Cosine Latitude

950nm−to−750nm Reflectance Ratio

1.15

0.75

1.15

1

A

0.16

0.18

0.2

0.22

1
0.95
0.9
0.85
0.8

0.55

0.14

1.05

0.5

0.75
0.08

0.09

0.1

0.11

750nm Reflectance

1.1

90

1.05

85

1

80

0.95

75

0.9

70

0.85

65

0.8

60

15−17 wt% FeO
1.0−2.0 wt% TiO2
0.08

0.1

0.12

55

0.14

0.16

0.13

0.14

0.15

0.16

0.17

0.18

0.18

0.2

0.22

50

750nm Reflectance

0.15

0.16

0.17

0.18

D

1.1

950nm−to−750nm Reflectance Ratio

95

Distance from Highlands (km)950nm−to−750nm Reflectance Ratio

1.15

0.7

1.15

100

C

0.75

0.12

750nm Reflectance

1.05
1
0.95
0.9
0.85
0.8
0.75
0.08

0.09

0.1

0.11

0.12

0.13

0.14

750nm ReflectanceFig. 15. Color variation of mare surfaces that are at least 50 km from the nearest highlands (compare with Fig. 4, which characterizes the entire lunar surface, and Fig. 10,
which characterizes all of the maria). (A) Spectral characteristics of pixels sampled from regions with between 15 and 17 wt% FeO and between 1 and 2 wt% TiO2. Points arecolor-coded according to cosine latitude, as in Fig. 4. (B) Compilation of latitudinal trends across the 13 compositional bins compared to the swirl- and impact-related trends(red, green, and blue lines). (C) Spectral characteristics of the same pixels as in (A), except that points are color-coded according to distance from the highlands: blue points
correspond to locations that are 50 km from the mare/highland boundary whereas red points correspond to locations that are 100 km or more from the nearest highlands(pixels within 50 km of the nearest highlands are excluded). The black trend line represents a least squares best fit through the data points with the end points indicating thecolor predicted by the best-fit line at locations 50 km (white circle) and 100 km (black circle) from the mare/highland boundary. (D) Compilation of the 13 highland-distancetrend lines (gray lines) compared to the swirl- and impact-related trends (red, green, and blue lines). (For interpretation of the references to color in this figure legend, thereader is referred to the web version of this article.)

4. Discussion
The latitudinal color variation we observe is unlike the color
trends associated with impacts but statistically equivalent to thoseobserved at swirls (Figs. 10 and 11), suggesting a common mechanism. It has been proposed that the swirl-related color trend could
be the result of magnetic and/or electric field-related alteration of
the regolith microstructure (Pieters et al., 2014), electrostatic sorting of fine dust (Garrick-Bethell et al., 2011), or similar mixing ofcompositionally distinct materials (Blewett et al., 2011). However,
it is not clear how such mechanisms could also produce the latitudinal color variation we report here. Instead, we propose that
reduced solar wind flux is the most likely common mechanism
behind the latitudinal and swirl-related color trends. Solar wind
flux is reduced both with increasing latitude (due to the increasing
incidence angle), and very likely at swirls, where strong magnetic
fields may be partially shielding the surface from the impinging

solar wind (Hood and Schubert, 1980; Hood and Williams, 1989;
Hemingway and Garrick-Bethell, 2012). Micrometeoroid flux, in
contrast, varies with latitude but should be unaffected by magnetic
fields (Richmond et al., 2003) and therefore cannot account for thecolor variations observed at swirls.
The observed latitudinal and swirl-related variations in spectral
characteristics (i.e., those described by the b parameter) suggest adependence on average flux rather than on the total accumulationof solar wind ions. A single grain saturates after only
&100,000 years (Hapke, 2001) such that even a surface shieldedby as much as 90% would saturate after only &1 myr. Consistent
with this constraint, swirls often exhibit diffuse morphologies,
with gradual transitions from the bright parts to darker background soils. In these regions, mean solar wind flux could varysmoothly between the bright parts of swirls and the darker background soils. Hence, if swirls are the result of locally altered solar
wind weathering, their color must be controlled not by total accu-

77

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

12
20
11
15
10
10

9

19

20

21

22

23

24

25

26

64
60

63
62

59

61
58

60
59

57

58
57

56

56
55
−12

5

−11

−10

−9

−8

−7

−6

−5

Longitude (degrees E)

Longitude (degrees E)
14

61

0.17

0.096

12

0.092

11

0.09
0.088

10

0.086

9

60

Latitude (degrees N)

0.094

0.165

Clementine 950nm Reflectance

Latitude (degrees N)

13

0.16
0.155

59

0.15
58

0.145
0.14

57

0.135
0.13

56

0.125

0.084
8
18

19

20

21

22

23

55

−4

24

25

Clementine 950nm Reflectance18

65

Clementine Phase Angle (degrees)

25

Latitude (degrees N)

Latitude (degrees N)

13

8

61

30

Clementine Phase Angle (degrees)

14

55
−12

26

−11

−10

Longitude (degrees E)

−9

−8

−7

−6

−5

−4

0.12

Longitude (degrees E)

14

61

0.275

0.34
0.335

60

11

0.265

10
0.26

0.33
0.325

59

0.32
58

0.315
0.31

57

0.305
0.3

56

9

Beta

12

Latitude (degrees N)

0.27

Beta

Latitude (degrees N)

13

0.295
8
18

19

20

21

22

23

24

25

26

0.255

Longitude (degrees E)

55
−12

−11

−10

−9

−8

−7

−6

−5

−4

0.29

Longitude (degrees E)

Fig. 16. Phase angle effects on 950 nm reflectance mosaics and derived b. Left panels show a low-latitude region in Mare Tranquillitatis. Right panels show a high-latitude
region in Mare Frigoris. Top panels show the phase angle corresponding to the imagery collected across the regions. The middle panels show Clementine 950 nm reflectance,
illustrating a bias toward lower reflectance values for imagery collected at relatively higher phase angles. The bottom panels show the derived parameter b, illustrating thatthe phase angle bias means that b is underestimated for higher phase angle imagery.

mulation of solar wind ions, but instead by some flux-dependentequilibrium—for instance between regolith gardening and solarwind-induced alteration of exposed grain surfaces.
These observations lead us to propose a new model of how
space weathering operates for solar wind and micrometeoroids.
Over length scales of a few kilometers, the surface exhibits considerable variability in a, owing to the appearance of fresh craters(low a) among the darker background soils (high a), whereas variability in b is low (Figs. 1B, 2B, 3B). Our interpretation is that theequilibrium b is reached so rapidly that we do not observe variability in its value over &kilometer scales, whereas the evolution of aoccurs gradually, as impact craters transition into mature soils overlonger timescales. We propose a model in which fresh impact craters begin with high albedo and low 950 nm/750 nm band ratios(the hypothetical ‘‘hyperfresh” point illustrated in Fig. 17, whichshould depend only on local mineralogy), then rapidly evolve toan equilibrium value of b (controlled by solar wind flux), andfinally follow the steeper progression toward larger values of a,as they gradually mature toward the local equilibrium color. Boththe rapid weathering to a solar wind flux-dependent b and thegradual weathering toward the local saturation level of a involve
decreases in albedo and increases in the 950 nm/750 nm band
ratio. However, the initial rapid weathering process (change in b)
has a proportionally greater effect (by a factor of &3.6) on albedo

78

D.J. Hemingway et al. / Icarus 261 (2015) 66–79

5. Conclusions

increa

sing

r
inc
ing
eas

l
dua
gra erings
ath
we crater
ils
of
o
s
into ow )l(at(transition from
fresh craters tomature soils)ldua
gra eringhs
at
we crater
ils
of
o
s
into igh )h(atrapid decreashypothetical“hyperfresh”
surfacee inFig. 17. Inferred optical evolution process. For a given composition, all impact
craters large enough to excavate fresh material should begin with the samecharacteristic color (the hypothetical ‘‘hyperfresh” point at right). In this model,
following the impact event, the freshly exposed material experiences rapid
weathering until it reaches an equilibrium value of b, controlled by solar wind
flux. This rapid color change occurs primarily in albedo, but is accompanied by asmall change in the 950 nm/750 nm band ratio. Subsequently, the crater material
matures gradually (increases in a) until it reaches the local saturation maturity
level of the surrounding, well-developed soils. This gradual maturation involves
additional darkening, but is also accompanied by a proportionally greater band
ratio increase than during the initial mode of rapid weathering.

(Fig. 17). In this model, the trends are parallel (see also Staid andPieters, 2000; Wilcox et al., 2005), as the soil matures toward distinct solar wind flux-dependent equilibrium colors, rather thanconverging to a common point, contrary to expectations for a simple mixing process.
The differences in optical effects may reflect differences in theway solar wind and micrometeoroid bombardment affect lunar
soils. For example, the darkening may be due primarily to theaccumulation of nanophase reduced iron (Hapke, 2001; Nobleet al., 2007) generated both by solar wind and micrometeorites,
whereas the increased 950 nm/750 nm band ratio, which reflects
both suppression of spectral features and increased continuum
slope, may also be influenced by impact vitrification (Adams andMcCord, 1971) and the soil’s impact-induced disintegration into
increasingly finer grains (Pieters et al., 1993), neither of which
depend on solar wind.
In spite of the uncertainties regarding the precise mechanism
for the observed latitudinal trends, our results help to quantify
the effects of latitude-dependent space weathering, which may
need to be accounted for when interpreting spectral measurements at different latitudes (Zuber et al., 2012; Cohen et al.,2014). The production and retention of hydroxyl (OH) groups, forinstance, reportedly varies with latitude (Clark, 2009; Pieters
et al., 2009; Sunshine et al., 2009; McCord et al., 2011; Hendrix
et al., 2012) as well as at swirls (Kramer et al., 2011). The latitudinal variation in space weathering effects we report here may
influence the interpretation of the spectral observations behindsuch findings.
Our results also suggest that it may be possible to use variations
in surface color to quantify the reduction in solar wind flux atswirls. For example, the magnitude of color variation at the Reiner
Gamma swirl is approximately equivalent to the color variation
observed between the equator and 60" latitude, suggesting that
this magnitude of color variation corresponds to a &50% reduction
in solar wind flux. This type of analysis, in conjunction with othermethods, may in turn help in estimating the strength of surface
fields at swirls and in making predictions that can eventually betested with near-surface magnetic field and solar wind flux
measurements.

Our analysis reveals a systematic latitudinal variation in thenear-infrared spectral properties of the lunar surface, and in particular, across the maria. Specifically, low latitude mare regions tend
to be darker and have higher 950 nm/750 nm reflectance ratios
than high latitude mare regions, such as the noticeably bright Mare
Frigoris. This latitudinal trend persists across a range of distinct
compositions, confirming that it is not an artifact of the fact that
regions with the highest iron and titanium content happen to beconcentrated at low latitudes. Our analysis also shows that thetrends are not significantly affected by contamination from thehighlands, nor can they be artifacts of phase angle biases, as confirmed by our comparison with the constant phase angle LOLA
reflectance data. Furthermore, the spectral characteristics of thelatitudinal color trend are statistically equivalent to those observed
at lunar swirls; higher latitude regions appear more like the brightparts of swirls. We propose that reduced solar wind flux, whichshould occur both at swirls and toward higher latitudes, is thecommon mechanism behind the observed color variations. We
suggest a process by which freshly exposed materials initiallyexperience rapid changes in color until reaching a solar windflux-dependent equilibrium, followed by a more gradual period
of optical maturation driven mainly by micrometeoroid impacts.
If correct, this model could help quantify the distinct effects ofsolar wind versus micrometeoroid weathering and may be important for the interpretation of spectral observations made at different latitudes on the Moon and other airless bodies throughout theSolar System.
Acknowledgments
This work was supported by the BK21 plus program through the
National Research Foundation (NRF), funded by the Ministry of
Education of Korea. Partial support was provided by the UCSC/
NASA Ames University Affiliated Research Center, Aligned Research
Program; the Alfred P. Sloan Foundation; and NASA Grant
NNX13AJ51G. We thank Carle Pieters for important comments
and suggestions, Paul Lucey for supplying the LOLA reflectancedata, and the two reviewers for their helpful comments andsuggestions.
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