Planetary Surface Research Articles

The CosmoQuest Moon Mappers Community Science Project: The Effect of Incidence Angle on the Lunar Surface Crater Distribution

The CosmoQuest virtual community science platform facilitates the creation and implementation of astronomical research projects performed by citizen scientists. One such project, called Moon Mappers, aids in determining the feasibility of producing crowd-sourced cratering statistics of the surface of the Moon. Lunar crater population statistics are an important metric used to understand the formation and evolutionary history of lunar surface features, to estimate relative and absolute model ages of regions on the Moon's surface, and to establish chronologies for other planetary surfaces via extrapolation from the lunar record. It has been suggested and shown that solar incidence angle has an effect on the identification of craters, particularly at meter scales. We have used high-resolution image data taken by the Lunar Reconnaissance Orbiter's Narrow-Angle Camera of the Apollo 15 landing site over a range of solar incidence angles and have compiled catalogs of crater identifications obtained by minimally trained members of the general public participating in CosmoQuest's Moon Mappers project. We have studied the effects of solar incidence angle spanning from approximately 27.5 deg to approximately 83 deg (extending the incidence angle range examined in previous works), down to a minimum crater size of 10 m, and find that the solar incidence angle has a significant effect on the crater identification process, as has been determined by subject matter experts in other studies. The results of this analysis not only highlight the ability to use crowd-sourced data in reproducing and validating scientific analyses but also indicate the potential to perform original research.

Planetary climate under extremely high vertical diffusivity

Aims. Planets with large moon(s) or those in the habitable zone of low-mass stars may experience much stronger tidal force and tide-induced ocean mixing than that on Earth. Thus, the vertical diffusivity (or, more precisely, diapycnal diffusivity) on such planets, which represents the strength of vertical mixing in the ocean, would be greater than that on Earth. In this study, we explore the effects of extremely high diffusivity on the ocean circulation and surface climate of Earth-like planets in one asynchronous rotation orbit. Methods. The response of planetary climate to 10 and 100 times greater vertical diffusivity than that found on Earth is investigated using a fully coupled atmosphere–ocean general circulation model. In order to perform a clear comparison with the climate of modern Earth, Earth’s orbit, land–sea configuration, and present levels of greenhouse gases are included in the simulations. Results. We find that a larger vertical diffusivity intensifies the meridional overturning circulation (MOC) in the ocean, which transports more heat to polar regions and melts sea ice there. Feedback associated with sea ice, clouds, and water vapor act to further amplify surface warming. When the vertical diffusivity is 10 (100) times the present-day value, the magnitude of MOC increases by ≈3 (18) times, and the global-mean surface temperature increases by ≈4 °C (10 °C). This study quantifies the climatic effect of an extremely strong vertical diffusivity and confirms an indirect link between planetary orbit, tidal mixing, ocean circulation, and surface climate. Our results suggest a moderate effect of varying vertical ocean mixing on planetary climate.

Insight into martian crater degradation history based on crater depth and diameter statistics

Impact craters are widely used to investigate the geologic history of planetary surfaces. Their morphology bears clues on past surface processes that affected it such as erosion, sedimentary deposit and volcanic processes and their density is widely used to date planetary surfaces.In this study we propose to combine crater Density and morphology into a single representation, Crater Size and Depth Frequency Distribution (CSDFD) that offers a snapshot not only at the age but also at the obliteration history of the crater population. Using simple models, we interpreted the CSDFD in term of crater obliteration rates. This methods can be applied on any body featuring impact craters. We present here the results obtained on Mars, using a global crater database.The obliteration rates obtained from CSDFD are consistent with known geologic history of Mars. Reaching thousands of m/Gy during Noachian and rapidly dwindling as soon as 3.5 Gy. Moreover, our method highlighted an continuous activity of Northern Lowlands surface during Amazonian we interpret to be linked with the formation of the vastitas borealis formation. This study offer a continuous vision in time and space of Martian obliteration history.

W-Band Photonic Receiver for Compact Cloud Radars.

We introduce an RF-photonics receiver concept enabling the next generation of ultra-compact millimeter wave radars suitable for cloud and precipitation profiling, planetary boundary layer observations, altimetry and surface scattering measurements. The RF-photonics receiver architecture offers some compelling advantages over traditional electronic implementations, including a reduced number of components and interfaces, leading to reduced size, weight and power (SWaP), as well as lower system noise, leading to improved sensitivity. Low instrument SWaP with increased sensitivity makes this approach particularly attractive for compact space-borne radars. We study the photonic receiver front-end both analytically and numerically and predict the feasibility of the greater than unity photonic gain and lower than ambient effective noise temperature of the device. The receiver design is optimized for W-band (94 GHz) radars, which are generally assessed to be the primary means for observing clouds in the free troposphere as well as planetary boundary layer from space.

Sensitivity of Pollutant Concentrations to the Turbulence Schemes of a Dispersion Modelling Chain over Complex Orography

Atmospheric circulation over mountainous regions is more complex than over flat terrain due to the interaction of flows on various scales: synoptic-scale flows, thermally-driven mesoscale winds and turbulent fluxes. In order to faithfully reconstruct the circulation affecting the dispersion and deposition of pollutants in mountainous areas, meteorological models should have a sub-kilometer grid spacing, where turbulent motions are partially resolved and the parametrizations of the sub-grid scale fluxes need to be evaluated. In this study, a modelling chain based on the Weather Research and Forecasting (WRF) model and the chemical transport model Flexible Air Quality Regional Model (FARM) is applied to estimate the pollutant concentrations at a 0.5 km horizontal resolution over the Aosta Valley, a mountainous region of the northwestern Alps. Two pollution episodes that occurred in this region are reconstructed: one summer episode dominated by thermally-driven winds, and one winter episode dominated by synoptic-scale flows. Three WRF configurations with specific planetary boundary layer and surface layer schemes are tested, and the numerical results are compared with the surface measurements of meteorological variables at twenty-four stations. For each WRF configuration, two different FARM runs are performed, with turbulence-related quantities provided by the SURface-atmosphere interFace PROcessor or directly by WRF. The chemical concentrations resulting from the different FARM runs are compared with the surface measurements of particulate matter of less than 10 µm in diameter and nitrogen dioxide taken at five air quality stations. Furthermore, these results are compared with the outputs of the modelling chain employed routinely by the Aosta Valley Environmental Protection Agency, based on FARM driven by COSMO-I2 (COnsortium for Small-scale MOdelling) at 2.8 km horizontal grid spacing. The pollution events are underestimated by the modelling chain, but the bias between simulated and measured surface concentrations is reduced using the configuration based on WRF turbulence parametrizations, which imply a reduced dispersion.

A Geoscientific Review on CO and CO2 Ices in the Outer Solar System

Ground-based telescopes and space exploration have provided outstanding observations of the complexity of icy planetary surfaces. This work presents our review of the varying nature of carbon dioxide (CO2) and carbon monoxide (CO) ices from the cold traps on the Moon to Pluto in the Kuiper Belt. This review is organized into five parts. First, we review the mineral physics (e.g., rheology) relevant to these environments. Next, we review the radiation-induced chemical processes and the current interpretation of spectral signatures. The third section discusses the nature and distribution of CO2 in the giant planetary systems of Jupiter and Saturn, which are much better understood than the satellites of Uranus and Neptune, discussed in the subsequent section. The final sections focus on Pluto in comparison to Triton, having mainly CO, and a brief overview of cometary materials. We find that CO2 ices exist on many of these icy bodies by way of magnetospheric influence, while intermixing into solid ices with CH4 (methane) and N2 (nitrogen) out to Triton and Pluto. Such radiative mechanisms or intermixing can provide a wide diversity of icy surfaces, though we conclude where further experimental research of these ices is still needed.

The Survey of Lava Tube Distribution in Jeju Island by Multi-Source Data Fusion

Lava tubes, a major geomorphic element over volcanic terrain, have recently been highlighted as testbeds of the habitable environments and natural threats to unpredictable collapse. In our case study, we detected and monitored the risk of lava tube collapse on Jeju, an island off the Korean peninsula’s southern tip with more than 200 lava tubes, by conducting Interferometric Synthetic Aperture Radar (InSAR) time series analysis and a synthesized analysis of its outputs fused with spatial clues. We identified deformations up to 10 mm/year over InSAR Persistent Scatterers (PSs) obtained with Sentinel-1 time series processing in 3-year periods along with a specific geological unit. Using machine learning algorithms trained on time series deformations of samples along with clues from the spatial background, we classified candidates of potential lava tube networks primarily over coastal lava flows. What we detected in our analyses was validated via comparison with geophysical and ground surveys. Given that cavities in the lava tubes could pose serious risks, a detailed physical exploration and threat assessment of potential cave groups are required before the planned intensive construction of infrastructure on Jeju Island. We also recommend using the approach established in our study to detect undiscovered potential risks of collapse in the cavities, especially over lava tube networks, and to explore lava tubes on planetary surfaces using proposed terrestrial and planetary InSAR sensors.

Rectangular drainage pattern evolution controlled by pipe cave collapse along clastic dikes, the Dead Sea Basin, Israel

AbstractRectangular drainage networks are characterized by right‐angle bends and confluences. The formation of such drainage patterns is commonly associated with orthogonal sets of fractures, making them an outstanding example for structurally controlled landscape evolution. However, this association remains largely circumstantial because little is known about how rectangular drainages mechanistically link to orthogonal fractures. We investigated these linkages in the hyper‐arid Ami'az Plain located within the Dead Sea Basin in Israel. The Ami'az Plain is penetrated by hundreds of sub‐vertical clastic dikes (mode‐I fractures infilled with sediments) and is also incised by a rectangular canyon system. Numerous caves extend from the banks and heads of the canyon system. Based on field surveys and analysis of high‐resolution airborne LiDAR data, we mapped the Ami'az Plain drainage network and its associated landforms, including sinkholes. Our analysis revealed that the subaerial tributaries of the canyon system and the strike of the clastic dikes show similar orientations. In addition, subsurface mapping with a ground‐based scanning LiDAR, together with field experiments, demonstrated that the caves and sinkholes in the Ami'az Plain are spatially associated with clastic dikes and that the caves formed through piping erosion along dikes. Based on these findings, we suggest that clastic dikes act as efficient infiltration pathways to the subsurface, where flow along clastic dikes induces internal erosion that forms pipe caves. The sinkholes form by collapses of cave roofs. Coalescence of sinkholes and seepage erosion where dikes intersect canyon heads generate new tributaries and act to extend existing ones. Fluvial erosion and subsequent bank collapse modify the canyon network. Our findings emphasize the critical role of subsurface erosion, caves and sinkholes in linking fractures to drainage pattern evolution, and provide a new process‐based framework to interpret rectangular drainage networks on Earth and possibly other planetary surfaces.

Agents of Exploration and Discovery

Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the problem, identify component functions, describe current abilities, and note remaining limitations. Finally, I discuss obstacles that the community must overcome before it can develop integrated agents of exploration and discovery.

Dehydration rate of the glycine‐MgSO4·5H2O complex and the stability of glycine expelled from the complex by in situ Raman spectroscopy under Mars‐relevant conditions

AbstractIn this work, we studied the dehydration process of the glycine‐MgSO4·5H2O complex under Mars‐relevant conditions (99% CO2 and 0.6% H2O under ultra violet (UV) irradiation exposure at 7‐mbar pressure and high vacuum conditions: 8 × 10−5 and 5 × 10−5 mbar) by in situ Raman spectroscopy inside a planetary atmosphere and surface chamber (PASC). This work provides quality Raman spectra taken under simulated planetary conditions (to be integrated in a database), as Raman spectroscopy forms part of the current and upcoming NASA and ESA Mars planetary missions. The results demonstrate that Raman spectroscopy can be used to calculate rates of dehydration of the glycine‐MgSO4·5H2O compound to study the chemical stability with respect to photodecomposition (1) of metal‐bound glycine molecules forming the complex and (2) glycine expelled from the complex, both under Mars‐simulated conditions; finally, Raman spectroscopy can also be used to quantify intermolecular interactions in terms of local pressures. Importantly, advanced detection of water molecules as part of a complex with astrobiological interest under planetary conditions plays a crucial role in planetary missions.

Diurnal climatology of correlations between the planetary boundary layer height and surface meteorological factors over the contiguous United States

AbstractThe correlations between the planetary boundary layer height (PBLH) and surface meteorological factors, including the surface wind speed (RWS), temperature (RTS), specific humidity (RSH), pressure (RPS), and lower tropospheric stability (RLTS), are investigated using hourly profiles from the Aircraft Meteorological Data Reports at 54 major airports over the contiguous United States (CONUS) and surface observations from weather stations. At the diurnal cycle, the RWS and RTS are positive and the RPS and RLTS are negative across the CONUS, while the RSH is negative over most areas but tends to be positive in the inland humid region. At the annual cycle, the correlations show significant diurnal variations that cannot be well captured by routine twice‐daily radiosonde data. The RWS is significantly positive at night but less significant during the daytime over most regions. The RTS and RSH are positive and the RPS and RLTS are negative at the daytime, which are of the opposite sign at the nighttime, with exceptions in some coastal areas. The diurnal variations of the correlations at the annual cycle also exhibit significant geographic variability, with the diurnal variation generally stronger in central eastern humid areas, weaker in some coastal areas, and moderate in relatively arid or mountain areas. This study highlights that the correlations between the PBLH and meteorological variables depend on the thermal stratification and are affected by the local hydrological and synoptic factors.

Geological tasks during HI-SEAS planetary analog mission simulations, Mauna Loa, Hawai'i

The Hawai'i Space Exploration Analog and Simulation (HI-SEAS) project is a NASA-funded research program operating long-duration planetary analog surface mission simulations on Mauna Loa volcano, Hawai'i. During missions lasting from 4 to 12 months, crews of six analog astronaut participants live and work in an isolated habitat, communicating with a remote mission support team via a 20-min time delay. The main purpose of HI-SEAS is to study team effectiveness and adaptation over time in isolated, confined, and high autonomy mission scenarios. Among other duties, Crewmembers are tasked with routinely conducting geological fieldwork requiring extravehicular activity (EVA) in the environment surrounding the habitat. They must determine how they will accomplish these tasks, conduct the tasks themselves, and report results by a due date set by the remote science team. Here we describe the design, task parameters, and performance outcomes of HI-SEAS geology EVA tasks from four 6-person missions. We describe the assigned tasks, how the crews carried out their assignments, and the results of their work in terms of six performance metrics for each task: 1) number of days required for completion; 2) number of crewmembers participating; 3) number of EVAs required; 4) total EVA time required; 5) difference between required and planned EVA times; and 6) performance score evaluating how well crew met the task objective. We find weak evidence of a decrease in geology task performance during the third quarter of missions M2-M4. This dataset provides insights into varying crew performance over time for different mission durations.

Automated Geological Landmarks Detection on Mars Using Deep Domain Adaptation From Lunar High-Resolution Satellite Images

The diversity in geological characteristics on the planetary surface, such as distribution (density), size, shapes, floor structures, ages, and availability of various input data types such as optical, thermal images, and digital elevation maps pose numerous challenges for detecting geological landmarks (e.g., rockfalls, craters, etc.). Several automatic detection methods are proposed to identify geological landmarks. However, the insufficiency of the labeled dataset is a challenging problem. It requires exceedingly time-consuming and expensive manual annotation. In this article, we use the domain adaptation technique to transfer deep learning from the planetary surface to another (lunar surface into Martian surface). We test the feasibility of transfer learning of the convolutional neural networks in optical images and elevation maps to distinguish landmarks such as rockfalls and craters from the background. The experimental results demonstrate the effectiveness of the proposed method. It achieves high F1-scores compared to the state-of-the-art methods with 58.32<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>2.3 and 57.51<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>2.4 in detecting rockfall regions in optical lunar and Martian images. It also achieves 65.32<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>1.8, 67.39<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>2.4, 77.37<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>2.2, and 72.56<inline-formula><tex-math notation="LaTeX">$\,{\pm}\,$</tex-math></inline-formula>2.3 in detecting crater regions in optical images and digital elevation maps of Moon and Mars. This method can be a potential approach to identify landmarks for coming Mars missions.

ROMA: A Database of Rock Reflectance Spectra for Martian In Situ Exploration.

The ROMA database (ROck reflectance for MArtian in situ exploration, https://roma.univ-lyon1.fr) provides the reflectance spectra between 0.4 and 3–4 μm of various terrestrial, Martian, and synthetic samples, as a means to document reference measurements for comparison with data acquired by visible and near‐infrared spectrometers on planetary surfaces, with a focus on current and future Martian observations by the Perseverance (Mars 2020 mission) and Rosalind Franklin (ExoMars) rovers. The main specificity of this database is to include a significant fraction of spectra of unprocessed rock, which are more realistic analogs and often have different spectral features than the fine powders more commonly analyzed in reflectance spectroscopy. Additionally, these measurements were acquired with a spectrometer whose spot size is similar to those of the SuperCam instrument (Mars 2020 mission) at a few meters from a target. Supplementary information are provided in the ROMA database: higher‐level data (such as absorption band parameters) as well as sample mineralogy estimated by whole‐rock X‐ray diffraction analyses. Future comparisons with this database will help improve the interpretation of spectral measurements acquired on the Martian surface. This work introduces the aim of the library and its current state, but additional data on intact natural rock surfaces will likely be added in the future.

Deterministic Heading-Independent Celestial Localization Measurement Model

<h3>Abstract</h3> Planetary rover navigation frequently relies on dead reckoning and external infrastructure such as orbiting satellites. Celestial navigation techniques combine measurements of the Sun, stars, and gravity to provide autonomous absolute localization. This study examines the performance of digital star sextants (DSS)—a suite of sensors combining a star tracker and an inclinometer—on estimating position on the planetary surface. In particular, we discuss the estimation, calibration, and error analysis for an elevation-only measurement formulation that does not rely on ground-truth heading information. Field tests and Monte Carlo simulations provide validation of the proposed techniques. The real-world performance of the experimental system gives a mean single-orientation error of 296 m. The relative agreement between the predicted and observed error reveals a clear roadmap to help evaluate the impact of prospective sensor improvements on DSS performance.

Guest Editorial Wireless for Space and Extreme Environments

Spaceflight involves critical sensing and communication in extreme environments such as planetary surfaces, space vehicles, and space habitats. The many challenges faced in space sensing and communication are extremely diverse and overlap significantly with those found in many terrestrial examples of extreme environments such as extreme hot or cold locations, extreme high- or low-pressure environments, critical control loops in aircraft and nuclear power plants, high-speed rotating equipment, oil/gas pipelines and platforms, etc. All of these environments pose significant challenges for radio-frequency or optical wireless sensing and communication and will require the application of a broad range of state of the art technologies in order to generate reliable and cost effective solutions. Although the specific challenges vary significantly from the environment to environment, many of the solutions offered by sensing, communication, and statistical signal processing technologies can be applied in multiple environments, and researchers focusing on space applications can benefit greatly from understanding the problems encountered and solutions applied in alternative environments.

Гелиогеофизические явления, вызванные влиянием сторонних гравитационных сил

It is shown that an external gravitational action onto the planet core leads to the generation of its magnetic field. The natural phenomena as earthquakes, orogeny, oceanic currents, tides, time and solar day sudden changes, periodic solar activity, which can be easily explained with proposed mechanism of planets interaction are considered. Clear relationship of planets magnetic fields, their forms and value from solar activity, magnetic and gravitational fields measurements, oceanic level monitoring, as well as natural processes observation data may be accepted as evidence. Analysis of all the data leads to the unique conclusion about the influence of Earth’s core movement to many processes occurred and measured on the planet surface. Proposed approach allows integrating many separate processes to the single hole.

Processing and Analysis for Radio Science Experiments (PARSE): Graphical Interface for Bistatic Radar

Abstract Opportunistic bistatic radar (BSR) observations of planetary surfaces can probe the textural and electrical properties of several solar system bodies without needing a dedicated instrument or additional mission requirements, providing unique insights into volatile enrichment and supporting future landing, anchoring, and in situ sampling. Given their opportunistic nature, complex observation geometries, and required radiometric knowledge of the received radio signal, these data are particularly challenging to process, analyze, and interpret for most planetary science data users, who can be unfamiliar with link budget analysis of received echoes. The above impedes real-time use of BSR data to support mission operations, such as identifying safe landing locations on small bodies, as was the case for the Rosetta mission. To address this deficiency, we develop an open-source graphical user interface—Processing and Analysis for Radio Science Experiments (PARSE)—that assesses the feasibility of performing BSR observations and automates radiometric signal processing, power spectral analysis, and visualization of DSN planetary radio science data sets acquired during mission operations or archived on NASA’s Planetary Data System. In this first release, PARSE automates the processing chain developed for Dawn at Asteroid Vesta, streamlining the detection of DSN-received surface-scatter echoes generated as the spacecraft enters/exits occultations behind the target. Future releases will include support for existing Arecibo data sets and other Earth-based radio observatories. Our tool enables the broader planetary science community, beyond planetary radar signal processing experts, to utilize BSR data sets to characterize electrical and textural properties of planetary surfaces. Such tools are becoming increasingly important as the number of space missions—and subsequent opportunities for orbital radio science observations—continue to grow.

A Laboratory Open-Set Martian Rock Classification Method Based on Spectral Signatures

Rocks are one of the major surface features of Mars. The accurate characterization of the chemical and mineralogical composition of Martian rocks would yield significant evolutionary information about relevant geological processes and exobiological exploration. Many existing rock recognition systems generally assume that all testing classes are known during training. Over real planetary surfaces, the autonomous recognition system is likely to encounter an unknown category of rock that is crucial to the performance of the rock classification task. Therefore, we develop an open-set Martian rock-type classification framework based on their spectral signatures, with the subgoal of new/unknown rock-type recognition and category-incremental learning for expanding the recognition model. First, the spectral signatures of rock samples are captured to characterize their mineralogical compositions and physical properties, which serves as the input of the developed framework. To further produce the highly discriminative feature representation from the original spectral signatures, a Transformer architecture integrated with contrastive learning is constructed and trained in an end-to-end manner to force instances of the same class to remain close-by while pushing those of a dissimilar class farther apart. Following this, according to the extreme value theorem (EVT), category-specific distance distribution analysis is conducted to detect and identify new/unknown types of rock samples due to the isolated characteristics of new/unknown rock samples in the latent feature space. Finally, the recognition model is incrementally updated to learn these identified "unknown" samples without forgetting previously known categories when the associated labels are progressively obtained. The multispectral camera, a duplicated payload of the counterpart onboard the Zhurong rover, is used as the multispectral sensor for capturing the spectral information of the laboratory rock dataset shared by the National Mineral Rock and Fossil Specimens Resource Center for both qualitative and quantitative evaluation. Experimental results indicate the effectiveness and robustness of the developed in situ analysis framework.

Neural Path Planning With Multi-Scale Feature Fusion Networks

Path planning is critical for planetary rovers that perform observation and exploration missions in unknown and dangerous environment. And due to the communication delay, it is difficult for the planet rover to receive instructions from Earth in time to guide its own movement. In this work, we present a novel neural network-based algorithm to solve the global path planning problem for planetary rovers. Inspired by feature pyramid networks used for object detection, we construct a deep neural network model, termed the Pyramid Path Planning Network (P3N), which has a well-designed backbone that efficiently learns a global feature representation of the environment, and a feature pyramid branch that adaptively fuses multi-scale features from different levels to generate the local feature representation with rich semantic information. The P3N learns environmental dynamics from terrain images of planetary surface taken by satellites, without using additional elevation information to construct an explicit environmental model in advance, and can perform path planning policy after end-to-end training. We evaluate the effectiveness of the proposed method on synthetic grid maps and a realistic data set constructed from the lunar terrain images. Experimental results demonstrate that our P3N has higher prediction accuracy and faster computation speed compared to the baseline methods, and generalize better in large-scale environments.