NIRVSS Aboard CLPS

NASA initiated the Commercial Lunar Payload Services (CLPS) program for flights to the lunar surface. Astrobotic was awarded a NASA contract to accommodate NASA payloads onto their Peregrine lander Astrobotic Mission One (ABM-1). ABM-1 is scheduled to land near Lacus Mortis, 44N 25E, in 2021. The Near-InfraRed Volatile Spectrometer System (NIRVSS) has evolved over time and was chosen as a NASA payload for ABM-1 and the flight model is scheduled to be delivered to Astrobotic at the end of March 2020

Laboratory observations and simulations of phase reddening

The visible reflectance spectrum of many Solar System bodies changes with changing viewing geometry for reasons not fully understood. It is often observed to redden (increasing spectral slope) with increasing solar phase angle, an effect known as phase reddening. Only once, in an observation of the martian sur- face by the Viking 1 lander, was reddening observed up to a certain phase angle with bluing beyond, mak- ing the reflectance ratio as a function of phase angle shaped like an arch. However, in laboratory experiments this arch-shape is frequently encountered. To investigate this, we measured the bidirec- tional reflectance of particulate samples of several common rock types in the 400–1000 nm wavelength range and performed ray-tracing simulations. We confirm the occurrence of the arch for surfaces that are forward scattering, i.e. are composed of semi-transparent particles and are smooth on the scale of the particles, and for which the reflectance increases from the lower to the higher wavelength in the reflec- tance ratio. The arch shape is reproduced by the simulations, which assume a smooth surface. However, surface roughness on the scale of the particles, such as the Hapke and van Horn (Hapke, B., van Horn, H. [1963]. J. Geophys. Res. 68, 4545–4570) fairy castles that can spontaneously form when sprinkling a fine powder, leads to monotonic reddening. A further consequence of this form of microscopic roughness (being indistinct without the use of a microscope) is a flattening of the disk function at visible wave- lengths, i.e. Lommel–Seeliger-type scattering. The experiments further reveal monotonic reddening for reflectance ratios at near-IR wavelengths. The simulations fail to reproduce this particular reddening, and we suspect that it results from roughness on the surface of the particles. Given that the regolith of atmosphereless Solar System bodies is composed of small particles, our results indicate that the preva- lence of monotonic reddening and Lommel–Seeliger-type scattering for these bodies results from micro- scopic roughness, both in the form of structures built by the particles and roughness on the surface of the particles themselves. It follows from the singular Viking 1 observation that the surface in front of the lander was composed of semi-transparent particles, and was smooth on the scale of the particle size

Self-organizing map classification of the Berlin Emissivity Data Base

Introduction: Existing and planned space missions to planets and their satellites produce increasing volumes of spectral data. Understanding the scientific content in this large data volume is a daunting task. Various statistical approaches are available to assess such data sets. We apply an automated classification scheme based on Kohonen Self-Organizing maps (SOM) to thermal emission spectra of individual minerals from the Berlin Emissivity Data (BED) base [1- 3]. Currently the BED incorporates many minerals and materials that have been suggested as being present on Mercury and Mars based upon previous measurements [2]. Testing the ability of the SOM on carefully controlled laboratory samples represents one of several steps towards its application for automatic data processing on future missions with a higher degree of autonomy. Samples Studied: The samples studied here are listed in Table 1 along with a hierarchal labeling scheme previously used for SOM clustering of other mineral data [4-5]. Four grain sizes separates are available for each sample (0-25, 25-63, 63-90, and 90- 125 mm). Spectral Measurements: The spectral measurements were performed with a Fourier transform infrared spectrometer (Bruker VERTEX 80v), purged with dry air and equipped with a liquid-nitrogen-cooled HgCdTe (MCT) detector. A "warm" (60°C) and "hot" (90°C) blackbody, together with a gold plated standard sandpaper at room temperature were measured for calibration. The sample was placed in a 3 cm diameter aluminum cup and heated to 90°C in an oven for 24 hours to reduce adsorbed water, then was placed on a heating plate and heated from below to a constant temperature of 90°C. Further details on apparatus, data preparation, standard measurement procedures and emissivity calculation can be found in [1,2]. Thus, the new spectral data expand the wavelength domain of the previous measurements of the BED [3]. Clustering with the SOM: Previous work developed an automated unsupervised classification scheme based on SOMs that does not suffer from the limitations of the K-means and Isodata algorithms; requirement for predefining the number of clusters [4-5]. The SOM maps the clustering inherent within the input data to an output layer. Commonly there are two steps with application of the SOMs; training and testing. During training the cells of the SOM are randomly populatedwith data having known labels and as similar data are grouped together disjoint regions in the output layer are formed and are associated with the data labels. Here we apply the SOM only using the training phase to investigate how the emissivity spectra cluster. In this case we ask if similar data are associated with each other. Before the SOM analyses, we eliminate data from the spectra in regions where telluric CO2 can introduce artifacts and a few other regions where signal precision is relatively low. Results: The specific location of a sample in an output layer “cell” (boxes created by the grid in the figures) is due to two factors; initial random placement of spectra at the beginning of the SOM training and similarity with near-by spectra during training. So, in addition to location it is important to consider the strength of the boundaries between individual “cells”. The thickness of the grid lines indicates the absolute difference between spectra in adjacent cells. Thin and thick lines indicate a relatively small and large difference, respectively. Figures 1 and 2 show the results of applying the SOM to the a subset of the BED spectra two different times and indicates: Oxide (hematite) spectra form a distinct region with strong boundaries from the silicates (pyrope and enstatite) suggesting this material is readily recognized as being different from the silicates. A strong boundary separates the finest grain size sample from all others. Silicates (pyrope and enstatite) form at least two distinct regions with intermediate strength boundaries that separate the coarser and finer grain sizes of these two materials. The finest grain size pyrope spectrum is segregrated from the other fine-grained silicates by strong boundaries. The conclusions presented in this initial effort will benefit from additional analyses of other materials in the BED data set. One natural extension of this effort is analyses of the informational content contained within differing spectral regions that would provide the potential to increase the accuracy of any classification scheme (e.g. 4) References: [1] Maturilli, A., Helbert, J., Witzke, A., Moroz, L. (2006) PSS 54, 11, p. 1057-1064 [2] Maturilli, A., Helbert, J., Witzke, A., Moroz, L. (2007) LPSC 38, abstract 1281. [3] Maturilli, A., Helbert, J. (2007) European Planetary Science Congress, abstract EPSC2007-A-00281 [4] Roush T.L. &amp; R.C. Hogan (2007) Proc. IEEE 2007 Aerospace Conf., paper #1456. [5] Hogan, R.C. and T.L. Roush (2002) SOM classification of TES data, LPSC 33, abstract 1693 Acknowledgements: Portions of this work have been supported by NASA’s Planetary Geology and Geophysics Program and the German Research Foundation (DFG)</p

Visible and Near-Infrared (VNIR) reflectance spectroscopy of glassy igneous material: Spectral variation, retrieving optical constants and particle sizes by Hapke model

Silicate glasses with igneous compositions can be an important constituent of planetary surface material via effusive volcanism or impact cratering processes. Different planetary surfaces are mapped with hyper-spectrometers in the VNIR, and in this spectral range crystal field absorptions are useful in discriminating iron bearing silicate components. For these reasons studying glassy materials, and their optical constants, is an important effort to better document and understand spectral features of Solar System silicate crusts where glasses are present, but may be difficult to map. In our work we present a set of four different synthetic glasses, produced under terrestrial conditions, with variable composition and in particular an increasing amount of iron. The VNIR spectra show, for all the compositions, two absorptions are present near 1.1 and 1.9. μm but reflectance, slope and absorption shape varies with composition. We measured the reflectance of different particle sizes of the samples and used radiative transfer models to estimate the optical constants as a function of wavelength. We used the retrieved optical constants to estimate the particle size from the measured reflectances and the results fall within the known sieve range. We qualitatively discuss the effect of the shape and distribution of particles on the application of the model