Turn the heat up – A first look at MESSENGER's near-infrared spectra of Mercury using new high-temperature emissivity measurements

Analyzing the surface composition of Mercury's regolith from remote-sensing measurements is a challenging task. In support of the National Aeronautics and Space Agency's MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission and especially in preparation for the Mercury Radiometer and Thermal Infrared Spectrometer (MERTIS) instrument on the BepiColombo mission of the European Space Agency and the Japan Aerospace Exploration Agency, we are developing a Planetary Emissivity Laboratory (PEL) at Deutsches Zentrum für Luft- und Rahmfahrt (DLR) in Berlin. The PEL allows measurement of the emissivity of Mercury-analogue materials at grain sizes smaller than 25 μm and at temperatures of more than 400°C, typical for Mercury's low-latitude dayside. The PEL development follows a multi-step approach. We have already obtained emissivity data at mid-infrared wavelengths that show significant changes in spectral behavior with temperature indicative of changes in the crystal structure of the samples. We are currently installing a new calibration target that will allow the acquisition of emissivity data over the full wavelength range from 1 to 50 micrometer with good signal-to-noise ratio. Here we present initial data in the range 1 to 1.4 micrometer, the near-infrared wavelength coverage of the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument on MESSENGER. Even these early PEL measurements have important implications for the analysis of the spectral observations obtained during MESSENGER's first Mercury flyby on 14 January 2008 as well as the data to be obtained during the probe's second Mercury flyby on 6 October

Compositional Units on Mercury from Principal Component Analysis of MESSENGER Reflectance Spectra

We analyzed surface spectra from the Mercury Atmospheric and Surface Composition Spectrometer with a principal component approach and unsupervised classification techniques to identify and characterize surface units along the ground tracks

MASCS surface units from cluster analysis

First work on the retrieval of Mercury surface units from cluster analysis of MASCS/MESSENGER instrument data

Global Classification of MESSENGER Spectral Reflectance Data and a Detailed Look at Rudaki Plains

We suppose that Mercury surface compositional information can be derived from spectral reflectance measurements of MESSENGER/MASCS via statistical techniques. Unsupervised hierarchical clustering successfully identify surface region and relationship

Compositional units on Mercury from MESSENGER spectral observations: comparison of clustering techniques

The Mercury Atmospheric and Surface Composition Spectrometer (MASCS) obtained spectra of much of the surface of Mercury during the first two MESSENGER flybys of the planet [1]. The dataset have not been corrected for any effect due to observing geometry, but only converted to reflectance [2]. The characterization of spectral units is performed by statistical techniques. In order to extract the spectral shapes of the primary surface components exposed in the surface area analyzed, we applied an R-mode factor analysis [3] [4]. That leads to the evaluation of the eigenvectors of the covariance matrix and their abundances along the track. The results indicates that the NIR spectral range is carrying less information than the VIS portion and that the eigenvectors are unchanged if the full wavelength range is selected rather than limiting observations to the VIS. The analysis shows that seven eigenvectors are needed to reconstruct the original data, where each eigenvector can be regarded as a representative of a spectral class that varies in abundance along the track. The first eigenvector always displays a strong positive or “red” slope, carrying the effects associated to the viewing geometry and all eigenvectors show distinctive spectral signatures. The eigenvector abundances show marked geographical variation and a strong correlation with surface units mapped by MESSENGER’s Mercury Dual Imaging System (MDIS). We apply a decorrelation technique (Mahalanobis transformation [5]) to remove dependence on observation angle in the retrieved eigenvector abundances and then used the corrected abundances to classify or cluster the measurements and to identify spectral units. We used three clustering techniques and then we compare the output from the algorithms. At the same time, we make use of newly available high-temperature spectra from our Planetary Emissivity Laboratory [6] to assist in the identification of the components of each unit. Application to data from the first flyby provides us with confidence in the ability of these techniques to extract physical properties of surface materials