Assessing remotely sensed data of the icy bodies with a spectroscopic tool.

Introduction: In this abstract we present a tool developed mainly in preparation of the ESA JUpiter ICy moons Explorer (JUICE) mission, scheduled to launch in April 2023 and arrive at the Jupiter system in July 2031, which however can be applied also to hyperspectral data acquired by past and ongoing missions such as Galileo, Cassini, and Juno.
Laboratory and telescopic spectroscopy measurements in the VIS—NIR are the primary observational data for identifying the composition and/or mineralogy of the icy satellites of giant planets. This technique allows one to discern between exogenous and endogenous compounds, and to identify the regions of the satellites in which the exchange of material between the surface and the shallow subsurface has been more frequent in the past.
We started from laboratory spectra of chemical compounds known or expected to exist on the surface of icy satellites and small icy bodies to build an interactive Excel database that provides a quick and easy way to derive spectral endmembers that can be used to perform spectral unmixing.

Methods: We consider multiple chemical species relevant for the surfaces of icy satellites, trans-Neptunian objects (TNOs) and comets, such as volatiles, mineral salts, organics, tholins, cyanide/nitrile compounds, and exogenous products. About 100 chemical compounds and over 1600 laboratory spectra were collected, and it is routinely updated once new laboratory data relevant for the icy surfaces are made available. This large amount of data is recovered from past literature focusing on laboratory measurements (mostly from the 1990s onward) and from multiple public databases such as SSHADE [1], Goddard Space Flight Center database [2, 3], RELAB [4] and USGS Spectral Library [5]. 

The Excel database is organized in different columns: sample name, reference, DOI, spectral range, measured quantity (e.g., reflectance, absorbance, transmittance, optical constants, etc.), lab/instrument, temperature, grain size, and comments. 

Each spectrum has a hyperlink that directly opens the related ASCII file. This is important for a quick view and comparison of the spectra. To this aim, we also have made available spectral tools written in Visual Basic for Application (VBA). These tools are easily accessible by right-clicking on the hyperlink in the Excel spreadsheet, and need no installation of separate add-ons.
The Visualize tool permits the quick-look of the spectral profile as found in the database (Fig. 1). The Convolution tool convolves the original spectral profile to a specific instrumental function (currently available are: Galileo/NIMS, Cassini/VIMS, Juno/JIRAM, and JUICE/MAJIS). At the end of the processing  spectral profiles pre and post-convolution are displayed (Fig. 2) and a TXT file and a CSV file are saved.

Conclusions: In the context of the future ESA JUICE mission, tools and techniques are needed to analyze the new hyperspectral data of the surfaces of the icy Galilean satellites. To this end, we have built a database of laboratory spectra extending from the visible to the near-infrared conceived and organized in an interactive Microsoft Excel environment in such a way as to make their visualization and use as simple and fast as possible by a large audience. A further advantage of this database is to be transversal to the analysis of the spectroscopic data of past and ongoing missions that have explored the external solar system, in particular Galileo, Cassini and Juno. Furthermore, our tool could be updated to perform convolution for other instrumental responses as well.

Acknowledgments: This work is supported by the Italian Space Agency under the ASI-INAF grant agreement n. 2018-25-HH.0: “Scientific activities for JUICE”.

References: [1] Schmitt B. et al. (2018), SSHADE: "Solid Spectroscopy Hosting Architecture of Databases and Expertise" and its databases. OSUG Data Center. Service/Database Infrastructure, DOI: 10.26302/SSHADE. [2] Gerakines P. A. and Hudson R. L. (2020), https://science.gsfc.nasa.gov/691/cosmicice/constants.html. [3] Hudson R. L. et al., https://science.gsfc.nasa.gov/691/cosmicice/spectra.html. [4] Milliken R. (2020), DOI: 10.17189/1519032. [5] Kokaly R. F. et al. (2017), USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61, DOI: 10.3133/ds1035