Francesca Altieri

Rock fractures on planetary bodies provide critical insights into composition and geologic history. On Earth, fracture mapping is performed manually on-site or remotely through drone, aircraft, or orbital photography. On other planets, fracture mapping mainly relies on orbital data, with on-the-ground confirmation only possible in very limited areas. To date, a repeatable and clearly defined workflow for fracture mapping from orbital data has not been presented. Here, we present a workflow protocol for producing comparable and consistent fracture data using orbital imaging. We present a case study of Oxia Planum, Mars, the landing site for the European Space Agency's ExoMars Rosalind Franklin Rover mission. On Mars, the High Resolution Imaging Science Experiment (HiRISE) provides over 100,000 0.25-0.50 m/px orbital images, equivalent to very-high resolution orbital imagery of Earth. Existing fracture mapping products around Mars are not directly comparable due to the interpretive nature of fracture mapping and a bias which leads to characterizing the most clearly visible fractures. This study leverages field fracture mapping expertise to present an unbiased, reproducible approach, and provides recommendations for basic statistical assessment of fracture data. We found that fracture density is reproduced to ±5%, and minimum visual resolvability of fracture lengths is 0.5 cm on screen. Comparison of fracture data with bedrock characteristics confirms the general unit descriptions for the area, where visually bright, clay-rich rocks are more fractured than other units, with significantly higher numbers of fractures and sum fracture lengths (387% and 360%, respectively).

Spectral and hyperspectral data provide essential information on the surface composition of rocky planetary bodies, through which we can reconstruct and understand their origin and geologic evolution. In this field, applications of advanced tools, such as machine learning‐based algorithms, are steadily increasing. This study presents a newly developed, open‐source, Python‐based tool and workflow for the identification and mapping of spectral units with CRISM near‐infrared (NIR) data using unsupervised machine‐learning (clustering) techniques. Data pre‐processing, dimensionality reduction, clustering, and visualization are integrated into an interactive user‐friendly environment. The tool employs two different established clustering algorithms: K‐means and Gaussian Mixture Models. Dimensionality reduction methods include the use of projection‐based and manifold‐based dimensionality reduction techniques, respectively Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). We showcase the abilities of the tool on a set of selected CRISM images. We identify multiple spectral units, from which we retrieve spectral signatures ranging from mafic minerals, with broad absorption bands, to Fe/Mg phyllosilicates and sulfates, which exhibit multiple narrow absorption features. Results highlight how clustering methods can support and enhance the findings derived from classical analysis of spectral parameters, for instance, by highlighting subtle spectral variations. Moreover, with minor tuning and modifications, the tool can be applied to different spectral ranges as well as to spectral or hyperspectral data from other planetary surfaces.

Shallow lunar subsurface characterization is a key requirement for future exploration activities, particularly for in situ resource utilization and the identification of protected environments for human and robotic operations. This work presents the preliminary design and performance assessment of an orbital very high frequency (VHF) radar sounder tailored to the detection of subsurface water ice deposits and lava tubes at depths relevant to exploration. The analysis combines physically based modeling of acquisition geometry, electromagnetic properties, and surface roughness with quantitative evaluation of signal-to-noise and signal-to-clutter ratios. Results indicate that surface clutter constitutes the primary limitation for subsurface detectability in orbital sounding, thereby driving both instrument design and mission geometry. Quantitative performance bounds are derived for penetration depth and spatial resolution, providing guidance for identifying regions where subsurface access may be achieved with reduced operational risk. One-dimensional electromagnetic simulations further demonstrate the advantages of operating in the VHF regime. While lower-frequency systems retain sensitivity to some subsurface interfaces, their limited vertical resolution prevents reliable separation of closely spaced structures, such as the roof and floor of lava tubes. In contrast, the proposed VHF sounder enables clear separation of multiple subsurface interfaces, allowing geometric characterization of cavities and improved discrimination of ice-bearing layers. These results establish the feasibility and relevance of a VHF orbital radar sounder as a dedicated tool for shallow lunar subsurface investigations in support of future exploration missions.

Oxia Planum, the landing site of the Rosalind Franklin Mission (RFM), is located between the ancient highlands of Arabia Terra and the younger plains of Chryse Planitia [1]. The region preserves clear evidence of past water-rock interactions, particularly layered clay-rich deposits that are considered prime targets for astrobiological investigation [2, 3]. This study focuses on a selection of Regions of Interest (ROIs), areas with the highest concentrations of ferromagnesian clays as identified through remote sensing analyses. We derive local digital terrain models from stereo photogrammetry and quantify horizontal and vertical accuracy [4], a key requirement for interpreting water-related processes and stratigraphic relationships in the low-relief Oxia Planum. Our objective is to examine how clay distribution relates to other geologic elements, in particular fractures [5, 6], to better constrain their geologic interpretation and stratigraphic context [7], contributing to strategies for guiding the selection of drilling sites once the rover arrives on Mars. This work is funded by the Italian Space Agency (ASI) [Grant ASI-INAF n. 2023–3–HH.0]. References: [1] Quantin-Nataf et al. (2021) Astrobiology 21, 345–366. [2] Mandon et al. (2021) Astrobiology 21, 464–480. [3] Brossier et al. (2022) Icarus 386, 115114. [4] Trisic Ponce et al. (2026), this conference. [5] Apuzzo et al. (2025) PSS 267, 106169. [6] Rasmussen et al. (2026), this conference. [7] Fawdon et al. (2024) Journal of Maps 20, 2302361.

Spectral and hyperspectral data provide essential information on the surface composition of rocky planetary bodies, through which we can reconstruct and understand their geologic evolution and environmental history. The study of compositional data is commonly approached through the definition and mapping of spectral parameters such as band depths, band areas, and spectral slopes. Applications of more advanced tools, such as machine learning-based automatic detection systems are steadily increasing in the planetary science field. We present a newly developed, Python-based, open source tool to perform unsupervised clustering on CRISM near-infrared (NIR) hyperspectral data. The tool employs two different algorithms: k-Means and Gaussian Mixture Models. Data can be fed to these algorithms following some pre-processing steps and dimensionality reduction, which include the use of projection-based and manifold-based dimensionality reduction techniques (Principal Component Analysis and Uniform Manifold Approximation and Projection). With this tool, we identify and cluster a great variety of spectral signatures, ranging from mafic minerals with broad absorption bands to Fe/Mg phyllosilicates and sulfates, which exhibit multiple narrow absorption features. We showcase the abilities of the tool on a set of selected CRISM images. Results highlight how automated clustering methods can greatly support and enhance the findings derived from classic analysis of spectral parameters. Moreover, with minor tuning and modifications, the tool can be broadly applied both to different spectral ranges and to spectral or hyperspectral data from any planetary surface.

Extensive research over the past two decades has shown that early Mars likely had a warmer, wetter climate with widespread water activity. Ferromagnesian (Fe,Mg‐rich) clay deposits are compelling markers of these ancient environments, helping reconstruct Mars' hydrologic evolution, assess past habitability, and guide future exploration. This study analyzes hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard NASA's Mars Reconnaissance Orbiter , focusing on regions along the Martian crustal dichotomy—where clay deposits occur at the boundary between the ancient southern highlands and the younger northern lowlands. We systematically surveyed ∼1500 CRISM targeted observations (1–2.6 μm) to identify ferromagnesian clays, distinguish them from other hydrated minerals, and characterize compositional differences between Fe‐ and Mg‐rich species using diagnostic absorptions around 1.4, 2.3, and 2.4 μm. Results reveal spatial variations in clay mineralogy: Fe‐rich nontronites are prevalent around Mawrth Vallis, while Mg‐rich saponites are more locally distributed in Nili Fossae and Libya Montes. Oxia Planum—the Rosalind Franklin rover landing site—exhibits more compositionally intermediate clays such as vermiculites and ferrosaponites. These differences may reflect variations in the iron and magnesium abundance or in the iron oxidation state. Moreover, a recurring absorption near 2.5 μm suggests co‐occurring carbonates like magnesite and siderite, increasing the potential for biosignature preservation. These findings refine our understanding of Mars' aqueous history and offer an important mineralogical context for future rover and sample return missions. They also emphasize the need for a next‐generation orbital imaging spectrometer to succeed CRISM and extend its legacy.

We produced several mineral mixtures and measured their reflectance in the VNIR range (350–2,500 nm) with the aim of reproducing the spectral features observed by the rovers Perseverance and Curiosity in the Jezero and Gale Crater respectively. We chose two specific case‐study spectra of the rovers namely the Bellegarde #5 abrasion patch in Jezero and the Voyageurs target drill in Gale. After selecting a number of mineral components representative of the two sites we first produced several binary (two components) mixtures and measured their reflectance. The binary mixtures are characterized by systematic variations of mineral proportions and were used to assess specific aspects of the relationships between spectral properties and mineralogical composition of mineral mixtures. Based on the results from the binary mixtures we then produced multicomponent (4, 5 or 6 components) mixtures through an iterative approach in which the spectrum of each produced mixture is compared with a target spectrum and its mineralogical composition is adjusted to match the target spectrum as closely as possible. The retrieved mineral composition of the final mixtures was eventually used to infer the conditions of formation of the investigated target emphasizing how the mineralogical composition of a laboratory mixture can give insights into the environmental conditions experienced by the target rock.

Equatorial Layered Deposits (ELDs) are sedimentary landforms found at equatorial latitudes of Mars showing repetitive bedding and commonly associated with hydrous minerals. These deposits are important archives of Mars' past aqueous conditions yet in most cases their formation mechanisms are still debated and elusive. Here we provide a detailed characterization of the mineralogy and stratigraphy of three different exposures of ELDs in Meridiani Planum, where such formations are found within small impact craters and are dated back to the Noachian‐Hesperian boundary. Our analysis highlights a varying degree of mixture between polyhydrated Mg sulfates and Fe/Mg phyllosilicates within the beds of all three chosen targets, with several alternating strata of sulfate‐rich and phyllosilicate‐rich materials. In one case, Al‐phyllosilicates are also found along with their Fe/Mg counterpart. This is a much more varied mineral assemblage than previously reported for ELDs in this area. We propose a formation mechanism which explains the observed interbedding as a result of surface ponding of groundwaters, aqueous alteration of atmospherically‐sourced basaltic materials and water table/pH oscillations (wet/dry cycles) leading to recurrent sulfate precipitation. Our findings show how local environments play a substantial role in recording small‐scale variations of complex aqueous processes, generally not observed at regional and global scales on Mars.