Introduction

Lunar Vertex, NASA’s first Payloads and Research Investigations on the Surface of the Moon (PRISM) delivery, will explore the Reiner Gamma lunar swirl (Figure 1) (Blewett et al., 2025). Swirls consist of high and low albedo variations in the form of loops and ribbons sometimes associated with crustal magnetic “anomalies”. Different hypotheses have been proposed to explain the presence of swirls (summarized in Blewett et al., 2021), including magnetic shielding of the surface from the darkening effects of the solar wind, scouring by gas and dust during the collision of a comet's coma or a meteoroid swarm, and accumulation of fine-grained dust in response to magnetic or electric fields associated with the magnetic anomaly. Studies have investigated the photometric properties of the uppermost regolith at swirls without reaching a consensus regarding their origin but suggest that solar wind shielding and dust levitation/sorting may both play a role (e.g., Denevi et al., 2016; Chrbolková et al. 2019; Kinczyk et al., 2025). Geomorphological studies suggest that on-swirl regions are lower than the off-swirl region by ~4 m at Reiner Gamma (Weirich et al., 2023), supporting the dust migration hypothesis, or local topographic variations pre-dating the formation of the swirls.

In this study, we investigate potential differences in regolith properties between on-swirl and off-swirl regions at Reiner Gamma to provide context for the Lunar Vertex mission. Our first objective is to quantify morphometric parameters from the bowl-shaped crater population to investigate potential dust migration mechanisms which may contribute to preferential crater infill on-swirl, or different regolith properties. Our second objective is to estimate regolith thickness using the non-bowl-shaped crater population to see if variations in local regolith thicknesses exist, and in turn, if dust migration mechanisms or differences in pre-impact topography could contribute to these variations.

Datasets and methods

We trained and used a YOLOv5-based model (e.g., Bickel et al., 2025) to detect craters across Reiner Gamma in hillshade images derived from Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) digital terrain models (DTMs) at spatial resolutions from 2 to 5 meters. We developed “MorphoPy”, a Python-based automated crater morphology characterization tool (Martinez et al., 2026), which calculates morphometric parameters (e.g., depth, diameter, wall slope, rim slope, circularity, eccentricity, freshness) on DTMs for craters that have 36 semi-profiles each containing 10 valid measurements. Morphometric parameters are calculated for craters with a diameter (D) equal to or larger than 20 times the spatial resolution of the input DTM, here D≥40 m. In parallel, we manually identified non-bowl-shaped craters (e.g., flat floored, concentric craters, mound) in LROC NAC images on Quickmap and estimated regolith thicknesses using the formula of Bart et al. (2011).

Figure 1. Map of Reiner Gamma showing the location of NAC DTMs used to investigate crater morphology overlain by the intensity of the magnetic field (Ravat et al., 2020) (swirl outline from Denevi et al., 2016).

Preliminary results

The YOLOv5 algorithm identified ~70 000 craters, and “MorphoPy” calculated morphometric parameters for ~25 000 of those. To investigate a subset of the freshest primary craters, we extracted values for craters having a circularity ≥ 0.9, opposite rim slope < 8˚ and a measurable rim for ≥ 50% of their half-profiles. Plots of depth versus diameter values (Figure 2) suggest that most of the freshest primary craters in the Reiner Gamma region have d/D values centered around ~0.1, especially for strength-dominated craters (D < 400 m). Depth versus diameter trends suggest that off-swirl craters are systematically deeper (hence have higher d/D values) than their on-swirl counterpart for a given crater diameter. Analyses will be conducted next to see if the trends are statistically different.

Figure 2. Depth versus diameter values for the freshest on-swirl and off-swirl craters in DTMs of different spatial resolution (solid line d/D=0.2, dashed line d/D=0.1, dotted line d/D=0.05).

We identified 918 non-bowl-shaped craters on Quickmap, and calculated regolith thickness for the 503 craters that exhibit a clear rim (apparent diameter, D_A) and “inner feature” (measured at the base of the normal crater wall slope, D_F). Bart et al. (2014) suggest that craters where D_F/D_A varies between 0.2 and 0.7 and where D_A < 300 m are well suited to infer regolith thickness. In our case, 467 craters respect such values (296 off swirl, 171 on swirl) and yield a mean regolith thickness of 6.6 m off-swirl versus 6.1 on-swirl and median values of respectively 4.1 and 4.2 m. These values are consistent with those reported for mare surfaces (3.1–7.8 m; Cooper et al., 1974; Nakamura et al., 1975; Bart et al., 2011). The “regolith thickness” values calculated range between ~1 and 50 m. Impact craters that encounter a strength transition in the target have non-bowl-shaped morphologies. The strength transition can be a layer of regolith over bedrock but can also be a strength transition farther beneath the surface such as layering in the basalt (Bart et al, 2014). A hot spot analysis (Getis-Ord Gi) on the 467 regolith thickness measurements suggests that some high (hot spot) and low (cold spot) regolith thickness values cluster spatially and are statistically significant. Hot spots mostly occur on-swirl and towards the southern portion of Reiner Gamma, while cold spots mostly occur off-swirl and towards the northern portion of Reiner Gamma. The region at ~7.5˚N, 59.5˚W notably exhibits cold spots (regolith thickness values on the order of 1-3 m) in proximity to hot spots (“regolith thickness” or strength contrast at 5-50 m).

Figure 3. Hot spot analysis on 467 regolith thickness measurements.

Bart et al. (2011) Icarus 215, 485–490.

Bart et al. (2014) Icarus 235, 130–135

Bickel et al. (2025) GRL, 51, 24, e2024GL110674.

Blewett et al. (2021) Bulletin of the AAS, 53, 4.

Blewett et al. (2025) 56^th LPSC, abstract #1233.

Chrbolková et al. (2019) Icarus, 333, 516-527.

Cooper et al. (1974) Rev. of Geophysics, 12, 3, 291-308.

Denevi et al. (2016) Icarus, 273, 53-67.

Kinczyk et al. (2025) Planet. Sci. J., 6, 57.

Nakamura et al. (1975) Nature, 13, 57-66.  

Weirich et al. (2023) lanet. Sci. J., 4, 212.

How to cite: Lemelin, M., Martinez, L., Calas, G., Lagneaux, A., Roy, M., T. Bickel, V., Belleau-Magnat, G., Diotte, F., and T. Blewett, D.: The Structure of the Regolith at Reiner Gamma: Context for the Lunar Vertex Mission, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-124, https://doi.org/10.5194/epsc2026-124, 2026.

Radiometric measurements of the (sub-)surface thermal emission are sensitive to the microphysical structure and thermophysical properties of the regolith. For the interpretation of these measurements, thermal models are required. Bürger et al. (2024) developed a microphysical thermal model for the lunar regolith, which more directly simulates regolith properties such as the grain size and volume filling factor.  In this study (Bürger et al., 2026), we derive global regolith grain size and density-stratification by matching modeled surface temperatures and microwave brightness temperatures with measurements from the infrared radiometer Diviner on board the Lunar Reconnaissance Orbiter (LRO) (Paige et al., 2010) and the Microwave Radiometer (MRM) on board Chang’E-2 (Wang et al., 2010; Zheng et al., 2019). The radiometric observations in the infrared and in the microwave range are complementary, as infrared measurements are sensitive to surface thermal emission, while the microwave measurements are sensitive to the thermal emission from subsurface layers.

It is important to note that the microwave radiometer receives the thermal emission from a range of depths within the regolith, with the overall penetration depth being controlled by measurement frequency and the dielectric properties of the regolith. Therefore, in order to derive synthetic microwave brightness temperatures, a radiative transfer model is required and this study uses the model presented in Feng et al. (2020) and Siegler et al. (2020). Furthermore, we use only the 19.35 GHz and 37 GHz channel data of MRM, because the two lowermost frequency channels of MRM are believed to suffer from a calibration issue (Feng et al., 2020; Hu et al., 2017; Hu & Keihm, 2021).

We find that the regolith grain size and density-stratification can be unambiguously constrained when fitting both datasets – Diviner and MRM. In more detail, we derive for the equatorial highlands a global regolith grain radius of 45⁺⁶_-4 µm and a deep layer bulk density of 1800⁺⁷⁰_-90 kg m^-3. These parameters describe the highland regolith well for all latitudes < 40° and are in good agreement with grain size and bulk density measurements from returned Apollo samples. Figure 1 illustrates the Diviner regolith temperatures and the MRM 37 GHz and 19.35 GHz measurements in the lunar highlands at the equator together with the simulated surface and microwave brightness temperatures resulting in the above described best fit. Figure 2 illustrates the resulting bulk-density profile together with best-estimate bulk density values inferred from Apollo data (Mitchell et al., 1974), and the range of bulk densities determined from Apollo core tube and drill core measurements (Carrier et al., 1974; Carrier et al., 1991). Finally, we also investigated the latitudinal dependence of lunar regolith properties and find that both data sets – Diviner and MRM – can be best fit with a poleward decrease in deep layer bulk density.

Figure 1: The best-fit simulations (green line) together with the measured Diviner regolith temperatures (left), MRM 37 GHz (center), and 19.35 GHz (right) brightness temperatures in the highlands at the lunar equator. The measurements are presented as a function of local time and the individual measurements (small dots) are binned (black circles) with a bin-size of 0.5 hours.

Figure 2: The resulting bulk-density profile (green line) together with constraints from Apollo measurements.

References

Bürger et al. (2024), JGR Planets, 129(3). Bürger et al. (2026), A microphysical thermal model for the lunar regolith: Determining the lunar regolith properties using a combination of LRO/Diviner and Chang’E-2/MRM data, accepted for publication in A&A. Carrier et al. (1974), The Moon, 10, 183. Carrier et al. (1991), in Heiken G. H., Vaniman D. T., French B. M., eds, Lunar Sourcebook, A User’s Guide to the Moon, 475–594. Feng, J. et al. (2020), JGR Planets, 125(1). Hu et al. (2017), Icarus, 294, 72. Hu & Keihm (2021), IEEE Geoscience and Remote Sensing Letters, 18, 1781. Mitchell et al. (1974), Apollo soil mechanics experiment S-200, Space Sciences Laboratory Series 15, Issue 7, Univ. of California, Berkeley. Paige et al. (2010), Space Sci. Rev., 150(1–4), 125–160. Siegler, M. A. et al. (2020), JGR Planets, 125(9). Wang et al. (2010), Science China Earth Sciences, 53, 1392. Zheng et al. (2019), Icarus, 319, 627-644.

How to cite: Bürger, J., Feng, J., Siegler, M., and Blum, J.: Lunar regolith grain size and density-stratification derived from LRO/Diviner and Chang’E-2/MRM data, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-192, https://doi.org/10.5194/epsc2026-192, 2026.

We examine lunar regolith space weathering as a function of wavelength, terrain type, and latitude using multiple datasets to quantify global and regional trends in surface reflectance and emission. Distinguishing space-weathering effects from bulk compositional controls is essential for interpreting spatial spectral variability and for identifying anomalous surface units. A statistically significant decrease in weathering with increasing latitude is observed in mare regions at all wavelengths analyzed, although the strength of the trend varies with wavelength. The strongest correlation occurs in the far-ultraviolet, consistent with its high sensitivity to space-weathering products such as submicroscopic iron. In the mare, the latitudinal dependence is well described by an equator-anchored cosine function, supporting a solar-zenith-controlled weathering process. Highlands terrains also exhibit statistically significant latitude-dependent behavior in ultraviolet and near-infrared observations, but the pattern is less monotonic and includes structured spatial variability, indicating a more complex interplay among weathering state, composition, and local geologic context. These results show that latitude-dependent space-weathering gradients are globally important but are also modulated regionally, including by longitude. Such gradients may bias spectral interpretations of regolith composition, particularly when applying commonly used compositional algorithms across terrains or spatial scales without accounting for maturity effects. Although terrain-specific approaches for mare and highlands have been proposed, the implications of latitude-dependent weathering have not yet been fully incorporated into compositional modeling or reconciled with interpretations based on optical maturity.

How to cite: Waller, D., Cahill, J., Retherford, K., Byron, B., Poston, M., Magaña, L., and Hendrix, A.: Variation in Lunar Regolith Space Weathering Relative to Latitude and Wavelength, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-310, https://doi.org/10.5194/epsc2026-310, 2026.

The lunar radiation environment presents harsh conditions for both manned and unmanned lunar missions due to the lack of atmospheric and magnetic shielding from primary radiation, such as solar energetic particles and galactic cosmic rays. These highly energetic particles pose a significant risk for both biological tissue and electronics, and therefore set constraints on shielding, mission duration and material choices.

During impact on the lunar regolith, secondary radiation in the form neutrons, protons, electrons and gamma rays is generated and partially emitted from the lunar surface. This contributes to the overall radiation level experienced by humans and spacecraft near the lunar surface. Secondary radiation measurements therefore grow increasingly relevant with the current international efforts to return to the Moon and establish manned lunar bases within the next decade.

Due to specific absorption and emission lines from each element altering the regolith’s response to primary radiation, secondary radiation measurements are capable of determining elemental abundances in the lunar regolith. Gamma-ray and neutron spectroscopy (GRNS) can quantify abundances of elements, such as H, Fe, Mg, Si, Al, Na, O and Ti. GRNS methods can be applied both from orbit – for course mapping of larger areas, and on rovers for localizing resources on a much finer scale. Mapping reserves of such elements enables in-situ resource utilisation and resource mining, as it supports the identification of landing sites with suitable resources.

To predict the local radiation environment and prepare elemental abundance mapping missions, we developed a simulation toolkit of the interaction between primary cosmic rays and the lunar regolith, in addition to the subsequent secondary radiation response. Given primary radiation spectra from cosmic ray models, such as CREME96, the DLR GCR model¹, and SAPPHIRE, the particle propagation in the lunar regolith is simulated using Geant4 with BDSIM². Responses are determined for variations in lunar regolith properties including elemental soil composition, hydrogen content, temperature, as well as changes in primary radiation spectra. Figure 1 shows the block diagram of the simulation toolkit.

Figure 1 Lunar Secondary Radiation Simulation Toolkit Concept

Predicted secondary radiation spectra variations, such as epithermal neutron depletion or presence of spectral lines for different soil and irradiation conditions such as shown in Figure 2, provide crucial data for both instrument design and data processing of future GRNS missions, such as the Gamma-Ray-including-Neutrons Spectrometer (GRiNS)³ that was proposed onboard the SER3NE mission⁴. Our toolkit predicts the secondary radiation flux in a given orbit, and with that contributes to the dimensioning of the active detection area and anti-coincidence shield needed.

Figure 2 Neutron flux for various hydrogen contents in FAN. Epithermal neutrons within orange marked energy range as defined from Gd cut-off to fast neutron range

Secondary radiation flux predictions furthermore provide insight into the surface radiation environment on the lunar surface and can aid in manned and unmanned lunar mission planning by predicting more precise radiation doses and analysing the effect of soil compositions on the local radiation environment, such as ground level enhancement effects.

In this report, we introduce our simulation framework, verify the results, and demonstrate its application potential on the example of the GRiNS instrument in the SER3NE orbit.

Acknowledgements  

We would like to thank our colleagues Sam Holdcroft and Rebecka Wahlén at UiO/CENSSS for the input on the GRiNS instrument, and Stephanie Werner at UiO/PHAB and the SER3NE team for the mission details, science objectives and their discussions and reviews.

References

1) Daniel Matthiä, Thomas Berger, Alankrita I. Mrigakshi, Günther Reitz. A ready-to-use galactic cosmic ray model. Adv Space Res. 2013;51(3):329-338. doi:10.1016/j.asr.2012.09.022

2) Nevay LJ, Boogert ST, Snuverink J, et al. BDSIM: An accelerator tracking code with particle–matter interactions. Comput Phys Commun. 2020;252:107200. doi:10.1016/j.cpc.2020.107200

3) Kohfeldt A, Wahlén R, Holdcroft S, Teodoro LFA, Werner S. Element Abundance Mapping with the SER3NE Gamma-Ray and Neutron Spectrometer. Copernicus Meetings; 2025. doi:10.5194/epsc-dps2025-1439

4) Werner SC. The SER3NE Mission to Hunt for Water and Other Volatiles on the Moon. Copernicus Meetings; 2025. doi:10.5194/epsc-dps2025-1451

How to cite: Eschler, J., Herbst, K., and Kohfeldt, A.: Lunar Secondary Radiation Soil Response Toolkit and Applications for GRNS, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-126, https://doi.org/10.5194/epsc2026-126, 2026.

South Korea’s first lunar probe, the Korea Pathfinder Lunar Orbiter (KPLO, DANURI), was launched in August 2022 and successfully entered lunar orbit in December of the same year. KPLO is currently conducting a mission with the KPLO Gamma-Ray Spectrometer (KGRS), developed by the Korea Institute of Geoscience and Mineral Resources (KIGAM). KGRS detects gamma-ray energies from 30 keV to 12 MeV and consists of a LaBr3 gamma-ray spectrometer and a Boron-Loaded Plastic Scintillator (BLPS) containing 5 % boron for background rejection. The primary scientific objective of KGRS is to collect gamma-ray spectral data from the lunar surface and to produce elemental abundance maps, which provide critical constraints on the bulk composition of the lunar surface and subsurface. Such elemental maps are directly relevant to the planning of future in situ robotic and human exploration missions, including site selection for resource utilization and ground-truth validation of orbital datasets.

KGRS data are processed through multiple steps, including positional data integration and exclusion of unusable data, in order to make them suitable for scientific analysis. KGRS data are currently available to the public through the KARI Planetary Data System (KPDS) website in Raw, PP, and CAL formats. All KGRS data transmitted to KIGAM are monitored daily, tracking both engineering parameters (e.g., instrument temperature) and science data, enabling the detection of transient events such as solar flares and gamma-ray bursts.

Since 2023, KGRS has accumulated more than three years of continuous gamma-ray spectral data from the lunar surface. The total gamma-ray count decreased by approximately 15% between 2023 and 2025. The decreasing trend is consistent with enhanced heliospheric modulation during the approach to solar maximum, which suppress the galactic cosmic-ray (GCR) flux responsible for inducing lunar gamma-ray emissions.

The orbit of KPLO was modified twice in 2025. Following initial operations in a near-circular orbit at approximately 100 km altitude, the orbit was lowered to approximately 60 km in February 2025. In late September 2025, KPLO transitioned into a frozen orbit with altitudes ranging from approximately 60 to 200 km.

This presentation reports on how KGRS spectral data vary as a function of time and changing mission environments, including orbital altitude modifications, over the course of the KPLO mission, and discusses the scientific implications of long-term lunar gamma-ray datasets in support of future lunar landing-site selection and exploration planning.

How to cite: Kim, S., Kim, K. J., and Hong, I.: Temporal Variations in KGRS Gamma-Ray Data over the KPLO/DANURI Mission, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-427, https://doi.org/10.5194/epsc2026-427, 2026.

Introduction

The lunar poles have been identified as key targets for future lunar missions because of the presence of Permanently Shadowed Regions (PSRs), which are thought to uncover large quantities of water ice. In this context, upcoming missions plan to carry Full-Polarimetric Ground Penetrating Radars (FP-GPR), such as the Chang’E 7 Lunar Penetrating Radar [2], and the Lunar Ground Penetrating Radar (LGPR), currently in development at LATMOS [9]. This work focuses on showing the benefit of GPR polarimetric observations for the interpretation of the radargrams in terms of subsurface structures.

CPR: a radar signature of water ice?

The lunar PSRs are located near the poles, where the low obliquity of the Moon with respect to the ecliptic plane prevents direct sun illumination. As a result, the interior of these craters can reach extremely low temperatures, forming long-term cold traps in which water ice may accumulate over geological timescales.

Although the presence of water ice has been confirmed at the surface of the PSRs [1], its occurrence in the subsurface remains under investigation. In 2009, the LCROSS (Lunar Crater Observation and Sensing Satellite) mission crashed a rocket into the Cabeus crater, revealing water in the ejecta plume [3]. Since then, numerous instruments have scrutinized the PSRs, searching for buried water ice.

Synthetic Aperture Radars (SARs), such as Lunar Reconnaissance Orbiter’s Mini-RF or DFSAR onboard Chandrayaan-2, have revealed anomalously high values of Circular Polarization Ratio (CPR) within the PSRs, and have sparked a debate, as they may indicate the presence of water ice, either as a matrix with rocky inclusions or as inclusions within the lunar regolith [8]; but could also arise from depolarizing phenomena unrelated to water ice, such as surface roughness or angular scatterers [7].

Here, the objective is to investigate how in situ FP-GPR measurements can contribute to a better understanding and interpretation of radar polarimetric products, including the CPR.

WISDOM / LGPR, two in-situ FP-GPR

Depending on their operating frequency, FP-GPRs can probe the subsurface to depths ranging from few meters to kilometers, and with vertical resolution spanning from few centimeters to meters. For instance, WISDOM (Water Ice Subsurface Deposit Observation on Mars), the FP-GPR of the ExoMars Rosalind Franklin rover mission, is designed to probe Mars shallow subsurface with a vertical resolution of few centimeters [6]. At LATMOS (France), a successor to WISDOM is currently under development for lunar exploration. Called LGPR, it will operate at lower frequencies to probe larger depths. Given the strong similarities between the two instruments and the availability of data from previous field tests, this study uses WISDOM as the basis for numerical simulations of FP-GPR measurements.

WISDOM is an FP-GPR that operates from 0.5 to 3 GHz. It can transmit and receive in two orthogonal directions of polarizations, providing four polarimetric configurations: 00, 01, 10 and 11 (Fig. 1).

Figure 1: Polarimetric configurations and illustration of WISDOM mounted on the Rosalind Franklin Rover.

Methodology

FP-GPRs can derive a wide range of polarimetric observables, including the CPR, by computing the Stokes Parameters, which are independent of the polarization basis [4]. However, FP-GPRs are often wideband instruments, providing one CPR value for each operating frequency, while orbital radars yield a single CPR value per observation.

As a result, FP-GPRs offer a three-fold analysis of the CPR, developed and investigated for WISDOM/LGPR using numerical simulations and experimental data:

• CPR as a function of frequency
• CPR averaged on the whole frequency band
• Subsurface CPR mapping

A previous study on CPR in icy media containing air and rock inclusions showed that CPR increases both with the number of inclusions, consistent with increasing depolarization phenomena, and with frequency [10]. Building on these results, the present work focuses on the development of subsurface CPR mapping.

Results: from simulations to experimental data  

The CPR subsurface mapping approach was tested on simple simulated environments. Figure 2 compares the permittivity distribution of a medium, with heterogeneous/homogeneous layers and smooth interfaces, its RGB radargram (a false-color radargram with 00 in Red, 11 in Green and the average of 01 and 10 in Blue), and the corresponding CPR subsurface map. Smooth interfaces appear yellow in the RGB radargram, and are associated with low CPR values, consistent with weak depolarization. In contrast, heterogeneities produce partial depolarization, appearing cyan or magenta and corresponding to higher CPR values.  

Figure 2: Permittivity distribution, RGB radargram, and CPR map of the simulated medium.

Finally, the subsurface mapping approach was applied to polarimetric data acquired during a WISDOM field test in Svalbard (Arctic Norway), focusing on a profile collected above the meanders of a buried ice cave. Figure 3 shows four regions exhibiting clear depolarization signatures:

• Two regions identified as the ice cave meanders, and a potential meltwater channel (white dashes in Fig. 3). [5]
• Two additional regions of depolarization, difficult to identify in the RGB radargram due to weaker intensity (red dashes in Fig. 3), requiring further investigation.

Figure 3: RGB radargram of the ice cave profile, and corresponding CPR map.

These results show the need for a combined quantitative and qualitative approach to identify regions of depolarizations and the complementarity of polarimetric observables.

Conclusion

Results from simulations and experimental data show the advantage of using polarimetric data to better understand subsurface depolarization phenomena from complex media. Next steps involve mapping other polarimetric products such as the degree of polarization, or polarimetric decompositions, but also comparing orbital datasets from Mini-RF with numerical simulations of CPR subsurface maps resulting from FP-GPR measurements at the surface of craters, and orbital simulations.

References

[1] Li et al., 2018, PNAS, 115, 36

[2] Shen et al., 2025, Space Sci Rev, 221, 98

[3] Colaprete et al., 2010, Science 330, 463–468

[4] Raney et al., 2011, IEEE, 99, 808-823

[5] Brighi et al., 2026, submitted in Geophysics

[6] Ciarletti et al., 2017, Astrobiology, 17(6-7), 565–584

[7] Fa et al., 2013, JGR, 118(8), 1582-1608

[8] Spudis et al., 2013, JGR, 118(10), 2016-2029

[9] Le Gall et al., 2026, ELS 2026

[10] Harrar et al., 2025, EPSC-DPS 2025

How to cite: Harrar, L., Le Gall, A., Ciarletti, V., Brighi, E., Hervé, Y., Oudart, N., and Brighi, G.: Towards subsurface mapping of polarimetric products from Full-Polarimetric Ground Penetrating Radar in situ measurements, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-468, https://doi.org/10.5194/epsc2026-468, 2026.

The Indian Space Research Organisation’s (ISRO) Chandrayaan-2 lunar mission carries the Dual-Frequency Synthetic Aperture Radar (DFSAR), which acquires fully polarimetric (quad-pol) observations at L- and S-band wavelengths [1]. This format measures the full scattering matrix for each pixel (HH, HV, VH and VV) enabling a range of polarimetric decompositions to be used to characterise surface and near-subsurface scattering mechanisms. At the lunar south pole where low solar incidence angles create many permanently shadowed regions (PSRs), radar provides a key means of investigating these otherwise inaccessible environments.

Given the capabilities of DFSAR, Cabeus crater was chosen for this study as it was designated as a site of interest in a study completed by Lemelin et al. [2], and the selected swath also intersects with a large PSR. The analysed area is close to the LCROSS Centaur stage impact site where near-infrared absorption spectra attributed to water vapour and ice, and ultraviolet emissions attributable to hydroxyl radicals were detected in the impact plume debris [3]. The crater itself is one of the coldest craters on the Moon [3], and has a substantial fraction of water equivalent hydrogen (WEH) content of values around 0.5%wt [4].

The chosen DFSAR single look complex (SLC) swath was processed within PyPolSARPro to generate Pauli, Cloude-Pottier, Yamaguchi 4-component, and target scattering vector model (TSVM) decompositions, enabling the comparison of the scattering responses. A polarimetric whitening filter (PWF) was also used to create an image where heterogeneous areas were isolated and highlighted against the background. For example, the volumetric component of the Y4R decomposition, originally developed to represent randomly oriented vegetation scatterers, was used to understand the volumetric return of surface and near-subsurface regolith. Finally, a ratio of the Y4R odd and volume components was used to generate a surface classification map identifying areas of dominant surface, dominant volume, and mixed return in a discrete and quantifiable way.

As shown in Fig.1, the Pauli RGB image indicates surface scattering as the dominant mechanism across the region. An enhanced volumetric signal is associated with the crater walls, which is consistent with buried boulders, blocky material or potentially volatile-bearing regolith. This is reinforced by the Y4R decomposition, which identifies a reduction in surface scattering and more volume scattering across the crater walls. Regions located south of the crater are characterised by smoother, surface-dominated scattering. In contrast, the region north of the crater exhibits enhanced heterogeneity, with strong volumetric signals associated with the rims of smaller impacts. Analysis of the PWF image further highlights the northern region’s heterogeneity and enhances linear features in the crater walls.

Fig.2 presents the resulting surface classification map. The northern region is predominantly volume dominated, whereas the smoother southern region is consistent with the Y4R surface component as seen in Fig.1. By categorising scattering behaviour into discrete regimes, ratio maps enable easy identification of regions where surface and subsurface heterogeneity may indicate volatile-bearing or structurally complex regolith. This can therefore support site prioritisation for future lunar exploration and in situ investigation.  

We acknowledge the use of data from the Chandrayaan-II, second lunar mission of the Indian Space Research Organisation (ISRO), archived at the Indian Space Science Data Centre (ISSDC). This work was supported by the UK Space Agency and the Science and Technology Facilities Council [grant number: UKRI2555].

 [1] Bhiravarasu et al., 2021, Planetary Science Journal; [2] Lemelin et al., 2021, Planetary Science Journal; [3] Colaprete et al., 2010, Science; [4] Sanin et al., 2015, Icarus

How to cite: McVann, P., Marino, A., Ghail, R., Gallardo i Peres, G., Mason, P., and Knight, C.: Comparison of Polarimetric Decomposition Techniques for Scattering Classification in Cabeus Crater. , Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1034, https://doi.org/10.5194/epsc2026-1034, 2026.

The cumulative effects of impact cratering on the Moon have given rise to a surficial fine-grained impact gardened regolith that is 10s of meters thick, along with deeper layers of thick impact ejecta and in situ fractured crust. Collectively, the thick ejecta deposits and underlying fractured crust are referred to as the megaregolith, but its physical properties and thickness remain poorly understood. Impact generated porosity can affect the bulk density, seismic velocities, and thermal conductivity of the crust. Knowledge of how porosity varies with depth and with location is thus important for understanding the Moon’s impact history, geologic evolution and thermal evolution.

The gravity field of a planet is directly related to its internal density structure, and the high-resolution measurements made by the Gravity Recovery and Interior Laboratory (GRAIL) mission allow to investigate how density (and by inference porosity) varies both laterally and with depth in the crust. The work of Besserer et al. (2014) made use of an early GRAIL-derived gravity model to invert for the depth dependence of density in thecrust using assumed linear and exponential density profiles. Since then, substantially improved gravity models have been developed using the complete set of observations collected by the GRAIL mission. Recent models reach spherical harmonic degrees as high as 1200 (Goossens et al., 2020), 1500 (Konopliv et al., 2014), and even 1800 (Park et al., 2025). These new gravity models, combined with improved analysis techniques, allows us to revisit the subsurface density structure of the Moon.

In this study, we employed a localized spectral analysis method (Wieczorek & Simons, 2005; 2007) to compute the effective density spectrum which was then used to constrain the depth dependence of density in the lunar crust. We restricted our analyses to the polar regions where the quality of the gravity field was best. In contrast to previous studies that imposed a density profile of a specific form, we assumed that the density structure could be approximated by Nconstant density layers, and we investigated models with 2 to 5 independent layers. As a result of the large number of inversion parameters, we used a Bayesian inversion approach with a Markov Chain Monte Carlo sampling of the density profiles. For our localized analyses we used a spherical cap size of 15° with a spectral bandwidth of 43 and employed 14 windows with concentration factors greater than 0.99. When computing the misfit between the model and observations, we used spherical harmonic degrees between 250 and 780, in contrast to 250 to 550 as used in the earlier study by Besserer et al. (2014). For the model results presented here, we only use density profiles increasing with depth.

Our results reveal a distinct two-layer density structure near both poles (see Figure 1 for an example centered on the South Pole). The models are characterized by an upper megaregolith layer that is about 1-2 km thick with porosities of ~15–20% and a less porous underlying layer with porosities of 7-13%. These two layers are interpreted as being composed of allochthonous impact ejecta and autochthonous in situ fractured crust, respectively. The depth of the lower layer extends to at least 11 km, beyond which our inversion approach loses sensitivity to density. We show these porosities substantially reduce crustal bulk seismic velocities and thermal conductivity.

Figure 1. Posterior distributions of density as a function of depth for layered models centered over the South Pole, where density is constrained to increase with depth. Panels (a) to (d) show the results for 2-, 3-, 4-, and 5-layer models, respectively. The yellow line in each subplot indicates the median density as a function of depth; the solid and dashed red lines represent the maximum posterior density and its uncertainty in the high- and low-density groups, and the solid and dashed horizontal green lines represent our estimate of the transition depth and its uncertainty. Density and depth were sampled in the ranges of 1000–4000 kg/m³ and 0–50 km, respectively.

Reference:

Besserer, J., Nimmo, F., Wieczorek, M. A., Weber, R. C., Kiefer, W. S., McGovern, P. J., Andrews‐Hanna, J. C., Smith, D. E., & Zuber, M. T. (2014). GRAIL gravity constraints on the vertical and lateral density structure of the lunar crust. Geophysical Research Letters, 41(16), 5771–5777.

Goossens, S., Sabaka, T. J., Wieczorek, M. A., Neumann, G. A., Mazarico, E., Lemoine, F. G., Nicholas, J. B., Smith, D. E., & Zuber, M. T. (2020). High‐Resolution Gravity Field Models from GRAIL Data and Implications for Models of the Density Structure of the Moon’s Crust. Journal of Geophysical Research: Planets, 125(2), e2019JE006086.

Konopliv, A. S., Park, R. S., Yuan, D., Asmar, S. W., Watkins, M. M., Williams, J. G., Fahnestock, E., Kruizinga, G., Paik, M., Strekalov, D., Harvey, N., Smith, D. E., & Zuber, M. T. (2014). High‐resolution lunar gravity fields from the GRAIL Primary and Extended Missions. Geophysical Research Letters, 41(5), 1452–1458.

Park, R. S., Berne, A., Konopliv, A. S., Keane, J. T., Matsuyama, I., Nimmo, F., Rovira-Navarro, M., Panning, M. P., Simons, M., Stevenson, D. J., & Weber, R. C. (2025). Thermal asymmetry in the Moon’s mantle inferred from monthly tidal response. Nature, 641, 1188–1192.

Wieczorek, M. A., & Simons, F. J. (2005). Localized spectral analysis on the sphere. Geophysical Journal International, 162(3), 655–675.

Wieczorek, M. A., & Simons, F. J. (2007). Minimum-variance multitaper spectral estimation on the sphere. Journal of Fourier Analysis and Applications, 13(6), 665–692.

Wieczorek, M. A. (2024). Lunar shape models (LDEM_shape_pa) [Dataset]. SHTOOLS. https://shtools.github.io/SHTOOLS/python-datasets.html.

How to cite: Yang, J. and Wieczorek, M.: Density structure of the Moon’s crust beneath the polar regions as revealed by gravity data., Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-729, https://doi.org/10.5194/epsc2026-729, 2026.

PROSPECT Overview: The Package for Resource Observation and in-Situ Prospecting for Exploration, Commercial Characterisation and Testing (PROSPECT) is a payload in development by ESA for use at the lunar surface.  PROSPECT is being prepared for flight to the lunar south polar region as part of the NASA CLPS program.

PROSPECT will perform an assessment of the volatile inventory in the near surface lunar regolith (down to ~1 m), and complete elemental and isotopic analyses to determine the abundance and origin of any volatiles discovered. PROSPECT also has ISRU capabilities and will aim to complete in-situ extraction of oxygen (and solar wind implanted volatiles) from lunar minerals, which will constitute potential science return from anywhere on the Moon.

PROSPECT is comprised of the ProSEED drill module and the ProSPA analytical laboratory plus the Solids Inlet System (SIS), a carousel of sealable ovens for evolving volatiles from regolith.  The ProSEED drill is capable of collecting two icy samples of different sizes and mechanical properties in a single sampling operation, one of up to 45 mm³ and a second up to 8 cm³, with the smaller sample delivered to ProSPA for analyses.  The drill rod also has integrated temperature sensors and a sensor to measure the electrical permittivity of the lunar soil along the borehole to give an indication of the presence of water ice and small subsurface structures in the surrounding regolith.

The ProSPA laboratory will receive samples from the drill, seal them in miniaturized ovens, and process them via ramped (EGA), stepped (isotopic) or single step (ISRU) heating up to ~1000 °C, completing physical and chemical processing of released volatiles, and analyzing the obtained constituents via Ion Trap (ITMS) or Magnetic Sector (MS) mass spectroscopy.

ProSEED and ProSPA will also each carry small cameras.  The ProSEED Imaging System (IS) has multispectral capabilities via 6 LEDs, which can illuminate the surface with wavelengths ranging from 451 to 970nm.  This will provide images and ‘video’ of the drill working area to monitor activities and deliver contextual scientific information.  ProSPA’s Sample Camera (SamCam) has its own specific illumination unit with similar capabilities to the ProSEED IS and will image the samples before they are sealed in the ovens, providing information on their morphology, grain size, volume and mineralogy.

Operations Planning: PROSPECT Science Team (ST), led by the ESA Project Scientist, comprises ~40 experts within Europe, including 8 ‘Investigation Leads’.  Together, the ST is responsible for supporting all activities related to the scientific exploitation of PROSPECT instrumentation.  Recent focus has been on developing nominal operational scenarios that will allow PROSPECT to achieve its high priority science objectives and maximise science return within the tight operational constraints available.  Two fundamental operational scenarios have been defined, depending upon whether regolith at the landing site is found to be icy or dry.

Conceptually, operations for PROSPECT will be ‘front-loaded’ for science, pushing for high impact science activities and the highest priority science objectives as early as possible rather than building up towards them.  This is mainly driven by the limited operational lifetime on the lunar surface, which will also mean that there will be very little operator interaction and limited tactical planning available.

The baseline plan includes two ‘vertical surveys’, each of which includes the acquisition and analysis of 4 subsurface samples.  The general pattern of activities would be to run an ‘Evolved Gas Analysis’ on the first sample using the ion trap (ITMS), and then a magnetic sector (MS) analysis on the second sample which is taken from a similar depth.  In combination, this would provide a complete chemical analysis with abundance and isotopic measurements for key species at that depth in the borehole.  For the very first sample, the ISRU demonstration would also be attempted after the EGA, in order to maximise the use of that sample.

Samples 3 and 4 would follow a similar pattern with an EGA followed by MS, but they would be acquired deeper than the initial samples.  The permittivity sensor is embedded in the drill ~40cm from the drill tip, and will be used regularly as the drill descends below 40cm.  This sampling pattern will allow PROSPECT to explore the vertical distribution of various species within the borehole, which is one of the payload’s primary objectives.  With these 4 sampling and analysis sequences, the first ‘vertical survey’ is complete.

A second borehole would follow a similar operational pattern but with different sampling depths, and provide lateral profiling for any ices, volatiles and species of interest.  In combination, the two vertical surveys would allow for PROSPECT to meet its priority objectives.  An additional ISRU demonstration may be attempted in the second borehole at a different depth to the original in order to explore the impact of grain size on the process.

The differences between the icy and dry scenarios will primarily be in the various parameters and instrument modes used.  The dry scenario may also allow for ‘hot’ operations, with fewer cooling periods required to manage icy volatile loss.

The high level plan will be further detailed as PROSPECT builds towards operations, defining the parameters to use for sampling depths, oven heating profiles, gas processing etc.  This will also allow for more fine tuning of the priorities. 

Additional Science Team Activities: The Science Team will also be completing several activities in support of the operations planning and to prepare for data calibration and interpretation.  These efforts are needed to support PROSPECT all the way from now through launch, operations, data exploitation, and data archive delivery.  Work will include aspects of ground calibrations, testing of command sequences, and end-to-end testing to understand how each aspect of the payload is expected to perform.  In addition, data pipelines will be developed to manage the data returned as quickly and effectively as possible, processing the data from telemetry through to a calibrated level.

How to cite: Heather, D. and the PROSPECT Science and Project Team: The ESA PROSPECT Payload: Status and Operations Planning, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-807, https://doi.org/10.5194/epsc2026-807, 2026.

Introduction: The accurate removal of thermal emission from near-infrared hyperspectral observations is a prerequisite for the reliable detection and quantification of OH/H₂O absorptions on the lunar surface. The Moon Mineralogy Mapper (M³), operating aboard Chandrayaan-1 across the 0.43–3.00 µm spectral range [1], is particularly affected by this problem at wavelengths beyond ~2 µm, where thermal contamination can substantially alter the shape and depth of hydration-related absorption features. Lunar surface temperatures vary dramatically over the course of a lunar day, ranging from ~40 to ~400 K [2] as a function of latitude, local solar time, solar incidence angle, surface albedo, and regolith thermophysical properties. Existing thermal correction strategies for M³ data include data-driven approaches [3], laboratory-calibrated empirical models [4], and advanced thermophysical models (TPMs) [5,6]. While each method has demonstrated utility, the laboratory-based empirical approach [4], which remains widely used in both local and global hydration studies, exhibits known limitations in transferability across diverse surface compositions and illumination geometries [7]. TPMs, despite their physical rigor and ability to account for anisothermal effects, remain computationally demanding for both global-scale and local-scale applications. In this context, we present LENNA (Lunar thermal-Emission Neural Network Approach), a supervised Machine-Learning framework for rapid, Diviner-consistent prediction of bolometric temperatures from M³ observations, whose outputs are coupled with a Hapke-based radiative transfer model [8] to retrieve spectral directional emissivity and OH/H₂O-related absorption signatures (OHIBD [5] and ESPAT [9]).

Methods and data: LENNA is implemented as a fully connected feed-forward neural network trained to predict Diviner-like bolometric temperatures from geometrical and M³-derived input parameters on a pixel-by-pixel basis. The nine input features combine geometric quantities — latitude, local solar time (LST), and the topographically corrected cosine of the incidence angle — with selected spectral quantities, including the mean reflectance over 0.89–1.62 µm as a proxy for surface albedo, the reflectance values at four spectral channels in the 2.54–2.67 µm range sensitive to residual thermal emission, and the spectral slope between 2.02 and 2.67 µm.

Training targets are Diviner bolometric temperatures [2] derived from spatially and temporally co-registered M³ and Diviner observations, matched within a LST tolerance of ±0.05h. The training dataset encompasses several regions of interest (ROIs) distributed across diverse latitudes, surface compositions, optical maturities, and illumination conditions. Model performance is evaluated via a random split approach and a Leave-One-ROI-Out (LORO) cross-validation strategy. Eight additional spatially independent test ROIs — spanning anorthositic highlands, pyroclastic deposits, and lunar maria — are used to assess generalization capability and the potential influence of spatial or temporal leakage. Predicted bolometric temperatures are subsequently coupled with a Hapke-based radiative transfer formulation to retrieve thermally and photometrically corrected reflectance spectra.

Results and conclusions: LENNA reproduces Diviner-derived bolometric temperatures with high fidelity across all testing ROIs, outperforming the empirical benchmark [4] in terms of both precision and bias. The LORO cross-validation yields an overall standard deviation of ~8.8 K and an RMSE of ~9.4 K, with pixel-level biases remaining below 5 K across all tested regions. Propagation of this uncertainty into thermally corrected reflectance spectra yields maximum absolute uncertainties of approximately ±0.40% at 2.54 µm and ±2.50% at 3.00 µm, at least in testing ROIs. The distribution of normalized reflectance residuals at 2.54 µm is broadly consistent with a near-Gaussian behavior, suggesting that systematic effects remain limited across the tested conditions. The LENNA-predicted near-noon bolometric temperature maps (Figure 1) reproduce the expected trends, in agreement with the global Diviner reference dataset [10]. The 2.54 µm directional emissivity map derived via the Hapke formulation (Figure 2) reveals spatially coherent patterns correlated with surface composition and albedo, consistent with independent mineralogical analyses. Comparison with an advanced TPM [6] over two hydration-rich near-noon regions, such as Copernicus crater and the western border of Mare Crisium, shows broadly consistent spatial distributions of OH/H₂O.

The ESPAT latitudinal profiles derived from LENNA (Figure 3) indicate systematically higher hydroxyl/water abundances than those reported in [9] at the same LSTs, in agreement with previous studies employing physically more complete correction frameworks (e.g., [5,6]).

Although calibrated on M³-derived features, LENNA is potentially transferable to other VIS-NIR imaging spectrometers operating in the same reference domain, provided their spectral range is at least partially overlapping with that of M³. Promising candidates include the Chang'e-5/LMS and Chang'e-6/LMS, Chandrayaan-2/IIRS, and instruments that acquired lunar observations during flyby operations, such as JUICE/MAJIS.

Future developments will aim to map the OH/H₂O distribution across the entire lunar surface by expanding the set of representative training ROIs and/or input parameters, in order to better capture data-related effects such as latitudinal/longitudinal striping [5].

Acknowledgments: The authors acknowledge support from the Space It Up project, funded by the Italian Space Agency and the Italian Ministry of University and Research. Contract n. 2024-5-E.0 – CUP n. I53D24000060005.

References:

[1] Green, R.O. et al. (2011) Journal of Geophysical Research, Planets, 116(E10).

[2]  Paige, D. A. et al. (2010) Science, 330, 6003.

[3] Clark, R.N. et al. (2011) Journal of Geophysical Research, Planets, 116(E6).

[4] Li, S. & Milliken, R.E. (2016) Journal of Geophysical Research, Planets, 121(10).

[5] Wöhler, C. et al. (2017) Science Advances, 3(9).

[6] Wohlfarth, K. et al. (2023) Astronomy & Astrophysics, 674(A69).

[7] Clark, R.N. et al. (2024) The Planetary Science Journal, 5(9).

[8] Hapke, B. (2005) Theory of Reflectance and Emittance Spectroscopy.

[9] Li, S. & Milliken, R.E. (2017) Science Advances, 3(9).

[10] Williams, J.–P. et al. (2017) Icarus, 283(300-325).

Figure 1. Bolometric temperature maps for both nearside and farside using LENNA on part of near-noon M³ observations (OP2C [1]).

Figure 2. 2.54 µm directional emissivity maps for both nearside and farside using LENNA on part of near-noon M³ observations (OP2C [1]).

Figure 3. The latitudinal trend of the ESPAT parameter computed at ~2.85 µm in analogy with [9].

How to cite: Colaiuta, F., Tosi, F., Zambon, F., Pratesi, G., Baldetti, C., and Bellucci, M.: Retrieving Lunar Bolometric Temperature, Directional Emissivity, and Hydration Signatures with LENNA: A Machine-Learning Framework for Moon Mineralogy Mapper (M3) Data, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-616, https://doi.org/10.5194/epsc2026-616, 2026.

Introduction:

Permanently shadowed regions (PSRs) of the Moon are thought to act as cold traps for volatiles, due to their extremely low temperatures (<110 K) [1,2].

The Moon Mineralogy Mapper (M³) spectral analysis [3] revealed absorptions of water ice at about 1.25, 1.5, and 2.0 µm in several permanently shadowed areas at both lunar poles. However, latest results based on radiance contrast, obtained by Danuri ShadowCam, [4-7] show that the ice-bearing areas identified by M³ exhibit no significant brightness contrast relative to their surroundings, raising questions about the reliability and interpretation of the previous M³ detections.

In this work, we analyze M³ spectral data to address this apparent discrepancy and reassess the evidence for exposed water ice in lunar PSRs, which represents a key factor for future in situ robotic investigations and human exploration.

Methods:

To search for water ice, we applied a procedure based on the identification of the 1.25, 1.5, and 2.0 µm absorption bands. Pixels were classified as positive water-ice detections when their spectral properties were consistent with thresholds defined using a laboratory spectrum of an intimate mixture containing 5 wt% water ice and 95 wt% lunar highland simulant. To assess the robustness of the detections, we applied the same procedure to non-PSR regions, located between 69°S and 75°S latitude, where water ice is not expected to be stable. Since the analysis relies on very low-signal data, we also evaluated the linearity of the instrument response in these pixels by comparing the spectra of high- and low-signal regions. Finally, we adopted a statistical approach in which a simulated dataset was generated to reproduce the spectral distortions induced by the non-linear instrumental response and noise, in order to determine whether the combination of these effects can account for the apparent ice detections in the M³ data.

Results:

Figure 1 reports the average spectra of the detected pixels within a South Pole Region of Interest (SP ROI; Fig. 1a), including a PSR, and within a lower-latitude region between 69°S and 75°S (Fig. 1b), where surface water ice is not expected [8]. Figures 1c and 1d show the spatial distribution of the detected pixels in the SP ROI and in the lower-latitude region, respectively. The occurrence of the same spectral features in both areas raises serious concerns on the robustness of these detections. In addition, the detected pixels are frequently aligned along individual detector samples, suggesting a link with instrumental effects rather than with localized surface properties. Overall, these results indicate that the apparent water-ice signatures observed at the South Pole are more likely related to data artifacts or systematic effects than to the presence of an actual surface ice deposit.

Figure 1: Mean spectra of the detected pixels are shown in figure (a, b) for the SP ROI and the lower latitude region 69° S - 75° S, respectively. The spectra are normalized to their mean value. Vertical dashed lines in the mean spectra indicate the 1250, 1500 and 2000 nm absorption band positions of water ice [9]. Positive detections (yellow dots) distribution in the SP ROI and in the lower latitude region are shown in panels c and d respectively.

We conducted a linearity analysis of the data and found that spectra become progressively distorted from high to low radiance levels. The shape of the distortion is very similar to the Radiance Calibration Coefficient (RCC) used in the instrument calibration pipeline.

We then carried out a statistical analysis to assess whether random noise, combined with the non-linear spectral response of the instrument, could account for the apparent water-ice detections. To this end, we generated a simulated dataset by applying the non-linear distortion derived from the linearity analysis and injecting M³-like noise. Figure 2 shows the scatterplot between the number of water ice detections in the simulated and in the M³ observation across different reflectance bins.

Figure 2: Detections in simulated and original datasets.

The strong linear anticorrelation between the number of detections and reflectance, observed in both datasets, confirms that the apparent water-ice spectral signatures in the M³ data are more likely produced by instrumental non-linearity than by the presence of surface water ice.

Conclusions:

In conclusion, we show that water ice-like spectral signatures can occur in M³ data even in regions where surface ice is not expected to be stable. Our analysis indicates that these detections can be explained by the combined effects of the instrument’s non-linear spectral response and the low signal-to-noise ratio of the data. However, this work does not rule out the presence of surface or subsurface water ice in lunar PSRs. Rather, it highlights that M³ observations at very low signal levels are affected by instrumental effects that can significantly bias the detection of water-ice signatures. Future in situ investigations of the lunar poles will therefore be crucial to assess the presence and distribution of water ice, by providing complementary datasets, higher signal-to-noise measurements, and instruments specifically designed to operate under extremely low-illumination conditions.

Aknowledgments:

This work is granted by Accordo ASI – INAF n. 2024-64-HH.0.

References

[1] Watson, K. et al. (1961). JGR, 66(9), 3033–3045. [2] Urey, H. C. et al. (1952). Physics Today, 5(8), 12-12. [3] Li, S. et al. (2018). PNAS, 115(36), 8907-8912. [4] Ando, J. et al. (2025). The PSJ, 6(3), 62. [5] Williams, J.-P. et al. PSJ 5, 209 (2024). [6] Mahanti, P. et al. J.A.S.S. 40, 131–148 (2023). [7] Li, S. et al. (2026) Sci. Adv. 12, eaec8211 [8] Hayne et al. (2021), Nature Astronomy 5, 169–175; [9] Clark, R. N. (1981) JGR 86, 3087–3096;

How to cite: Massa, G., De Sanctis, M. C., Altieri, F., Raponi, A., and Besse, S.: On the reliability of Water Ice Signatures in Moon Mineralogy Mapper (M3) Spectral Data, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1102, https://doi.org/10.5194/epsc2026-1102, 2026.

The Rashid-2 mission of the Mohammed Bin Rashid Space Centre (MBRSC) is scheduled for launch at the end of 2026 as part of the Firefly Blue Ghost 2 payload. Equipped with three optical cameras, a thermal camera, four Langmuir probes, and a material adhesion / abrasion determination experiment, this ~10 kg rover is similar to the Rashid-1 mission (e.g., Flahaut et al., 2024). Rashid-2 aims to investigate the properties of the lunar regolith, plasma interactions at the lunar surface, and, more broadly, to improve our understanding of the Moon’s formation and evolution.

This study focuses on the geological mapping of the Rashid-2 landing region, located on the northeastern rim of Nassau F crater (177.7° W; 23.82° S). Remote sensing data from the Clementine, Lunar Prospector, LRO (LOLA, WAC, Diviner, Mini-RF), and Kaguya (TC and MI) missions were processed and integrated into a Geographic Information System (GIS), complemented by mineralogical information derived from Chandrayaan-1 M³ spectral imager data.

Our analysis shows that the landing ellipse is located near the boundary between the interior of the basin and its outer rim, also referred to as the heterogeneous annulus of the South Pole-Aitken (SPA) basin. These regions are characterized by distinct lithologies. The landing site is situated on a ridge likely composed of superimposed ejecta from Nassau F (dated at ~4.2 Ga), Orlov Y, Nassau D, and Orlov (dated at ~3.8 Ga), as well as secondary craters potentially originating from the Orientale basin-forming impact (~3.8 Ga).

M³ spectra are relatively featureless or indicative of weak pyroxene signatures, consistent with mature and mixed highland material. Kaguya-derived mineral maps, combined following Frueh et al. (2025), suggest an anorthositic norite to anorthositic gabbro composition. Distinct compositions including gabbros, gabbronorites, and norites are observed in the ejecta of nearby craters such as Birkeland (d = 82 km) and Rumford (d = 61 km), suggesting that SPA impact melt may be present at depths greater than 4 km and could have been excavated in the study region by larger impacts.

Our updated regional geological map, along with new insights into the local crustal stratigraphy, will be presented and discussed at the conference.

How to cite: Flahaut, J., Delvoye, Z., Wöhler, C., Füri, E., and Al Matroushi, H.: The Nassau F Crater Region within the South Pole–Aitken Basin: Geological Context for the Rashid-2 Rover Landing Site, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-593, https://doi.org/10.5194/epsc2026-593, 2026.

Introduction: Multiple missions are currently planned to explore the lunar south polar region and study the occurrence of water in and around permanently shadowed regions [1]. As part of the Rover Permittivity Sensor (RPS) for the European Space Agency [2,3], a contribution to the Rashid-3 mission [4], we have developed several testbeds to investigate the soil interaction of wheel-based sensors. RPS includes two sensing electrodes on the rover wheel to measure the dielectric permittivity of the lunar soil along the traverse and infer its potential ice content and density. For such contact-based sensors, the mechanical coupling to the soil is a major source of uncertainty in measurement quality. Thus, it is important to characterise this interface with respect to soil sinkage and compaction, considering reduced gravity and the wide range of the particle size distribution. Furthermore, due to the suspected abrasive nature of lunar regolith, mechanical wear is expected at the wheel-soil interface. For missions to the lunar south pole or similar destinations, the extremely wide temperature range of the environment also needs to be considered, as it might alter the mechanical properties. Currently, no standardised environmental testing procedure exists for this scenario. The two custom-designed testbeds presented here allow studying both the geotechnical interaction between the wheel-based sensors and the soil, and the wear over the sensors' lifetime.

Soil interface testbed: The setup features a wheel analogue installed on a movable gantry and can be installed inside a dust-tolerant thermal-vacuum chamber [5]. With this system, the wheel with a functional RPS sensor can be pressed against or rolled over (ice-bearing) regolith samples, while measuring load and displacement. This enables studies of sensor-soil interaction effects on the permittivity measurement, as well as the compaction of the soil. The system is planned to include a gravity-offloading system and a cooled regolith bed, with target temperatures of around 150 K. Fig. 1 shows the initial testing setup for the vertical axis, before integration into a full gantry.

Wear testbed: The setup comprises a turntable platform on which a wheel mock-up can continuously be rolled. Both the turntable and the wheel are motorised, allowing defined slip ratios to be set. The turntable includes a ring-shaped trench, 89 cm in outer radius, filled with regolith simulant, up to 10 cm deep and 13 cm wide. Bigger rocks can also be placed in the trench to simulate higher surface roughness. The wheel is free to move vertically and is offloaded with counterweights to set specific wheel loads for different gravity or rover conditions. Fig. 2 shows the wheel wear testbed after first room temperature tests. A liquid nitrogen evaporation cooling system is being implemented to cool the wheel during operation and test the robustness of wheel and its surface-mounted sensors in a lunar polar environment.

Fig. 1: First iteration of the soli interface testbed vertical axis, used for static sinkage tests

Fig. 2: Wheel wear testbed

Conclusion: The two testbeds provide a combined capability for testing RPS wheel-based sensors with glass-fibre-reinforced polymer (GFRP) caps under various relevant lunar environment aspects (temperature, vacuum, ice-regolith mixtures) and motion types (static, rolling, slip, continuous/steady-state rolling), while studying load, wheel/sensor sinkage, and wear. Furthermore, the testbeds can also be used to study other similar wheel-based systems and materials, making them a useful tool for exploration rover development programs.

Acknowledgments: Part of this work has been performed in the context of the Rover Permittivity Sensor project for the European Space Agency (Contract No. 4000148937/25/NL/PA/dp).

References: [1] Reiss, P. (2024), PNAS, 121(52). [2] Reiss, P. et al. (2025), European Lunar Symposium. [3] Gscheidle, C. et al. (2025), EPSC. [4] Almatroushi, H. et al. (2025), EPSC. [5] Witzel, T. et al. (2025), EPSC.

How to cite: Sesko, R., Laub, P., Sedlmair, F., Chauhan, S., Eckert, L., Gscheidle, C., and Reiss, P.: Experimental investigation of the soil interaction and wear of lunar rover wheel-based sensors, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-816, https://doi.org/10.5194/epsc2026-816, 2026.

Introduction. This work is carried out within the framework of the LUMIO (Lunar Meteoroid Impact Observer) mission, an ESA 12U CubeSat aimed at detecting and characterizing meteoroid impacts on the lunar far side [1,2]. From a quasi-halo orbit around the Earth–Moon L2 point, LUMIO uses the LUMIO-Cam instrument to observe impact flashes in the visible and near-infrared, enabling estimation of radiated and kinetic energy through luminous efficiency and providing constraints on the meteoroid flux.

To interpret these observations, it is essential to model the evolution of ejecta clouds generated by hypervelocity impacts and to assess the influence of material properties and vaporization processes.

Methods. We conduct numerical simulations using the LIMARDE code to study the dynamical evolution of ejecta clouds generated by impacts. The simulated regolith properties are defined consistently with numerical impact studies, where the lunar surface is represented as a porous regolith layer with properties varying depending on mechanical conditions. Impact conditions are not prescribed analytically, but are derived from the outputs of iSALE shock physics simulations, which provide physically consistent ejecta velocity distributions and material responses following hypervelocity impacts. In addition, thermodynamic processes are incorporated by modeling partial vaporization of the regolith, which generates a transient gas plume characterized by velocities of several hundred m/s and short characteristic timescales. 

Simulations are conducted for different materials and configurations, including cases with and without vaporization. Dust grains are modeled as non-spherical axially symmetric particles with characteristic sizes of ~50 μm and ~100 μm, and densities of 2700 kg/m³ for plagioclase and 3450 kg/m³ for pyroxene [3,4].

Results. We performed simulations of the initial ejecta distribution produced by an impact crater of approximately 30 m in radius, considering two dust particle cases corresponding to pyroxene and plagioclase. The particle sizes are constrained by observed lunar regolith morphology, spanning typical grain distributions from equatorial to polar regions.

The simulations provide the spatial distribution of deposited particles on the lunar surface as a function of distance from the crater, as well as the influence of vaporization processes.

Figure 1 shows the number of particles deposited as a histogram of radial distance from the crater center, considering a vaporization phase lasting approximately 1 s after the impact (which can be considered as an upper limit). We simulate the vaporization process as resulting from the melting of lunar surface material, using the following meteoroid impact scenario: a 30 cm meteoroid with a density of 2900 kg/m³ impacting at a velocity of 21 km/s.

 The results indicate that a significant fraction of the ejecta is deposited relatively close to the impact site, with about 40% of the particles settling within 8 km. Over the full simulation duration of 10 minutes, less than 10% of the particles remain in motion, and no particles reach or exceed the lunar escape velocity.

Figure 2 illustrates the variation in particle velocities after the vaporization phase has ended. Both accelerated and decelerated particles are observed, reflecting the interaction with the transient gas plume. However, deceleration affects approximately 30% of particles in the plagioclase case and about 40% in the pyroxene case.

These results show that vaporization influences the redistribution of ejecta and that variations in material properties contribute to constraining the thickness of the deposition layer as a function of the impactor characteristics and the resulting crater size.

Future Work. We would like to investigate the deposition of lunar dust in polar regions and to study ejecta timescales. In addition, future work will focus on constraining the most plausible composition of the particles involved in the thermodynamical processes responsible for generating lunar impact flashes.

Fig. 1. Deposited particle distribution of pyroxene and plagioclase in the lunar ejecta LIMARDE simulations. The vaporization phase lasts approximately 1 s, while the total simulation duration is 10 minutes.

Fig. 2. Acceleration and deceleration of the two types of particles (pyroxene and plagioclase) resulting from the vaporization process following the impact.

[1] Cipriano, A., et al. (2018). LUMIO: Lunar Meteoroid Impacts Observer. Frontiers in Astronomy and Space Sciences.  

[2] Topputo, F., et al. (2023). LUMIO mission design and operations. Icarus.

[3] Qin, L., Yue, Z., Gou, S., Zhang, Y., Wei, G., Shi, K., Zhang, X., & Yang, B. (2025). Structure and Formation Mechanism of Lunar Regolith. Space Science & Technology, Article 0219. 

[4] Yu, S., Yu, M., Xiao, X., Huang, J., & Xiao, L. (2025). Thermophysical Properties of Lunar Regolith Revealed by Thermal-Infrared Observations of the LRO Diviner Radiometer. The Astrophysical Journal. 

Acknowledgements. This work was supported by the Italian Space Agency (ASI) within the LUMIO project (ASI-PoliMi agreement n. 2024-6-HH.0).

How to cite: Ivanovski, S. L., Calderone, L., Martellato, E., Rice, P., and Luther, R.: Modeling Lunar Impact Ejecta with Vaporization under Hypervelocity Meteoroid Impacts, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1230, https://doi.org/10.5194/epsc2026-1230, 2026.