Icarus 333 (2019) 323–342

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Iron content determines how space weathering flux variations affect lunar
soils

T

James McFaddena, Ian Garrick-Bethella,b, , Chae K. Simb, Sungsoo S. Kimb, Doug Hemingwayc⁎aEarth and Planetary Sciences, University of California, Santa Cruz, USA
School of Space Research, Kyung Hee University, Republic of Koreac
Carnegie Institution for Science, Washington, DC, USAb

ABSTRACTPrevious work has established that the solar wind and micrometeoroids produce spectral changes on airless silicate bodies. However, the relative importance of these
two weathering agents, the timescales over which they operate, and how their effects depend on composition have not yet been well determined. To help addressthese questions we make use of the fact that solar wind and micrometeoroid fluxes vary with latitude on the Moon. Previous work has shown that this latitudinallyvarying flux leads to systematic latitudinal variations in the spectral properties of lunar soils. Here we find that the way in which a lunar soil's spectral properties varywith latitude is a function of its iron content, when we consider soils with 14–22 wt% FeO. In particular, a 50% reduction in flux corresponds to a significant increase
in reflectance for 14 wt% FeO soils, while the same flux reduction on 21 wt% FeO soils is smaller by a factor of ~5, suggesting that this brightening effect saturates forhigh FeO soils. We propose that lower iron soils may not approach saturation because grains are destroyed or refreshed before sufficient nano- and micro-phase ironcan accumulate on their rims. We compare our results to the spectral variations observed across the Reiner Gamma swirl, which lies on a high‑iron surface, and find it
has anomalous brightness compared to our predictions. Swirls in Mare Marginis, which lie on a low iron surface, exhibit brightness differences that suggest reductions
in solar wind flux between 20 and 40%. Our inferences suffer from the limited latitudinal extent of the maria and the convolution of micrometeoroid flux and solar
wind flux changes with latitude. Superior constraints on how space weathering operates throughout the inner solar system would come from in situ measurements ofthe solar wind flux at lunar swirls.

1. Introduction
Space weathering is the mechanical and chemical alteration of anairless body's soil by solar wind plasma and micrometeoroids. These
alterations change the soil's visible to near-infrared reflectance spectra,
complicating the interpretation of its mineralogy (Domingue et al.,2014; Hapke, 2001; Pieters and Noble, 2016). Several methods havebeen developed to quantify these spectral changes. One early techniqueused 750 and 950-nm bands to construct the spectral parameter OMAT
to map surface maturity (Lucey et al., 2000b). These two bands werealso combined to determine the amount of iron (FeO) present in a soil
(Lucey et al., 2000a; Lucey et al., 1995; Wilcox et al., 2005). Recently,
more sophisticated spectral modeling techniques have been developed
to estimate iron abundances (Lemelin et al., 2015), and space weathering products (Trang and Lucey, 2019; Trang et al., 2017). A numberof these and other methods have been used to argue that soil spectravary due to differential exposure to solar wind as a function of latitude(Hemingway et al., 2015; Trang and Lucey, 2019; Yokota et al., 2011),
or passage through the Earth's magnetotail (Sim et al., 2017; Trang andLucey, 2019).
Particularly relevant to this work are the latitudinal variations in the⁎

spectral parameter β, defined by Hemingway et al. (2015), to isolate thespectral signature of lunar swirls as distinct from other geologic features, such as immature crater rays and ejecta (Fig. 1a). Briefly, β wasderived from trends observed in plots of 750-nm-reflectance vs. theratio of 950 to 750-nm-reflectance, across the range of pixels foundinside and outside of swirls. Previous work had shown that normal,
non-swirl pixels plotted in this type of plot could be separated according to the pixel's degree of space weathering (“maturity”) or ironcontent (Lucey et al., 2000a; Lucey et al., 2000b; Wilcox et al., 2005).
Surprisingly, however, it was found that the brightest swirl pixels were
not simply immature in the manner typically predicted for this type ofplot, but rather they formed entirely new trends that partially mimickeda decrease in iron (Garrick-Bethell et al., 2011). This new trend wasthen parameterized by β. The similarity between variations in β atswirls and β across different latitudes, as discovered by Hemingway
et al. (2015), simultaneously supported the hypotheses that swirls are
partially shielded from the solar wind by the underlying rock magnetization (Hood and Schubert, 1980) and that solar wind plays an important role in causing spectral changes due to space weathering (e.g.
Hapke, 2001; Vernazza et al., 2009).
There are at least two unresolved problems with previous studies ofCorresponding author at: Earth and Planetary Sciences, University of California, Santa Cruz, USA.
E-mail address: igarrick@ucsc.edu (I. Garrick-Bethell).

https://doi.org/10.1016/j.icarus.2019.05.033
Received 29 January 2019; Received in revised form 8 May 2019; Accepted 14 May 2019
Available online 31 May 20190019-1035/ © 2019 Published by Elsevier Inc.

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750 nm ReflectanceFig. 1. (A) Definition of the parameter β derived from swirl trends, and distinct from impact trends, observed at Reiner Gamma swirl. (B) Illustration of the similar
nature of the spectral change of the lunar maria with latitude, across various iron bins, compared to the spectral changes observed across swirls. Prior work
Hemingway et al. (2015) discovered the two trends are parallel. In this work we report the systematic decrease of the length of each line within each iron bin, while
the overall shape of the spectral trend is the same across iron bins. Both figures are after those in Hemingway et al. (2015).

the Moon's latitudinal spectral changes. The first is that they do notdecouple solar wind and micrometeoroid effects. If micrometeoroids
are largely confined to the ecliptic plane, which is the case for larger
impactors (Gallant et al., 2009; Le Feuvre and Wieczorek, 2008), theirflux should decrease with latitude, just as the solar wind flux. Micrometeoroids likely produce nanophase (< 100 nm) (Loeffler et al., 2008;
Sasaki et al., 2002) and microphase (> 100 nm Britt and Pieters, 1994;
James et al., 2001; Lucey and Riner, 2011) metallic iron particles thatdarken and alter a soil's spectrum (collectively referred to herein as
submicroscopic iron, following Trang and Lucey, 2019). Solar wind
may also be capable of producing submicroscopic iron (Loeffler et al.,2009) (but see Christoffersen et al., 2015), or at least changing the soil
spectra via some other mechanism (Fu et al., 2012b; Marchi et al.,2005; Strazzulla et al., 2005). Complicating the matter further, nanophase iron particles may coalesce into microphase iron (Pieters andNoble, 2016), possibly making their abundance partially dependent on
both solar wind and micrometeoroids. Remote sensing observations
support the importance of micrometeoroids: grain size (Jeong et al.,2015) and microphase iron both decrease with latitude (Trang andLucey, 2019).
The second unresolved problem is that previous work did not determine how latitudinal spectral variations depend on composition,
particularly FeO content. As the regolith is gardened through meteoroid
impacts, immature soil grains are introduced into the soil, and maturesoil is destroyed or melted (e.g. (Gault et al., 1974)). Thus, the steadystate abundance of submicroscopic iron on a surface likely depends on
324

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90°N

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Fig. 2. (A) Clementine 750-nm-reflectance restricted to the maria. (B) Same as panel A, but with a maria-highland boundary standoff distance of 22.5 km. (C) Sameas panel A but with a maria-highland boundary standoff distance of 45 km. All maps show the entire Moon in simple cylindrical projection centered on the nearside.

the timescale of the gardening process relative to the timescale foraccumulation of submicroscopic iron on rims of exposed grain. Assuming the soil concentration of FeO influences the rate of accumulation, higher iron soils should have higher abundances of submicroscopic iron. Alternatively, one might imagine that soils of all ironcontent eventually saturate their rims with submicroscopic iron if theiraccumulation times are much faster than the regolith gardening timescales. Or, perhaps there is a level of FeO above which grains start toexhibit saturation in submicroscopic iron. A possibly related effect wasreported in Trang and Lucey (2019), who found that nanophase ironsaturates at ~17 wt% FeO, but they did not explain the saturation asfunction of the flux of weathering agents.
The first objective of this paper is to determine how spectra change
as a function of latitude for a given FeO content. The second is tocompare those trends with those observed at swirls, where the micrometeoroid flux is likely uninterrupted, to gain some insight into theimportance of micrometeoroids in space weathering, possibly as afunction of FeO. A hint that the latitudinal variation depends on FeO
content can be found in Hemingway et al. (2015). A reproduction of
their work in Fig. 1b shows the spectral trends characteristic of swirls
and the lunar maria across different latitudes, for soils of various FeO
content. The similar slopes of these lines and the lines at swirls imply
325

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Fig. 3. Cumulative distribution of Lunar Prospector titanium (wt% TiO2), restricted to the lunar maria. The two bins used in our nominal analysis are indicated withblack lines (0–2 wt% and 2–4 wt% TiO2).

that high latitude regions and swirls change their spectra in similar
ways due to reduced solar wind flux, as discussed above. However, thelength of these lines was not considered in Hemingway et al. (2015).
The length of the lines decreases with increasing FeO, implying thateven though the spectral trends are the same across surfaces withvariable FeO content, the total range of variation in β decreases athigher FeO concentrations. In contrast, lower FeO soils on the maria are
more susceptible to spectral changes, as shown by their longer lines(here we are ignoring the lines with very low iron that fall on thehighlands, which also exhibit shortening). In the next sections we
present a more quantitative characterization of this effect on the lunar
maria. We restrict our analysis to the lunar maria because they exhibit
the spectral effects of space weathering more strongly, as reported
previously (Hemingway et al., 2015; Lemelin et al., 2016). This stronger
effect of weathering in the maria is presumably due to the overall
higher abundances of FeO, compared to the highlands, and the fact that
FeO is putatively the dominant source of submicroscopic iron. Anotherreason we restrict our coverage to the maria is that the range of ironcontent is smaller over the highlands, especially when considering ouranalysis must avoid iron quantitates that are intermediate between
highlands and maria (see Methods).
The second objective of this paper is to use the results from themaria to calculate the predicted spectral changes at two lunar swirls
found on surfaces with different FeO concentrations. The solar wind
flux at swirls is unknown, but we can explore the assumption that theflux is reduced to nearly zero at the swirl associated with some of thestrongest magnetic fields on the Moon: Reiner Gamma.

(GRS) iron (wt% FeO) at a resolution of 2 pixels per degree (ppd)(Lawrence et al., 2002), to characterize the surface FeO content. As
discussed in Hemingway et al. (2015), GRS iron abundances are notaffected by space weathering or interpretations of lunar spectra. Other
global-scale spectrally derived maps of FeO are available (Lemelin
et al., 2015), and we note there may be differences between their FeO
estimates and those obtained from LP.
We then combine GRS iron with three different measures of spectral
variation. The first is Clementine 750 nm reflectance (R750), the second
is Lunar Orbiter Laser Altimeter (LOLA) reflectance at 1064 nm (RLOLA)(Lucey et al., 2014), and the third is the spectral parameter β, discussed
above. We used R750 and β datasets sampled at 16 ppd. The resolutionof the LOLA data is 4 ppd. We also bin our data according to LP GRS
titanium (wt% TiO2) (Prettyman et al., 2006) with a map resolution of
0.5 ppd. We avoid spectrally derived estimates of titanium (e.g. Satoet al., 2017), due to possible interference with the spectral effects weare measuring.
We also use R750 from the Kaguya Multiband Imager (MI) (Ohtake
et al., 2008) and the parameter β derived from Kaguya 750-nm and
950-nm-reflectance, to assess whether there are any differences compared to Clementine data. Unlike Clementine data, the Kaguya data we
use are corrected for topographic slope. We used data sampled at aresolution of 16 pixels per degree (ppd). The derivation of β using
Kaguya data from three lunar swirls is given in Appendix A.
As in Hemingway et al. (2015), we limited our analysis to maria
pixels that are sufficiently distant from the maria-highland boundary.
Lunar Prospector GRS iron data have a spatial footprint of ~45 km2(Lawrence et al., 2002), and therefore the iron values of the maria
measured close to a highland boundary will consist of a mixture of ironfrom the highlands and maria. In this study, we restrict our analysis of
the maria to four standoff distances from the nearest highlands
boundary: 22.5, 45, 67.5, and 90 km (Fig. 2). Hemingway et al. (2015)
showed that using data 50 km from the maria-highland boundary produced no change in the spectral trends we observed. Here we show thatthe results at half the GRS iron footprint length scale (22.5 km) arenearly identical to those at 45 km, and these estimates form the basis of

2. Methods
To determine how iron influences the spectral response at high latitudes, we use a map identifying the lunar maria based on topographic
roughness, as in previous work (Hemingway et al., 2015; Kreslavsky
et al., 2013). Using topographic roughness to define the maria-highlands boundary avoids defining it with spectral data, which may varywith latitude. We use Lunar Prospector (LP) gamma-ray spectrometer326

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±60

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Fig. 4. 750-nm-reflectance vs. cos(latitude) for eight iron bins on the lunar maria, for 0–2 wt% TiO2 and reflectance pixels restricted to a standoff distance of 22.5 km
from the maria-highland boundary. Black lines show the best-fit line. Fig. 5 shows the best-fit line slopes for each iron bin.

our conclusions. The standoff distances of 67.5 and 90 km are extreme
values explored to test the robustness of the effects we observe. In
practice, to remove maria pixels within a given distance from thehighland boundary, we progressively remove adjacent pixels until thedesired distance is achieved.
We divided GRS iron into bins of 1 wt% FeO and then further subdivided our analysis by GRS titanium, as in Hemingway et al. (2015).
We use two TiO2 bins in our nominal analysis: 0–2 and 2–4 wt%. Fig. 3
shows the distribution of TiO2 restricted to the maria, illustrating that
~85% of the lunar maria are covered by TiO2 values up to 4 wt% (later,

we also consider the full range of TiO2 up to 8 wt% to determine if ourresults are artifacts of lower data availability at high latitudes). An
example of how R750 changes with latitude, for eight iron bins, for
0–2 wt% TiO2 is shown in Fig. 4. As in Hemingway et al. (2015), we usecos(latitude), instead of simply latitude, since the cosine function provides a linear decay of solar wind and (approximately) micrometeoroid
flux with latitude. We exclude maria pixels with FeO < 14 wt% since
we were concerned that such pixels may harbor a component of highlands contamination. We excluded iron content > 22 wt% FeO since thenumber of pixels available above this iron bin is limited to near327

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Fig. 5. (A) Rate of change of Clementine 750-nm-refletance with cos(latitude), dR750/dcos(latitude), for eight iron bins on the lunar maria, three titanium bins, andmaria-highland standoff distances of 22.5 and 45 km (see legend). Red lines show data restricted to ± 45° of the equator (and 0–8 wt% TiO2, see Fig. 8). (B) Same as
panel A but for LOLA reflectance. (C) Same as panel A but for the spectral parameter β (Clementine derived). All data points are at the center of each iron bin, i.e.
every half integer. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)328

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Kaguya 750 nm

0

A

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Fig. 6. (A) Same as Fig. 5a, but for Kaguya 750-nm-reflectance. (B) Same as for Fig. 5b, but for the spectral parameter β derived from Kaguya data.

equatorial regions. The minimum latitudinal extent of any of the eightiron bins used was ± 45° from the equator, which was present in the
21–22 wt% iron bin. We calculated the best-fit slopes for each of the cos(latitude) plots such as those in Fig. 4, via a simple least squares line fit.
We then repeated the analysis for the 2–4 wt% TiO2 bin, and then again
for a maria-highland standoff of 45 km.

Interestingly, in the case of RLOLA, the rate of change dRLOLA/dcos(latitude) becomes positive for > 20 wt% FeO (Fig. 5b). This would
imply the maria actually darken at high latitudes for high FeO concentrations. However, the apparent positive valued slope may be an
artifact of the lower resolution of the LOLA data compared to the
Clementine data (4 ppd. vs. 16 ppd). We attempted to resolve thisproblem by improving our statistics: we performed the dR750/dcos(latitude) vs. FeO analysis in one large titanium bin of 0–8 wt% TiO2, but
the positive valued slopes persisted and were nearly identical. Furthermore, we expanded our analysis to one additional higher FeO bin(22–23 wt% FeO), which was afforded to us by the larger number ofpixels when aggregating all titanium bins. However, the slope increased
to +0.035, suggesting a monotonic increase of the slope above zerofrom ~20 wt% FeO. Hence, we have no completely satisfactory explanation for this effect. If the effect is real (i.e. not an artifact of statistics), it may be due to the unique geometry of the LOLA observation
at zero phase angle, somehow coupled to the FeO content of the soil.
This remains a puzzle for future study.
Note that formal confidence limits on the slopes in Fig. 5 are small
due to the large number of points, and so they are not shown. Instead,the uncertainty on the slope is dominated by data coverage, crater rays,

3. Results: maria trends
Overall, we find that R750 increases with decreasing cos(latitude)(i.e. R750 increases poleward), as expected. Moreover, for high ironbins, this rate of increase, dR750/dcos(latitude) (i.e. the slope of thebest-fit line, see Fig. 4), slows dramatically (i.e. its negatively valued
slope approaches zero, Fig. 5a). Similar trends are observed for RLOLA(Fig. 5b), and β (Fig. 5c). Later, we show that this effect is not due
simply to higher FeO materials being generally darker in their pristine
state (Section 4.1). Similar trends are also observed for Kaguya R750 and
β (Fig. 6), illustrating that this effect is not an artifact of the dataset
used. For Kaguya data, the trend in β is more robust (i.e. the slope ismore uniformly increasing), perhaps due to the different topographic
calibration of these data.

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Clementine 750 nm
-0.02

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67.5 km, TiO2 0-8%
90 km, TiO2 0-8%

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Fig. 7. Same as Fig. 5a, but for maria-highland boundary standoff distances of 67.5 and 90 km, for all titanium data (0–8 wt% TiO2). Note the different y-axes
between this figure and Fig. 5a.

imperfectly removed highland pixels, etc. A number of such potential
problems were addressed by Hemingway et al. (2015) and shown not to
influence the global-scale trends they observed, suggesting they do notplay a role in our observations. But again, we do not exclude their rolein some local deviations, including a positive valued dRLOLA/dcos(latitude) in Fig. 5b.
We also used a more extreme maria-highland standoff distance todetermine whether the latitudinal trend could be an artifact of highlands contamination of the GRS FeO abundances near maria-highlands
boundaries. We performed our analysis excluding maria pixels within
67.5 and 90 km of the maria-highlands boundary, but we included alltitanium data (0–8 wt%), due to the smaller number of pixels available
with such a large standoff distance. Using one large titanium bin isjustified by the observation that titanium did not affect the results when
they were binned by 0–2 and 2–4 wt% TiO2 (Figs. 5 and 6). Overall, we
found that the trend of dR750/dcos(latitude) increasing with high iron
persisted (Fig. 7).
In our analysis we were also concerned that the limited latitudinalrange of data in the higher iron bins (e.g. < 45° for 21–22 wt% FeO, seeFig. 4h) could be distorting our results. For example, it might be thatthe true R750 vs. cos(latitude) trend is more like a polynomial or someother function that starts out nearly flat near the equator, and increases
more rapidly with increasing latitude, such that a line fit close to theequatorial region yields lower slopes than a line fit to a larger latitudinal range. Or, the true trend may indeed be linear, but simply poorly
fit due to the lower latitudinal range of pixels at high iron bins.
Therefore, we repeated the same analysis for all eight iron bins, but
limiting ourselves to data up to ± 45° of the equator – the same range as
the iron bin with the smallest latitudinal range (21–22 wt% FeO,
Fig. 4h). In this case, we had fewer pixels to work with, and wetherefore again extended our analysis to include all titanium bins
(0–8 wt% TiO2). We found the trend of dR750/dcos(latitude) increasing
with high iron persisted even when the fit was restricted to data ± 45°,
albeit with some greater fluctuations (red lines in Fig. 5 and data in
Fig. 8). Note that by limiting our analysis to ± 45° of the equator we
excluded the high-latitude Mare Frigoris.
In summary, we conclude the increase of dR750/dcos(latitude) withiron is not an artifact of the lower latitudinal coverage of the higher

iron bins. The increase is also not limited to a certain range of TiO2
values. In the remainder of our work we rely on the more robust analysis of data from all maria latitudes (black and blue lines in Fig. 5).
4. Discussion
4.1. Predicted maria 750-nm-reflectance at the equator and ± 60° N
We next compared the observed R750 at low latitudes with thepredicted R750 at high latitudes. Note that this predicted value included
latitudes above the range of actual observations, for some higher ironbins. The low latitude R750 was computed from the mean R750 value
from pixels within ± 5° of the equator (Fig. 9a). In this case, we had
ample pixels to work with up to an iron bin of 23–24 wt%. We also
compared these means to the predicted values at the equator from thebest-fit lines (Fig. 9b). We found good agreement between the predictedequatorial value and the empirical equatorial mean values (compare
Fig. 9a and b), as expected.
Surprisingly, we found that there was only a minor differenceof < 0.01 in the equatorial reflectance across all iron bins between 14
and 24 wt% FeO. That is, the darkest state that any soil can reach on the
lunar maria (i.e. at the equator), due to solar wind and micrometeoroid
effects, is apparently nearly independent of its iron content.
When we calculate the predicted R750 value of the soil at higher
latitudes, based on the best-fit slopes and y-intercepts from the analysis
above (Fig. 5), we find that the predicted R750 steadily increases forlower iron bins, as expected. In particular, we computed the predicted
R750 value at 60° N, which represents a 50% reduction in weathering
flux compared to the equator (Fig. 9). We found that the highest ironbin (21–22 wt% FeO) only brightens by about 0.01 at 60° N, while thelowest iron bin (14–15 wt% FeO) brightens by about 0.05. That is, thehighest iron soils do not appreciably brighten at high latitudes, compared to their low iron counterparts.
Note that one might also attempt to predict the R750 value at higher
latitudes from merely the slopes shown in Figs. 5 and 6, and the meanvalues at the equator (Fig. 9a). However, this ignores the importance ofthe best-fit y-intercepts in predicting the actual spectral properties at agiven latitude.

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±60

±45

±45

0.15

0.1

14 - 15 wt. % FeO
0.5

0.6

0.7

0.8

0.9

0.15

0.1

15 - 16 wt. % FeO
0.05
0.4

1

0.5

0.6

cos(latitude)

750 nm reflectance

750 nm reflectance

0.1

16 - 17 wt. % FeO
0.5

0.6

0.7

0.8

0.9

1

0.8

0.9

1

0.8

0.9

1

0.8

0.9

1

17 - 18 wt. % FeO
0.5

0.6

0.7

cos(latitude)
0.2

750 nm reflectance

750 nm reflectance

1

0.1

0.05
0.4

0.2

0.15

0.1

18 - 19 wt. % FeO
0.5

0.6

0.7

0.8

0.9

0.15

0.1

19 - 20 wt. % FeO
0.05
0.4

1

0.5

0.6

cos(latitude)

0.7

cos(latitude)
0.2

750 nm reflectance

0.2

750 nm reflectance

0.9

0.15

cos(latitude)

0.15

0.1

20 - 21 wt. % FeO
0.05
0.4

0.8

0.2

0.15

0.05
0.4

0.7

cos(latitude)

0.2

0.05
0.4

Equator

0.2

750 nm reflectance

750 nm reflectance

0.2

0.05
0.4

±60

Equator

0.5

0.6

0.7

0.8

0.9

0.15

0.1

21 - 22 wt. % FeO
0.05
0.4

1

cos(latitude)

0.5

0.6

0.7

cos(latitude)

Fig. 8. Same as Fig. 4, but the data and fit are limited to data falling between ± 45° of the equator, and all titanium bins are included (0 to 8% wt% TiO2). Fig. 5
shows the best-fit line slopes for each iron bin (red lines in that figure). (For interpretation of the references to colour in this figure legend, the reader is referred to the
web version of this article.)

4.2. Comparisons with spectral variations at swirls

degree. Reiner Gamma is fortuitously near the equator (7° N), simplifying our analysis. Reiner Gama is found on a surface with approximately 21 wt% FeO (Fig. 10). The R750 at Reiner Gamma varies from abackground (non-swirl) value of ~0.095 to a peak near 0.15. These
values are taken from pixels outside of fresh craters, within the brightregions shown in Fig. 11 (black boxes). The peak R750 at Reiner Gamma(shown in Fig. 9) is substantially brighter than the equatorial value forthis iron bin, and nearly equals the predicted R750 values for lower ironbins at 60° N. Therefore, we conclude that the trends we observe are not

An important question is whether the differences in R750 at 60° N in
Fig. 9 across different iron bins are simply due to higher iron soils being
darker than lower iron soils, and their pristine unweathered state isreached at higher latitudes. To answer this question, we compare ourpredicted spectral values at 60° N with observations at swirls on highand low iron mare surfaces. We do not know the solar wind flux reduction at swirls, but they are likely arresting at least protons to some331

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Reiner Gamma (RG) swirl

22.5 km, TiO2 0-2%
22.5 km, TiO2 2-4%
45 km,12TiO2 0-2%
45 km, TiO2 2-4%

14

750 nm reflectance

A
0.15
0.13

Predicted
values
at 60° N= 50% solar

0.12

reduction
0.11
0.1
0.09

18

20

Marginis swirls brightest pixels (Fig. 14)

0.16

0.14

16

Observed
mean
values atequator{Unexplainedby this work

22

24

RG swirl
brightest
pixels (Fig. 11)

0.16
0.15

RG predicted for 0.14
zero solar wind
0.13

RG predicted if? micrometeoroid

darkening varies
with latitude

{

0.1
0.09
0.08

0.08
0.07

12

14

16

18

20

22

0.07
24

Gamma ray iron (wt. % FeO)
0.16
0.15

750 nm reflectance

0.14
0.13
0.12

0.16

B

Predicted
values
at 60° N= 50% solar
reduction

0.11
0.1
0.09

Predicted
values atequator22.5 km, TiO2 0-2%
22.5 km, TiO2 2-4%
45 km, TiO2 0-2%
45 km, TiO2 2-4%

{

0.14
0.13
0.12
0.11

{

0.1
0.09

0.08
0.07

0.15

0.08

12

14

16

18

20

22

0.07
24

Gamma ray iron (wt. % FeO)
Fig. 9. (A) Bottom curves: Mean Clementine R750 values ( ± 1σ) for pixels within ± 5° of the equator, for eight iron bins, two titanium bins, and maria-highland
boundary standoff distances of 22.5 and 45 km. There are no data at 19.5 wt% FeO for the 0–2 wt% TiO2 bin with a standoff distance of 45 km. Top curves: R750
values predicted at 60° N using the best fit linear relationships between R750 and cos(latitude) (best-fit slopes shown in Fig. 5). Also shown are the FeO and brightest
empirically observed R750 values from the swirls considered in Section 4.3 (black dots, see Figs. 11 and 14). The gray range below the predicted Reiner Gamma
reflectance shows the expected additional darkening from micrometeoroid weathering, if the latitudinal trends are a combination of both solar wind and micrometeoroid reduction with latitude. (B) Same as in part A except the bottom curves are the 750-nm-reflectance value at the equator obtained from the best-fit linear
relationship between R750 and cos(latitude). All data points are at the center of each iron bin, i.e. every half integer.

merely due to higher iron soils having generally darker reflectances in
the absence of weathering.
We can now perform a more detailed comparison between our
predictions of the dR750/dcos(latitude) trend and the reflectance variations at swirls. Above we concluded that the highest and lowest ironbins only brighten by about 0.01 and 0.05, respectively, when theweathering flux is reduced by 50% (i.e. at 60° N, Fig. 9). We assume

that both solar wind flux and micrometeoroid flux control the soil
spectral response. We further assume that the spectral variations withlatitude embody both a solar wind and micrometeoroid component. If
the solar wind flux at Reiner Gamma is zero, we predict R750 to be
reduced by at least ~0.03. This value is simply (and somewhat crudely)obtained from twice the R750 difference between the equatorial pixels
and 60° N (=50% flux reduction) for FeO = 21 wt% (see Fig. 9a).

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wt. % FeO

18° N

24

24

22

20

18

14

16

22

26
24

22

22

20
12

21

20

10

20

16

20

12

10

16

18
16

18
22

21

14

22

20

14

8

20

10

6

12
22

12

10

20

14

8
22.5 km 45 km

8-4° N
288° E

20

8
6

16

308° E

6

Fig. 10. Lunar Prospector iron (wt% FeO) contours over Clementine 750 nm reflectance in the vicinity of Reiner Gamma swirl (white dashed box). Stereographic
projection centered at (7° N, 301° E).

Above we found a total R750 change of ~0.055 by examining the actualreflectance values at Reiner Gamma. Therefore, Reiner Gamma appears
to be anomalously bright at 750 nm by at least ~80%, relative to ourmodel, or more, if its solar wind flux is not reduced to zero. In Fig. 9a
we show the possible further expected reduction of brightness due tomicrometeoroids as a gray box under our predicted values from thebest-fit function to the latitudinal FeO trend (the vertical extent of thisbox is speculative). That is, we would have predicted the brightness at
Reiner Gamma to have increased by < 0.03, if our dR750/dcos(latitude)
trend is comprised of micrometeoroid and solar wind effects, since themicrometeoroid flux at Reiner Gamma is likely unimpeded (andthereby presumably causing darkening).
We offer two possible explanations that could help reduce the excess
model-derived component of brightness at Reiner Gamma: 1) The true
R750 vs. cos(latitude) function brightens non-linearly as the weathering
flux is reduced below the range we have fit. We have only performed
our analysis over the region from the equator to a maximum of ~65° N
representing a ~58% flux reduction (Fig. 4). However, when the flux
gets close to zero at higher latitudes, the R750 function may deviate
from linearity, and the brightness may increase markedly. Other work
has reported non-linear reflectance changes as a function of exposure to

space weathering agents (Kohout et al., 2014; Nesvorný et al., 2005;
Vernazza et al., 2009). 2) Our analysis is sufficiently in error eventhough the true R750 vs. cos(latitude) function is nearly linear, and weare not able to accurately predict the flux within ~0.025, which is thedeviation of Reiner Gamma from predictions in the case of complete
solar wind shielding. Since we do not know the actual solar wind flux
reduction at Reiner Gamma, and we do not know the actual contribution of micrometeoroids to weathering, it is not possible to conclusively
rule in favor of either of these explanations, and the high reflectance atReiner Gamma remains a puzzle.
Interestingly, Reiner Gamma's top 10% brightest pixels, equal toR750 > 0.14, represent the top 5% brightest pixels on the entire lunar
maria (Fig. 12). Recall that Reiner Gamma falls on a high iron surface
near the equator, and such surfaces are typically the darkest on themaria (Fig. 9a). The fact that Reiner Gamma's brightest pixels are thebrightest on the maria further illustrates the peculiarity of its ≳80%
excess brightness.
Finally, we can compare our predictions with observations of reflectances at swirls on a mare surface with low iron content: Mare
Marginis. Much of Mare Marginis and its swirls are close to highland
boundaries, making their GRS iron abundances indeterminate.

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Clementine 750 nm reflectance
8.57° N

A

0.16
0.15
0.14
0.13
0.12
0.11
01
.

12.5 km
6.10° N299.68° E

8.57° N

0.09

301.75° E

B

0.32

0.31

03
.

0.29

0.28

0.27

12.5 km
6.10° N299.68° E

301.75° E

0.26

Fig. 11. (A) Clementine 750-nm-reflectance at Reiner Gamma. (B) Spectral parameter β (Clementine derived) at Reiner Gamma. Black dashed boxes illustrate thebrightest portions of Reiner Gamma and are discussed in the text. Both maps are cylindrical projection.

However, in central Mare Marginis we find a peak iron concentration of
14% FeO (Fig. 13). We assume this value is representative of the marevalues elsewhere in the region. This iron abundance represents thelowest iron bins we examined (Fig. 9). Two well-defined swirls innorthwest Mare Marginis have R750 of ~0.135 and ~0.155 (black boxes
L and H, respectively, in Fig. 14). The peak brightness outside of fresh
craters is ~0.16, shown in Fig. 9. The background (non-swirl) reflectance is approximately 0.115, making the total increase in R750
~0.02 and ~0.04, respectively. Again, assume the solar wind and micrometeoroids both vary with latitude. From Fig. 9 we see that thepredicted R750 change here is ~0.05 for a flux reduction of 50% (using

the 14–15 wt% FeO bin). Extrapolating linearly, we would conclude
these swirls are shielded by at least ~20% and 40%, respectively (thevalue may be larger if they are darkened by micrometeoroids). Unlike
Reiner Gamma, there is no excess brightness. However, as with Reiner
Gamma, we do not know the actual solar wind flux reduction at theseregions, or the contribution from micrometeoroids.
4.3. Implications for weathering mechanisms
Our results suggest that the highest iron (> 21 wt% FeO) soils arenearly saturated by submicroscopic iron across the weathering fluxes

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Bright regions of Reiner Gamma (Fig. 11)

Cumulative distribution

A
1
0.8
0.6
0.4
0.2
0
0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

Clementine 750 nm reflectance

Cumulative distribution

B

Bright regions of Reiner Gamma (Fig. 11)

1
0.8
0.6
0.4
0.2
0
0.25

0.26

0.27

0.28

0.29

0.30

0.31

0.32

0.33

0.34

Clementine

Bright regions of Reiner Gamma

C
Cumulative distribution

1
0.8
0.6
0.4
0.2
0

0.14

0.16

0.18

0.2

0.22

0.24

LOLA 1064 nm reflectanceFig. 12. Reflectance data from the entire lunar maria. (A) Cumulative distribution of Clementine 750-nm-reflectance. (B) Cumulative distribution of the spectral
parameter β. (C) Cumulative distribution of LOLA 1064 nm reflectance. The peak non-crater value obtained from the regions in black boxes in Fig. 11 is shown forcomparison. The map of LOLA reflectance is not shown in Fig. 11 due to its low resolution (4 ppd).

observed (i.e. between 0° and ± 65° N, Fig. 9). This finding is broadly
consistent with Trang and Lucey (2019) who found that submicroscopic
iron abundances saturate in high iron soil, but it is difficult to directlycompare their results with ours, as their saturation effect is a summation across all latitudes and terrains. In addition, their iron abundanceis derived from MI data (Lemelin et al., 2015), while ours is from theLunar Prospector GRS.
We suggest that a process operates to prevent sufficient accumulation of submicroscopic iron in low-iron grains, while high iron soils
approach saturation. As discussed in Section 1, under this interpretation, grains of any iron content would eventually saturate with submicroscopic iron if left exposed for long enough periods of time. Low

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wt. % FeO

22° N

8

8

6

13

8

6

14

8

12

8

10

8

8

6

11

12

8

10
14

10

10

14

12

8

12
10

9
12

6

10

6

8

10
10

8

6

7

6

6° N

45 km
6

8

8

22.5 km
6
95° E

78° E

Fig. 13. Lunar Prospector iron contours (wt% FeO) over Clementine 750 nm reflectance at Mare Marginis. The dashed white box indicates the area discussed in the
text and shown in Fig. 14. Stereographic projection centered at (18° N, 86° E).

iron grains have less available iron, and therefore reach this saturation
state more slowly. At high latitudes, low iron grains reach this stateeven more slowly, due to the lower solar wind flux. The process competing against accumulation of submicroscopic iron on all such grains islikely the continual destruction and creation of grains through micrometeoroid bombardment, or regolith gardening. This interpretationsuggests the timescales of the solar-wind-induced nanophase iron saturation and gardening must therefore be roughly similar. For example,
if the nanophase iron saturation timescale were much longer, then there
would be no observations of such saturation on the Moon, which wehave reported. At the moment, given our lack of understanding of themechanisms behind production of submicroscopic iron, estimating theactual timescales will be difficult. Laboratory particle irradiation experiments suggest 105 years of exposure to the solar wind would be
required to saturate a grain's optical changes Hapke (2001). In contrast,
the time to produce vapor deposited rims or amorphize lunar soil grainsis ~106–107 years (Keller and Zhang, 2015). Much work remains to linkthe various processes operating over these timescales.
This model for saturation of high iron soils also works if submicroscopic iron is not produced by the solar wind. As discussed in the
introduction, there is only limited work demonstrating the exact chemical or physical change (e.g. nanophase or microphase iron production) that arises from ion irradiation and which produces the classic

darkening of silicate soils in space. It is possible that grain rim amorphization is the dominant effect of ion irradiation (Christoffersen et al.,2015). Such changes, either on their own, or in combination with micrometeoroid bombardment, may exhibit grain darkening as a functionof exposure time. In other words, whatever the exact nature of the grain
rim changes taking place with exposure to the solar wind, our suggestion remains that its saturation timescale may compete with gardening
and rejuvenation of the soil.
An interesting implication of the above model is that the solar wind
flux appears to be fortuitously nearly saturating at the equator for alliron bins. This can be seen by the nearly equal reflectances of equatorial
pixels across all iron bins (Fig. 9). This implies that the equatorial rates
of soil destruction and refreshing are slow enough relative to theequatorial submicroscopic iron production rate, to permit near-saturation of these soils at any iron bin.
Finally, we highlight yet another complication of our model andspace weathering in general: micrometeoroids likely produce submicroscopic iron, but they are also the same agent that refreshes thesoil. If micrometeoroid flux substantially decreases with latitude, thenits importance in producing spectral changes at high latitudes is not
only reduced, but its ability to refresh those soils is similarly reduced.
How these effects may balance at varying latitudes is clearly uncertain.

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Clementine 750 nm reflectance
18.11° N

0.17

A

0.16

H

0.15

0.14

L
0.13

0.12

0.11
15.61° N
82.56° E
18.11° N

84.94° E

0.35

B
0.34

H

0.33

0.32

L

0.31

0.30

0.29
15.61° N
82.56° E

84.94° E

Fig. 14. (A) 750-nm-Clementine reflectance at northwest Mare Marginis (Fig. 13). (B) Spectral parameter β (Clementine derived) at northwest Mare Marginis. Blackdashed boxes illustrate the mare swirls discussed in the text. Both maps are cylindrical projection. Both maps are saturated at the high end of the values shown due tothe presence of bright highlands material. This saturation does not affect the spectral range occupied by swirl portions of the map.

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4.4. Extrapolation to Vesta

further studies of pristine and weathered reflectances of lunar andVesta-like materials can be compared with these predictions to help
predict the magnitude of solar wind weathering at Vesta (if any), and towhat degree its surface might be shielded by a magnetic field.

We can also predict the effect of solar wind weathering on Vesta's
surface. Vesta does not exhibit the same darkening and reddening attributed to lunar-style space weathering (LSSW) (Blewett et al., 2016;
Pieters et al., 2012). In contrast, regolith churning and mixing of exogenous materials appear to control the spectra of the surface (Denevi
et al., 2016). However, nanophase iron does appear in at least one
howardite regolith breccia (Noble et al., 2011), albeit at smaller
abundances than in lunar soils. Furthermore, LSSW appears to be important for other asteroids (Vernazza et al., 2009). One hypothesis forthis discrepancy is that Vesta has a crustal magnetic field to stand off
the solar wind (Fu et al., 2012a; Vernazza et al., 2006), just as at lunar
swirls. Our results allow us to predict the amount of spectral change fora Vesta surface of a given iron content in the absence of a magnetic
field, at Vesta's semi-major axis of 2.36 AU.
The surface of Vesta is composed mostly of material similar incomposition to howardite and eucritic meteorites (McSween et al.,2014), which have typical FeO concentrations of ~17–19 wt% (Warren
et al., 2009). Assuming the solar wind flux decreases like the square of
the solar distance, Vesta's equator experiences a factor of (1/2.362) = 0.18 reduction compared to the lunar equator. This flux isequivalent to the flux received at a latitude of 80° N on the Moon, which
is outside our analysis range for all of our iron bins. However, setting
this limitation aside, and proceeding assuming that the lunar GRS
18–19 wt% FeO bin is sufficiently calibrated to permit direct comparison to the howardite and eucrite FeO content, we can calculate thespectral change at Vesta relative to the same material at the lunar
equator. We obtain ΔR750 = 0.05 for 80° N. This value is similar to the~0.06 change from an extrapolation of results in Fig. 4 of Sim et al.
(2017). Sim et al. (2017) studied the solar wind's effect on space
weathering by examining the spectra of crater walls, as a function of
their orbit-averaged exposure to the magnetotail (where solar wind
fluxes are reduced). However, their study used data across all iron bins,
so it is not possible to make a direct comparison between our prediction
and their results. We also ignored the differences in micrometeoroid
speeds at lunar and Vesta semi-major axes; the slower values at thelatter may increase the expected change in reflectance. Ultimately,

5. Conclusions
We have found that iron content influences a soil's spectral responseto changes in weathering fluxes at high latitudes. High iron soils(> 21 wt% gamma-ray FeO) only vary in R750 by ~0.01 for flux reductions of ~50%, while low iron soils (14 wt% gamma-ray FeO) experience a five-fold larger change of ~0.05. This is consistent with theidea that accumulation and potential saturation of a soil grain withsubmicroscopic iron, or some other optically active physical and chemical changes on the rim, is moderated by continual destruction andcreation of new grains. Our proposed interpretation is that high irongrains saturate quickly, before they can be destroyed.
Our conclusions are limited by the latitudinal coverage of maria
surfaces of diverse iron content. In particular, the highest iron bin we
utilized only reaches ± 45° latitude from the equator. We also postulatethat non-linear brightening may take place at very low weathering
fluxes. Either of these effects could help explain the anomalous ~80%
brightness we find at Reiner Gamma, which contains the top 5%
brightest pixels on the entire maria. Ultimately, however, in situ ornear-surface measurements of solar wind flux at lunar swirls of different
iron compositions (e.g. Reiner Gamma and Mare Marginis) would offer
a definitive quantification of how the solar wind affects spectral properties of airless silicate bodies.
Acknowledgements
This work was supported by the BK21 plus program through the
National Research Foundation (NRF), funded by the Ministry of
Education of Korea. CKS acknowledges the support from Basic Science
Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2016R1A6A3A11931484).
SSK was supported by the NRF funded by the Ministry of Science andICT (2019R1A2C1009004).

Appendix A. Derivation of β from Kaguya data
The spectral parameter β was derived using MAP-level, 2048-ppd mosaics of Kaguya Multiband Imager (MI), downloaded from Kaguya archive(http://darts.isas.jaxa.jp/planet/pdap/selene/). Geometric distortions caused by the topography had been corrected by MI team making use of theparallax between adjacent observational bands of the instrument. Hemingway et al. (2015) defined the spectral parameters α and β as

= R750

R950
/ m1
R750

= R750

R950
/m2
R750

where R750 and R950 are 750 nm and 950 nm reflectances, respectively, and m1 and m2 are the slopes of the swirl and impact-related trends,
respectively. In Figs. A1–A3, the value of m2 is the slope of the designated background area for three swirls (marked by red). The value of m1 is foundfrom the best-fit line through the medians of each spectral region shown. Averaging over the three swirl areas yields m1 = -2.5 ± 0.3 andm2 = −7.3 ± 0.4. These values are lower than those found in Hemingway et al. (2015) using Clementine data: m1 = −1.6 ± 0.2 andm2 = −5.7 ± 0.5. We ascribe most of the discrepancy to the differences between the reflectances of Clementine/UVVIS and Kaguya/MI (Ohtake
et al., 2010).

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Fig. A1. (A) Kaguya/MI reflectance map of Reiner Gamma swirl at 750 nm. The five colored boxes of interest are the same as those in Fig. 1 of Hemingway et al.
(2015). (B) 750 nm reflectance and 950 nm/750 nm band ratio of pixels sampled from the boxes of corresponding colors in (A). Only 1% of the pixels inside each box
are shown for clarity. The dashed black line is a linear regression fit of the red point group and has a slope of m2. The black circles represent the median of each group.
The solid black line is a linear fit through the medians and has a slope of m1. (C) Map of spectral parameter α (refer to Hemingway et al., 2015 for detailed
description). (D) Map of β parameter.

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Fig. A2. Same as Fig. A1 but for Mare Ingenii swirls.

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Fig. A3. Same as Fig. A1 but for Mare Marginis swirls.

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