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Author’s Accepted Manuscript

Characterization of A photometric anomaly inLunar mare Nubium

Viktor Korokhin, Yuriy Shkuratov, VadymKaydash, Alexander Basilevsky, LarysaRohachova, Yuri Velikodsky, NickolayOpanasenko, Gorden Videen, Dmitry Stankevich,Olena Kaluhina

PII: S0032-0633(16)00019-2DOI: http://dx.doi.org/10.1016/j.pss.2016.01.011Reference: PSS4131

To appear in: Planetary and Space Science

Received date: 22 July 2015Revised date: 26 October 2015

Accepted date: 14 January 2016

Cite this article as: Viktor Korokhin, Yuriy Shkuratov, Vadym Kaydash,Alexander Basilevsky, Larysa Rohachova, Yuri Velikodsky, NickolayOpanasenko, Gorden Videen, Dmitry Stankevich and Olena Kaluhin

Characterization of A photometric anomaly in Lunar mare Nubium, Planetar and Space Science, http://dx.doi.org/10.1016/j.pss.2016.01.011

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CHARACTERIZATION OF A PHOTOMETRIC ANOMALY IN LUNAR MARE NUBIUM

Viktor Korokhina,*, Yuriy Shkuratova, Vadym Kaydasha, Alexander Basilevsky b, Larysa Rohachovaa,

Yuri Velikodskya,c

, Nickolay Opanasenkoa, Gorden Videen

d,e, Dmitry Stankevich

a, Olena Kaluhina

a

a Institute of Astronomy, Kharkiv V.N. Karazin National University, 35 Sumskaya St, Kharkiv, 61022, Ukraine

b Institute of Geochemistry and Analytical Chemistry, RAS, 19 Kosygin St., Moscow, 117975, Russia

c National Aviation University, Cosmonaut Komarov Ave. 1, Kiev 03680, Ukraine

d Army Research Laboratory AMSRL-CI-EM, 2800 Powder Mill Road, Adelphi Maryland 20783, USA

e

Space Science Institute, 4750 Walnut St. Suite 205, Boulder CO 80301, USA

*Corresponding author. Tel.: +38-057-707-5064, E-mail address: [email protected] (V.V. Korokhin)

Submitted to

Planetary and Space Science

Page 42

Figures 21


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Abstract

A novel approach of constructing photometrically seamless mosaics of reflectance, color-ratios,

and phase-curve slopes using LROC WAC images has been developed, which can be used to map the

photometric parameters of the lunar surface. The approach takes into account both geometric

corrections with data on local topography and photometric conjunctions using our simple photometric

model. New mosaics obtained with the technique allow more reliable studies of structural and

chemical characteristics of the lunar surface. This approach has been applied to analyze the

photometric anomaly (21.6S, 17.7W, ~40 km in size) in Mare Nubium detected earlier with our Earth-

based observations. For each point of the scene the parameters were calculated using the least-square

method for several tens of source WAC images. Clementine mosaics also were used in the analysis,

e.g., in order to estimate the parameter of maturity degree Is/FeO. The anomaly has low FeO and TiO2

abundance and reveals a higher slope of the phase function than surroundings. Thermal data from

LRO Diviner measurements do not show anomalies in this region. We consider this area as a shallow

flooding of an elevated formation of highland composition, the material of which could have been

excavated and mixed up with upper layers of the lunar surface through meteoroid impacts. The

anomalous behavior of the phase function can be explained by the difference of surface structure in

the anomaly and surrounding regions on the scale of less than several centimeters. This may be due to

larger quantities of small fragments of rocks and clumps on the surface and/or the presence of

agglomerates having open structure.

Keywords: Moon; Surface; Mare Nubium; Photometry; Colorimetry; Mosaics; Phase Function;

Surface Anomaly; Chemical Composition; Lunar Reconnaissance Orbiter (LRO); Wide Angle Camera

(WAC); Clementine UVVIS data.


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1. Introduction

Optical remote sensing remains a powerful and prospective tool to study the lunar surface.

Significant information has been obtained from measurements of the bi-directional reflectance A of

the lunar regolith as a function of wavelength λ (e.g., Pieters, 1978; Hapke, 1993; Shkuratov et al.,

2011). Spectral observations suggest information about composition and maturity degree of the lunar

regolith. The bi-directional reflectance also is a function of phase angle α. Recently, due to increasing

accuracy, the classical photometric investigations, which are based on measurements of the phase-

angle dependence of brightness, have gotten a second wind, allowing one to find structural anomalies

of the lunar surface (e.g., Shkuratov et al., 2011). Detailed studies of such areas are of great interest,

as this potentially can provide a new view on the formation and evolution of the lunar surface.

The dependence of brightness of lunar areas on the phase angle α is described by the phase

function. The features of this function contain information about the structure of the lunar surface. To

study surface variations of phase-function parameters, for each pixel of a lunar image, a phase-angle

dependence of brightness should be measured with appropriate accuracy. The simplest way to find

photometric anomalies is a phase-ratio method, which has been suggested by Shkuratov et al. (1994)

and developed in several papers (e.g., Velikodsky et al., 2011; Shkuratov et al., 2010; 2011; Kaydash

et al., 2012; Shkuratov et al., 2012a; Shkuratov et al., 2013; Kaydash et al., 2014). This technique

implies calculating the ratio of two images acquired at different phase angles. This is not an easy task,

as the images should be photometrically unified and geometrically coregistered. This unification

results from calculation of so-called equigonal albedo Aeq (see definition below Sec. 2.3).

Applying the method of phase-ratios to data of ground-based observations, Shkuratov et al.

(2010) found several regions displaying anomalous photometric functions (photometric anomalies) in


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the south-west portion of the lunar disk. These areas manifest themselves for phase ratios in different

ranges, including even the small-angle ratio f (2°)/ f (10°) (Kaydash et al., 2009), where f (α) is the phase

function (see below). The anomalies are photometrically unique because they do not follow the

inverse correlation between equigonal albedo and steepness of phase curve, which is known for the

Moon (e.g., Shkuratov et al., 2010; 2011). These regions demonstrate too great a slope of the phase

curve, which implies higher surface roughness. No abnormal thermal inertia, magnetic, and radar

features were found to be associated with the regions (Shkuratov et al., 2010). It was suggested that

these anomalous areas could result from bombardment of the lunar surface by a swarm of meteorites

composed of small impactors, although they also could be due to the excavation of unusual material

from beneath the regolith layers (Shkuratov et al., 2010).

Among these areas, the compact spot centered at 21.6S, 17.7W (~40 km in size) has been

detected (see Fig. 1A in Shkuratov et al., 2010). This formation shows an anomalously high slope of

phase function between the phase angles α = 24º and 43º (Fig. 1B in Shkuratov et al., 2010). The slope

was characterized with the phase-ratio Aeq(24°)/ Aeq(43°), where Aeq(α) is the equigonal albedo at a

given phase angle α. The Earth-based data (Shkuratov et al., 2010) provided a spatial resolution of

about 1 km in the center of the lunar nearside. This area is brighter than the surroundings in the case

of the image of color-ratio C (750/415 nm) = ACSR(750 nm)/ ACSR(415 nm) (Fig. 1C in Shkuratov et al.,

2010) and obscure for the image of color-ratio C (950/750 nm) = ACSR(950 nm)/ ACSR(750 nm) (Fig. 1D

in Shkuratov et al., 2010), where ACSR( λ) is Clementine spectral reflectance. Later we began to study

and characterize this anomaly in other data (Korokhin et al., 2015).

To better study this photometric anomaly, we construct 75 m-resolution maps of the parameter

characterizing the phase-curve slope (see definition below) and color-ratios for the area using data of

the NASA Lunar Reconnaissance Orbiter (LRO) LROC WAC (Robinson et al., 2010). We also use

100-m resolution image data obtained with Clementine UVVIS camera (Eliason et al., 1999), which


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have an order of magnitude higher spatial resolution than the Earth-based data. Analysis of these maps

allows us to estimate not only structural features, but also the chemical composition and maturity

degree of the lunar soil in this formation and neighboring areas. Figure 1 presents a color map

constructed using the WAC data. The large red-yellow spot in the scene center is the photometric

anomaly, revealing an excess of reflectivity in red light.

Developing the investigation (Shkuratov et al., 2010), we considered, first of all, a possibility to

confirm the photometric anomaly in this area using data of higher resolution. We here do not use

directly the phase-ratio technique; we map a parameter that governs a 1-parametric phase function

fitting to the experimental data. Unfortunately, the available LROC WAC RDR mosaics

(http://wms.lroc.asu.edu/lroc/view_rdr/WAC_GLOBAL) cannot be used for this purpose

straightforwardly, since they (1) are not photometrically accurate enough, and (2) do not use all

available data for the full range of phase angles α for the region under study. Thus, we start in Section

2 with raw images acquired with the LROC WAC. Our WAC mosaics and the mentioned Clementine

UVVIS data also are used to assess the regolith chemical composition and its maturity degree. For

interpretation of the obtained results we also incorporate in the analysis our polarimetric Earth-based

observations, Chandrayaan-1 M3 maps, as well as the LRO and Kaguya digital terrain models and

infrared LRO Diviner measurements.

2. Mapping photometric parameters with WAC images

2.1. The need for new mosaicing

The largest high-quality and continuously replenished data-set for lunar photometric

investigations has been obtained with the NASA LRO mission (Robinson et al., 2010). The LRO is

equipped with the wide angle camera (WAC) that is one among different instruments onboard. The


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LROC WAC provides a continuous photometric survey of the lunar surface in seven spectral bands:

five of them are in the visible (415, 566, 604, 640, and 689 nm) and two cover the near UV (321 and

360 nm) range (Robinson et al., 2010). The instrument produces unique information for photometric

investigations of the lunar surface, because it provides multiple coverage (up to hundreds of times) of

the same areas under different illumination/observation conditions.

An important feature of the LRO mission is low polar orbits of the spacecraft with a mean

altitude of 50 km in the nominal mission phase. Such orbits, providing relatively high spatial

resolution, lead to significant geometric distortion of LROC WAC images due to the effect of parallax

caused by the lunar topography. We have proposed a new approach to compensate for the parallactic

effect (Korokhin et al., 2014). This gives us an opportunity to construct geometrically seamless

mosaics on the base of WAC images. However, these images are photometrically influenced by wide

variations of the phase angle (up to 60°) and photometric latitude and longitude (see definition of

these angles in Section 2.3). Straightforward mosaicing leads to inconsistencies on the borders of

different images. Therefore, it is impossible to use WAC data directly for constructing good mosaics

without photometric corrections. This task has been considered, of course, when the LROC WAC

mosaics were built; however, the technique used for photometric corrections has shortcomings that

have been partially considered by Shkuratov et al. (2012b). For instance, Figure 2 shows a fragment of

the WAC mosaic for the studied area, where a vertical seam is clearly seen. Such quality of seaming is

not sufficient for our purposes.

We here develop a novel approach, using a new photometric function, for constructing the

photometrically seamless mosaics of reflectance, color-ratios, and phase-curve slopes using LROC

WAC images. This allows us to bring available images obtained for a given area of the lunar surface

to the same photometric condition and to provide their correct comparison and use.


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2.2. Data selection and initial processing

A set of LROC WAC images has been downloaded from the NASA archive

http://wms.lroc.asu.edu/lroc/search, using the following criteria: (1) the image contains the site with

coordinates 21.6S, 17.7W; (2) the range of incidence angle for the center of the image is i ≤ 90º; and

(3) the spatial resolution of images is not worse than 150 m/pixel. As a result, 293 images have been

selected. The algorithm used for initial processing of WAC data is described in detail by Korokhin et

al. (2014). Here, we shortly outline the principal steps of the processing.

The first step is the transformation of the PDS-format data into FITS that is basic for our

software. As is known, a source WAC image consists of a set of initial framelets (Robinson et al.,

2010). Each framelet is a sub-image from the color-filtered part of the CCD with an angular field of

view (FOV) about 60° in the direction perpendicular to orbital motion (along the selenographic

longitude) and 1.2° along the orbital-motion direction (along the selenographic latitude). Series of

framelets in a WAC image usually cover a long meridional strip of the lunar surface with small

overlapping between framelets as a result of near nadir scanning of the polar-orbiting spacecraft.

Framelets corresponding to all filters are stored into a single source file. Therefore, the second step is

a split of WAC images into separate spectral components.

The third important step is the correction of each framelet for the geometrical distortion caused

by the WAC wide-angle optical system with a 60° FOV. In (Korokhin et al., 2014) we have shown that

the standard model of distortion published by Robinson et al. (2010) does not provide sufficient

compensation of this effect and have proposed our own empirical model:

k = 1 + k 1·r + k 2·r 2+ k 3·r

3 + k 4·r 4 + t x· x + t y· y, (1)

where k i are coefficients of radial distortion (k 1 = −0.0000153, k 2 = −8.225·10−7

, k 3 = −5.103·10−11

, k 4

= −5.905·10−13); t x and t y serve to compensate for the tilt of the CCD array along rows and columns,

respectively (t x = 4.50·10−7 pix−1, t y = 4.41·10

−5 pix−1); r is the distance of the pixel from the optical


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center; x and y are coordinates of the pixel counted from the optical center; r 2 = x

2 + y2; and the values

r , x, and y are measured in pixels.

We also have determined the shift of the optical center from the pre-flight defined position, OC X

= – 1.7 pix, OC Y = – 0.5 pix, and the difference between the optical center and the center of projection

(the intersection point of the perpendicular from the lens center and the CCD array): PC X = – 0.7 pix,

PC Y = 0.25 pix. The values of the last two parameters differ slightly from those reported by Korokhin

et al. (2014). These parameters have been obtained after republication of the LROC CDR data on the

NASA server at the beginning of 2014. We should note that after this republication, the inexplicable

correction of a frame acquisition time ΔT = 112 ms for the northward flight direction (Korokhin et al.,

2014) is no longer needed. Also we have found that for the 415 nm filter, it is necessary to correct the

focal length with coefficient 1.0035 to compensate the difference of scale along the X axis relative to

the rest of the WAC filters. This difference probably is caused by residual сhromatic aberration of the

WAC optics.

The fourth step takes into account the parallactic effect caused by the influence of the local lunar

topography. We exploit the Library of Planetary Cartography (LPC) (Shalygin et al., 2003;

http://www.astron.kharkov.ua/dslpp/cartography/carthography.pdf) for coordinate calculations and

projection transformations. The current version of the LPC addresses spherical and ellipsoidal planets

as well as with planets of any shape described with a single-valued function, e.g., using a map of

elevations relative to the reference sphere. We here use the NASA Digital Terrain Model GLD100

retrieved from LROC WAC stereo-pairs (Scholten et al., 2012) as a carrying elevation map. Although

the map has a nominal resolution of 100 m, they allow substantial compensation of parallactic shifts.

The fifth and final step is the transformation of each framelet into a cylindrical equiangular

projection and the construction of mosaics. Note that each color source image generates two mosaics:

the main part of the image and the image from overlapped portions of the source framelets. Resulting


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mosaics are calculated with the angular resolution at the central pixel of the corresponding source

images. All files containing the albedo mosaics are accompanied by auxiliary files containing the

parameters of projection for each framelet calculated using the SPICE information system (Acton,

1996). This information is used for calculation of photometric conditions for each point of the images.

2.3. Producing photometrically seamless mosaics

After the described initial processing, we obtained a set of WAC images that must be brought to

the same photometric conditions. For this purpose, it is necessary to take into account many

circumstances, e.g., the variations of phase angle within one frame that may reach 60°, or the fact that

a point of the surface is observed in different filters at different phase angles; the difference for

neighboring filters is 2.232º. Therefore, for reliable mapping of the photometric and colorimetric

parameters of an area, it is necessary to use all available images for a given scene.

To bring the diverse images to the same photometric conditions, we use the following formula

(Hapke, 1993; Shkuratov et al., 2011):

A( λ, ) = f ( λ, ) D( λ, ) , (2)

where A( λ, ) is the apparent albedo (radiance factor) at a given wavelength λ; f ( λ, ) is the phase

function; D( λ, ) is the disk function describing the brightness distribution over the lunar disk at a

fixed phase angle ; the angles and are the photometric latitude and longitude that can be found

from the incidence and emergence angles i and e using the well-known expressions (e.g., Hapke,

1993; Shkuratov et al., 2011):

tan = (cos i / cos e cos ) / sin , cos = cos e/cos , (3)

In this work, we use the following formula for the phase function:

0, exp f A , (4)


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where η( λ) is the parameter characterizing the phase-curve slope, A0( λ) is the normal albedo (when

α = i = e = 0°). Equation (4), having only two free parameters (η and A0), describes the phase

dependence of brightness of the lunar surface over a wide range of phase angles (1°..100°) with

quality not worse than the formulas proposed earlier: the 4-parametric formula by Akimov (1988b),

f ( ) = A1 exp( – μ1α) + A2 exp( – μ2α) (5)

and even the 6-parametric formula by Velikodsky et al. (2011),

f ( ) = A1 exp( – μ1α) + A2 exp( – μ2α) + A3 exp( – μ3α). (6)

To demonstrate this we calculated the absolute mean errors of each approximation for the area shown

in Fig. 1. We estimate that for the mare surface the errors are 0.0177, 0.0175, and 0.0175 for Eqs. (4),

(5), and (6), respectively, and the same for the highland portion of the scene is 0.0315, 0.0304, and

0.0313, respectively.

Formula (4) is empirical, however, we note that a similar expression with square root of α has

been theoretically justified in (Shkuratov et al., 1994). Equation (4) has an additional advantage in

comparison with (5) and (6). It is easily linearized by taking the logarithm

0ln , ln f A , (7)

which allows effective searching the parameters of the linear regression.

The disk function D( λ, ) depends very slightly on λ in the case of the Moon (Hapke, 1993;

Shkuratov et al., 2011), and we hereafter consider this function spectrally independent. We use Eq. (2)

with the lunar disk function by Akimov (1979; 1988a,b) (see also (Shkuratov et al., 2011)):

cos

, , cos coscos 2 2

D

,

where is measured in radians, and is the coefficient related to roughness (Akimov et al., 1999;

2000); ν = 0.34 for maria and ν = 0.52 for highlands, and we used below the average value = 0.43.

This function describes the scattering by the lunar surface much more precisely than the Lommel-


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Seeliger and Minnaert scattering laws (Akimov, 1988b; Kreslavsky et al., 2000; Shkuratov et al.,

2011; 2012b).

Using the disk function (8), a useful characteristic named the equigonal albedo Aeq can be

introduced (e.g., Shkuratov et al., 2011; Velikodsky et al., 2011). This is defined as

, ,0, / 2 , ,0, / 2eq A A f D (see Eq. (2)); Equation (8) shows ,0, / 2 D = 1.

For each point of the lunar surface, the parameters A0 and η are found using coefficients of

linear regression (see Eq. (7)) with the least-square method. Results of the calculation are maps of A0

and η, shown in Fig. 3. The maps are presented in the cylindrical equirectangular projection with a

resolution of 0.0025° per pixel, i.e. approximately 75 m on the lunar surface. In the procedure we use

several tens (from 50 to 120) of source WAC images (Fig. 4a). In Fig. 4b, the relative mean-square

errors of the approximation of experimental phase curves by Eq. (4) are shown, and the average error

is 0.7%.

To avoid the influence of the opposition effect and the shadows from topographical details, we

use the following restriction for phase angles 10° < 0°: incidence and emergence angles are

limited by 50°. In order to reduce the influence of shaded areas, points with A > 0.01...0.02

(depending on the photometric band) were used solely for the approximation.

We also carry out an iterative procedure that allows us to reduce the influence of errors of the

radiometric calibration of the WAC CDR data. After the first iteration (the fitting procedure), the

normalization factor for each WAC image is calculated as the ratio of the corresponding averaged

values of calculated apparent albedo ACalc (see Eq. (2)) and observed apparent albedo AObs:

Sc j = Acalc/ Aobs , (9)

where j is the index of source WAC images. During the second iteration, values of observed apparent

albedo are multiplied by this normalization coefficient. This allows us to reduce residual errors of the

approximation of the phase curve by 4 to 5%, depending on the WAC spectral band.


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For calculations of photometric angles when taking into account the parallaxes and local

topography slopes of the lunar surface, the Library of Planetary Cartography (LPC) (Shalygin et al.,

2003; http://www.astron.kharkov.ua/dslpp/cartography/carthography.pdf) is used.

The available SPICE kernels provide coordinates linking LROC WAC images with a precision

only of 70 – 100 m (Mazarico et al., 2012), i.e. 1 – 2 pix. To compensate these spatial errors we carry out

automatic coregistration of source images to the mosaics obtained by the method described above.

Maps of the equigonal albedo are coregistered using the correlation algorithm and bi-linear sub-pixel

interpolation. The observed map is generated by dividing the source reflectance values by the disk

function (Eq. (8)), taking into account topography. The model map is calculated using Eq. (4). After

the coregistration, the procedure of approximation-normalization-approximation (see above) is

repeated. As calculations show, for the studied area the longitudinal and latitudinal shifts are,

respectively, up to 0.005° (150 m on the surface) and 0.0035° (105 m). This is in agreements with the

Mazarico et al. (2012) data. The spatial resolution of the mosaics generated after the coregistration

increases significantly.

We display the quality of our approach with Fig. 2b that reproduces a portion of the scene

shown in Fig. 1 in comparison to the existing mosaics (cf. Fig. 2a). The map shown in Fig. 3a also

demonstrates a seamless distribution of A0 over the mapped area. Bounds of the source WAC

framelets and random noise are invisible. The studied photometric anomaly is seen as a spot at the

center of the scene, which has a more-or-less sharp boundary in several places. Maps of the phase

slope parameter η in blue and red light (Fig. 3b,c) are also seamless. In spite of some residual

influence of local topography, the photometric anomaly is well discernible in the parameter η. This

confirms the conclusion of Shkuratov et al. (2010) about the anomalous behavior of the phase

function of this area. Using available images for the region we built phase curves of absolute

reflectance for three typical 1-pixel sites: inside and outside the anomalous area and the highland


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formation to the south-east of the anomaly. Figure 5 shows the phase curves, from which one can see

that the anomaly reveals itself in increasing brightness at decreasing phase angle beginning from 40º

and that this effect is small.

3. Mapping spectral parameters

3.1. Color ratio mapping

The seamless mosaics allow one to construct color-ratio maps using any pair of wavelengths in

the visible range λ = 415, 566, 604, 643, and 689 nm. Using the apparent albedo images, these ratios

can be calculated at different α in the range from approximately 10º – 100º. Examples of maps of the

color-ratios A0(689 nm) / A0(415 nm) and η(689 nm)/η(415 nm) are shown in Fig. 6. The investigated

photometric anomaly shows up in the A0(689 nm) / A0(415 nm) map (see also Fig. 1). The same was

found by Shkuratov et al. (2010). In contrast, the ratio η(689 nm)/η(415 nm) indicates spectral

neutrality of the anomaly in the wavelength range 415 – 689 nm. It should be noted that both the

distributions of η are influenced mainly by roughness. Albedo is a secondary factor affecting mostly

η(689 nm).

For specific tasks, albedo values at wavelengths other than those of the WAC set may also be

required, e.g., we may need to compare LROC WAC and Clementine UVVIS images. Therefore, we

obtained the parameters of the regression equation in each point (pixel) of the surface using albedo

values for the five WAC bands. Then we use it for conversion to the required λ. In the wavelength

range 415 – 689 nm, lunar spectra can be described approximately by a linear function:

A0( λ) = k 1( λ – λ0) + k 0, (10)

where k 1 and k 0 are the coefficients of the linear regression, λ0 = 415 nm. Figures 7a,b show

distributions of these coefficients. A similar analysis can be carried out for the parameter η:


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η( λ) = l 1( λ – λ0) + l 0, (11)

where l 1 and l 0 are the corresponding coefficients of the linear regression (l 1 is negative). The

equations (10) and (11) show that A0 increases with wavelength; whereas, η decreases with λ and,

hence, with albedo. The maps of l 1 and l 0 are presented in Fig. 7c,d. The images shown in Fig. 7 are

solely supporting, though they also demonstrate that the studied anomaly stands out clearly in the

cases of the maps of k 0, k 1 and l 0. This anomaly is almost invisible for the parameter l 1. The latter, in

particular, means that the phase function f (α) of the anomaly amplitude is spectrally neutral in the

WAC spectral range. The equations (10) and (11) make it possible to calculate the values of A0 and η

for any wavelength within the spectral range and a little outside its edges.

3.2. Connection between the WAC and Clementine photometric systems

Most modern methods of prognosis and analyzing the chemical and mineral composition of the

lunar surface (e.g., Lucey et al., 1995; 1998; 2000a,b; 2004; Gillis et al., 2003; Pieters et al., 2002;

2006; Shkuratov et al., 2003a,b; 2005a; Korokhin et al., 2008; Lin Li, 2011) incorporate laboratory

chemical/mineral and maturity degree determinations of size particle separates of lunar soils from

Apollo landing sites, and spectrophotometric data of the same samples presented in the LSCC

(RELAB) photometric system (http://www.planetary.brown.edu/relabdocs/relab_disclaimer.htm and

http://www.planetary.brown.edu/pds/LSCCsoil.html) using Clementine UVVIS wavelengths (415,

750, 900, 950, 1000 nm). The LSCC data include measurements of regolith samples selected to be

representative of various lunar basalt compositions having different maturity (Taylor et al., 1999,

2001). The laboratory spectral measurements were carried out with the RELAB spectrometer in the

range 0.4 – 2.5 m at α = i = 30°, e = 0 (Pieters, 1999). The RELAB measurements of regolith

samples from the Apollo-16 landing site were used for the radiometric (absolute) calibration of

Clementine UVVIS data. Unfortunately, the Clementine calibration significantly differs from the

WAC and several other measurements (Shkuratov et al., 2001; 2011; Velikodsky et al., 2011). This


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leads to a somewhat paradoxical situation, when well-calibrated data should be brought to the

eccentric Clementine system in order to use available techniques for the prognosis of chemical and

mineral composition. We used the following procedures:

1. Maps of the normal albedo A0 and the parameter η for the Clementine wavelengths 415 and

750 nm using Eqs. (10) and (11) and corresponding regression coefficient distributions are calculated.

The wavelength λ = 750 nm is a little beyond the upper limit of the WAC range (689 nm); however,

between these wavelengths, the lunar spectrum is linear and can be extrapolated.

2. Maps of apparent albedo AWAC for the phase angle α = 30º, incidence angle i = 30º, and

emergence angle e = 0º (the RELAB photometric condition) using Eqs. (2), (3), and (4) also are

calculated.

3. We normalize the WAC scales of apparent albedo using linear functions ARELAB =

1.3959 AWAC + 0.0144 (for λ = 415 nm), and ARELAB = 1.1191 AWAC + 0.0242 (for λ = 750 nm). These

formulas have been obtained from comparison of the albedo over the investigated area for the WAC

after previous steps and acquisition of the Clementine mosaics.

The obtained maps of Aeq allow us to construct WAC maps of albedo and color ratio in the

Clementine photometric system, i.e. to calculate A(750 nm) and color-ratio C (750/415) =

A(750 nm)/ A(415 nm). The maps can be used for further applications.

4. Estimates of chemical composition and maturity degree

4.1. Applying Lucey's method to the WAC data

As has been noted, there are several methods for the estimating the chemical composition and

maturity of the lunar soil using colorimetric (spectrophotometric) data (e.g., Lucey et al., 1995, 1998,

2000a,b, 2004; Blewett et al., 1997; Gillis et al., 2003; Pieters et al., 2002; Shkuratov et al., 2003a,b,


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2005a; Korokhin et al., 2008; Lin Li, 2011). The technique by Lucey et al. (1995, 1998, 2000a,b,

2004) is the most popular. This is based on results of laboratory optical studies of lunar samples,

which show regularities in arrangement of points corresponding to soils of different maturity and

chemical composition on the correlation diagrams A(750 nm) – C (950/750 nm) and A(750 nm) –

C (415/750 nm). It has been found that samples with a similar iron (titanium) content, but different

maturity degree form sequences, oriented approximately in the direction of a singular point of the

diagrams (the ‘‘overmature’’ point). The distance from this point is the parameter of the optical

maturity OMAT : the shorter the distance, the greater the maturity. This is an approximate approach,

whose shortcomings were considered in several papers (e.g., Starukhina and Shkuratov, 2001);

however, it suggests very plausible results. Using the mentioned correlation diagrams, empirical

formulas for assessments of abundance of FeO and TiO2 have been found (Lucey et al., 1995, 1998,

2000a,b).

950 nm / 750 nmFeO % 17.43 arctan 7.56

750 nm

A A y

A y

, (12)

5.98

2

415 nm / 750 nmTiO % 3.71 arctan

750 nm

A A z

A

, (13)

where A( λ) is albedo, y = 1.19, and z = 0.42. Both iron and titanium are the main transition elements in

the lunar regolith materials, and, therefore, they significantly influence the properties of spectral

reflectivity.

Formula for the optical maturity (parameter OMAT ) also was obtained using the diagram

A(750 nm) – C (950/750 nm):

2

2 950 nm750 nm

750 nm Fe

AOMAT A x y

A

, (14)


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where x = 0.08. The parameter OMAT Fe is closely related (see below) to the maturity degree I s/FeO

that is the amount of nano-phase Fe0 in regolith particles normalized to the FeO content, where I s is

measured by ferromagnetic resonance techniques (e.g., Morris 1976, 1978).

Unfortunately the WAC filters allows one to use only Eq. (13) to estimate the abundance of

TiO2, not FeO. Lucey et al. (1998) have noted a possibility to exploit for approximate estimations

another optical maturity parameter (OMAT Ti) using the diagram A(750 nm) – C (415/750 nm):

2

2 415 nm750 nm

750 nmTi

AOMAT = A z

A

. (15)

This parameter does not have an obvious physical meaning, but is undoubtedly useful for an

independent discrimination of lunar surface types.

Formulas (14) and (15) indicate that the smaller the OMAT Fe and OMAT Ti, then the higher the

regolith maturity. It should be observed, however, that the use of Eq. (15) requires great caution, since

the OMAT maps constructed with Eqs. (14) and (15) are noticeably different (Shkuratov et al., 1999).

A map of abundance of TiO2 obtained using Lucey's method is shown in Fig. 8a. It is seen that

the photometric anomaly has noticeably lower titanium content than the surrounding area, but higher

than for typical highland. Note that the prognosis of TiO2 abundance for low as well as high values is

not reliable in Lucey’s approach, which suggests overrated abundances. Figure 8b presents the map of

OMAT Ti. This figure shows that the optical maturity of soils in the anomaly area also slightly differs

from surrounding regions. We also illustrate differences between the maps of TiO2 abundance

determined using WAC and Clementine data (Fig 8c,d). Comparison of these TiO2 maps shows higher

quality of the WAC version. Moreover, the LRO WAC map reveals a higher resolution as compared to

the Clementine one.


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4.2. Chemical composition and maturity degree using Clementine UVVIS data

We use Clementine UVVIS data (Eliason et al., 1999) for the determination of FeO content (Fig.

9a) and the parameter of optical maturity degree OMAT Fe (Fig. 9b). We specifically exploit the

Clementine 100 m mosaics and apply Eq. (12). Additionally, in order to construct a map of I s/FeO

(Fig. 10a), we use LSCC data (http://www.planetary.brown.edu/relabdocs/relab_disclaimer.htm and

http://www.planetary.brown.edu/pds/LSCCsoil.html) and apply the Artificial Neural Network (ANN)

method (Korokhin et al., 2008). Results of the mapping presented in Fig. 9a show that the abundance

of FeO inside the anomaly area is somewhat lower than in surrounding areas, although the contrast

between the anomaly area and its surroundings is noticeably higher for TiO2. As anticipated,

comparison of the maps OMAT Ti (Fig. 8b) and OMAT Fe (Fig. 9b) reveals very weak correlation of

these parameters. We note that the borders of the photometric anomaly and chemically distinct areas

are close to each other, but often are not exactly coincident.

We also have found the following useful relationship between the parameters OMAT Fe and

I s/FeO (the correlation coefficient equals 0.92):

I s/FeO = – 267 OMAT Fe + 97. (17)

The maps in Figs. 8b, 9b, and 10a clearly demonstrate that characteristic values of the maturity degree

in the anomaly area do not differ significantly from its surroundings.

The map of OMAT Ti reveals a dark ring around the border of the photometric anomaly, which

is not detected in the parameter OMAT Fe and I s/FeO. There is no direct interpretation of the feature of

OMAT Ti, since the physical meaning of OMAT Ti is debatable (Lucey et al., 1998; Shkuratov et al.,

1999); nevertheless, this ring is not an artifact, as its traces are in the source images.

The composition mapping based of LSCC data (Pieters et al., 2006; Shkuratov et al., 2007)

also allows a determination of the agglutinate content in the lunar soil. Agglutinates are glassy

aggregates of particles, which are formed from micrometeorite strikes of the lunar regolith. The


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abundance of impact glasses in agglutinates is rather high, up to 50%. There is a very strong

correlation between I s/FeO and agglutinate abundance. The LSCC data base also contains information

about volcanic (pyroclastic) glasses. In contrast to agglutinate glasses, the abundance of pyroclastic

material is much lower in the LSCC samples; it usually is several percents. Nevertheless, the LSCC

data allow maps of the abundance of volcanic glasses, although the reliability of such maps is

somewhat lower than in case of the parameter I s/FeO (Fig. 10b).

The mean size of lunar regolith particles correlates with the parameter I s/FeO: the lower the

maturity degree, the higher the mean size (Morris, 1976; 1978; McKay et al., 1991). Unfortunately,

there are not reliable remote-sensing methods to assess variations of such sizes, although several

attempts have been undertaken. Using a technique proposed by Shkuratov et al. (2003), which is

based on the use of LSCC and Clementine UVVIS data, we construct a distribution of the mean size

of particles. We find that in the anomalous area, the size is absolutely the same as its surroundings,

and therefore, we do not show here these result.

5. Discussion

5.1. Brief characterization of the studied region

Using LROC WAC data we confirm results obtained by Shkuratov et al. (2010) concerning the

photometric anomaly: the steepness of the phase curves of the area is higher than that of mare

surroundings. As reported in the previous study (Shkuratov et al., 2010), the area has been identified

by an inverse dependence of the slope of phase function on albedo in comparison with the typical

lunar response. The steepness difference between the anomaly and adjoint areas is the same at λ = 415

and 689 nm. We also confirm that the anomalous area has an excess of red color and slightly higher

albedo than its surroundings. This, in particular, suggests that the anomaly area is distinct in


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composition from the neighboring mare regions: the regolith of the anomaly has lower contents of

iron and titanium. On the other hand, the abundance of FeO and TiO 2 is certainly higher than for

highland areas. The maturity degree I s/FeO of the regolith inside the anomaly is somewhat lower than

outside.

We proceed with the characterization of the region analyzing the geologic situation. Figure 11

displays older and younger types of terrains clearly seen on the image corresponding to large phase

angles. The types are presented, respectively, by various lavas. The photometric anomaly roughly

outlined in Fig. 11 is located on the lava plain covered by impact craters that occasionally form

clusters. In many cases these are secondary craters. Sometimes the craters form chains associated with

cracks. Moreover, the plain is complicated by wrinkle ridges that are moderate compression structures

(overthrusts) that are characteristic of lunar maria. The lava plain also has sinuous rilles that are

channels cut by lava floods due to heating and mechanical erosion. The ridges and rilles can be seen

inside and outside the anomaly area. The geologic situation in the region does not directly show

features that could point out the origin of the anomaly; therefore, it is necessary to use additional

independent data and analysis.

5.2. Feasible reasons of the anomaly

There are theoretically five causes affecting the steepness of the phase curve of regolith-like

surfaces. These are (Hapke, 1993; Shkuratov et al., 2005; 2011) the following: (1) regolith porosity;

(2) a complicated topography, e.g., the presence of rock fragments; (3) a peculiarity of the phase

function of individual particles; (4) incoherent multiple scattering, and (5) coherent backscattering

enhancement. The latter mechanism contributes solely to the opposition effect of bright particulate

surfaces only at small phase angles; thus, this is definitely not our case. We consider the first four

points in more detail.


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1. The regolith porosity is a complex function of the composition and features of structure

generation of a light-scattering surface. Variations of the porosity, which depend on the mean size of

regolith particles and the optical heterogeneity of the particles, are important characteristics affecting

the shadowing effect that significantly influences the surface phase function; ordinarily, the smaller

the particles, the higher the porosity. Therefore, fine regolith is expected to have higher values of η,

which is observed for the anomaly. The smaller mean size of particles is observed if the lunar regolith

is mature; in this case the size is ~70 m (McKay et al., 1991). For immature regolith this size may be

equal to 120-150 m. Contamination by pyroclastic materials may decrease this parameter, as the

mean particle size of the material is about 40 m (Heiken et al., 1974). Finally, the anomaly area can

be notably contaminated by highland materials that are involved in the impact formation of the local

regolith. The mean size of particles of mature highland regolith is lower than that of the mare regolith

(Engelhardt et al., 1976). Moreover, results of our ray-tracing computer modeling show that the

mixture of dark and bright materials may significantly influence the regolith phase function

(Stankevich and Shkuratov, 2004).

As has been noted, there are not yet remote-sensing techniques for the reliable determination

of the mean size of regolith particles and lunar surface porosity. The technique suggested by

Shkuratov et al. (2003) uses a poor base of lunar samples with different sizes of particles. An

alternative method to estimate this parameter is grounded in polarimetric measurements (Shkuratov

and Opanasenko, 1992) that are discussed below. Both these approaches have not yet been confirmed

with independent measurements. Another reason for surface variations of porosity can be related to

the regolith formation from lunar materials of different compositions.

2. The shadow effect and, hence, the parameter η can be greatly increased if a large enough

quantity of stones and rock fragments cover the surface (Shkuratov and Helfenstein, 2001; Shkuratov

et al., 2005b). The presence of such rock fragments in large quantities with sizes > 10 cm may be


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established with thermal inertia measurements using the LRO Diviner imager. The LRO Diviner

measures both reflected solar and emitted lunar infrared radiation in nine spectral channels with

wavelengths ranging from 0.3 to 400 microns (Paige et al., 2010). The measurements enable mapping

and characterization of surface properties such as the thermal inertia, which allows determinations of

rock abundance and the silicate minerals with measurements of the Christiansen frequencies

(Bandfield et al., 2011; Song et al., 2013). Figure 12 presents map of the abundance of stones > 10 cm

on the lunar surface (http://pds-geosciences.wustl.edu/missions/lro/diviner.htm). As can be seen for

these parameters, there are no hints of peculiarities inside the photometric anomaly. Thus, the regolith

of the area does not have abnormally large numbers of stones and rocks > 10 cm, and its mineral

composition is rather ordinary for this region. The LROC NAC images with resolution 1 m also

confirm the normal abundances of large stones within this region.

3. The phase function (indicatrix) of individual particles may in principle contribute noticeably

to the resulting phase function of the lunar surface. If the photometric anomaly is due to single-

particle scattering, we may generally expect that the phase function of linear polarization degree also

is abnormal, as these two parameters are very closely connected to each other (e.g., Bohren and

Huffman, 2004). Polarimetry is a powerful tool of lunar surface studies (Shkuratov et al., 2011; 2015).

It has been shown that polarimetric anomalies found due to Earth-based observations at large phase

angles may relate to surface variations of particle size and regolith porosity (Shkuratov, 1981;

Shkuratov and Basilevsky, 1981; Shkuratov and Opanasenko, 1992; Dollfus, 1998; Shkuratov et al.,

2011; 2015).

The photometric anomalies described by Shkuratov et al. (2010), including the one considered

here, also are polarimetrically unusual. Figure 13a displays an image of the distribution of

polarimetric anomalies detected at a phase angle near 93º in blue light; the arrows 1 and 2 point out,

respectively, the anomaly under study and another photometric anomaly also detected Shkuratov et al.


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(2010). One could conclude that if the anomaly clearly shows up both on the photometric and

polarimetric images, its origin relates to the unusual scattering properties of individual particles, but

this conclusion is not valid.

The notion of the polarimetric anomaly appeared from analyzing the correlation between the

polarization degree and albedo (the diagrams log P – log A). The regression line of the correlation can be

described as follows (Dollfus and Bowell, 1971):

log logeq max A a P b . (18)

Shkuratov and Opanasenko (1992) found that at 430 nm, the coefficients a = 0.795 and b = – 1.871, if

the equigonal albedo Aeq is determined at = 6º, and P max equals P (90º). The deviations from the

regression line (18) are physically informative. First of all, the deviations depend on which kind of

albedo we use in Eq. (18). The equigonal albedo Aeq measured at small phase angles and at αmax differs

from each other by the influence of phase function of the lunar surface.

It is convenient to study the deviations from the regression line, considering b in Eq. (18) as a

parameter of the polarimetric anomaly a

eq maxb A P . Imaging the parameter was initiated in several

studies (Shkuratov, 1981; Shkuratov and Opanasenko, 1992; Dollfus, 1998), but this technique is not

yet fully developed. The maps presented in Fig. 13a,b correspond to Aeq calculated at α = 93º and 10º,

respectively. Figure 13a shows that the photometric anomaly simultaneously is the polarimetric one

(see arrow 1). On the other hand, Fig. 13b does not reveal this anomaly and confirms that the

polarimetric anomaly is due to the difference of Aeq(α). Thus, the polarimetric anomaly paradoxically

is not actually polarimetric. This paradox is a result of the definition of polarization degree that clearly

includes the normal albedo: 0/ P const A (Dollfus and Bowell, 1971; Shkuratov and Opanasenko,

1992). The results presented in Fig. 13a,b suggests that the possible peculiarity of the single-scattering

indicatrix hardly is the cause of the anomalous photometric properties of the area.


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4. Incoherent multiple scattering may abate the photometric anomalous properties, as the

interparticle scattering weakens the shadow effect. The effect of incoherent multiple scattering clearly

exists in the case of the Moon. It can be seen from Eq. (11) and a comparison of Figs. 3b and c, where

the distributions of the parameter η are presented in a common η-scale.

Lunar spectral measurements indicate that albedo typically increases with λ until ~ 2.5 m,

where thermal emission begins to contribute to the total radiance. The role of multiple scattering,

which weakens the shadow effect, also should increase. Hence, the parameter η decreases with λ, and

in principle at some λ the photometric anomaly may disappear on η maps. We verify this hypothesis

using data from the Moon mineralogy mapper (M3) of the Indian spacecraft Chandrayaan-1 (Pieters et

al., 2009a).

We calculate phase-ratio distributions in two very different wavelengths, λ = 0.75 and 2.90 m.

Data at the longer wavelength were corrected with the thermal emission model by Clark (2009).

Figure 14a-d shows the brightness distribution at λ = 0.75 m, where the phase-ratio images

constructed from M3 images M3G20090110T154845 (average phase angle 34º) and

M3G20090206T225721 (average phase angle 54º) at the two mentioned wavelengths. In spite of this,

variations of phase angle over the images are a few tens of degrees, the images in Fig. 14b,c indeed

are phase ratios, although they have large uncertainties. The anomaly is clearly seen in Fig. 14b

(shorter wavelength), but disappears at λ = 2.9 m (Fig. 14c). Thus, at this wavelength, multiple

scattering may entirely compensate the influence of surface roughness. To stress the difference

between phase ratios in the two wavelengths, we calculate the color ratio of the phase ratios; the

photometric anomaly here is especially visible (Fig. 14d).

5.3. On the origin of the anomaly

Taking into account the described possible mechanisms of the anomalous formation, we

discuss here four hypotheses of its nature: (A) a swarm impact, (B) a volcanic flooding in the anomaly


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boundary, (C) pyroclastic deposits, and (D) highland material contamination. We consider these four

possibilities in more detail.

(A). A recent impact by a compact swarm of small meteoroids plowing the surface was

suggested as a mechanism by Shkuratov et al. (2010). This can easily explain higher values of η and

slightly lower maturity. At the same time, this hypothesis has significant shortcoming. The scales of

roughness generated by the impact should be less than several tens of centimeters, since LROC NAC

data with resolution of 0.5 m does not show any prominent peculiarity in roughness. The plowing with

a swarm of small impactors may provide shallow damage of the regolith and cannot explain the

distinction in composition due to excavated material from beneath layers of the regolith. Finally, such

swarms (if they can be formed in the Moon-Earth system at all) may live a very short time that harshly

decreases the feasibility of the mechanism.

(B). We may assume that the anomalous area contains peculiar lava formations with unusual

chemical properties. We tried to find the boundaries of the lava fluxes using DTM maps constructed

with the LROC WAC data providing images with substantial across-track stereo coverage (Scholten et

al., 2012). A fragment of such a map is presented in Fig. 15a with contour of the anomaly. The

absolute accuracy of the DTM has been estimated with altimetry data from the LRO LOLA instrument

(e.g., Smith et al., 2010). Figure 15b displays results of visualization of the DTM data. We use

illumination at the angular height of the Sun equaling 3º. The height and spatial resolutions of the

images is near 2 m and 200 m, respectively. We here see extremely strengthened topography solely

without albedo variations. The images were constructed with the Lambertian photometric function of

each relief element. Many wrinkle ridges are exhibited in the region. Excepting several small places

we do not find any correlation between the anomaly border and the topography. Thus, the layer of the

material making the area abnormal should be thinner than the DTM height resolution. In Fig. 16 we

confirm this result displaying the south sharp border of the anomaly and for comparison DTM data


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from Kaguya imaging with a resolution of 50 m (Haruyama et al., 2012). No border can be found in

this topography as well.

C. The photometric anomaly is brighter than its surroundings at small phase angles. Thus,

supposing pyroclastics, we expect the existence of bright pyroclastic material. Usually lunar volcanos

produce dark halos. The crater (depression) shown by the arrow in Figs. 3a and 17 can be considered

as a candidate to be a pyroclastic source. This depression is surrounded by a dark halo. If the anomaly

is related to the pyroclastic deposits, it should be young, as the upper regolith layer is considerably

reworked during 200-300 million years and becomes optically insignificant. The depression does not

appear in LROC NAC images as a young formation. It partially cuts three craters on the left and one

crater on the right. This tectonic structure in principle may be a source of the pyroclastic, but it should

be old, since these lineaments are smooth.

The Chandrayaan-1 M3 data have allowed for a mapping of the compositional diversity of the

lunar surface and the discovery of the 3m absorption band related to OH and H2O possibly contained

in the regolith (e.g., Pieters et al., 2009b). Recently, it was shown that large pyroclastic deposits on the

lunar surface exhibit increased hydration; unlike nearby regions, these areas are highly hydrated,

exhibiting up to ~1000 ppm H2O (Li and Milliken, 2014). We verify the pyroclastic hypothesis using

Chandrayaan-1 M3 spectra. In Fig. 18 we show an albedo image at 750 nm and color ratio = 3 2/ A A ,

where 2 A = ( A2776.6 + A2816.5 + A2856.4)/3 and 3 A = ( A2896.3 + A2936.3 + A2976.2)/3. Examples of spectra for

the three sites shown in Fig. 18a can be found in Fig. 18c. Figure 18b clearly shows that the anomaly

manifests itself as an area having higher ratio. If we suppose that this color ratio characterizes the

short wing of the water band, then this result indicates lesser OH/H2O abundance than its

surroundings. Finally, Fig. 10b shows that the abundance of volcanic gasses inside the anomalous area

is lower than in the rest of the mare territory. Thus, summarizing, we may conclude that the

pyroclastic origin of the anomaly is hardly possible.


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D. The last hypothesis of the anomaly origination seems perhaps to be most realistic. We

suppose that initially the lava inside and outside the anomaly had the same age and composition. The

only difference is that the lava layer inside the area is rather thin due to the topography of the highland

substrate in this region (Fig. 19). Then the layer in the boundary of the area was partially destroyed by

rather large meteoroids and a portion of the substrate material has been excavated on the top and then

was involved in the regolith formation processes. This flooded highland elevation can be assumed to

be a hill, or possibly, a rhyolite extrusion similar to the formations Helmet and Hansteen Alpha. These

formations have an excess of red color and are easily found in images of color ratio red/blue (Malin,

1974; Head and McCord, 1978). The studied photometric anomaly also has a noticeable reddish hue

(see Fig. 1).

The highland regolith contains lower abundances of impact glasses and agglutinates, but more

highland breccias that are structurally more compact than agglutinates (Rode et al., 1979; McKay et

at., 1991). This may result in the difference in the porosities of the regolith upper layer of the mare

and highland materials, and hence influences the phase curve. On the other hand, agglutinate particles

having a significant amount of impact glasses have roundish shapes (Rode et al., 1979). This can

produce less shadowing from particles than for very open agglomerates that may have a hierarchical

structure composed by glued mineral fragments if the amount of gluing glass in the agglomerate is

rather small. The presence of particles with a hierarchical (prefractal) structure can significantly

enhance the shadowing effect (Shkuratov and Helfenstein, 2001), creating a photometric anomaly. A

mixture of mare and highland materials can explain the composition difference between the anomaly

area and its surroundings, since the highland material has low TiO2 and FeO abundance.

Anomalous behavior of the phase function also may be explained by a difference of surface

structure in the anomaly and surrounding regions on the scales of less than several centimeters,

imperceptible in the camera image with LROC NAC and undetectable by the LRO instrument


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Diviner. This may be due to the presence of unusually large numbers of small rock fragments and

clumps on the regolith surface. Such surface-structure elements are ubiquitous in all in situ images of

the Moon (see, e.g., Fig. 20).

For hypothesis D, the photometrical sharpness of the anomaly’s boundary can be explained

naturally by the substrate topography features: inside the shallow flooding the pre-mare material

contamination is important, while outside, the regolith has typical mare composition. Such a model

explains the absence of the boundary in elevation maps, as the lava flooding is the same. Nevertheless,

we have tried to find the boundary using LROC NAC data of high resolution. Figure 21a displays a

southern portion of the map shown in Fig. 3b. The red boxes in this figure correspond to two

fragments of the LROC NAC mosaics presented in Fig. 21b,c. The anomaly boundary cannot be

identified reliably in the mosaics, although in some cases its slight traces may be detected (see arrow

in Fig. 21c).

6. Conclusion

A method of constructing seamless mosaics of photometric parameters of the lunar surface

(albedo and parameter of the phase-curve slope) using LROC WAC color images has been developed.

Our approach takes into account both geometric corrections with data on local topography and

photometric conjunctions using a simple photometric model that is more accurate than ones used in

other works. Mosaics obtained using this method allows one to study optical, structural and chemical

characteristics of the lunar surface with higher accuracy than before. The proposed method has been

applied to a photometric anomaly in Mare Nubium identified earlier by Shkuratov et al. (2010). It

should be emphasized that at present, only LROC WAC data together with the technique described


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above make it possible to calculate phase functions of lunar surface sites with spatial resolution m

with high accuracy (see, e.g., Fig. 3). The following results have been obtained:

1. Maps of the normal albedo and the parameter η that characterizes the phase-curve slope for

5 WAC spectral bands have been constructed. We here use two maps constructe d for λ = 415 and 689

nm in order to show the spectral neutrality of the anomaly amplitude in this spectral range. The maps

cogently confirm the existence of a photometrically anomalous area in Mare Nubium found by

Shkuratov et al. (2010), which manifests itself in deviation from the inverse correlation between the

slope of phase function and albedo.

2. Maps of abundance of FeO and TiO2 in the lunar soil have been constructed using the Lucey

method and the ANN (artificial neural network) regression technique using LRO WAC, Clementine

UVVIS data, and laboratory measurements of lunar soil samples LSCC. The maps demonstrate lower

concentration of titanium and iron in the anomaly; the content values are between the highland and

mare ones.

3. The same approach based on LSCC data allowed mapping the parameters OMAT Fe and

OMAT Ti, as well as the maturity degree and volcanic glass abundance distributions. The regolith of the

photometric anomaly has somewhat low maturity I s/FeO and volcanic glass content.

We analyzed four hypotheses of the origin of the photometric anomaly: a swarm impact,

volcanic flooding inside the area, pyroclastic deposits, and contamination with highland materials

extracted from beneath the regolith layer. The first hypothesis (Shkuratov et al., 2010) cannot explain

chemical difference inside the anomaly and its sharp borders on the scale m. The second model

implies the coincident boundaries of photometric anomaly and lava floods visible in elevation maps.

The DTM models produced by LRO and Kaguya teams do not reveal this. The pyroclastic hypothesis

is also vulnerable. For instance, it conflicts with Chandrayaan-1 M3 measurements that do not exhibit

the 3-m water signature in spectra of the anomaly, as can be anticipated.


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We suggest a model of the area origin that may be a shallow flooding of an elevated formation

of highland composition, which is partially denuded by meteoroid impacts. A mixture of mare and

highland materials can explain the composition differences between the area and its surroundings. The

anomalous behavior of the phase function can be explained by the difference of structure of the

surface in the anomaly and surrounding regions on the scale of less than several centimeters. This may

be due to larger quantities of small fragments of rocks and clumps on the surface and/or the presence

of agglomerates having open structure.

Acknowledgment. The authors gratefully acknowledge the research support from NASA grant

NNX11AB25G Lunar Advanced Science and Exploration Research (LASER) “Imaging photometric

and polarimetric remote sensing of the Moon”. The authors thank the SELENE(KAGUYA) TC team

and the SELENE Data Archive for providing the SELENE(KAGUYA) data. SELENE is a Japanese

mission developed and operated by JAXA.


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References

Akimov, L.A., 1988a. Light reflection by the Moon. I. Kinemat. Phys. Celest. Bodies 4(1), 3 – 10.

Akimov, L.A., 1988b. Light reflection by the Moon. II. Kinemat. Phys. Celest. Bodies 4(2), 10 – 16.

Akimov, L.A., Velikodsky, Yu.I., and Korokhin, V.V., 1999. Dependence of lunar highland brightness

on photometric latitude. Kinemat. Phys. Celest. Bodies 15(4), 232 – 236.

Akimov, L.A., Velikodsky, Yu.I., and Korokhin, V.V., 2000. Dependence of latitudinal brightness

distribution over the lunar disk on albedo and surface roughness. Kinemat. Phys. Celest. Bodies

16(2), 137 – 141.

Acton, C.H., 1996. Ancillary data services of NASA’s navigation and ancillary information facilty.

Planet. Space Sci. 44, 65 – 70.

Bandfield, J.L., Ghent, R.R., Vasavada, A.R., Paige, D.A., Lawrence, S.J., Robinson, M.S., 2011.

Lunar surface rock abundance and regolith fines temperatures derived from LRO Diviner

Radiometer data. J. Geophys. Res., 116, E00H02, doi:10.1029/2011JE003866

Blewett, D., Lucey, P., Hawke, B.R., Jolliff, B., 1997. Clementine images of the lunar sample-return

stations: refinement of FeO and TiO2 mapping techniques. J. Geophys. Res. 102, 16,319 – 16,325.

Bohren, C.F., Huffman, D.R., 2004. Absorption and Scattering of Light by Small Particles. Wiley-

VCH Verlag GmbH & Co (530 pp).

Clark, R.N., 2009. Detection of adsorbed water and hydroxyl on the Moon. Science 326, 562 – 564.

Dollfus, A., 1998. Lunar surface imaging polarimetry: I. Roughness and grain size. Icarus 136, 69 –

103.


33/89

Dollfus, A., Bowell, E., 1971. Polarimetric properties of the lunar surface and interpretation. I.

Telescope observation. Astron. Astrophys. 10, 29 – 53.

Eliason, E.M., McEwen, A.S., Robinson, M.S., Lee, E.M., Becker, T.L., Gaddis, L., Weller, L.A.,

Isbell, C.E., Shinaman, J.R., Duxbury, T., Malaret, E. 1999, Clementine: A global multi-spectral

map of the Moon from the Clementine UVVis imaging instrument. Lunar Planet.Sci. Conf. 30th,

LPI, Houston, USA, pp. 1933 – 1934.

Engelhardt, W., Hurrle, H., Luft, E., 1976. Microimpact-induced changes of textural parameters and

modal composition of the lunar regolith. Lunar Planet. Sci. Conf. 7th, LPI, Houston, USA, pp.

373 – 392.

Gillis, J., Jollif, B., Elphic, R., 2003. A revised algorithm for calculating TiO2 from Clementine

UVVIS data: a synthesis of rock, soil, and remotely sensed TiO2 concentrations. J. Geophys. Res.

108 (E2).

Hapke, B., 1993. Theory of reflectance and emittance spectroscopy, Cambridge Univ. Press, 450 p.

Haruyama, J., Hara S., Hioki, K. et al., 2012. Lunar global digital terrain model dataset produced from

Selene (Kaguya) terrain camera stereo observations. Lunar Planet.Sci. Conf. 43rd, LPI, Houston,

USA, Abstract #1200.

Head, J.W., McCord, T.B., 1978. Imbrian-age highland volcanism on the Moon: The Gruithuisen and

Mairan domes, Science 199, 1433 – 1436.

Heiken, G.H., McKay, D.S., Brown R.W., 1974. Lunar deposits of possible pyroclastic origin.

Geochim. Cosmochim. Acta 38, 1703-1718.


34/89

Kaydash, V.G., Gerasimenko, S.Yu., Shkuratov, Yu.G., Opanasenko, N.V., Velikodskiy, Yu.I.,

Korokhin, V.V., 2009. Photometric function variations observed on the near side of the Moon:

Mapping. Solar System Res., 43(2). 89 – 99.

Kaydash, V., Shkuratov, Y., Videen, G., 2012. Phase-ratio imagery as a tool of lunar remote sensing. J.

Quant. Spectrosc. Radiat. Trans. 113, 2601 – 2607.

Kaydash V., Shkuratov, Y., Videen, G., 2014. Dark halos and rays of young lunar craters: a new insight

into interpretation. Icarus 231, 22 – 33.

Korokhin, V.V., Kaydash, V.G., Shkuratov, Yu.G., Stankevich, D.G., Mall, U., 2008. Prognosis of TiO2

abundance in lunar soil using Clementine and LSCC data: nonlinear approach. Planet. Space Sci.

56, 1063 – 1078.

Korokhin, V.V., Velikodsky, Yu.I., Shalygin, E.V., Shkuratov, Y.G., Kaydash, V.G., Gorden, V., 2014.

Retrieving lunar topography from multispectral LROC images. Planet. Space Sci., 92, 65 – 76,

http://dx.doi.org/10.1016/j.pss.2014.01.008.

Korokhin, V.V., Kaluhina, O.O., Velikodsky, Yu.I., Shkuratov, Yu.G., Kaydash, V.G., Rohachova, L.V.,

Videen, G., 2015. A photometric anomaly in the mare Nubium. Lunar and Planet. Sci. Conf. 46th.,

LPI, Houston, 1343.pdf.

Kreslavsky, M.A., Shkuratov, Yu.G., Velikodsky, Yu.I., Kaydash, V.G., Stankevich, D.G., Pieters, C.M.,

2000. Photometric properties of the lunar surface derived from Clementine observations. Journ.

Geophys. Res. Planets. 105. E8. 20,281 – 20,295.

Li, S., Milliken, R.E., 2014. Quantitative mapping of hydration in lunar pyroclastic deposits: insights

into water from the lunar interior. Lunar Planet. Sci. Conf. 45th. LPI, Houston, Abstract #2012.


35/89

Lin Li, 2011. Quantifying TiO2 abundance of lunar soils: Partial least squares and stepwise multiple

regression analysis for determining causal effect. Journal of Earth Science, 22, 549 – 565.

Lucey, P.G., Taylor, G.I., Malaret, E., 1995. Abundance and distribution of iron on the Moon. Science

268, 1150 – 1153.

Lucey, P., Blewett, D., Hawke, R., 1998. FeO and TiO2 Concentrations in the South Pole-Aitken

Basin: implication for mantle composition and basin formation. J. Geophys. Res. (Planets) 103,

3701 – 3708.

Lucey, P.G., Blewett, D.T., Bradley, L.L., 2000a. Lunar iron and titanium abundance algorithm based

on final processing of Clementine ultraviolet – visible images. J. Geophys. Res. 105, 20,297 –

20,305.

Lucey, P.G., Blewett, D.T., Taylor, G.J., Hawke, B.R., 2000b. Imaging of the lunar surface maturity. J.

Geophys. Res. 105, 20,377 – 20,386.

Lucey, P.G., 2004. Mineral maps of the Moon. Geophys. Res. Lett. 31(8), L08701.

doi:10.1029/2003GL019406.

Malin, M., 1974. Lunar red spots: Possible pre-mare materials. Earth Planet. Sci. Lett., 21, 331 – 341.

Mazarico, E., Rowlands, D.D., Neumann, G.A., Smith, D.E., Torrence, M.H., Lemoine, F.G., Zuber,

M.T., 2012. Orbit determination of the Lunar Reconnaissance Orbiter. J. Geodesy, 86, 193 – 207.

McKay, D., Heiken, G., Basu, A., Blanford, G., 1991. The lunar regolith. In: Heiken, G.H., Vaniman,

D.T., French, B.M. (Eds.), Lunar Sourcebook, 1991. Cambridge University Press, New York NY,

pp. 285-356.


36/89

Morris, R.V., 1976. Surface exposure indices of lunar rocks: A comparative FMR study. Proc. Lunar

Planet. Sci.7, 315 – 335.

Morris, R.V. 1978. The surface exposure (maturity) of lunar soils: Some concepts and Is/FeO

compilation. Proc. Lunar Planet. Sci. 9, 2287 – 2297.

Paige, D.A., Foote, M.C., Greenhagen, B.T., et al., 2010. The Lunar Reconnaissance Orbiter Diviner

Lunar Radiometer Experiment. Space Sci. Rev., 150, 1-4, 125-160.

Pieters, C., 1978. A summary of spectral reflectance data. Lunar Planet. Sci. Conf. 9th, LPI Houston,

USA, pp. 2825 – 2849.

Pieters, C.M., 1999. The Moon as a spectral calibration standard enabled by lunar samples: the

Clementine example. In: Proceedings of the Workshop on New Views of the Moon II :

Understanding the Moon through the Integration of Diverse Datasets, Abstract no. 8025, LPI

Houston, USA.

Pieters, C.M., Stankevich, D.G., Shkuratov, Y.G., Taylor, L.A., 2002. Statistical analysis of the links

between lunar mare soil mineralogy, chemistry and reflectance spectra. Icarus 155, 285– 298.

Pieters, C.M., Shkuratov, Y.G., Kaydash, V.G., Stankevich, D.G., Taylor, L., 2006. Lunar soil

characterization consortium analyses: pyroxene and maturity estimates derived from Clementine

data. Icarus 184, 83 – 101.

Pieters, C.M., 19 colleagues, 2009a. The Moon Mineralogy Mapper (M3) on Chandrayaan-1. Curr.

Sci. 96 (4), 500 – 505.

Pieters, C.M. and the M3 Science Team, 2009b. Character and spatial distribution of OH/H2O on the

surface of the Moon seen by M3 on Chandrayaan-1. Science 326, 568 – 572.


37/89

Robinson, M.S. et al., 2010. Lunar Reconnaissance Orbiter (LROC) Instrument Overview, Space Sci.

Rev., 150, 81 – 124.

Rode, O.D., Ivanov, A.V., Nazarov, M.A. et al., 1979. Atlas of photomicrographs of the surface

structures of lunar regolith particles. Academia. Prague. p. 20.

Scholten, F., Oberst, J., Matz, K.-D., Roatsch, T., Wählisch, M., Speyerer, E.J., Robinson, M.S., 2012.

GLD100: The near-global lunar 100 m raster DTM from LROC WAC stereo image data. J.

Geophys. Res., 117, doi:10.1029/2011JE003926.

Shalygin, E.V., Velikodsky, Yu.I., Korokhin, V.V., 2003. Formulas of the perspective cartographic

projection for planets and asteroids of arbitrary shape. Lunar and Planet. Sci. 34-rd. Abstract

#1946. LPI Houston.

Shkuratov, Y.G., 1981. Connection between the albedo and polarization properties of the Moon.

Fresnel component of reflected light. Soviet Astronomy, 25, 490 – 494.

Shkuratov, Y.G., Basilevsky, A.T., 1981. An attempt at mapping of parameter of surface microporosity

of lunar regolith: Correlation between albedo and polarization properties of the Moon. Lunar

Planet. Sci. XII, pp. 981 – 983. LPI Houston.

Shkuratov, Yu.G., Helfenstein, P., 2001. The opposition effect and the quasi-fractal structure of

regolith: Theory. Icarus 152, 96 – 116.

Shkuratov, Y., Opanasenko, N., 1992. Polarimetric and photometric properties of the Moon: Telescope

observation and laboratory simulation. 2. The positive polarization. Icarus 99, 468 – 484.

Shkuratov, Y., Helfenstein, P., 2001. The opposition effect and the quasi-fractal structure of regolith:

Theory. Icarus 152, 96 – 116.


38/89

Shkuratov, Y., Starukhina, L., Kreslavsky, M., Opanasenko, N., Stankevich, D., Shevchenko, V., 1994.

Principle of undulatory invariance in photometry of atmosphereless celestial bodies. Icarus 109,

168 – 190.

Shkuratov, Y.G., Kaydash, V.G., Opanasenko, N.V., 1999. Iron and titanium abundance and maturity

degree distribution on lunar nearside. Icarus 137. 222 – 234.

Shkuratov, Y.G., Kaydash, V.G., Kreslavsky, M.A., Opanasenko, N.V., 2001. Absolute calibration of

the Clementine UVVIS Data: Comparison with ground-based observation of the Moon. Solar

System Res. 35 (1), 29 – 34.

Shkuratov, Y.G., Stankevich, D., Kaydash, V., Omelchenko, V., Pieters, C., Pinet, C., Chevrel, S.,

Daydou, Y., Foing, B., Sodnik, Z., Josset, J.-L., Taylor, L., Shevchenko, V., 2003a. Composition

of the lunar surface as will be seen from SMART-1: a simulation using Clementine data. J.

Geophys. Res. 108 (E4). 1-1 – 1-12.

Shkuratov, Y.G., Pieters, C.M., Omelchenko, V.G., Stankevich, D.G., Kaydash, V., Taylor, L., 2003b.

Estimates of the lunar surface composition with Clementine images and LSCC data. Lunar

Planet. Sci., (34th Abstract #1258. LPI Houston).

Shkuratov, Y., Kaydash, V., Stankevich, D., Pinet, P., Chevrel, S., Daydou, Y., 2005a. Derivation of

elemental abundance maps at intermediate resolution from optical interpolation of Lunar

Prospector Gamma-Ray spectrometer data. Planet. Space Sci. 53, 1287 – 1301.

Shkuratov, Yu.G., Stankevich, D.G., Petrov, D.V., Pinet, P.C., Cord, A.M., Daydou, Y.H., 2005b.

Interpreting photometry of regolith-like surfaces with different topographies: shadowing and

multiple scatter. Icarus 173, 3 – 15.


39/89

Shkuratov, Y.G., Kaydash, V.G., Starukhina, L.V., Pieters, C., 2007. Lunar surface agglutinates:

mapping composition anоmalies. Solar System Res., 41, 177 – 185.

Shkuratov, Y., Kaydash, V., Gerasimenko, S., Opanasenko, N., Velikodsky, Yu., Korokhin, V., Videen,

G., Pieters, C., 2010. Probable swirls detected as photometric anomalies in Oceanus Procellarum.

Icarus 208, 20 – 30. doi:10.1016/j.icarus.2010.01.028.

Shkuratov, Y., Kaydash, V., Korokhin, V., Velokodsky, Y., Opanasenko N., Videen, G., 2011. Optical

measurements of the Moon as a tool to study its surface. Planet. Space Sci. 59, 1326 – 1371.

Shkuratov, Y., Kaydash, V., Videen, G., 2012a. The lunar crater Giordano Bruno as seen with optical

roughness imagery. Icarus 218, 525 – 533.

Shkuratov, Y., Kaydash, V., Petrov, D., Korokhin, V., Stankevich, D., Videen, G., 2012b. A critical

assessment of the Hapke photometric model. Journ. Quant. Spectrosc. Rad. Transfer 113(18),

2431 – 2456.

Shkuratov, Y., Kaydash, V., Sysolyatina, X., Razim, A., Videen, G., 2013. Lunar Surface Traces of

Engine Jets of Soviet Sample Return Probes: The Enigma of the Luna-23 and Luna-24 Landing

Sites. Planet. Space Sci. 75. 28 – 36.

Shkuratov, Y., Opanasenko, N., Korokhin, V., Videen, G., 2015. The Moon. In: Polarimetry of Stars

and Planetary Systems / Eds.: L. Kolokolova, J. Hough, and A.-C. Levasseur-Regourd.

Cambridge University Press. United Kingdom, pp. 303 – 319.

Smith, D.E., Zuber, M.T., Neumann, G.A., et al., 2010. Initial observations from the Lunar Orbiter

Laser Altimeter (LOLA), Geophys. Res. Letters, 37, L18204, doi:10.1029/2010GL043751.

Song E., Bandfield, J.L., Lucey, P.G., Greenhagen, B.T., Paige, D.A., 2013. Bulk mineralogy of lunar


40/89

crater central peaks via thermal infrared spectra from the Diviner Lunar Radiometer: A study of

the Moon's crustal composition at depth. J. Geophys. Res., 118, 689 – 707, doi: 10.1002/jgre.20065

Stankevich, D., Shkuratov, Y., 2004. Monte Carlo ray-tracing simulation of light scattering in

particulate media with optically contrast structure. J. Quant. Spectrosc. Radiat. Transf. 87, 289 –

296.

Starukhina, L.V., Shkuratov, Y.G., 2001. A theoretical model of lunar optical maturation: effects of

submicroscopic reduced iron and particle size variations. Icarus 152, 275 – 281.

Taylor, L.A., Pieters, C.M., Morris, R.V., Keller, L.P., McKay, D.S., Parchen, A., Wentworth, S.J.,

1999. Integration of the chemical and mineralogical characteristics of lunar soils with reflectance

spectroscopy. Lunar Planet. Sci. Conf. 30th, Abstract no. 1859, LPI Houston, USA.

Taylor, L.A., Pieters, C.M., Morris, R.V., Keller, L.P., McKay, D.S., 2001. Lunar mare soils: Space

weathering and the major effects of surface correlated nanophase Fe. J. Geophys. Res. 106,

27,985 – 28,000.

Velikodsky, Y.I., Opanasenko, N.V., Akimov, L.A., Korokhin, V.V., Shkuratov, Y.G., Kaydash, V.G.,

Videen, G., Ehgamberdiev, Sh.A., Berdalieva, N.E. 2011. New Earth-based absolute photometry

of the Moon. Icarus, 214, 30 – 45 (10.1016/j.icarus.2011.04.021).


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Figure captions

Figure 1. A fragment of a color map constructed using WAC data (colors R = 689 nm, G = 415 nm,

and B = 321 nm are matched with the mosaic fragment). The red spot (the photometric anomaly) in

the centrum of the frame is clearly seen. Images are extracted from mosaics avalable in ACT-REACT-

QuickMap (http://target.lroc.asu.edu/q3/) provided by Applied Coherent Technology Corporation.

Figure 2. Fragments of the WAC mosaic: (a) LROC WAC RDR mosaics

(http://wms.lroc.asu.edu/lroc/view_rdr/WAC_GLOBAL), (b) image synthesized using WAC images

and Kaguya DTM (Haruyama et al., 2012). The seam is clearly visible only in the panel (a).

Figure 3. Maps of the following photometric parameters: (a) the normal albedo A0 at 689 nm (the

arrow show a dark halo crater), (b) and (c) are distributions of phase curve slope η at 415 nm and 689

nm, respectively. The images (b) and (c), being presented in the common scale, manifestly

demonstrate that the phase slope at 415 nm is higher than at 689 nm.

Figure 4. (a) the distribution of the number of images used for the scene and (b) the relative mean-

errors of approximation of measured phase curve by Eq. (4) at λ = 415 nm.

Figure 5. Typical phase curves of 1-pixel sites located in the anomaly, surrounding, and highland

areas. The anomaly begins to reveal itself at phase angles < 40º.

Figure 6. (a) distribution of the color-ratios A0(689 nm) / A0(415 nm) and (b) η(689 nm) / η(415 nm).

The anomaly is spectrally neutral, although the phase slope depends on albedo (cf. Fig. 5).

Figure 7. Maps of the coefficients of linear regressions (10) and (11). For A0 the panels (a) and (b)

present k 0 and k 1, respectivly; for η the panels (c) and (d) present the same for l 0 and l 1.


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Figure 8. Results of application of Lucey's method to WAC data. Panels (a) and (b) show the maps of

abundance of TiO2 and the titanium optical maturity OMAT Ti. Panels (c) and (d) show fragments of the

maps of TiO2 abundance determined, respectively, using WAC and Clementine data, in order to show

difference in quality and resolution.

Figure 9. Results of application of Lucey's method to Clementine UVVIS data. The panels (a) and (b)

present the maps of abundance of FeO and the iron optical maturity OMAT Fe.

Figure 10. The maps constructed using LSCC and Clementine data with the ANN regression

(Korokhin et al., 2009) for (a) the maturity degree ( I s/FeO) and (b) the content of volcanic glasses.

Figure 11. The main geological units in the region.

Figure 12. The map of rock abundance from the LRO instrument Diviner. Scale bar shows rock areal

fraction (0-1). Maximal abundances (~0.35) in the scene are observed for crater slopes.

Figure 13. Polarimetric images obtained with Earth-based observations. Panels (a) and (b) show

distributions of the parameter a

eq maxb A P a

eq maxb A P at Aeq = 93º and Aeq = 10º, respectively.

Figure 14. Images built using Chandrayaan-1 M3 data: (a) the image M3G20090110T154845 (the

average α = 34º); (b) the phase ratio M3G20090206T225721 (the average α = 54º) to

M3G20090110T154845 calculated at λ = 0.75 m; (c) the same as in (b), but at λ = 2.9 m; (d) the

color-ratio of the phase-ratios [ A0.75 m(54°)/ A0.75 m(34°)]/[ A0.29 m(54°)/R 0.29 m(34°)]. The anomaly is

visible only at λ = 0.75 m.

Figure 15. Contour of the anomaly on the topography maps. The panel (a) is a fragment of the

elevation map GLD100 (Scholten et al., 2012) and the panel (b) is shading map calculated from

GLD100. The resolution of the images is near 200 m.


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Figure 16. The panel (a) is a fragment of Fig. 3b. The panels (b) and (c) are the same as in Fig. 15, but

with higher resolution. Kaguya elevation data were used for the south portion of the photometric

anomaly. The resolution of the images is near 50 m.

Figure 17. A depression (crater) that can be a pyroclastic source inside the anomaly. (a) our A0-image

(see also Fig. 3a); the dark halo is seen around (arrow). (b) a fragment of LROC NAC mosaic; the

lines emphasize possible tectonic deformations of the depression.

Figure 18. Distribution of spectral parameters for M3 image M3G20090110T154845. (a) A750 nm, (b)

color ratio ( A2896.3 + A2936.3 + A2976.2)/( A2776.6 + A2816.5 + A2856.4), and (c) selected spectra for locations

SP1, SP2, and SP3 shown with circles.

Figure 19. A model of the lava flooding of the anomaly area and surroundings.

Figure 20. An astronaut footprint in lunar soil nubbins and small pieces of rocks shown by arrows on

the mature regol

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