Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1

Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1

Journal Pre-proof Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1 P.R. Kumaresan, J. Saravanavel, K. ...

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Journal Pre-proof Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1 P.R. Kumaresan, J. Saravanavel, K. Palanivel PII:

S0032-0633(18)30279-4

DOI:

https://doi.org/10.1016/j.pss.2019.104817

Reference:

PSS 104817

To appear in:

Planetary and Space Science

Received Date: 2 August 2018 Revised Date:

26 August 2019

Accepted Date: 29 November 2019

Please cite this article as: Kumaresan, P.R., Saravanavel, J., Palanivel, K., Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1, Planetary and Space Science (2020), doi: https://doi.org/10.1016/j.pss.2019.104817. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Lithological Mapping of Eratosthenes Crater Region using Moon

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Mineralogy Mapper of Chandrayaan-1

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Kumaresan P R1 Saravanavel J2 Palanivel K3

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Campus, Tiruchirappalli - 620 023 Tamil Nadu, India.

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Email: [email protected], ORCIDiD: 0000-0001-9138-1993

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2

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Campus, Tiruchirappalli - 620 023 Tamil Nadu, India.

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Email: [email protected], ORCIDiD:0000-0002-3233-8730

Research Scholar, Departmentof Remote Sensing, Bharathidasan University -Khajamalai

Assistant Professor, Departmentof Remote Sensing, Bharathidasan University -Khajamalai

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3

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Tiruchirappalli - 620 023 Tamil Nadu, India.

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Email: [email protected].

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Abstract

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Selenological exploration (lunar geology) involves mapping and delineation of different rock

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types and minerals present in the Moon. Various lunar missions with multi-spectral sensors

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provided preliminary information about the lithological and mineralogical occurrences on the

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Moon. The Chandrayaan-1 Moon Mineralogy Mapper (M3), provided hyperspectral data

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having high spatial resolution of 140 m and 85 continuous spectral bands of Electro Magnetic

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Spectrum from 430 - 3000 nm. This data utilized for identification of distinctive absorption

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bands near 1000, 1250, 2000 and 2500 nm regions. These absorption features are playing

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vital role in mapping of various minerals and rock types on the lunar surface with higher

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precision. The present study aimed to map the compositional diversity in and around the

Professor, Departmentof Remote Sensing, Bharathidasan University -Khajamalai Campus,

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Eratosthenes crater region of the lunar surface using M3 data of Chandrayaan-1. The initial

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studies carried out using Clementine ultraviolet and visible (UVVIS) warped color-ratio

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mineral map and M3 data based band shape, band depth and band ratio algorithms have led to

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understand the lithological diversity of the study area. These preliminary studies revealed that

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the Eratosthenes crater region contains mare basalt, anorthosite, highland soil, pyroxene,

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Olivine, etc. At the next stage, the detailed compositional variability of the study area was

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analyzed using Spectral Information Divergence (SID) algorithm using various thresholds.

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Finally, The SID analysis showed that the spatial distribution of olivine, pigeonite, augite and

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norites present within the study region. The present study further revealed that the SID could

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be an effective detection tool rather than as a classification tool.

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Keywords: Moon Mineralogy Mapper, Lunar lithology, Eratosthenes crater, Mineralogical

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discrimination.

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1.0 INTRODUCTION

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Hyperspectral remote sensing techniques have high potentials to derive significant

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information about the lithological and mineralogical composition of the earth and planetary

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surface by analyzing the subtle changes in spectral curve and its absorption features. The

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mineral composition, crystal structure, grain size, space weathering, regolith cover, etc.,

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controls the shape, strength and absorption features of the spectra (Burnsand Roger, 1993).

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Therefore, distinguishing and delineation of the composition of lunar minerals using

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hyperspectral data is a challenging task. Distinct absorption features near 1000, 1250 and

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2000 nm in visible and near-infrared spectral region can identify the lunar minerals.

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Chandrayaan-1 Moon Mineralogy Mapper (M3) is an imaging spectrometer that provides data

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on 85 continuous and narrow spectral regions in between 430 and 3000 nm with 140 m

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spatial resolution. This spectral region is very much useful in identification of various lunar

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minerals and lithological units such as olivine, pyroxenes, plagioclase, basalt, anorthosite,

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norite, etc. A lot of information on the mineralogical and lithological compositional diversity

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of lunar surface have been brought out using the Chandrayaan-1 Moon Mineralogy Mapper

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(M3) data by several researchers. For example, Mustard et al., 2011 brought out the central

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peak of the Aristarchus crater is consist of plagioclase and the rim part of the crater enriched

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by Olivine. Besse et al., 2011 examined the Marius hill volcanic region and distinguished the

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olivine and pyroxene rich basaltic units in the plateau region. Klima et al., 2011a identified

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prominent distribution of low‐Ca pyroxene in South Pole–Aitken Basin (SPA), north and

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south of Mare Frigoris region. Bharti et al., 2014 have brought out the compositional

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variation of near and far-side transition zone of the lunar surface. Varatharajan et al., 2014

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assessed the temporal and spatial heterogeneity of Mare basalts in western nearside,

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Moscoviense, and Orientale basin.

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For the present research work, a study area covering 15, 060 sq. km, extending from

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17º0' N-12º0' N to 13º0' W-11º0' E, has been selected in the equatorial region of near side of

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the Moon (Fig. 1a). The Eratosthenes crater and its related impact materials are covering

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most part of the study area. This is a deep impact crater named after the ancient Greek

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astronomer Eratosthenes of Cyrene. The Eratosthenes crater has a well defined rim with a

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central peak and classified under complex craters (Fig. 1b). This crater was formed

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approximately about 3.2 billion years ago. The crater region is falling in between the mare

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imbrium to the north and the sinus aestuum to the south. The northeastern part of this crater is

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the western terminus of the montes apenninus mountain range. This kind of complex, large

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and deep impact crater usually have mineralogical heterogeneities in laterally as well as

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vertically (Wilhelms and Pieters, 1985; Pieters, 1986 and Pinet et al. 1993). Further,

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Eratosthenes crater has not been studied in detail using the high-resolution spatial and

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spectral M3 data, and hence the present study has been undertaken.

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Figure 1: (a) Key map showing the location of the Eratosthenes crater region and study area. (b) Major morphological features of the Eratosthenes crater region prepared using on LROC WAC and LOLA DEM data.

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2.0 MORPHOLOGY OF ERATOSTHENES CRATER REGION

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Firstly, the morphology of the Eratosthenes crater visualized based on the terrain character

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and topographic variations using Wide Angle Camera (WAC) and Lunar Orbiter Laser

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Altimeter (LOLA) Digital Elevation Model (DEM) of Lunar Reconnaissance Orbiter (LRO)

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(Fig. 2a). The spatial resolution of WAC is 100 m and LOLA DEM is 118m (Robinson et al.,

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2010; Sato et al., 2014; Scholten et al., 2012).The 3D GIS based visualized WAC image

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wrapped over LOLA DEM of Eratosthenes crater shown in Figure 2b. The diameter and

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depth of the crater is 65.35 km and 6.5 km. Therefore, this crater considered as a deep impact

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crater, which has well-defined circular rim (1), terraced inner wall (2), central peak (3),

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irregular floor (4) and outer ejecta blanket (5, Fig. 2). The E-W (A-Aˈ) and N-S (B-Bˈ)

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profiles drawn using the LOLA DEM clearly shows the terraced inner walls (2) central peak

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(3) and irregular floor (4, Fig. 1b, 2c & 2d). It lacks a ray system of its own, but overlaid by

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rays from the prominent crater Copernicus located to 220 km west of Eratosthenes crater. It

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provides evidence that Eratosthenes carter formed before the Copernicus crater and after the

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major imbrium basin.

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Figure 2: (a) Color coded LOLA DEM (b) 3D Visualization of the Eratosthenes crater region. (c) East-West topographic profile A-Aˈ. (d) North-South topographic profile B-Bˈ.

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3.0 MATERIALS AND METHODOLOGY

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3.1 Data

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For the present study, three numbers of M3 datasets such as M3G20090206T105850,

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M3G20090206T145451 and M3G20090206T124510 downloaded from the Lunar Orbital

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Data Explorer (ODE). These three datasets are Level-2 global mode data products, which are

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pixel located, thermally and photometry corrected reflectance data captured during the OP2C

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optical period with 140m spatial resolution.The meta data consists of location or LOC file

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comprising three bands such as Longitude (Degrees East 0-360), Latitude and Radius of the

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lunar surface at pixel level. Geographic Lookup Table (GLT) file developed from the latitude

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and longitude bands and geo-referenced using ENVI classic image processing software. The

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geo-referenced datasets projected onto Moon sinusoidal projection that is more suitable for

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equatorial regions preserving the shape and distance. The geo-referenced datasets mosaicked

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to have a single image of the study area. As the mosaicked image has the photometric error

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with adjacent tiles, it has been rectified using cross track illumination correction process

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(Besse et al., 2013b).

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3.2 Methods

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Preliminary studies carried out by interpretation of Clementine UVVIS warped colour ratio

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mosaic image and colour composite image developed using band shape algorithms and

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Integrated Band Depth analysis (IBD) from M3 data. Further, the detailed mineralogical and

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lithological units of the study area mapped using the Spectral Information Divergence (SID)

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algorithm using endmembers collected from the image. These end members selected from the

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M3 data through a series of analysis such as dimensionality reduction through MNF

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(Minimum Noise Fraction), PPI (Pure Pixel Index) and identification of extreme spectrally

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pure end members in n-dimensional visualizer. Those endmembers named after spectral

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matching with relab spectral library. The M3 data classified based on end members using SID

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algorithm. The lithological and compositional diversity map of the study area was prepared

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by integrating the above outputs. Flowchart showing the methodology adopted in the present

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study (Fig. 3).

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Figure 3: Schematic methodology applied in this study.

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3.2.1 Clementine UVVIS warped colour ratio mosaic

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Clementine multispectral data provide a first time global view of composition and

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mineralogical diversity of the lunar surface (Tompkins and Pieters 1999; Pinet at al. 2000;

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Chevrel et al. 2009; Yan et al. 2010). Clementine mission acquired images of lunar surface

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using ultraviolet, visible (UVVIS) and near infrared (NIR) spectral region (Nozette et al.,

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1994; McEwen and Robinson, 1997) with a spatial resolution of 100-200 meters per pixel.

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Global Clementine UVVIS warped colour-ratio mosaic mineral map, available at USGS

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website (https://astrogeology.usgs.gov). This colour composite image generated using band

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ratio technique as 750/415 nm bands for the red channel, 415/750 nm for the blue channel,

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and 750/1000 nm ratio to the green channel (Lucey et al. 2000). The red channel represents

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areas that are low in titanium or high in glass content, the green channel is sensitive to the

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amount of iron in the surface and the blue channel reflects the surfaces with high titanium

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(Fig. 4a).

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3.2.2 Band shape algorithms

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The band shape algorithms such as band strength (bs), band curvature (bc) and band tilt (bt)

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are analyzed at particular wavelengths to identify the diversity and availability of minerals on

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the lunar surface (Borst et al., 2012; Sivakumar and Neelakantan 2015;Sivakumar et al.,

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2017). The bs, bc and bt are calculated with band ratios of 1009.95/750.44 nm, 750.44/910.14

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+ 1009.95/910.14 nm and 910.14/1009.95 nm respectively (Table-1). The colour composite

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image generated using bs, bc and bt revealed that the blue colour is due to higher band

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strength (bs) values due to low mafic and weathered anorthosites of high land region. Higher

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value in band curvature (bc) indicates the presence of low calcium pyroxene which is shown

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in red to orange in colour composite image and medium band tilt (bt) value shown in yellow

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indicates the presence mafic minerals.

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Table 1: Band shape algorithms and their representation in colour composite. Name of Band shape algorithms

Algorithms

Band curvature

(750.44+ 1009.95) /910.14 nm

Channel allotted in Colour Composite Image Red

Band tilt

910.14/1009.95 nm

Green

Band strength

1009.95/750.44 nm

Blue

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3.2.2.1 Band curvature:

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Band curvature (bc) is calculated by applying (750.44+ 1009.95) /910.14 nm formula. This

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captures variations in center of the 1000 nm absorption feature exhibited by Fe2+ ion

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(Tompkins and Pieters, 1999; Pieters et al., 2001; and Bhattachariya et al. 2011). Therefore, it

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differentiates low-Ca pyroxene (LCP) bearing noritic rocks and high-Ca pyroxene bearing

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gabbroic rocks. This parameter value will decreases from LCP to HCP and further lower

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values to olivine rich rocks. While colour composite generation, red channel is assigned to

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‘bc’ so LCP bearing rocks will appears red in colour.

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3.2.2.2 Band tilt:

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Band tilt calculated by dividing 910.14/1009.95 nm wavelength bands. Mafic minerals have

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strong 1000 nm absorption feature like high-Ca pyroxene and olivine. This parameter helps in

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sensing towards strong Fe2+ absorption (Dhingra, 2008). Green channel allotted in colour

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composite generation for this parameter so areas with high-Ca pyroxene and/or olivine

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appears green in colour. Therefore, it is difficult to differentiate high-Ca pyroxene and

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olivine.

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3.2.2.3 Band Strength:

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Dividing 1009.95/750.44 nm wavelength bands, band strength is calculated. Actually, it

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implemented to know the 1000 nm absorption feature and compositional variations across the

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lunar surface (Lucey et al. 1995, Isaacson and Pieters, 2009). Matured soils and anorthosites

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are lacks in 1000 nm absorption feature and therefore have higher values according to this

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formulation compared to unweathered regions that are rich in mafic minerals with strong

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1000 nm absorption feature. This parameter assigned to blue channel and therefore

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anorthosites appears in deep blue and highly matured soils of highland region shows

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significant blue component in colour composite image.

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3.2.3 Integrated Band Depth Analysis

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The Integrated Band Depth (IBD) analysis is portraying the band depths for 1000 and 2000

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nm absorption features in order to obtain the spectral variations related to mafic and felsic

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minerals, surface maturity and weathering due to exposure of space (Mustard et al., 2005;

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Mustard et al., 2011). The following equations used to bring out the IBD for 1000 and 2000

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nm absorption features:



  1000 = 1 −  "

  2000 = 1 − 

(789 + 20 )  (789 + 20 )

(1658 + 40 )  (1658 + 40 )

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Where, R = reflectance at a particular wavelength and Rc = continuum removed reflectance

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The 1578.86 nm wavelength region is free from lunar mafic silicate absorption therefore the

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IBD of 1000 and 2000 nm band depths together with band reflectance at 1578.86 nm is used

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to produce the RGB colour composite image that implies compositional characteristic and

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surface lithological variations. For estimation of IBD at 1000 nm and 2000 nm, the

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continuum offsets selected from 699 nm to 1578 nm and from 1578 nm to 2537 nm. The

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colour composite image generated by assigning red channel to IBD-1, green to IBD-2 and

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blue to the 1578.86 nm reflectance band. This IBD colour composite image was used to

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identification of various lithological and mineralogical units of lunar surface (Cheek et al.,

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2011; Klima et al., 2011b; Besse et al., 2013a).

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3.2.4 Optical Maturity

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Optical maturity (OMAT) is the assessment of exposure of the lunar surface to space

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weathering. Prolonged exposure of lunar surface to space leads to increase in maturity of the

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soil and regolith. OMAT depends on geologic setting of the particular region, size of the

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crater and in addition to age of the geologic unit (Lucey et al., 2000a& b; Ajith Kumar and

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Shashi Kumar, 2014; Sun et al., 2016). Optical maturity image of the study area was prepared

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using band ratio of 940 nm and 740 nm (Bharti et al., 2014). Higher value of OMAT

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represents unweathered surface whereas lower value represents weathered surface.

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3.2.5 Endmember Extraction

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Endmember extraction is one of the fundamental tasks in hyperspectral data analysis. An

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endmember is an ideal and pure signature for an individual class. The dimensionality of the

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data reduced using the Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI)

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algorithm (Boardman, 1995) to identify the compositionally unique endmember spectra from

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the

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(http://www.planetary.brown.edu/relabdocs/relab_disclaimer.htm)

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mineralogical and lithological characters of the endmembers using spectral analyst tool of

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ENVI image processing software.

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3.2.6 Spectral Information Divergence (SID)

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The SID algorithm classifies the pixels based on the similarity between the reference and

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unknown spectra. SID models the band-to-band variability due to uncertainty caused by

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randomness. The SID classification derived based on the divergence and calculates the

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probabilistic behaviors

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der Meer, 2006; Chang, 2000). Smaller the divergence, the pixels are likely similar. Pixels

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with a measurement greater than the specified maximum divergence threshold are not

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classified.

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examines the geometrical

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Further, SID is more effective in capturing the subtle spectral variability than SAM (Naresh

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Kumar et al., 2011). In the present study, image classified using different thresholds to

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identify the optimum threshold for classification of the various mineralogical units. In order

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to find robustness of classified images of different thresholds, mean spectra collected for

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various lithological and mineralogical units where compared with the pure endmembers

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spectra.

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M3

data.

When

These

endmembers

between

compared

the

with

compared

reference

spectral

characters between

two

with

and

angle

relab

spectral

and

unknown

mapper

identified

spectra

(SAM),

spectral signatures or

the

library the

(Van

SID

pixel vectors.

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4.0 RESULTS AND DISCUSSION

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Clementine UVVIS Colour ratio image depicts the broad mineralogical diversity of the study

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region. In Eratosthenes Crater region, red colour indicates accumulation of low Titanium soil

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and glassy agglutinates. The yellow-green area in the mare is effect of concentration of mafic

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minerals. The central peak appears in yellow colour indicates presence of mafic may be

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olivine / pyroxene. The blue colour indicates relatively high titanium rich soils (Greeley et al.

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1993; Pieters et al. 1994;Lucey et al. 2000; Kramer et al. 2011) (Fig. 4a).

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The RGB colour composite developed from the band curvature (bc), band tilt (bt) and band

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strength (bs) of Eratosthenes Crater region using M3 data is shown in Figure 4b. The higher

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values of band curvature (bc) indicates presence of low calcium pyroxene (LCP- pyroxene

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with high Fe/Mg) and suggest possibility of noritic rocks in the Lunar surface and same

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appears as orange to red colour (Borst et al., 2012). The most of the newly formed craters in

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highland region are with LCP and noritic in composition in the study area (1, Fig. 4b). Higher

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band tilt value in composite image generally appears green to yellow colour indicates mafic

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rich rocks (3, Fig. 4b). In Eratosthenes crater region, central peak enriched with olivine

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appears quetzal green in nature (2, Fig. 4b). The bluish to purple colour indicates the higher

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band strength probably low mafic and weathered anorthosites and soils derived from the

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highland region (Tompkins et al., 1997; Sivakumar et al., 2017).

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The red colour in the IBD colour composite image indicates strong absorption in 1000 nm

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and no absorption in 2000 nm suggest presence of olivine. The central peak of the

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Eratosthenes crater exhibits presence of olivine (4, Fig. 4c). Green colour in IBD composite

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image is representing strong absorption in 2000 nm. The yellowish green colour in and

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around crater region revealed the mare / basaltic regions. The yellow colour associated with

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the fresh impact craters implies that the fresh craters rich in pyroxene and show the strong

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absorption in both 1000 nm and 2000 nm (5, Fig. 4c). Presence of yellow colour near central

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peak and eastern outer rim of the crater in IBD based colour composite image are not due to

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pyroxene rich materials, it is because of shadow effects (6, Fig. 4c & Fig. 5).

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Figure 4: (a) The Clementine UVVIS warped colour-ratio mineral map subset for Eratosthenes crater region. (b) M3 Colour Composite prepared using Band shape algorithms techniques - 1) Norite, 2) Olivine, 3) High calcium pyroxene; (c) Colour Composite image of Integrated Band Depth analysis technique – 4) Olivine, 5) Pyroxene, 6) Yellow colour in central peak and eastern rim of the crater is due to shadow effect; (d) Optical Maturity (OMAT), higher value represents unweathered surface (7).

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Figure 5 clearly explaining the presence of yellow colour in the shadow region of the study

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area. A small crater in the northwestern corner of the study area shows yellow colour in its

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eastern part due to shadow effects (b, Fig. 5). Similarly, near central peak and eastern rim of

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the Eratosthenes crater also have yellow colour due to shadow of central peak and rim (c, d,

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Fig. 5). Further, Shadow effects have not generated such type of errors in SID classified

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images. For example, figure 8 showing presence of olivine only in the central peak region

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and there is no traces of pyroxene near central peak area. The weaker or no absorption along

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1000 nm and 2000 nm represented as blue in colour indicate the presence of low mafic /

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anorthositic / high land soil of the study area. Most of montes apenninus located to the

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northeastern side of Eratosthenes crater is highland region and made up of anorthositic

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composition or rich in plagioclase feldspar.

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Figure 5: (a) M3 reflectance image of band 1578.86 nm region; IBD colour composite image showing yellow colour due to shadow effects in (b), (c) and (d).

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The optical maturity (OMAT) image, generated from the band ratio of 940 nm and 740 nm,

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shows the degree of maturity and exposure stage of the lunar surface. Higher OMAT values

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represent the un-weathered surface and lower OMAT values indicate the weathered surface.

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The rim and fresh impact craters (7, Fig. 4d) are exhibit the un-weathered surface.

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At the next stage, M3 data classified based on endmembers for mapping of different types of

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lithological and mineralogical units. The endmembers or pure pixels identified based on

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extreme spectral responses by rotating different axes in n-dimensional visualizer. Compare

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the pure pixels or endmembers spectra with the relab spectral library using spectral analyst

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module of ENVI image processing software to identify and naming (Fig. 6). Based on the

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strong absorption in 1000 nm and no or minute absorption in the 2000 nm, the mineral

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olivine was identified in the study area. The Figure 6a vividly displays perfect match in

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between the image spectra and relab spectra of mineral olivine. The analysis between image

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spectra and relab spectra suggests the presence of both orthopyroxene and clinopyroxene in

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the Eratosthenes crater region (Fig. 6). The mineral pyroxenes generally have the

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distinguishing absorption features near the 1000 nm and 2000 nm. The position and shift on

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these two major absorption features based on the percentage of concentration of calcium and

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magnesium. The ortho and clino pyroxenes differentiated through the position of peak of

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absorption along the 1000 nm and 2000 nm.

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absorption features are fall below the 1000 nm and 2000 nm and nearly along the 900 nm and

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1900 nm (Fig. 6b). Further, the orthopyroxene indicates presence of the noritic rocks in the

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study area. The clinopyroxene pigeonite and clinopyroxene augite were demarcated based on

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the subtle changes in the position of absorption features along the 1000 nm and 2000 nm. The

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clinopyroxene pigeonite was identified by falling of these two absorption features along the

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950 nm and 2200 nm (Fig. 6c). Similarly, clinopyroxene augite was recognized by the typical

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1000 nm and 2200-2300 nm absorption features (Fig. 6d).The spectral matching analyses

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revealed that the study area is vested with diverse lithological and mineralogical units such

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olivine, orthopyroxene (norite), clinopyroxene pigeonite and clinopyroxene augite (Fig. 6a-

For the orthopyroxene, these two major

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d). In order to know the position of various endmembers in principal components space,

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scatter plots prepared in-between the various MNF bands. For example, scatter plot between

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the MNF first and second order shows in Figure 7.

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Figure 6: Plots represent the image derived endmembers with their best matching RELAB Library spectra.

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Figure 7: Scatter plot between MNF1 and MNF2 showing the position of end members.

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The Spectral Information Divergence (SID) algorithm used to identify the spatial distribution

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of the various mineralogical units in the investigative region. It classifies the pixels based on

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the similarity between the reference (named pure spectra) and unknown (pixel) spectra

315

(Yousefi et al., 2016). Smaller the divergence, more likely the pixels are similar. The image

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classified using various SID thresholds such as 0.1, 0.2, 0.3, 0.4 and 0.5. The thresholding is

317

a trial and error approach that may result in over or under estimation (mapping) of a target

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class. When the image was classified using various thresholds, the distribution of

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mineralogical units increases with varying accuracy (Fig. 8-11). As the lunar surface is a

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mixture of lunar soil (regolith), glass, pyroclastic materials, various minerals and rock types,

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identified the optimum thresholds for various mineralogical units based on the spectral

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similarity between the endmember spectra and mean spectra of different threshold classes.

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Generally, lunar crusts with impact craters have spectral mixture of different composition and

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mineralogy (Pinet et al. 1993; Tompkins et al. 1994; Mustard 1998; Chevrel et al. 2009).

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To determine the optimum threshold for olivine, norite orthopyroxene, clinopyroxene augite

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and clinopyroxene pigeonite, collected the mean spectra for different thresholds and

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compared with respective endmembers (pure pixels). The mineral olivine is mostly associated

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with the central peak of the Eratosthenes crater region. The comparison of the end member

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spectra with mean spectra of different thresholds for olivine shows that the mean spectra of

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0.1 and 0.2 thresholds of SID have best match with the end member spectra of olivine (Fig.

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8b-8e). The mean spectra of other successive thresholds are gradually deviating from the end

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member spectra due to mixture of other lithological and mineralogical units (Fig. 8e). The

334

analysis between the end member spectra and mean spectra of different threshold shows that

335

the SID threshold of 0.1 and 0.2 is suitable for olivine as far as the study area is concerned.

336 337 338 339 340 341

The norite orthopyroxene is occurs in fresh craters of montes apenninus region (Fig. 9). The

342

relation in-between the end member spectra and mean spectra of different thresholds shows

343

that the mean spectra of 0.1 threshold have perfect correlation with the end member spectra

344

of norite orthopyroxene. The mean spectra of other thresholds i.e. 0.2 to 0.5 are differing

345

from the end member spectra due to mixture of other materials (Fig. 9b-9e). The above

346

analysis clearly shows that the SID threshold as 0.1 is more suitable for norite orthopyroxene.

347 348 349 350 351

Figure 9: (a) Northernpart of study area rugged mountains region contains presence of norite orthopyroxene; (b), (c) & (d) Classified image with thresholds of 0.1, 0.2 and 0.5. (e) Spectral plot shows the mean spectra of different threshold and end member spectra of norite orthopyroxene.

Figure 8: (a) Eratosthenes central peak region contains olivine; (b), (c) & (d) Classified image with thresholds of 0.1, 0.2 and 0.5. (e) Spectral plot shows the mean spectra of different threshold and endmember spectra of olivine.

352

The clinopyroxene augite is associated with the most of the fresh craters of the mare region.

353

The analysis of end member spectra of augite in conjunction with mean spectra of different

354

thresholds shows that there is no much variation in spectral characters up to thresholds of 0.2.

355

The mean spectra beyond 0.2 thresholds are deviating from the endmember spectra (Fig. 10b-

356

10e). Beyond 0.2 thresholds, the typical absorption feature of augite in 2200-2300 nm region

357

is shifting towards 2000 nm indicates that the lowering calcium content with increasing of

358

magnesium.

359 360 361 362 363 364

Figure 10: (a) Fresh craters in mare basalt region contain clinopyroxene augite; (b), (c) & (d) Classified image with thresholds of 0.1, 0.2 and 0.5; (e) Spectral plot shows the mean spectra of different threshold and end member spectra of clinopyroxene augite. The clinopyroxene pigeonite occurs on a fresh crater found in the northern rim of the

365

Eratosthenes crater (Fig. 11a). To identify the optimum SID threshold, compare the end

366

member spectra with mean spectra of different thresholds of pigeonite shows that there is no

367

variation in spectral characters up to 0.5 thresholds. However, the depth of absorptions

368

features along 950 nm and 2200 nm are gradually increases towards lower thresholds and at

369

the same time positions of these absorption features are not changed (Fig. 11b-11e). The

370

reduction of absorption depth towards higher thresholds may be due to grain size variation

371

(Pieters, 1983).

372 373 374 375 376

Figure 11: (a) Clinopyroxene pigeonite occurs in the fresh crater found on the rim of the Eratosthenes crater; (b), (c) & (d) Classified image with thresholds of 0.1, 0.2 and 0.5; (e) Spectral plot shows the mean spectra of different threshold and end member spectra of clinopyroxene pigeonite.

377 378 379 380 381 382 383 384

Figure 12: (a) Image derived endmembers (named with their best matching with RELAB spectral library); (b) Continuum removed spectra of endmembers; (c) M3 classified (SID) image (Threshold 0.1) with image-derived end members with background of reflectance band 1578.86nm; (d) Fresh crater in highlands showing norite orthopyroxene; (e) Fresh crater on the north side rim of Eratosthenes crater showing clinopyroxene pigeonite (f) Central peak of Eratosthenes crater showing olivine; (g) Small fresh crater in bottom showing clinopyroxene augite.

385

The above analyses indicated that the SID threshold of 0.1 produced consistent results with

386

the RGB derived color composite images (Fig. 4 & 12).The present study highlight that the

387

classification of hyperspectral imagery with different thresholds for varying mineralogical

388

units may provide good result instead of a common threshold. Further, SID with 0.1 threshold

389

can be treated as a detection tool rather than the classification tool (Fig. 12).

390

6. SUMMARY AND CONCLUSION

391

The present study extensively used the visible and near infrared region of the Moon

392

Mineralogy Mapper (M3) of Chandrayaan-1 to determine the lithological diversity and

393

mineralogical composition of the Eratosthenes crater region. The spectral and spatial

394

resolution of M3 has opened a window to understand the diversity of lunar surface. Band

395

shape algorithms and IBD depth technique based colour composite images generated from

396

the M3 data reflect the lithological diversity of the study area. The endmembers identified

397

through series of analysis and their matching with relab spectra further attribute the

398

compositional diversity of lunar surface. The classification image generated using SID

399

algorithm with different thresholds shows that the spatial distribution of various

400

mineralogical units of Eratosthenes crater region. The analysis between endmember spectra

401

and mean spectra of different thresholds shows that the image classified with threshold of 0.1

402

is more accuracy when compared to others. Further, mineralogical and lithological

403

information derived from Band shape algorithms and IBD depth techniques are best match

404

with 0.1 threshold. The present study revealed that the SID could be an effective method and

405

detection tool for mineralogical mapping and potentially used for exploration in unknown

406

areas as it measures the discrepancy between each pixel spectrum and a reference spectrum.

407

The presence of mantling mineral olivine in the central peak indicates the deep impact of the

408

crater. The study demonstrated that the efficiency of M3 data in unraveling the lithology and

409

mineralogy of lunar surface due to its spatial and spectral character.

410

ACKNOWLEDGEMENT

411

This research study was carried out under the Chandrayaan-1 AO programme. We thank M3

412

Team, Chandrayaan-1 mission and Indian Space Research Organization (ISRO) for making

413

the availability of datasetin the public domain through web portals. We express our sincere

414

thanks to Prof P.Pinet and anonymous reviewers for critical review of our manuscript and

415

many insightful comments and suggestions.

416

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Highlights 1. The study explored the potential of visible and near infrared hyperspectral image-cube of M3 data in lithological and mineralogical mapping of Eratosthenes crater region. 2. Eratosthenes crater region is vested with divergent lithological/mineralogical composition such as olivine in central peak, norite in fresh craters of highland regions and fresh craters of mare basalt exhibits clinopyroxene pigeonite and augite. 3. Classified image generated with different thresholds of Spectral Information Divergence (SID) algorithm. 4. Mean spectra collected from 0.1-0.5 threshold image were compared with pure end member spectra of olivine, orthopyroxene, clinopyroxene augite and clinopyroxene pigeonite. 5. Comparative analysis shows that average spectra collected from 0.1 threshold of SID is best match with pure/end member spectra. 6. Classified image generated with 0.1 threshold is best match with the color composite images generated from band shape algorithms and integrated band depth analysis.