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Abstract We created high-resolution shape models of Phobos and Deimos using stereophotoclinometry and united images from Viking Orbiter, Phobos 2, Mars Global Surveyor, Mars Express, and Mars Reconnaissance Orbiter into a single coregistered collection. The best-fit ellipsoid to the Phobos model has radii of (12.95 ± 0.04) km × (11.30 ± 0.04) km × (9.16 ± 0.03) km, with an average radius of (11.08 ± 0.04) km. The best-fit ellipsoid to the Deimos model has radii of (8.04 ± 0.08) km × (5.89 ± 0.06) km × (5.11 ± 0.05) km with an average radius of (6.27 ± 0.07) km. The new shape models offer substantial improvements in resolution over existing shape models, while remaining globally consistent with them. The Phobos model resolves grooves, craters, and other surface features ~ 100 m in size across the entire surface. The Deimos model is the first to resolve geological surface features. These models, associated data products, and a searchable, coregistered collection of images across six spacecraft are publicly available in the Small Body Mapping Tool, and will be archived with the NASA Planetary Data System. These products enable an array of future studies to advance the understanding of Phobos and Deimos, facilitate coregistration of other past and future datasets, and set the stage for planning and operating future missions to the moons, including the upcoming Martian Moons eXploration (MMX) mission. Graphical Abstract

Dimorphos was the target of the Double Asteroid Redirection Test (DART) mission. This paper summarizes the properties of an updated shape model of Dimorphos, describes the differences between the updated shape model and an earlier version published by Daly, Ernst, Barnouin et al. (doi: https://doi.org/10.1038/s41586-023-05810-5 ), summarizes the data products associated with this model, and explains where the products can be accessed. The updated shape model benefited from improved methods of incorporating limb information, which will accelerate future shape modeling efforts for other objects with limited imaging data. The updated shape model is similar to the earlier model but slightly smaller (−2.8% change in volume) than the previous Dimorphos global shape model, and the updated shape is slightly more elongated. The additional analysis reported here supports an oblate preimpact shape for Dimorphos. This result indicates that the postimpact elongation of Dimorphos derived from ground-based observations is evidence for a large crater or global reshaping of the asteroid due to the DART impact. The updated global shape model of Dimorphos, as well as the earlier version, will be available in the Planetary Data System Small Bodies Node and through the public Small Body Mapping Tool.

Stereophotoclinometry (SPC) was the prime method of shape modeling for NASA’s OSIRIS-REx mission to asteroid Bennu. Here we describe the extensive testing conducted before launch to certify SPC as NASA Class B flight software, which not only validated SPC for operational use but also quantified the accuracy of this technique. We used a computer-generated digital terrain model (DTM) of a synthetic asteroid as the truth input to render simulated truth images per the planned OSIRIS-REx observing campaign. The truth images were then used as input to SPC to create testing DTMs. Imaging sets, observational parameters, and processing techniques were varied to evaluate their effects on SPC's performance and their relative importance for the quality of the resulting DTMs. We show that the errors in accuracy for SPC models are of the order of the source images’ smallest pixel sizes and that a DTM can be created at any scale, provided there is sufficient imagery at that scale. Uncertainty in the spacecraft’s flight path has minimal impact on the accuracy of SPC models. Subtraction between two DTMs (truth and simulated) is an effective approach for measuring error but has limitations. Comparing the simulated truth images with images rendered from the SPC-derived DTMs provides an excellent metric for DTM quality at smaller scales and can also be applied in flight by using real images of the target. SPC has limitations near steep slopes (e.g., the sides of boulders), leading to height errors of more than 30%. This assessment of the accuracy and sensitivity of SPC provides confidence in this technique and lessons that can be applied to future missions.

The OSIRIS-REx mission used stereophotoclinometry (SPC) to generate digital terrain models (DTMs) of its target asteroid, Bennu. Here we present a suite of preflight tests conducted to identify the observing geometry and number of images needed to create DTMs that would enable successful navigation around and to the surface of the asteroid. We demonstrate that high-quality DTMs can be generated by using only five images: four that are focused on topography, in which the spacecraft’s viewing geometry brackets the target (north, south, east, and west), and a fifth that measures the target’s albedo variation, taken from near local noon. We further show that the first 10 iterations of the SPC process can meaningfully improve DTM quality, including in the case of a suboptimal input image set, whereas after 10 iterations the DTM quality approaches an asymptotic maximum. We distill our findings into recommendations for observation planning that can be applied by other missions intending to use SPC to model the shape and terrain of their target.

Lunar swirls have been traditionally considered to be unaffected by topographic changes. Yet, correlations between areas of high albedo and lower elevations are observed in regions of the Mare Ingenii swirl. Here, we apply similar techniques used at Mare Ingenii to determine if correlations between swirl units and topography also exist within Reiner Gamma. We generate topography using the techniques of stereophotoclinometry to Lunar Reconnaissance Orbiter Camera Narrow Angle Camera images to derive meter- to submeter-scale topography. We choose a 50 km ^2 study region with a 2.6 m ground sample distance (GSD), and within this region there is a 1 km ^2 subregion with a 0.8 m GSD. We use surface reflectance data at multiple viewing geometries to classify these regions into different swirl units using machine learning techniques. Statistical analyses of the data show mean height variations between on- and off-swirl of ∼4 m, with on-swirl at a lower elevation. It is not clear how this scale of elevation difference influences the formation of swirl units, but it supports postulations of dust migration and magnetic sorting contributing to their formation.