Formulation of spectral indexes from M3 cubes for lunar mineral exploration using python

IntroductionThe scientific exploration of planetary bodies is enhanced using spectral indexes, and specific band combinations/operations that allow the interpretation of the compositional properties of planetary surfaces. The best hyperspectral sensor for the study of the Moon is M3 onboard Chandrayan-1 (Pieters et al., 2008), it has 86 channels, and covers the range between 450 to 3000 nm, a region that shows the main properties of the rock-forming minerals of the Moon. Although the data of M3 has been widely used with different techniques, there is no unified set of spectral indexes for this instrument, and the ones defined are usually produced in proprietary software. In this work, we compiled spectral indexes from several sources and recreated them in python.MethodsWe compiled spectral indexes from the literature, namely the ones defined by Zambon et al. (2020), Bretzfelder et al. (2020), and Horgan et al. (2014). Before applying the indexes, an M3 cube was processed in ISIS3 (Laura et al., 2022) and filtered in python to reduce the noise. Subsequently, the spectral indexes were replicated according to the procedures described by the authors and compared with the original results. Most of the process was done with common scientific libraries such as rioxarray (Guillies, 2013), OpenCV (Bradski, 2000), specutils (Earl et al., 2022), and NumPy (Harris et al., 2020).ResultsWe were able to reproduce fourteen indexes with high fidelity. All of them are formulated to highlight the spectral features around the absorptions in 1000 nm and 2000 nm, which are the location with the major expressions from olivine and pyroxenes. Comparing our results with the ones in the literature, we found that the color ramps are similar in both results and that the surface features showcased in both cases are consistent with each other and their known compositions.Discussion and conclusionsSmall differences between the original indexes and the ones recreated here are expected, due to variations in the internal methods across libraries, the different ways of preprocessing and filtering, and the quality of the original cubes. Further comparison and validation of the procedures is planned.Nevertheless, we believe that the results are consistent enough to be used as scientific inputs, thus providing an open-source alternative for the analysis of spectral indexes of the surface of the Moon. This work is in progress, and the code is going to be available via EuroPlanet GitHub organization (https://github.com/europlanet-gmap), as well as in the Space Browser of the EXPLORE platform (https://explore-platform.eu/space-browser).AcknowledgmentsThis project has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation program under grant agreement No 101004214.References Bradski, G. (2000). The OpenCV Library.Bretzfelder et al., (2020). Identification of Potential Mantle Rocks Around the Lunar Imbrium Basin.Earl et al., (2022). astropy/specutils: V1.9.1&amp;#160;Gillies, S. &amp; others. (2013). Rasterio: Geospatial raster I/O for Python programmers.&amp;#160;Harris et al., (2020). Array programming with NumPy.Horgan et al., (2014). Near-infrared spectra of ferrous mineral mixtures and methods for their identification in planetary surface spectra.Laura et al., (2022). Integrated Software for Imagers and Spectrometers&amp;#160;Zambon et al., (2020). Spectral Index and RGB maps.