Planetary surfaces

Abstract:

Introduction: The Lunar Thermal Mapper (LTM) is a multispectral infrared radiometer, built by the Oxford Physics Instrumentation Group for the Lunar Trailblazer mission; a small satellite launched in February 2025 under NASA’s Small Innovative Missions for Planetary Exploration (SIMPLEx). Trailblazer aims to advance our understanding of the lunar water cycle by mapping surface temperature, water abundance, distribution and form (OH, H2O, ice) and silicate lithology (i.e., Si-O Christiansen spectral feature). LTM was developed to improve upon existing infrared instrumentation in lunar orbit (e.g., Diviner Lunar Radiometer Experiment, hereafter referred to as Diviner) to provide higher resolution temperature estimations and refine interpretations of thermophysical properties at the surface [2, 3]. Accurately determining surface temperatures on airless bodies is essential for deriving emissivity spectral features (such as the Christiansen Feature and Restrahlen bands, which are diagnostic of silicate lithologies) that are representative of the surface. Temperature errors can affect spectral shape, resulting in the misidentification of surface composition [5, 8].  Our team compared six methods for estimating LTM’s brightness temperature (BT), including the temperature retrieval approach used by Diviner, to (1) determine which method provides the most representative surface temperature and (2) assess how variations in BT estimation affect derived emissivity spectral shape. Despite challenges facing the Trailblazer mission, refining methods for BT estimation remains relevant to the planetary community, as future missions continue to depend on infrared instrumentation and accurate BT retrievals for remote compositional interpretation (e.g., LEAP, L-CIRiS, Europa Clipper).   Instrumentation: LTM is a 15-channel infrared imager that covers a range between 6 to 100 µm [2,3]. LTM advances infrared compositional analysis by incorporating eleven narrowband compositional filters across the 6.25 to 10 µm range. This expanded spectral coverage enables more precise characterization of key features, such as the Christiansen Feature, Reststrahlen bands, and transparency features, which are essential for identifying spectral endmembers (Table 1) [2,3].   LTM builds upon Diviner, a nine-channel instrument that has a broad spectral range from 0.3 to 400 µm (Table 1) [1]. Diviner’s three narrowband compositional channels, 7.55–8.05 µm (Channel 3), 8.10–8.40 µm (Channel 4), and 8.38–8.60 µm (Channel 5), are specifically tuned to capture the Christiansen Feature (CF), an emissivity peak that is diagnostic of broad silicate mineralogy and sensitive to variations in silica content [1,4].  Table 1: LTM and Diviner observational parameters.    Methodology: To assess BT and emissivity retrieval techniques for LTM, we measured four lunar analog samples under controlled laboratory conditions to retrieve high-resolution emission spectra. These laboratory spectra were down-sampled to match LTM’s narrowband spectral resolution. Six BT estimation methods were tested to determine how effectively each method preserved laboratory spectral shape and temperature. The following section describes the laboratory setup and the BT estimation methods examined in this study. Laboratory: Using the PASCALE (Planetary Analogue Surface Chamber for Asteroids and Lunar Environments) in conjunction with a Bruker 70V Fourier Transform Infrared (FTIR) spectrometer, we conducted thermal infrared measurements of four volcanic lunar analogue samples; dunite (Twin Sisters -1 and -2), basalt (BIR-1) and rhyolite (RGM-1) under controlled ambient conditions (350 K, 1000 mbar, N2 atmosphere) [4]. The integration of PASCALE with FTIR allows for the acquisition of thermal emission spectra (as opposed to typical laboratory reflectance), offering a more representative analog of data collected by orbiting infrared instrumentation. Spectra were measured across ~6000 to 350 cm⁻¹ at a resolution of 4 cm⁻¹. Quality assurance and calibration procedures followed established protocols outlined in [6,7,8].  BT Estimations: To evaluate BT performance at LTM’s spectral resolution, each sample’s measured radiance was convolved with LTM’s filter response to simulate instrument-resolution radiance. The resulting spectra were converted to BT using the Planck function. Seven distinct methods were applied to the LTM-resolution BT data to determine the maximum BT values for each sample (Table 2). Emissivity was subsequently derived as the ratio between the observed LTM-resolution radiance and an ideal blackbody at the retrieved maximum BT for each method across all samples. The accuracy of the BT estimation methods was assessed by comparing the resulting emissivity spectra and maximum BT values to the full laboratory reference data (350K and full resolution emissivity). Additionally, a focused comparison with Diviner’s BT retrieval method was conducted to identify method-specific discrepancies and evaluate cross-instrument consistency.Table 2: BT estimation methods Results & Discussion: Six BT estimation methods were applied to laboratory emissivity spectra of four lunar analogue samples (dunite, basalt, and rhyolite), as shown in Figure 2. The associated standard errors (SE) for each method are reported in Table 3. Among the tested approaches, four methods (3rd degree polynomial, quadratic, spline and narrowband maximum) showed close agreement with high-resolution laboratory spectra (Figure 2). Temperature variations across compositions were minor, with low SE values (Table 3).  Since the spline fit did not significantly outperform the simpler polynomial or narrowband methods, lower complexity approaches are preferred for LTM temperature retrievals, with a maximum SE of 3.42%.In contrast, due to limited spectral sampling, the Diviner method underestimates surface temperatures by up to 19 K (SE max: 5.55%) in the dunite (TS-2) sample. Expanding this analysis to include a broader range of lithologies or impacted processed samples would help assess whether the Diviner approach (and potential other methods with sparse spectral sampling) introduce systematic shifts in the Christiansen Feature (CF) position or affect the spectral shape relative to more spectrally resolved techniques.  Table 3: BT estimations and associated SE of temperature for dunite (TS-1, TS-2), basalt (BIR-1) and rhyolite (RGM-1). Fig 2: Six BT methods are fitted to laboratory emissivity spectra of four lunar analogues. Conclusion:Comparisons between BT estimation methods indicate the 3rd-degree polynomial, quadratic, and narrowband maximum methods offer the best agreement with laboratory data (SE max: 3.42%). Although Diviner’s method tends to underestimate surface temperatures (up to 19 K), it still preserves spectral shape and wavelength range, supporting the reliability of compositional interpretations. Expanding the dataset to include a broader range of compositions could confirm whether different approaches result in systematic shifts in the Christiansen Feature across different lithologies. This work enhances the accuracy of remote compositional interpretation and supports future exploration on airless bodies.

Abstract:

In this presentation, we will show efforts made to include accurate photochemical modelling of hydrogen chloride (HCl) and ozone (O3) in the Mars Planetary Climate Model in order to reconcile recent observations.The ExoMars Trace Gas Orbiter (TGO) has detected and characterised trace gases in the Martian atmosphere over several Mars years. With its data, upper limits of potential constituents have been constrained, the accuracy of species’ concentration measurements has been improved, and seasonal and spatial variations in the atmosphere have been observed. The wealth of data obtained has addressed several open questions about the nature of Mars’ atmosphere, while other measurements have revealed much that remains poorly understood. For example, models continue to struggle to reproduce ozone distributions, both spatially and temporally, as well as seasonal variations in atmospheric oxygen (O2), suggesting that some key photochemical interactions may be being overlooked. As another example, despite seven years of dedicated observations producing very low upper limits on atmospheric methane levels, there remains no unifying hypothesis that simultaneously explains the detections reported by other Mars assets at Gale Crater [e.g., 1-4].Hydrogen chloride—the first new gas detected by TGO [5,6]—has been investigated recently using the mid-infrared channel on TGO’s Atmospheric Chemistry Suite (ACS MIR) [7,8]. Observations show a strong seasonal dependence of HCl in the atmosphere, with almost all detections occurring during the latter half of the year between the start of dust activity and the southern hemisphere autumnal equinox. There are also unusual measurements of HCl, localised in both time and space, during the aphelion season. Chlorine-bearing species such as HCl are important to understand in the Mars atmosphere because on Earth they are involved in numerous processes throughout the planetary system, including volcanism, from which HCl on Earth ultimately originates. Further, chlorine species play a key role in atmospheric chemistry: they influence oxidative chemistry and variations in the aforementioned O2 and O3 concentrations (e.g., by catalysing the destruction of ozone), and by extension, potential CH4 in the Martian atmosphere [9]. However, much remains unknown about original source and sinks of HCl, as well as the factors controlling its distribution and variation.Here, we use the Mars Planetary Climate Model—a 3-D global climate model that includes a photochemical network—to investigate potential mechanisms accounting for patterns in ozone and HCl detections and interactions between them. We begin with the role of heterogeneous chemistry involving ice and dust aerosols, by implementing modelling developed for the Open University Mars Global Climate Model [10] and building on existing chlorine photochemical model networks [11,12,13]. Heterogeneous chemistry affects the abundances of oxidative species such as OH and HO2, and by extension, O and O3. In addition, we investigate how such processes can potentially serve as a mechanism for direct release and sequestration of HCl from the atmosphere. We also explore potential mechanisms behind the annual occurrence of spatially-constrained aphelion HCl, including volcanic sources, and we investigate the interplay between chlorine-bearing species and OH, HO2,O, and O3. Figure 1 shows the way that HCl appears during spring and summer in the southern hemisphere (solar longitudes 180-360°) when water vapour is present in the Martian atmosphere. Ozone behaves in the opposite manner and is present when water vapour abundances are low. As shown, these species are anti-correlated; we explore the important chemical pathways connecting them.Understanding the role of oxidative chemistry on HCl and other trace gases is key to achieving a more complete picture of processes occurring in the present-day Mars atmosphere, as well as processes that have shaped its evolution and habitability.Figure 1: Observations of CO, O2, O3 and HCl seasonally and across multiple Mars Years. Upper panel: CO and O2 observations from Curiosity’s Sample Analysis at Mars (SAM) instrument (stars; [14]) and the Mars Climate Database (lines; [15]). Lower panel: O3 and HCl observations from TGO’s ACS instrument [8]. MY=Mars Year; NH/SH=northern/southern hemisphere. Figure from Kevin Olsen.References:[1] Giuranna, M., et al. (2019). Nat. Geosci. 12, 326–332. [2] Korablev, O. et al. (2019). Nature 568, 517–520. [3] Montmessin, F. et al. (2021). Astron. Astrophys. 650, A140. [4] Webster, C. R. et al. (2015). Science 347, 415-417. [5] Korablev O. I. et al. (2021). Sci. Adv., 7, eabe4386. [6] Olsen K. S. et al. (2021). Astron. Astrophys., 647, A161. [7] Olsen K. S. et al. (2024a). JGR, 129, e2024JE008350. [8] Olsen K. S. et al. (2024b). JGR, 129, e2024JE008351. [9] Taysum, B. M. et al. (2024). Astron. Astrophys., 687, A191. [10] Brown M. A. J. et al. (2022). JGR, 127, e2022JE007346. [11] Rajendran, K. et al. (2025). JGR: Planets 130(3), p.e2024JE008537. [12] Streeter, P. M. et al. (2025). GRL 52(6), p.e2024GL111059. [13] Benne, B. et al. (2024). EPSC, pEPSC2024-1037. [14] Trainer, M. G. et al. (2019). JGR 124, 3000. [15] Millour, E. et al. (2022). Mars Atmosphere: Modelling and Observations, p. 1103.

Abstract:

NASA’s Lucy mission [1] successfully completed a flyby encounter with the main-belt asteroid (52246) Donaldjohanson on April 20, 2025, collecting data as part of a full-scale operational test for Lucy’s future Trojan encounters.  Donaldjohanson was known to be a C-type asteroid and based on our ground-based observations, to have a Fe-bearing phyllosilicate 0.7 µm absorption. Similar absorptions in spectra of CI, CM, and CR carbonaceous chondrites are indicative of aqueously altered mafic silicates [2-4]. Donaldjohanson is also a member of the 155 Mya Erigone family [5], which is dominated by objects that have also been inferred to be aqueously altered based on their visible 0.7 µm absorptions [6].The Multi-spectral Visible Imaging Camera (MVIC), part of Lucy’s L’Ralph instrument [7-8], was specifically designed to include a filter covering the 0.7 µm absorption to detect evidence of aqueous alteration on the mission’s primary Trojan targets. The Donaldjohanson encounter is thus an excellent opportunity to compare the performance and calibration of MVIC to ground-based data. Here, we will report on both these validation efforts and our exploration of the spatial variability of the 0.7 µm phyllosilicate absorption across the imaged surface of Donaldjohanson to understand potential variability with surface features and photometry, and in relation to other Erigone family objects.References: [1] Levison et al. (2021) PSJ. [2] Cloutis et al. (2011a) Icarus. [3] Cloutis et al. (2011b) Icarus. [4] Cloutis et al. (2012) Icarus. [5] Marchi et al., (2025) PSJ. [6] Morate, D., et al. (2016) A&A. [5] Reuter et al. (2023), SSR. [6] Simon, A.A., et al. 2025 PSJ.Acknowledgments: The Lucy mission is funded through the NASA Discovery Program (Contract No. NNM16AA08C).

Abstract:

Introduction: The surfaces of airless bodies, such as asteroid (101955) Bennu, are typically composed of a regolith mixture containing both coarse and fine particulates. Observations from NASA’s Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) mission demonstrated a discontinuity between the remote sensing derived thermophysical properties and thermal spectroscopy results, indicating that a fine layer of dust may be coating the large boulders and coarse regolith surface [1]. To better understand the impact of such a coating on the thermal infrared spectra measured at Bennu, this work developed experimental methods for simulating dust coverings using Space Resource Technology’s CI simulant, based on the bulk composition of the Orgueil meteorite [2].    Figure 1: FTIR Reflectance Spectra of Control Samples of CI simulant. Figure 2: Figure 2: Microscope Camera Images of Sample Surfaces (7%, 10%, 15%, 20%, 25%, 50% Fines wt) of CI simulant Methods: The CI simulant was first sieved into seven size fractions: 1000 mm. An unsieved sample was used as a control. The spectra of the eight samples were measured using a Bruker Vertex 70v Fourier Transform Infrared Reflectance (FTIR) spectrometer, normalized using a gold standard prior to and after measurements, in the range of 1000-650 cm-1 (Figure 1). The dust coating was simulated by placing increasing mass fractions of fine particulates (10% change in the spectral slope).  Implications for OSIRIS-Rex Findings: From the data returned by the OSIRIS-Rex Thermal Emission Spectrometer (OTES) [3],  thermal inertia modelling imply that the surface is porous;, however, the spectral findings indicate that the surface is composed of non-porous? rocks with thin dust coatings [4]. Our experiments find that as little as ~7 wt % of 7% wt) was sufficient to overwhelm and dominate mid-infrared emissivity spectra. The results indicate that the discontinuity in OTES data could be linked back to dust coating on the larger rocks and boulders.   References: [1] Tinker C. et al. (2023) RAS Techniques and Instruments (Vol. 2, Issue 1). [2] Landsman Z. et al. (2020) EPSC 2020. [3] Christensen P. R. et al. (2018) Space Science Reviews (Vol. 214, Issue 5). [4] Rozitis B. et al. (2022) JGR: Planets (Vol. 127, Issue 6). [5] Rivera-Hernandez F. et al. (2015) Icarus (Vol. 262). 

Abstract:

Introduction: On the 20th of April 2025, NASA’s Lucy mission [1] flew by the C-type main-belt asteroid (52246) Donaldjohanson (hereafter DJ). The encounter’s goal was to test the spacecraft and instruments during an observation sequence commensurate with those to be used on Lucy’s main targets – Jupiter’s Trojan asteroids. Data returned from the panchromatic Lucy LOng Range Reconnaissance Imager (L’LORRI, 450-850 nm, [2]) during this testing sequence reveal the asteroid to be bi-lobed and elongated shape (Fig. 1).DJ is a member of the Erigone collisional family, named after the parent body asteroid (163) Erigone (see references in [3]). Ground-based color observations (Fig. 2) show it to decrease in color towards shorter wavelengths, possibly due to the presence of hydrated materials [4].In this work, we present an analysis of color images taken by Lucy’s Multispectral Visible Imaging Camera (MVIC). MVIC consists of six time delay integration (TDI) charge-coupled devices (CCDs). TDI works by synchronizing the transfer rate of the image between CCD rows and the relative motion of the instrument allowing a high signal to noise image to be built up even for fast scans. It covers wavelengths between 375 nm and 950 nm using five color filters and a panchromatic one (see Table 1).Color Analysis: We focus our analysis on images acquired with the four wide band filters: violet, green, orange and near-IR. Our results will provide resolved color variations and contextualise DJ’s color with respect to ground-based observations of DJ, Erigone (Fig. 2), other members of the Erigone family, and the broader asteroid and small body populations.Filter Wavelength Violet 375-480 Green 480-520 Orange 520-625 Phyllosilicate 625-750 Near-IR 750-950 Panchromatic 350-950 Table 1 – MVIC filters [5]Figure 1 – (52246) Donaldjohanson as seen by the panchromatic Lucy L’LORRI instrument, taken on April 20, 2025 at 17:51 UTC.  Figure 2 – Ground-based normalized (at 0.55 µm) visible spectrum of DJ (blue) acquired with the Gran Telescopio Canarias compared to the Bus-DeMeo’s Cg-type (black) and the mean spectrum of the C-type members within the Erigone family (grey). Taken from [6]. Acknowledgments: The Lucy mission is funded through the NASA Discovery program on contract No. NNM16AA08C.References: [1] Levison et al. (2021) PSJ 2, 171. [2] Weaver et al. (2023), SSR 219, 82. [3] Marchi et al., (2025) PSJ 6, 59. [4] Vilas (1995) Icarus 115, 217-218. [5] Reuter et al. (2023), SSR 219, 69. [6] Souza-Feliciano et al. (2020), Icarus 338, 113463.