There is evidence that life on Earth originated in cold saline waters around scorching hydrothermal vents, and that similar conditions might exist or have existed on Mars, Europa, Ganymede, Enceladus, and other worlds. Could potentially habitable complex brines with extremely low freezing temperatures exist in the shallow subsurface of these frigid worlds? Earth, Mars, and carbonaceous chondrites have similar bulk elemental abundances, but while the Earth is depleted in the most volatile elements, the Icy Worlds of the outer solar system are expected to be rich in them. The cooling of ionic solutions containing substances that likely exist in the Icy Worlds could form complex brines with the lowest eutectic temperature possible for the compounds available in them. Indeed, here, we show observational and theoretical evidence that even elements present in trace amounts in nature are concentrated by freeze–thaw cycles, and therefore contribute significantly to the formation of brine reservoirs that remain liquid throughout the year in some of the coldest places on Earth. This is interesting because the eutectic temperature of water–ammonia solutions can be as low as ~160 K, and significant fractions of the mass of the Icy Worlds are estimated to be water substance and ammonia. Thus, briny solutions with eutectic temperature of at least ~160 K could have formed where, historically, temperature have oscillated above and below ~160 K. We conclude that complex brines must exist in the shallow subsurface of Mars and the Icy Worlds, and that liquid saline water should be present where ice has existed, the temperature is above ~160 K, and evaporation and sublimation have been inhibited.

Safe landings are imperative to accomplish NASA’s Artemis goal to enable human exploration on the Moon, including sample collection missions. However, a process known as plume surface interaction (PSI) presents a significant hazard to lunar landings. PSI occurs when the engine exhaust of a lander interacts with the surface ejecting large amounts of regolith particles at high velocities that can interfere with the landing, disturb the surface, and damage hardware. To better understand PSI, the particle impact event (PIE) sensor is being developed to measure the kinetic energy and the flux of ejecta during landings, to quantify the potential damage, and to quantify the ejecta displaced. Multiple parameters were estimated to define the PIE instrument requirements. These estimates demonstrate that ejecta can travel at velocities of up to 800 m/s and impact the surrounding area with energies of up to 400 µJ. A significant amount of ejecta can be deposited several 10 s of meters away from the landing site, modifying the surface and causing dust-related challenges. The PIE sensor will be launched for the first time in an upcoming lunar lander. Then, PIE measurements will be used to improve PSI prediction capabilities and develop mitigation strategies to ensure safe landings.

Airborne dust plays an active role in determining the thermal structure and chemical composition of the present-day atmosphere of Mars and possibly the planet’s climate evolution over time through radiative–convective and cloud microphysics processes. Thus, accurate measurements of the distribution and variability of dust are required. Observations from the Mars Global Surveyor/Thermal Emission Spectrometer Mars Mars Reconnaissance Orbiter/Mars Climate Sounder and Mars Express/Fourier Transform Spectrometer and the Curiosity Rover have limited capability to measure dust. We show that spacecraft occultation of the Martian atmosphere at far-infrared frequencies between 1 and 10 THz can provide the needed global and temporal data on atmospheric dust by providing co-located measurements of temperature and dust opacity from the top of the atmosphere all the way down to the surface. In addition, spacecraft occultation by a small-satellite constellation could provide global measurements of the development of dust storms.

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases.