TP3
We welcome contributions from any field of Mars science (observation or modelling) and exploration (robotic and human), in particular mission status and instrument overviews of latest scientific results and technical developments. These may include latest scientific results and mission overviews, as well as new challenges, for orbiters (Mars Express, ExoMars TGO, Odyssey, MRO, Tianwen-1, Hope), surface assets (Mars Science Laboratory, Mars2020), and future missions: Martian Moons eXploration (MMX), ExoMars Rosalind Franklin Mission, ESCAPADE, and beyond.
Session assets
ExoMars Trace Gas Orbiter (TGO): TGO will soon reach its tenth year at Mars: it arrived at Mars on 19 October 2016, and reached its nominal science orbit in April 2018.
Recent science highlights include (a) a new climatology of water vapor, tracing high-altitude water [1,2]; (b) further characterization of variations of HCl, including search for magmatic sources [3] and co-ordinated ground-based observations [4]; (c) study of the seasonal cycle of ice & dust deposition at Louth Crater [5].
The TGO spacecraft health is nominal and appears consistent with operation well into the 2030s.
Mars Express (MEX): MEX remains a highly productive mission as it enters its third decade of operation at Mars.
Recent science highlights include (a) discovery of englacial (internal) folding of the South Polar Layered Deposits, providing evidence of ice flow [1]; (b) an analysis of clays at Oxia Planum and Mawrth Vallis showing that they clay layers at both sites appear to have a common origin; (c) a new climatology of atmospheric dust from SPICAM; (d) detection of a huge enhancement of the M2 ionospheric layer in response to a solar storm, measured using spacecraft-to-spacecraft (MEX-to-TGO) radio occultation.
Mars Express operations are currently funded until end of 2026, with extension requested until end of 2029.
Acknowledgments: This abstract represents the work of hundreds of researchers across the MEX and TGO teams, as well as the spacecraft and science operations teams for these missions. MEX and TGO data are freely and publicly available at ESA’s Planetary Science Archive (https://psa.esa.int/). Digital Elevation Models from CaSSIS are available at https://cassis.oapd.inaf.it/archive/ .
References:
[1] A. Brines et al., JGR 2026, https://doi.org/10.1029/2024JE008916
[2] A. Brines et al., JGR 2026 https://doi.org/10.1029/2025JE009191
[3] Mege et al EPSC-DPS 2025, https://doi.org/10.5194/epsc-dps2025-985
[4] S. Faggi et al., JGR 2026, https://doi.org/10.1029/2025JE009105
[5] M. Azevedo et al., LPSC 2026
[6] L. Guallini et al, EPSL 2026, https://doi.org/10.1016/j.epsl.2025.119749
[7] Auré et al, Icarus 2026, https://doi.org/10.1016/j.icarus.2026.117113
[8] Fedorova et al, JGR 2026, https://doi.org/10.1029/2025JE009388
[9] Parrott et al., Nat Comm 2026, https://doi.org/10.1038/s41467-026-69468-z
How to cite: Wilson, C.: Europe’s Mars orbiters: Status & Highlights, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-270, https://doi.org/10.5194/epsc2026-270, 2026.
The High-Resolution Imaging Science Experiment (HiRISE) camera, orbiting Mars since 2006 on the Mars Reconnaissance Orbiter (MRO), has returned more than 102,000 large (gigapixel) images with scales as small as 25 cm/pixel covering 5.2% of the Martian surface. About 20% of these images are parts of stereo pairs providing 10000 anaglyphs of the martian surface. The HiRISE team has created over 1200 stereo Digital Terrain Models (DTMs; Figure 1) that provide the highest-resolution (1 m/px) planetary topography from orbit. HiRISE uses time delay integration (TDI) to image at a high signal-to-noise ratio in spite of the small view of each pixel and the fast groundtrack velocity. Thanks to our community targeting, rapid data release policy, and open distribution of analysis tools, the scientific impact of HiRISE has been extraordinarily high. A search for "HiRISE" and "Mars" in NASA ADS on 5 May 2026 yielded 2,687 refereed publications. There are 638 refereed publications from 2/2024 to 5/5/2026, published since the summary by McEwen et al. (2024, Icarus 419, id.115795). HiRISE has scouted and certified the landing sites for the Phoenix and InSight landers and the Curiosity, Perseverance, and Rosalind Franklin rovers. Furthermore, HiRISE images have helped to diagnose several failed landing attempts, allowed the Opportunity rover to avoid sand traps, monitored dust accumulation on Insight’s solar panels, and enabled Curiosity to choose drive paths to minimize wheel damage. HiRISE science results can be put into several major categories:
- Ancient Mars alteration: Widespread aqueous mineral alteration of ancient martian crust (and limited exposures of younger crust) has been a paradigm change over recent decades from orbiting spectrometers and other data. HiRISE data, especially with DTMs, enable placing the compositional data into stratigraphic context and extrapolation of some mineral exposures to smaller scales via the color data.
- Geologic processes: There have been hundreds of studies of martian stratigraphic, volcanic (lava and mud), impact, fluvial, mass wasting, tectonic, aeolian, glacial, and periglacial processes. Typically these studies use multiple datasets, but HiRISE provides the highest resolution views except where there have been successful landers, rovers, and drones.
- Ice in the mid latitudes forms an important climatic record and resource for future humans. HiRISE has determined the distribution of buried mid-latitude ice through the imaging of ice-exposing new impact craters including one located as equatorward as 35 N. HiRISE also characterizes thermokarst features that indicate the continued presence of buried excess ice as well as ice-exposing scarps that directly expose this ice.
- Polar science benefits from HiRISE’s polar orbit as well and the high signal-to-noise that its TDI system provides. Stereo DTMs allow for signal analysis of polar layered exposures that show orbital control of Mars’ climate over millions of years. HiRISE shows seasonal CO2 defrosting activity actively changing sand dunes and other surface features today. Dozens of avalanches in progress and mass wasting from polar scarps are measured in HiRISE data. Interannual change in dynamic CO2 ice landforms are measured and can be related to the current climate.
- Current non-polar activity has been shown by HiRISE to be surprisingly common. Dune migration is measured at all latitudes providing information on near-surface winds and the efficacy of aeolian processes today. Modern gully activity has been recorded by HiRISE in enough seasonal detail to determine that seasonal CO2 frost and not liquid water is the responsible agent. Recurring slope lineae mimic the appearance of seeping water tracks on Earth, but HiRISE DTMs and seasonal monitoring showed they are probably narrow thermally-triggered dry landslides. Thousands of newly-formed impact craters have been measured by HiRISE and constrain the present-day impact rate.
- Co-analysis with landed missions has been a part of hundreds of publications. Although landers and rovers provide high-resolution images of very small areas around the surface cameras, HiRISE images and topography provide essential geologic context. For example, the Amapari Marker bed (AMB), for which Curiosity indicated lacustrine deposition, has been mapped around Gale crater with HiRISE data, showing that the lake could have been up to 14-km across (Mondro et al. 2025, JGR Planets 130). In some cases there are dozens of publications even before landing has occurred, such as for the Rosalind Franklin rover.
- Computer vision (or machine learning) has been increasingly important given the very large volume of data from Mars. Such studies have transitioned in recent years from simply demonstrating the potential to producing significant science results. Examples include mapping the global distribution of pitted cones, whose distribution strongly supports an origin as mud eruptions, and finding new impact locations including smaller events where HiRISE can make follow-up diameter measurements, reducing the discrepancy between optical and seismic detections.
Figure 1. HiRISE DTMs archived with PDS plotted (green squares) on a map of Mars based on MOLA elevations.
How to cite: McEwen, A. and Byrne, S.: 20 Years of Mars Science from MRO HiRISE, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-169, https://doi.org/10.5194/epsc2026-169, 2026.
The Martian Moons eXploration (MMX) mission [1], led by the Japan Aerospace Exploration Agency (JAXA), is scheduled for launch in October 2026 and aims to investigate Phobos, Deimos, and the Martian atmosphere [2]. Among its payload, the MMX InfraRed Spectrometer (MIRS) [3] will observe the planet’s atmosphere in the infrared range (0.9–3.6 µm) [4], with the goal of improving our understanding of dust transport and water-ice cloud processes [5].
In preparation for the analysis of the first observations of the Martian atmosphere expected in autumn 2027, we simulate the I/F spectra that will be measured by MIRS in order to better constrain key aerosol (water ice and dust) properties [6], such as optical depth and particle radius. To this end, we use the pyRT_DISORT module from the DISORT (DIScrete-Ordinate-method Radiative Transfer) radiative transfer model [7,8] to generate the spectra across the MIRS spectral range.
These simulations require an accurate map of the surface albedo for specific martian regions defined by their given latitude and longitude coordinates. Surface albedo is a critical input parameter, particularly under clear atmospheric conditions [9], as it strongly influences the simulated I/F spectra. We therefore rely on an albedo map derived from OMEGA/MEX observations at 1.08 µm [10] (fig.1), which lies within the MIRS spectral range. However, due to instrumental and observational constraints, approximately 3% of the map contains missing data, or bright artifacts. To address this, we reconstructed these pixels using a complementary visible-wavelength albedo dataset from MARCI/MRO (band 5, 0.718 µm) [11], selected for its spectral proximity and global coverage. The resulting completed albedo map (fig. 2) provides continuous albedo coverage over the entire simulations’ domains, enabling the generation of realistic spectra for all pixels. Instrument-like noise provided by the CNES is then added to reproduce conditions representative of future MIRS observations.
Figure 1 : OMEGA albedo map at 1.08 µm [10].
Figure 2 : Improved OMEGA albedo map at 1.08 µm, after the dark and bright pixels corrections.
These synthetic spectra, to which we added noise, are then intended to serve as test observations for the development and validation of an inversion framework based on comparison with a precomputed Look-Up Table (LUT) (Table 1). The LUT itself is constructed from DISORT simulations through an extensive sensitivity analysis covering the main parameters controlling the signal, including surface albedo, aerosol optical depth, particle radius, and observation geometry (incidence, emission, and phase angles).
Table 1 : Final LUT defined from the sensitivity analysis.
The parameter values were chosen to ensure a uniform distribution of the resulting I/F spectra across the explored spectral domain, covering atmospheric conditions from optically thin (clear atmosphere) to increasingly optically thick (opaque atmosphere) cases. Preliminary tests have recently been initiated using a search algorithm inspired by face-recognition techniques, namely Facebook AI Similarity Search (FAISS) [12]. This approach is expected to enable efficient identification of the synthetic spectrum within the LUT that best matches an observed MIRS spectrum, and therefore retrieval of the associated aerosol properties.
Acknowledgments :
We thank M. J. Wolff and S.W. Lee for sharing the MARCI albedo map (personal communication). We thank the MMX JAXA teams for their efforts and CNES for the financial support and collaboration to build the MIRS instrument.
References :
[1] Kuramoto K. et al., Earth, Plan. and Space, 74, 12.
[2] Nakamura T. et al., Earth Plan. and Space, 2021, 73.
[3] Barucci M. A. et al., Earth, Plan. and Space, 2021, 73, 211.
[4] Barucci M. A. et al., Progress in Earth and Planetary Science, 2025, 12, pp.69.
[5] Määttänen A. et al., Space Science Reviews. 2024. 220. 10.1007/s11214-024-01092-z.
[6] Leseigneur Y. et al. (2025) EPSC-DPS2025-1672
[7] Stammes K. et al., Applied Optics, 1988, 27, 2502-2509.
[8] Connour K. and Wolff M. (2022) pyRT_DISORT: A pre-processing front-end to help make DISORT simulations easier in Python, version 1.0.0.
[9] D.F. Wellington, J.F. Bell, Icarus, Volume 349, 2020, 113766, ISSN 0019-1035.
[10] Ody A. et al., Journal of Geophysical Research, 2012, Vol. 117, E00J14.
[11] Bell, J. F.III, et al., J. Geophys. Res., 2009, 114, E08S92.
[12] M. Douze et al., "The Faiss Library," in IEEE Transactions on Big Data, vol. 12, no. 2, pp. 346-361, April 2026.
How to cite: Le Bail, G., Lasue, J., Stcherbinine, A., Leseigneur, Y., Bertrand, T., Gautier, T., and Théret, N.: Preparing MMX InfraRed Spectrometer (MIRS) observations : Simulations of the Martian Atmosphere for Aerosol Retrieval, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-581, https://doi.org/10.5194/epsc2026-581, 2026.
Mars 2020 Perseverance rover is currently exploring Jezero Crater on Mars, which contains an ancient lake-delta fan system. Since its landing in 2021 the rover has investigated different parts of the Jezero crater including the crater floor, the delta fan, the crater margin (Margin unit’) and the Neretva Vallis channel, identifying several different potentially habitable environments. Currently the rover is exploring the crater rim, which contains some of oldest rocks on Mars, when Mars might have been its most habitable.
One of Perseverance’s primary science goals is to collect samples for potential return to Earth (Farley et al. 2020). Since February 2021, Perseverance has sealed 33 tubes containing 27 rock cores, 2 regolith samples, one atmosphere sample and three witness tubes (Fig. 1). Of the samples collected, several collected from the fan front, the Margin unit and the Neretva Vallis channel have large potential for answering questions about past climate, habitability and the potential for life on Mars.
The rock cores collected at the fan front and Neretva Vallis are fine-grained clay-rich sedimentary rocks that were likely deposited in a lacustrine/deltaic environment (Bosak et al. 2024; Hurowitz et al. 2025). The fine-grained and phyllosilicate rich nature of these samples are beneficial for the preservation of biosignatures. The Neretva Vallis sample contains tantalizing signatures including organic material and phosphate and sulfide-rich reaction fronts that potentially could have been produced by life (Hurowitz et al. 2025; Murphy et al. accepted). Other more coarse-grained sedimentary samples, such ones collected at the fan top samples, which were likely deposited in a river setting, could inform about a Martian source to sink system, contain lithologies from greater Nilli Fossae area and be used for paleomagnetism studies.
The rock cores collected at the Margin unit contain abundant carbonate and silica which likely were formed by carbonation and serpentinization of ultramafic rocks (Williford et al. 2026). In addition to trapping CO2 and thus providing information about the past atmosphere and climate on Mars, this process could have provided energy and a habitat for potential microbes (Siljeström et al. in prep). Microcrystalline carbonates and silica are also known to preserve biosignatures.
The rock cores have also been collected at the crater rim including a serpentine sample and a phyllosilicate-rich sample will inform about potential habitability during the Noachian. The serpentine rock, collected at a potential megablock, is dominated by minerals formed from serpentinization, informing about Noachian water-rock interaction and providing energy for potential microbes. The phyllosilicate-rich sample was collected from an altered likely Noachian basement and could provide insights into water-rock interactions and a potentially habitable environment in Mars’ most ancient past.
In addition, the rock cores from the crater floor and rim which will be critical for constraining the timing of different events on Mars including aqueous activity in Jezero Crater and when and for how long Mars was habitable (Farley et al. 2022). They will also inform about Mars’ igneous history and potential impact processes on Mars.
Perseverance will continue exploring the crater rim and collecting samples for next couple of years, expanding the number of samples and that can be answered, if returned to Earth.
References:
Bosak, T. et al. (2025) AGU Advances, 5, e2024AV001241.
Farley K.A. et al. (2020) Space Science Reviews 216, 142.
Farley, K.A. et al. (2022) Science 377, eabo2196.
Hurowitz J.A., et al. (2025) Nature 645, 332–340.
Murphy A.E., et al. accepted, Science Advances
Siljeström, S. et al. in prep., JGR-Planets
Williford, K. et al. (2026) Science 391, eadu8264.
How to cite: Siljeström, S., Herd, C., Bosak, T., Farley, K., Stack, K., Benison, K., Czaja, A., Debaille, V., Hausrath, E., Hickman-Lewis, K., Mayhew, L., Sephton, M., Shuster, D., Simon, J., and Zorzano, M.-P.: The returned sample science of the samples collected by NASA Perseverance rover at Jezero Crater, Mars, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-444, https://doi.org/10.5194/epsc2026-444, 2026.
Aerosols on Mars are a primary element for studying the interaction between the solar radiation and the atmosphere and surface. Depending on properties such as aerosol number density, particle radius, or refractive index, the aerosols can provide positive or negative radiative feedbacks on the atmospheric dynamics. Previous studies have revealed large temporal and spatial variability in the aerosol optical properties, emphasizing the need for continuous monitoring throughout the day and at multiple locations. To address these measurements, the Radiation and Dust Sensor (RDS) [1] was included as part of the Mars Environmental Dynamics Analyzer (MEDA) [2] payload onboard the Perseverance rover of the Mars 2020 mission. The RDS instrument is composed of two sets of eight photodiodes (RDS-DP) and a sky-pointed camera (RDS-SkyCam). One set, oriented toward the zenith, captures radiation from 190 to 1200 nm, while the other, inclined 20° above the horizon at 45° azimuthal intervals, samples a single wavelength. The analysis of these observations, through a radiative transfer model [3], allows for the retrieval of key aerosol parameters such as aerosol opacity at different wavelengths and particle radius. However, the continuous deposition of dust over the sensors [4], since the beginning of the mission, introduces modifications in their optical response. In particular, the zenith-pointed photodiodes require angular response calibration due to the progressive accumulation of dust on their optical surfaces. Here, we present the calibration approach developed for the mission, essential to ensure the accuracy of aerosol property retrievals and reliable long-term atmospheric monitoring. Figure 1 shows the temporal evolution of the RDS-TOP 7 irradiance change derived from our calibration alongside Matscam-Z opacities [5], highlighting the impact of atmospheric conditions. In addition, we report ongoing radiative transfer retrievals of aerosol (dust and ice) optical properties during the first two years of the Mars 2020 mission. The relationship between vortex population and dust deposition rates will also be discussed.
Figure 1. Temporal evolution of the irradiance change measured by MEDA-RDS TOP 7 (blue) of the Mars 2020 mission at Jezero Crater, compared with atmospheric opacity at 630 nm measured by Mastcam-Z (red) over the first two years.
References:
[1] Apestigue, V., et al. “Radiation and Dust Sensor for Mars Environmental Dynamic Analyzer
Onboard M2020 Rover”. Sensor 22.8 (2022): 2907.
[2] Rodriguez-Manfredi, Jose Antonio, et al. “The Mars Enviromental Dynamics Analyzer, MEDA. A suite of enviromental sensors for the Mars 2020 mission.” Space science reviews 217.3 (2021): 1-86.
[3] Toledo, D., et al. “Measurement of aerosol optical depth and sub-visual cloud detection using the optical depth sensor (ODS)”. Atmospheric Measurement Techniques 9.2 (2016): 455-467.
[4] Vicente-Retortillo, A., et al. “Dust Accumulation and Lifting at the Landing Site of the Mars 2020 Mssion, Jezero Crater, as Observed From MEDA.” Geophysical Research Letters 51 (2024).
[5] Lemmon, M. T. Et al. “Dust, sand and winds within an active Martian storm in Jezero crater.” Geophysical Research Letters, 49.17 (2022): 0094-8276.
How to cite: Rodriguez-Veloso, R., Toledo, D., Apestigue, V., Arruego, I., Lorenzo-Corvo, C., Martínez, G. M., Lemmon, M. T., Jiménez-Martín, J. J., García-Menéndez, E., Hueso, R., Smith, M. D., Vicente-Retortillo, Á., Viudez-Moreiras, D., and Rodríguez-Manfredi, J. A.: Aerosol Optical Properties and Dust Deposition as observed by MEDA Radiation and Dust Sensor (RDS) at Jezero Crater, Mars, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-378, https://doi.org/10.5194/epsc2026-378, 2026.
Introduction: The European Space Agency’s ExoMars was conceived to search for signs of life on Mars. The design of the mission puts the science team in the best possible condition to search for biosignatures, using:
- a 2-m depth drill;
- the suite of 8 scientific instruments in the Pasteur Payload;
- the selected landing site, Oxia Planum, and;
- the surface exploration strategy that guides how the Rover and instruments are used together to achieve the mission objectives.
The Rosalind Franklin Mission (RFM) is a re-establishment of the ExoMars 2022 mission [1]. With European industrial prime contractor Thales Alenia Space, and important contributions from partner, NASA. RFM is on schedule for launch in the latter part of 2028 and to land at Oxia Planum in 2030.
Figure 1: The ExoMars Rosalind Franklin rover. Credit ESA/MLabspace
Pasteur Payload: The heart of the characterisation and analysis capabilities of the Rosalind Franklin Rover lies in the suite of complementary scientific instruments that comprise the Pasteur Payload. At macroscopic scales, the PanCam investigation [2], with its wide-angle multispectral stereo camera (WAC), and narrow-angle, high-resolution camera (HRC), working together with NavCam and LocCam navigation cameras, constitutes the eyes of the rover. An infrared spectrometer, Enfys [3], will reveal mineralogical signatures at targeted locations. The CLUPI instrument [4] serves as a geologist’s hand-lens, allowing close-up characterization of surface lithologies. The WISDOM ground penetrating radar [5] will reveal subsurface structures and survey potential drilling sites. Ma_Miss comprises an IR spectrometer head near the drill tip and will allow reconstruction of mineralogical stratigraphy in drilled boreholes [6]. In the rover’s Analytical Laboratory Drawer (ALD), the MicrOmega imaging Visible/IR spectrometer [7], Raman Laser Spectrometer, RLS [8], and Mars Organic Molecule Analyser (MOMA) [9] (which combines gas-chromatography and laser desorption with a linear ion trap mass spectrometer), work together to determine the mineralogy and organic composition of crushed samples.
Science Team Activities: The ExoMars Science Working Team (ESWT), ExoMars project, and industrial partners are nearing completion of refurbishment of the rover and its instruments. The ExoMars Rover Science Operations Working Group (RSOWG), chartered in 2019 by the ESWT, continues working to advance science readiness. The ‘Micro’ sub-group addresses topics regarding the spatial scale of the samples that will be extracted from down to 2m depth by the rover’s drill, their terrestrial analogues, and plans for their analyses, including by the three ALD instruments. Ongoing work regards the ‘Mission Reference Samples’ – a suite of natural and synthetic analogue samples most relevant to the landing site and mission objectives, which are under characterization by ground models of all the Pasteur Payload instruments. A set of geotechnical analyses are led by an Interdisciplinary Scientist (IDS) team focussing on sub-surface investigations [10], to maximize retrieval of information from drill telemetry in concert with visible images from CLUPI [4] and PanCam [2], infrared spectra from Ma_Miss [6] and information from the WISDOM radar [5].
Members of the ‘Macro’ sub-group continue geological interpretation of the landing site [e.g. [11], [12]] and continue to build interpretations from the published a high-fidelity geological map of Oxia Planum [13], the culmination of a 4-year team effort [14]. A dedicated co-author team is preparing the mission Strategic Science Plan (SSP), which traces mission science objectives to specific questions and hypotheses that are testable by Pasteur Payload instruments. Further, a Science Sampling Strategy (S3) is in development to provide tools for the science team to implement the priorities of the SSP into daily rover operations [15].
The European Entry Descent and Landing Module (EDLM) is in advanced development and will deliver the Rosalind Franklin rover to Oxia Planum. The module contains sensor packages that will support EDL and environmental characterisation at the surface for the time that the platform is operational after landing. Amongst them are the COMARS+ suite (Combined Aerothermal and Radiometer Sensors Instrument Package), four visible wavelength cameras for imaging the descent; and the Platform Atmospheric Characterisation Instrument Suite (PACIS), installed on the lander, which contains atmospheric pressure and temperature sensors, and a microphone. Telemetry from various sensors used during EDL supports the ExoMars Atmospheric Mars Entry and Landing Investigations and Analysis (AMELIA) team [16].
Continued Preparations for Operations: The Rover Operations Control Centre (ROCC, Turin) is maintained and updated, while continuing to host a schedule of tests and simulations, providing regular opportunities to exercise Science and Control Team processes. A Science Knowledge Management Programme (SKP) continues to support key expertise within the science and instrument teams, to retain and develop valuable team knowledge and experience that was built in preparation for the 2022 mission opportunity [17]. Preparations are well underway for rover field trials, to be held in 2026 and 2027 at a Mars analogue site, using a testbed rover and emulators of survey instruments, and building on experience from prior field tests [18], [19].
This presentation will explain how ESA, supported by industry and the payload teams and with our NASA partners, remains on track for a 2028 launch of the ExoMars Rosalind Franklin Mission. We will present the current level of advancement of the project and highlight the main science objectives and overall strategic plan for the mission.
References:
[1] J. L. Vago et al., Astrobiology, doi: 10.1089/ast.2016.1533.
[2] A. J. Coates et al., Astrobiology, doi: 10.1089/ast.2016.1548.
[3] A. Coates et al., Jul. 03, 2024. doi: 10.5194/epsc2024-927.
[4] J.-L. Josset et al., Astrobiology, doi: 10.1089/ast.2016.1546.
[5] V. Ciarletti et al., Astrobiology, doi: 10.1089/ast.2016.1532.
[6] M. C. De Sanctis et al., Astrobiology, doi: 10.1089/ast.2016.1541.
[7] J.-P. Bibring et al., Astrobiology, doi: 10.1089/ast.2016.1642.
[8] F. Rull et al., Astrobiology, doi: 10.1089/ast.2016.1567.
[9] F. Goesmann et al., Astrobiology, doi: 10.1089/ast.2016.1551.
[10] F. Altieri et al., Adv. Space Res., doi: 10.1016/j.asr.2023.01.044.
[11] I. T. Auré et al., Icarus, doi: 10.1016/j.icarus.2026.117113.
[12] J. D. Mcneil et al., JGRP, doi: 10.1029/2022JE007246.
[13] P. Fawdon et al., J. Maps, doi: 10.1080/17445647.2024.2302361.
[14] E. Sefton-Nash et al., LPSC#52, https://ui.adsabs.harvard.edu/abs/2021LPI....52.1947S
[15] F. Ferri et al., Space Sci. Rev., doi: 10.1007/s11214-019-0578-x.
[16] E. Sefton-Nash et al., LPSC2022, https://ui.adsabs.harvard.edu/abs/2022LPICo2678.2109S/abstract
[17] G. Ligeza, et al., Mars Through Time Int. Conf. https://www-mars.lmd.jussieu.fr/mtt2025/abstracts/Ligeza.pdf
[18] M. R. Balme et al., Planet. Space Sci., doi: 10.1016/j.pss.2018.12.003.
[19] M. Balme et al., EPSC 2020, doi: 10.5194/epsc2020-1073.
How to cite: Sefton-Nash, E., Vago, J. L., Joudrier, L., Zekri, E., Baglioni, P., Ball, A. J., Favaro, E. A., Ligeza, G., Tacconi, B., and Balme, M. R.: ExoMars Rosalind Franklin Mission (RFM) Update, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-411, https://doi.org/10.5194/epsc2026-411, 2026.
The ExoMars Rosalind Franklin Mission is designed to address one of the most fundamental questions in planetary science: whether life ever existed on Mars [1]. The selected landing site, Oxia Planum, exposes some of the oldest Noachian terrains (~4.1–3.7 Ga), characterized by widespread Fe–Mg phyllosilicate-rich deposits formed in the presence of water. These environments are considered highly favourable for the preservation of biosignatures [2, 3]. Achieving the mission’s scientific objectives depends on the rover’s ability to identify, prioritise, and sample the most promising geological targets, that is, those with the highest potential to harbour preserved biosignatures, and within the available operational resources (time, energy, and data volume).
This study has two main objectives:
First, to identify high-priority features for the ExoMars Rosalind Franklin rover. Priorities of relevant features are determined from the Strategic Science Plan (SSP, in preparation); the set of prioritised questions that are elaborated from overall mission science objectives. Then, the detection and spatial distribution of such features is interpreted using orbital observations [2, 3, 5] and the resulting geological map of Oxia Planum [4]. Together with predicted landing ellipses, we may calculate the relative importance of features in S3 according to their scientific priority, and the likelihood of encountering them during the rover surface mission phase.
Second, to develop a Science Sampling Strategy (S3) that incorporates these features and priorities into rover operations by defining how such features can be systematically recognised in situ and assessed for sampling. The S3 is designed to support science-driven decision-making during rover operations, particularly in relation to surface mission phases (see Experiment Cycle (EC) in [1]), at key decision points, such as “stay or go” at a site, “drill or not drill”, and the selection of appropriate subsurface sampling depth.
Identification of high-priority features: We identify two main groups of features: Group A, features known from orbital data within the landing ellipse, and Group B, features too small to resolve from orbit but that correspond to the first priority of the Science Strategic Plan (SSP) and are indicators of biosignature preservation. Group A includes Fe–Mg phyllosilicates, layered sedimentary units, fractures, bright patches, polygonal/honeycomb structures, and mounds. Their abundances within the map boundary [4] were quantified using ArcGIS (Fig. 1). Group B includes features inferred from interpretation of the landing site and analogues from Mars rover missions, limited to those relevant to the first SSP priority. These include carbonates, hydrated silica, hydrothermal textures (nodular, porous, vuggy), sulphates, mineralized veins, redox boundaries, concretions, iron oxides/hydroxides, and microbially induced sedimentary structures (MISS). Their potential occurrence at Oxia Planum is supported by geological context and indirect spectral evidence, such as silica detections near the sedimentary fan within the landing ellipse [5].
Figure 1: Group A high-priority features identified from orbital data and their abundances within the landing ellipse.
Exploration prioritisation: To integrate scientific value with operational feasibility, we introduce a feature prioritisation framework that evaluates targets based on two parameters: scientific interest (aligned with the Science Sampling Plan, SSP) and likelihood of rover encounter (Fig.2) This approach identifies a “golden spot” where features of high scientific value—particularly those related to the formation and preservation of life on Mars—coincide with a high likelihood of rover encounter. Accordingly, priority targets for rover exploration include Fe–Mg phyllosilicates, layered units, bright patches, hydrated silica and/or other hydrothermal features (e.g., nodular or porous textures), and potential microbially induced sedimentary structures (MISS), if present at the surface.
Figure 2: Exploration prioritisation for high priority features at Oxia Planum, their exploration interest based on SSP vs. likelihood of rover encounter.
Science Sampling Strategy (S3): The S3 incorporates high-priority features into a structured operational framework. It defines observations required to characterise geological context, recognise high-priority features in situ, and support sampling decisions.
The strategy is organised into three sequential stages (Fig.3). Observations are acquired across multiple spatial scales, from landscape context (PanCam WAC, NavCam, WISDOM)[6,7] to outcrop and target-scale investigations (PanCam HRC, CLUPI, Enfys)[6,8], with subsurface information provided by WISDOM [7] and Ma_Miss [9] (down to ~2 m depth). Interpretation integrates morphological, stratigraphic, and mineralogical data to reconstruct palaeoenvironmental conditions and assess biosignature preservation potential. Decision-making links these observations to operational decisions, including “stay or go” at a site, “go/no-go for drilling”, and drilling depth selection.
This workflow ensures observations are acquired and evaluated, enabling efficient decision-making. A key strength of the S3 framework is its adaptability. While anchored in orbital predictions, it allows incorporation of newly identified features, addressing uncertainties in orbital interpretations. S3 aims for consistency in preparing for decisions made in operations, so that decision-makers are provided with at least the minimum essential level of supporting information about a certain site or feature.
Figure 3: Science Sampling Strategy (S3) workflow.
Conclusions and preparations for operations : We present a Science Sampling Strategy (S3) that links high-priority geological targets at Oxia Planum with a structured, instrument-driven operational workflow for the Rosalind Franklin rover. The strategy enhances science-driven decision-making during rover operations and maximises mission return by focusing on environments most favourable for biosignature preservation.
The S3 will be further refined and validated through upcoming simulation campaigns and operational testing. In the near-term, it will be tested in the first of two ExoMars rover Field Tests, planned for mid-September 2026, providing a critical step toward readiness for surface mission operations.
References: [1] Vago, J. L. et al. (2017) Astrobiology 17, 471–510. [2] Quantin-Nataf, C. et al. (2021) Astrobiology, 21(3), pp.345-366. [3] Torres-Auré, I. et al. (2026). Clay continuity between Oxia Planum and Mawrth Vallis. Icarus, 117113.[4] Fawdon, P. et al. (2024) Journal of Maps, 20(1), p.2302361. [5] McNeil, J.D. et al. (2025) Journal of Geophysical Research: Planets, 130(9), p.e2025JE008989. [6] Coates, A. J. et al. (2017) Astrobiology 17, 511–541. [7] Ciarletti, V. et al. (2017) Astrobiology 17, 565–584. [8] Josset, J.-L. et al. (2017) Astrobiology 17, 595–61. [9] De Sanctis, M. C. et al. (2017) Astrobiology 17, 612–620.
How to cite: Ligeza, G., Sefton-Nash, E., Orgel, C., and Vago, J. L.: Science Sampling Strategy (S3) for High-Priority Features for the ExoMars Rosalind Franklin Rover – Where are They and How to Identify Them?, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-152, https://doi.org/10.5194/epsc2026-152, 2026.
Introduction: Mars is one among the Solar System bodies of highest interest for research about prebiotic chemistry, mainly because early Mars surface environment was similar to the Earth’s one at the time Life arose on our planet [1]. This is one reason why Mars surface has been intensively explored for decades, notably by the use of mobile rover probes, which allowed the first detection of indigenous organic molecules [2].
The Exomars mission has among its main objectives to characterize the organic matter present in its landing site, Oxia Planum, which is characterized by wide plains rich in clays [3]. Clay formation requires prolonged exposure to liquid water. The geochemical conditions necessary for the formation of such clays could facilitate prebiotic activity [4]. Therefore, Oxia Planum deposits could hold a record of formation and evolution of organic matter into molecules of biological interest. To prepare the interpretation of future data to be collected by the different instruments onboard the Rosalind Franklin rover, the Exomars science team organized a campaign of analyses performed on a variety of terrestrial samples, possibly analogs of Oxia Planum soils and rocks, that were distributed among the laboratories participating to the ExoMars project.
Mars Organic Molecule Analyzer (MOMA) is the main instrument dedicated to characterize the organic matter present in the collected samples. It is a dual-mode instrument aboard the rover, designed to detect and characterize organic molecules in subsurface samples down to 2 m depth, where radiation-induced degradation is minimized [5]. MOMA employs a combination of laser desorption (LD) and gas chromatography–mass spectrometry (GC-MS) with complementary sample preparation techniques: pyrolysis (up to 850 °C) which allows organic molecules to evolve (or be fragmented) into volatile species prior to their injection into the GC-MS analyzer. In addition to this vaporization technique, chemical derivatization using tetramethylammonium hydroxide (TMAH), to support molecule vaporization but limiting their fragmentation or N-methyl-N-(trimethylsilyl)trifluoroacetamide (MTBSTFA), and dimethylformamide–dimethylacetamide (DMF-DMA) for polar organic molecules of interest for prebiotic chemistry analyzable by GC-MS, such as amino acids or fatty acids [6].
My work focuses on the GC-MS analysis part. The goal of this study is to replicate MOMA GCMS protocols in the laboratory and asses the best parameters to confirm the detectability of biomarkers then understand the best conditions to operate MOMA. To this aim, each sample was subjected to sequential pyrolysis (250 °C, 400 °C, 550 °C, 700 °C, and 850 °C) and derivatization with TMAH, MTBSTFA, and DMF-DMA. All analyses were performed using laboratory-scale GC-MS systems configured to replicate MOMA operational parameters. Blank runs and procedural controls were conducted to assess contamination levels.
Results: We were able to detect organic molecules in all the samples, using MOMA analytical technics, though in various abundances and number, depending on the samples. Although the possibility of contamination cannot be ruled out, it remains minimal. We observed that the thermal treatment (temperature and duration of pyrolysis) had a significant impact on the nature of the organic molecules detected (Fig. 1). These results allowed us to determine that in order to extract as much information as possible from a sample, two pyrolysis temperature should be used successively. A first “low temperature” (550°C) pyrolysis to extract volatile compounds while limiting their alteration, and a “high temperature” (850°C) pyrolysis to extract molecules potentially trapped in refractory minerals that can thermally decompose above 550°C, with the drawback of aromatization processes that occur to organic molecules at such high temperatures.
Fig. 1. Distribution by chemical family of the number of compounds detected by pyrolysis GCMS for successive pyrolysis temperatures of 250°C, 400°C, 550°C, 700°C and 875°C, applied to the same fraction of the Modern Gypsum sample of the ExoMars mission sample exercise.
The derivatization techniques used allowed us to identify important biological molecules like amino acids or fatty acids. It was observed that the MTBSTFA (in large excess comparing to MOMA conditions) derivatization allowed the identification of a higher number of compounds. Results also show that for the same sample the amino acids identified depend on the derivatization agent used. Furthermore, even if DMF-DMA (also in large excess) derivatization does not allow to detect as much molecules as with MTBSTFA, DMF-DMA leaves room for chiral separation of the molecules. This observation demonstrates the derivatization technics are complementary to each other’s and to pyrolysis.
Conclusion: We demonstrated that the sample preparation technics used in the MOMA instrument to perform GCMS analyses, allow to detect organic molecules, including species of interest for prebiotic chemistry, in martian analog samples. This works also demonstrated the interest of a two-step pyrolysis when analyzing natural sample to prevent the alteration of the most volatile compounds. In addition, the derivatization technics allowed the identification of building blocks of life, proving the relevance and the complementarity of the technics boarding MOMA.
Though these results focus on laboratory technics reproducing MOMA, the MOMA team works together studying the same sample in more MOMA-like situation (subsystems, sample to reagent ratio …) in order to transpose our results to the actual instrument.
Acknowledgments: Thanks are due to the Exomars project team and the teams that provided the variety of samples to be characterized in the frame of the “ExoMars mission samples” campaign. T. Govekar acknowledges financial support from the Centre national d’études spatiales (CNES), France (ROR: https://ror.org/04h1h0y33) within the framework of the ExoMars mission. T. Govekar thanks University of Versailles Saint Quentin and CNES for his PhD grant funding.
References: [1] Cockell, et al. (2016), 16(1), 89-117. [2] Freissinet, et al. (2015). Journal of Geophysical Research: Planets. [3] Quantin-Nataf, et al. (2021). Astrobiology, 21(3), 345-366. [4] Pinnavaia, T. J. (1983). Science, 220(4595), 365-371. [5] Vago, et al. (2017). Astrobiology, 17(6-7), 471-510. [6] Goesmann, et al. (2017). Astrobiology, 17(6-7), 655-685.
How to cite: Govekar, T., Szopa, C., Freissinet, C., Buch, A., Bouhier, B., Couderc, O., Azemard, C., Stalport, F., and Yesil Sahan, F.: ExoMars analog sample campaign: preparing in situ research of organic matter with Gas Chromatography-Mass Spectrometry at Oxia Planum, Mars, with the MOMA experiment , Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-320, https://doi.org/10.5194/epsc2026-320, 2026.
Introduction: Rover-based geological investigations rely on multispectral imaging to distinguish lithological and mineralogical variability, helping to reconstruct palaeoenvironmental conditions on Mars [1-5]. However, the interpretation of geological targets from rover imagery is constrained by the discrete spectral sampling of multispectral imaging systems, potentially limiting discrimination of subtle spectral variations within compositionally heterogeneous materials. Evaluating how effectively rover multispectral observations preserve diagnostic mineralogical information is therefore essential for reliable geological interpretation and informed scientific target selection during planetary surface exploration.
The Panoramic Camera (PanCam) is a context imaging instrument onboard the ExoMars Rosalind Franklin rover [6-7]. The optical bench consists of a High-Resolution Camera (HRC) and two multispectral Wide Angle Cameras (WACs), enabling stereo imaging and visible to near-infrared observations across the 440-1000 nm wavelength range [7-9]. PanCam is designed to support geological characterisation and drill target prioritisation within the phyllosilicate-rich terrain of Oxia Planum [10], using a geology filter set optimised for the detection of ferric mineral signatures and broad mineralogical variability [9].
Here, we evaluate the mineralogical discrimination capabilities of PanCam using imagery acquired with the PanCam Training Model (TM), a functional replica of the flight instrument configured with the Right WAC (RWAC) and a filter wheel containing 11 of the 12 geology filters (excluding the 500 nm filter). Using diverse Mars analogue materials, we assess how effectively PanCam multispectral observations preserve diagnostic mineralogical information, while additionally exploring the potential of PanCam imagery to support the astrobiological objectives of the Rosalind Franklin mission.
Figure 1. PanCam TM HRC images of a) a polymict lithic impact breccia from the Ries crater, Germany; b) a peridotite mantle xenolith from Arizona, USA; and c) cryptoendolith horizons within Antarctic Dry Valley sandstones [12]. Scale bars each represent 10 cm.
Dataset and methods: Multispectral image datasets were collected under controlled laboratory conditions for ten Mars analogue samples selected to represent a range of mineralogical compositions, lithologies, and formation environments. The sample suite includes a clay-rich polymict lithic impact breccia collected during fieldwork at the Ries impact structure, Germany (Figure 1a), and an ultramafic mantle xenolith sourced from the San Carlos volcanic field, Arizona, USA (Figure 1b). Radiometric calibration was performed using the PanCam Operations Toolkit (PCOT) [11], supporting derivation of reflectance spectra, image enhancement products, and spectral parameter maps. Hyperspectral VIS-NIR measurements were also collected using a high spectral-resolution ASD RxSpec 700Z contact probe spectrometer (350–2500 nm) within the same regions of interest for comparative analysis.
Figure 2. a) Cropped PanCam TM RWAC image of a mantle xenolith sample (RGB: 670, 530, 440 nm) showing ROIs corresponding to b) olivine-rich and c) enstatite-rich regions. Panels b–c compare PanCam TM image-derived spectra (red) with corresponding ASD contact probe spectra (green).
Results: Spectra derived from RWAC images (Figure 2a) capture diagnostic features across diverse mineralogies, including olivine (Figure 2b), enstatite (Figure 2c), and materials not originally prioritised during filter selection, such as illite-bearing assemblages. Comparisons with ASD measurements, that were also resampled to the 11 bands of the TM, show strong agreement in spectral trends across the PanCam wavelength range, indicating minimal loss of diagnostic spectral information despite the reduced spectral resolution of multispectral imaging.
PanCam multispectral observations additionally resolve spectral variations associated with microbial colonisation (Figure 1c), with distinct endolithic horizons detectable at distances of 2 m. Extracted spectra exhibit features consistent with biologically associated pigments, including chlorophyll, melanin, and phycocyanin. Several analogue samples containing endolithic growth and photosynthetic pigments display spectral characteristics consistent with a diagnostic red edge feature within the PanCam wavelength range, suggesting that biologically derived spectral signatures may be distinguishable alongside mineralogical variability within PanCam imagery.
Summary: This work contributes to the development of a PanCam image-based spectral library of analogue materials and demonstrates the value of multispectral imaging for interpreting geologically heterogeneous targets on Mars. These results additionally highlight the potential of PanCam multispectral observations to investigate biologically derived spectral signatures within Mars analogue environments relevant to the astrobiological objectives of the Rosalind Franklin mission.
[1] Bell et al., 2004, Science [2] Bell et al., 2022, Science [3] Farrand et al., 2007, JGR: Planets [4] Royer et al., 2024, Nature [5] Harris et al., 2015, Icarus [6] Vago et al., 2017, Astrobiology [7] Coates et al., 2017, Astrobiology [8] Cousins et al., 2010, Astrobiology [9] Cousins et al., 2012, Planetary and Space Science [10] Quantin-Nataf et al., 2021, Astrobiology [11] Finnis et al., 2024, Geoscience Data Journal [12] Preston et al., 2015, Applied Spectroscopy.
How to cite: Warrilow, R., Preston, L. J., Tornabene, L. L., Dartnell, L., Osinski, G. R., Ballard, C., Hunt, T., and Coates, A.: Multispectral Analysis and Geological Characterisation Using the PanCam Training Model, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1180, https://doi.org/10.5194/epsc2026-1180, 2026.
Introduction
Ma_MISS is the visible and near-infrared spectrometer embedded in the drill system of the ExoMars Rosalind Franklin rover [1]. It will acquire spectral reflectance measurements along the walls of boreholes drilled into the Martian subsurface, down to depths of about 2 m, providing mineralogical and stratigraphic information in situ before samples are extracted and delivered to the rover analytical laboratory. Ma_MISS acquires single-point spectra through a small sapphire window on the side of the drill tool. Each spectrum is collected from a small spot (about 120 μm large) on the borehole wall. By exploiting the finely controllable rotation and vertical translation of the drill tool, Ma_MISS can build hyperspectral maps of the borehole wall, acquiring one spatial pixel at a time. A sequence of acquisitions interleaved with small drill rotations produces a “ring” scan, while a “column” operation consists of multiple rings acquired at different depths, with small vertical translations between consecutive rings.
Operations simulation and planning
The scientific return of Ma_MISS observations will depend strongly on the selected acquisition strategy. A Ma_MISS operation is defined mainly by the angular step between acquisitions, the vertical step between rings, the number of points per ring, and the number of rings. These parameters control the spatial sampling and the depth interval covered by a scan, but they also determine the time, data volume, and energy required to complete the operation. Since the time available for Ma_MISS observations during rover operations will be limited, operation planning requires explicit trade-offs between spatial resolution, depth coverage, and resource constraints.
We are developing software tools to support the planning and optimization of Ma_MISS acquisition sequences. The first component is a command-sequence simulator that estimates the duration and generated data volume from a proposed sequence of Ma_MISS commands. These estimates can be used in two complementary planning modes. In the first, the user fixes the spatial sampling and resource limits, and the tool determines the maximum depth interval that can be covered within the available time. In the second, the user specifies the depth interval to investigate and the available resources, and the tool searches for an acquisition strategy that maximizes the expected scientific return. This second approach is essentially a constrained optimization problem and requires a quantitative objective function. Importantly, the best strategy is not necessarily the one with the largest number of individual spectra: depending on the target material and on the scientific objective, a more regular sampling pattern, or a different balance between angular and vertical resolution, may preserve more useful information.
Simulations based on DAVIS measurements
To develop and test optimization strategies and to compare acquisition sequences meaningfully, it is necessary to simulate not only the required resources but also the hyperspectral datasets that would be produced by different Ma_MISS acquisition sequences. Previous work used synthetic borehole geometry models to rapidly generate simulated datasets [2]. Here we extend this approach using DAVIS, a Ma_MISS laboratory model designed to acquire spectra inside holes drilled in rock samples, with a measurement geometry and optical head representative of Ma_MISS [3]. Through automated rotation and translation actuators, DAVIS can be used to reproduce Ma_MISS acquisition sequences in rock samples.
As a first proof of concept, we acquired a high-spatial-sampling DAVIS scan over a limited region inside the hole of a drilled rock sample. The scan covered an angular sector of about 9° and a depth range of about 40 mm, using a rotation step of 0.5° and a vertical step of 0.1 mm. The resulting dataset contains 7200 spectra, arranged as 400 partial rings of 18 points each. Coarser acquisition strategies can then be simulated by subsampling this reference dataset. Figure 1 shows examples of such strategies, from the full 18 × 400 acquisitions dataset to progressively faster operations with fewer acquired points.
To compare the simulated strategies, each subsampled dataset was interpolated back onto the full grid using bilinear interpolation (Figure 2). The interpolated products can then be compared with the complete high-resolution dataset to quantify the information lost when the number of acquisitions is reduced. As an example metric, we used the Structural Similarity Index (SSIM) [4] to rank acquisition strategies according to their similarity to the full reference dataset. This provides a practical demonstration of how laboratory hyperspectral data can be used to define objective functions for Ma_MISS operation planning.
Figure 1. Each panel shows the results of a different acquisition strategy simulated from DAVIS data, with operation decreasing duration from left to right. The leftmost panel shows the full dataset. The hyperspectral dataset is represented as an RGB composite of three bands (660 nm, 532 nm, 480 nm respectively). The duration of each sequence is reported above each panel as a percentage of the duration of the full sequence.
Figure 2. Same as Figure 1, but with each simulated hyperspectral dataset interpolated back to the original size. The SSIM value is reported below each panel.
Future work
This preliminary test is based on a small spatial region and on a single, highly heterogeneous rock sample. Future work will extend the method by using DAVIS to simulate more complete Ma_MISS scans, including acquisitions over the full borehole circumference and larger depth intervals. We will also test multiple rock samples with different textures and degrees of heterogeneity, including layered, clastic, and veined materials. This will allow us to evaluate how the optimal Ma_MISS acquisition strategy depends on the geological target and to develop more robust metrics for quantifying scientific return and comparing different acquisition strategies.
Acknowledgements
This work is supported by the ASI grant ASI-INAF n. 2023-3-HH.0 and by the ESA Rosalind Franklin Mission’s Ma_MISS SKP (Science Knowledge Programme).
References
[1] De Sanctis M. C. et al. (2022), Planetary Science Journal, 3, 142. https://doi.org/10.3847/PSJ/ac694f
[2] Rossi L. et al. (2022), EPSC2022-391. https://doi.org/10.5194/epsc2022-391
[3] De Angelis S. et al. (2022), LPSC 53, Abstract #1796.
[4] Wang Z. et al. (2004), IEEE Transactions on Image Processing, 13, 600–612. https://doi.org/10.1109/TIP.2003.819861
How to cite: Rossi, L., Fonte, S., Bruschini, E., Jimenez Sanz, D., De Angelis, S., Altieri, F., and De Sanctis, M. C.: Simulating and optimizing Ma_MISS operations, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-846, https://doi.org/10.5194/epsc2026-846, 2026.
Introduction
Martian weather has been continuously monitored for more than 25 years using observations from orbiters, landers, and rovers. A paradigm shift would be to move toward simultaneous monitoring of global Martian weather using a constellation of satellites in high-altitude orbits, such as areostationary orbit [1, 2].
Improving weather monitoring has direct implications for weather forecasting, which is considered a key exploration-focused science topic, as it supports and enables future exploration objectives. At the same time, developing weather forecasting capabilities for an extraterrestrial planet has broader implications for atmospheric science, making it also an exploration-enabled science topic, since it represents science made possible by exploration platforms and capabilities [3].
Alongside improved observational monitoring of Martian weather, reliable forecasting requires key developments in the models used to simulate the Martian atmosphere, such as Global Climate Models (GCMs). Improving the representation of the dust cycle, in particular, is of paramount importance for correctly reproducing the observed seasonal and interannual variability of Martian dust storms [4].
Data assimilation and machine learning have emerged as complementary approaches for monitoring and forecasting Martian weather: data assimilation combines observations with atmospheric models to produce multi-annual retrospective analyses, or “reanalyses” [e.g., 5], while machine learning enables data-driven prediction [e.g., 6].
From Monitoring…
Using thermal infrared data from instruments such as the Thermal Emission Spectrometer onboard Mars Global Surveyor, the Thermal Emission Imaging System onboard Mars Odyssey, the Mars Climate Sounder onboard Mars Reconnaissance Orbiter, and the Emirates Infrared Spectrometer onboard the Emirates Mars Mission, we have produced a reconstruction of daily maps of column dust optical depth (CDOD) spanning 14 Martian years (MY 24–37). These maps represent one of the longest consistent, observation-based, multi-instrument climatological records produced to date for a key Martian weather-relevant variable [7]. Among many applications, they are used as “dust scenarios” in the Mars Climate Database [8]
However, the observational coverage remains spatially sparse, requiring interpolation techniques, such as kriging or the use of climatological values, to complete the gridded maps. To move beyond purely spatial interpolation, we have developed a dynamical interpolation approach inspired by data assimilation. In this approach, incomplete gridded CDOD maps with a 6-hour cadence are assimilated into the Mars Planetary Climate Model (PCM; see [9]) using an adaptation of the Analysis Correction assimilation scheme [10], in which analysis increments are gradually introduced into the model integration.
Figure 1 illustrates the scheme concept. Figure 2 presents an application to the reconstruction of the diurnal evolution of a regional dust storm over one sol, compared with both a model-only simulation and the incomplete gridded maps. Figure 3 shows a similar reconstruction for daily averages over multiple sols, compared with both the model-only simulation and complete maps produced by combining gridding and kriging.
Figure 1: Schematic of the implemented dynamical interpolation approach. Example for MY 36, SOY 584, Ls ~ 313°, MUT 21:00, using incomplete gridded maps at MUT 03:00, 09:00, 15:00, and 21:00 (a). Other boxes show: (b) Mars PCM background CDOD, (c) CDOD assimilation increment, (d) analysis equation, and (e) analysis-derived CDOD rescaling factor used to adjust the dust tracer before transport.
Figure 2: Comparison of the diurnal evolution of a regional dust storm (MY 36, SOY 584) among the PCM simulation, dynamical interpolation with the data assimilation scheme, incomplete gridded CDOD maps used as assimilation input (1-sol time window), and incomplete gridded maps reconstructed using time windows of up to 3 sols. All maps show extinction CDOD at 9.3 μm.
Figure 3: As in Figure 2 but showing the evolution of the same dust storm over 6 sols. Each map corresponds to a daily average. Kriging was also used for the bottom raw.
…to Forecasting
The products shown in Figures 2 and 3 represent a first step toward the implementation of a full data assimilation system for Martian weather, which could ultimately be used to produce initial conditions for numerical Martian weather forecasts. Although the underlying assimilation approach is established, the system is designed to be flexible and expandable, with future operational applications in mind.
A complementary approach to numerical weather forecasting is data-driven forecasting using machine learning (particularly deep learning models), which has recently shown promising results in terrestrial weather prediction. We have therefore begun exploring deep learning approaches by training convolutional forecasting models on a multi-annual reanalysis dataset [5] and multi-annual model-only simulations [4].
Figure 4 shows preliminary results from a ConvLSTM model applied to daily averaged CDOD prediction with a one-sol lead time. In this experiment, the model was trained on a single Martian year (MY 26) and tested on another year (MY 24), using 60% of the data for training and 40% for testing. We report the resulting model performance on the test set in Table 1. A skill score of 18% relative to persistence indicates that the model improves on a persistence forecast by 18% at a one-sol lead time. Because the standard-deviation ratio is below 1, the current model appears to underestimate variability. Similarly, a sharpness ratio below 1 suggests that the model still struggles to reproduce sharp gradients and tends to smooth spatial structures.
Figure 4: Example of CDOD field prediction using a ConvLSTM model. Left panel: observed field at the initial time (MY 24, SOY 457, LS ≈ 233°). Central panel: one-sol-ahead prediction produced by the model, using the seven preceding daily fields as input. Right panel: observed field corresponding to the prediction.
References
[1] Montabone et al., EPSC2021-625, https://doi.org/10.5194/epsc2021-625 (2021).
[2] Cardesin-Moinelo et al., EPSC2026-928 (2026)
[3] European Space Agency. The European Exploration Strategy: Explore2040. ESA (2024).
[4] Pierron et al., EPSC-DPS2025-724, https://doi.org/10.5194/epsc-dps2025-724 (2025).
[5] Valeanu et al., NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/cd037a9ea387438fabf4d674dbe53088 (2026).
[6] Uprety et al., Proceeding of the DARES’25-ECAI 2025 workshop, Bologna, Italy (2025)
[7] Lombard et al., EPSC-DPS2025-1765, https://doi.org/10.5194/epsc-dps2025-1765 (2025).
[8] Millour et al., EPSC2026-776 (2026).
[9] Forget et al., 7th Mars Atmosphere Modeling and Observation workshop, Paris, France (2022).
[10] Lorenc et al., Q. J. R. Meteorol. Soc., 117, 59–89 (1991)
How to cite: Montabone, L., Lombard, T., Guyon, V., Le Dantec, J., Pierron, T., Forget, F., Millour, E., and Boukhobza, Y.: Weather on Mars: From Monitoring to Forecasting, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1310, https://doi.org/10.5194/epsc2026-1310, 2026.
We summarize here the contributions of an international consortium of atmospheric and magnetospheric scientists, for the study and development of future Mars missions based on small satellites and compact instrumentation.
Past and present Mars orbiters have focused mostly on targeted, high-resolution observations, but lacked continuous global coverage. Yet key atmospheric phenomena—such as clouds and dust storms—and space weather processes, including solar wind interactions, aurorae, and radiation, require simultaneous, global, and sustained measurements to properly characterize Mars’ dynamic climate and environment. Continuous monitoring is essential both for scientific understanding and for enabling future robotic and human exploration.
“Mars GRADIVUS” is a Spanish ESA Prodex study led by a collaboration between INDRA/Deimos, IAA, Aurora SSC Space, INTA, UPV, and Thales Alenia Space. Its goal is to assess the technical feasibility of cost-effective CubeSats delivering high-value science at Mars using a New Space approach. Conceived as a flexible piggyback payload, it aims to demonstrate the scientific viability of a CubeSat dedicated to monitoring the Martian atmosphere and space weather. [Cardesin 2025, and proceedings of CPESS-8, Paths2Space & SEA2026]
Building upon this study, the “MACAWS” (Mars Constellation for Atmosphere and Space Weather Studies) mini-F proposal to ESA is a low-cost constellation of three spacecraft in high-altitude orbit (~17,000 km). Designed for the 2030s, MACAWS would provide the first quasi-global, simultaneous, high-cadence monitoring of Mars’ lower atmosphere and near-space environment across the full diurnal cycle, with a modular architecture supporting international contributions. [Montabone 2025 & 2026: EPSC-DPS2025 and 46th COSPAR 2026]
These efforts align with the priorities of European Space Agency (Voyage 2050, TerraNovae2030+), NASA’s Mars Exploration Future Program, and the International Mars Exploration Working Group (IMEWG), through its Access to Mars and Infrastructure for Lower-Cost Missions (LCM).
Keywords: Mars, Small Satellites, Mars Atmosphere, Space Weather, Global Monitoring
References:
[Cardesin 2025]: Mars Small Satellite Missions: Mars Gradivus Study and New ESA Mini-F Proposal for Mars Global Meteorology and Space Weather. CPESS-8 Proceedings https://spainportugal-eps.org/images/cpess-8/20250314093504_cpess-8-cardesinmoinelo.pdf
[Montabone 2026]: The Mars Constellation for Atmosphere and Space Weather Studies (Macaws) Mission Concept. 46th COSPAR Scientific Assembly 2026
[Montabone 2025]: Science Case for the MArs Constellation for Atmosphere and space Weather (MACAWS) mission concept EPSC-DPS2025-2067 https://doi.org/10.5194/epsc-dps2025-2067
How to cite: Cardesin-Moinelo, A., Montabone, L., Lopez-Valverde, M. A., and Hernandez-Bernal, J. and the Mars GRADIVUS and MACAWS science consortia: Small Missions for Mars Global Meteorology and Space Weather: Mars GRADIVUS Study and MACAWS Mini-F Proposal, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-928, https://doi.org/10.5194/epsc2026-928, 2026.
We present work supporting the preliminarily selected MeteorCam instrument for the LightShip-1 mission (shown in Fig.1). Earlier LightShip studies considered both nightside Martian fireball imaging and in situ dust impact detection [1], but our focus is the selected space-based meteor camera concept and how modelling can define its expected scientific return. This supports MEPAG Goal IV, Sub-Objective A2, which calls for characterization of the meteoroid environment around Mars before future exploration [2].
Fig.1 MeteorCam global architecture, with envelope dimensions 100x100x150 mm3
Using our Mars meteoroid simulation framework (example of simulated Mars meteors are shown in Fig.2), we evaluate the types of fireballs detectable from orbit with a SPOSH/MeteorCam-type instrument [3,4]. The platform altitude is 5720 km, with 20 degrees inclination and a 46 degree field of view covering the full Martian disc and part of the surrounding atmosphere. The current concept assumes 15 FPS, an apparent stellar limiting magnitude of +4 mag, a spectral range of 400–800 nm, and optical transmission above 85%.
Fig.2 Earth meteors re-simulated in the Martian atmosphere at corrected Mars speeds of 50 and 15 km/s.
To place MeteorCam in a broader measurement context, we also discuss a possible complementary dust analyzer and estimate the meteoroid flux it could encounter at Mars (shown in Fig.3). We consider particles larger than 0.1 mm, which are most relevant to spacecraft risk [5] and aligned with the MEPAG objective [2]. Such measurements would be valuable, but direct in situ detection of a statistically significant number of particles may be challenging because of realistic detector-area and mission-lifetime limits [1].
Fig.3 Preliminary Flux results of meteoroids ranging from 10-6 to 10 g, in the ram, wake and zenith direction at 5720 km from Mars
To address these low-number statistics, we compare the potential science return from direct impact measurements with that from observing meteors produced by mm-sized meteoroids, which are expected to be much more readily detectable than direct impacts from mm-sized particles. Our meteor predictions use 144 observed sporadic meteors with peak absolute magnitudes near +2 at Earth. Their physical properties were inferred using an erosion-fragmentation model [6] and dynamic nested sampling [7,8], then re-simulated in the Martian atmosphere [9]. Predicted Martian meteors reach peak absolute magnitudes of +2 to +7 (Median absolute magnitude profiles shown in Fig.4). The fastest events remain similar in brightness to their Earth counterparts, while slower ones become fainter. Most Martian luminous emission occurs between 55 and 110 km altitude, compared with 80 to 120 km at Earth, and trail lengths on both planets average about 20 km [9]. We also examine ground-based, low-orbit, and high-orbit cameras; wide and narrow fields of view; and possible spectral capabilities. This maps trade-offs among detection rate, spatial coverage, brightness sensitivity, and data quality.
Fig.4 Median absolute magnitude profiles as a function of height for the Earth observations of mm size meteoroids and the corresponding Mars predictions.
References:
[1] Ball, A. J. et al. (2025). A Strawman Mars Dust & Debris Monitor for LightShip. ESA-E3P-LS1-TN-010, Issue 1.0.
[2] Mars Exploration Program Analysis Group (MEPAG) (2020). Mars Science Goals, Objectives, Investigations, and Priorities: 2020 Version.
[3] Oberst, J. et al. (2011). The Smart Panoramic Optical Sensor Head (SPOSH): A camera for observations of transient luminous events on planetary night sides. Planetary and Space Science, 59, 1–9.
[4] Christou, A. A. et al. (2012). Orbital observations of meteors in the Martian atmosphere using the SPOSH camera. Planetary and Space Science, 60, 229–235.
[5] Moorhead, A., Cooke, B., Blaauw, R., Moser, D., & Ehlert, S. (2019). A meteoroid handbook for aerospace engineers and managers. NASA/TM-2019-220142.
[6] Borovička, J., Spurný, P., & Koten, P. (2007). Atmospheric deceleration and light curves of Draconid meteors and implications for the structure of cometary dust. Astronomy & Astrophysics, 473, 661–672.
[7] Speagle, J. S. (2020). dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society, 493, 3132–3158.
[8] Vovk, M., Brown, P. G., Vida, D., Lee, D., & Harmos, E. G. (2026). Inferring meteoroid properties with dynamic nested sampling: A case study of Orionid and Capricornid shower meteors. Icarus, 116963.
[9] Vovk, M., Brown, P. G., & Vida, D. (2026). From Earth Meteors to Mars: Predicting Where to See the First Martian Meteors. Manuscript submitted to Advances in Space Research.
How to cite: Vovk, M., Brown, P., Vida, D., Millinger, M., and Koschny, D.: Constraining the Martian Meteoroid Environment: Supporting the Preliminarily Selected MeteorCam for LightShip-1, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-66, https://doi.org/10.5194/epsc2026-66, 2026.
Analysing the surface expressions of Mars’ aqueous history is essential for reconstructing the environmental evolution of early Mars [1–3]. Oxia Planum, the landing site for ESA’s Rosalind Franklin rover [4,5], preserves extensive geomorphological and mineralogical evidence of sustained aqueous activity, including valley networks, sedimentary deposits, and widespread clay-bearing terrains indicative of prolonged water–rock interaction and environmental conditions potentially favourable for past habitability [4,6,7]. While the Oxia Planum landing ellipse has been extensively characterised, the surrounding catchment region remains comparatively underexplored at the regional scale, despite likely acting as a major sediment source and hydrological conduit delivering detrital and aqueously altered materials into the basin [6,8]. In this work, we investigate the mineralogical variability of the catchment using integrated CRISM, CaSSIS, and HiRISE datasets.
Figure 1. Targeted HiRISE observation (ESP_092719_1970) for this study showing an IRB colour composite of a potential chloride-bearing locality within the Oxia Planum catchment region.
CRISM observations reveal widespread low-calcium pyroxene-bearing terrains throughout the Coogoon Valles catchment, suggesting preservation of an LCP-rich mafic substrate across much of the regional crust, while more localised HCP-bearing materials indicate lithological variability within the crust. Distinct clay-bearing assemblages are additionally identified within altered sedimentary and channel-associated terrains from ratioed spectra, including extensive Fe/Mg-smectite-bearing units extending into the eastern catchment, and possible kaolinite-bearing signatures associated with crater rim terrains, potentially indicating localised alteration environments. These assemblages suggest spatially variable alteration of a predominantly mafic crustal substrate throughout the catchment system.
Targeted CaSSIS observations acquired for this investigation examine several bright-toned channel-associated deposits potentially consistent with chloride-bearing terrains [9] that exhibit distinct colour variability in false-colour NPB composites, colour band ratio composites, and decorrelation stretched imagery. Targeted HiRISE observations further reveal surface textures consistent with altered or evaporitic materials, alongside IRB colour characteristics comparable to previously identified chloride signatures (Figure 1) [10].
These observations provide new constraints on the mineralogical and hydrological evolution of the Oxia Planum catchment system and highlight the importance of regional-scale orbital investigations for reconstructing aqueous and sedimentary processes beyond the landing ellipse, providing broader geological context for assessing the habitability potential of the wider Oxia Planum system.
[1] Bibring et al., 2006, Science [2] Carr and Head, 2010, Earth and Planetary Science Letters [3] Carter et al., 2015, Icarus [4] Quantin-Nataf et al., 2021, Astrobiology [5] Vago et al., 2017, Astrobiology [6] Mandon et al., 2021, Icarus [7] Brossier et al., 2024, Journal of Geophysical Research: Planets [8] Fawdon et al., 2022, Journal of Geophysical Research: Planets [9] Osterloo et al., 2008, Science [10] Bickel et al., 2024, Scientific Data.
How to cite: Warrilow, R., Tornabene, L. L., Preston, L. J., and Coates, A.: Spectral characterisation of the Oxia Planum basin catchment area with CRISM, CaSSIS, and HiRISE., Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1193, https://doi.org/10.5194/epsc2026-1193, 2026.
Introduction
High-resolution Digital Terrain Models (DTMs) derived from HiRISE stereo observations are fundamental for the geomorphological analysis of the Martian surface, enabling meter-scale investigation of slopes, sedimentary bodies, aeolian landforms, impact-related features and candidate exploration terrains [1]. Open-source workflows based on ISIS and the NASA Ames Stereo Pipeline (ASP) have made HiRISE DTM generation broadly accessible to the planetary science community [2,3]. Since 2023, however, the intermittent failure of the RED4 CCD has produced a central data gap in many recent HiRISE observations. Because RED4 lies near the center of the focal plane (Figure 1), its loss disrupts CCD mosaicking, tone balancing and stereo processing more severely than edge-CCD losses, often causing standard pipelines to fail at the mosaicking or stereo-preparation stage. The HiRISE team has recently introduced a mitigation strategy [4] in which the overlapping IR10 CCD is radiometrically matched to the surrounding RED data and inserted in place of the missing RED4 as a synthetic component, named SYN4. Previous HiRISE stereo work has shown that these DTMs support ultrahigh-resolution topographic mapping and analyses of active Martian surface processes [5,6], while the September 2024 HiRISE/PDS release explicitly notes both the RED4 failure and the planned use of IR10 data to fill the gap for selected products [7]. While effective, this approach has so far been implemented within selected internal HiRISE pipelines and is not yet integrated into the openly available ASP/ISIS chain on which most external users rely.
Figure 1: HiRISE focal plane detector geometry and location of the RED4 gap. Relationship between RED4, IR10 and synthetic SYN4 component.
A RED4-aware ASP workflow
We present an adaptive HiRISE stereo processing workflow that integrates the SYN4/IR10 concept into a fully open-source ASP/ISIS chain. The workflow follows the standard sequence of image preparation, bundle adjustment, stereo correlation, point-cloud generation, DEM extraction and co-registration to reference topography, but introduces an automatic preprocessing step that checks the availability of RED4 for each input observation. When RED4 is missing, the workflow activates an IR10-based fallback prior to CCD mosaicking: IR10 is radiometrically matched to the adjacent RED CCDs and ingested as a SYN4 substitute, restoring the continuity of the RED mosaic before stereo reconstruction proceeds. This preserves the usability of stereo pairs that would otherwise fail at the mosaicking stage and keeps the rest of the chain unchanged, ensuring full reproducibility.
Test cases and validation
The workflow was tested on two HiRISE stereo pairs acquired in 2025 in Arabia Terra: ESP_088933_2200 / ESP_089012_2200 (convergence angle 28.0°) and ESP_089500_2200 / ESP_089645_2200 (convergence angle 12.4°). The two pairs were selected to evaluate the workflow under contrasting stereo geometries, an ideal high-convergence case and a marginal low-convergence case below the nominal HiRISE threshold. Both pairs were processed through the full pipeline, producing continuous DTMs across the RED4 gap with no visible seams or correlation drop-outs along the SYN4 strips (Figure 2, left). Vertical consistency was assessed against an independent HRSC reference DTM, chosen because its different instruments, mission and processing chain provide a fully external benchmark. A topographic profile crossing the SYN4 strip shows close agreement between the HiRISE and HRSC datasets along the entire ~9 km transect, with no detectable offset or discontinuity at the SYN4 boundaries (Figure 2, right). The agreement holds for both pairs, indicating that the IR10-based reconstruction does not introduce systematic vertical artefacts within the gap region, even in the marginal convergence-angle case.
Figure 2: Validation of the RED4-aware HiRISE workflow in Arabia Terra (~39.9°N, 26.5°E). Left: HiRISE DTMs generated from the two test stereo pairs (ESP_089500_2200/ESP_089645_2200 and ESP_088933_2200/ESP_089012_2200); red rectangles outline the RED4 gap, reconstructed through the IR10/SYN4 substitution. The black line indicates the NE–SW topographic transect. Right: ~9 km topographic profile across the SYN4 strip, comparing the HiRISE DTM (red) with the independent HRSC reference DTM (blue). The yellow box marks the most critical portion of the transect across the reconstructed gap; this sector is shown in detail in the upper inset, highlighting the continuity of the HiRISE topography and its agreement with the HRSC reference across the SYN4 boundaries.
Conclusion
By integrating the SYN4/IR10 concept into an open-source ASP-based workflow, this work provides a practical and reproducible solution for recovering scientific value from RED4-affected HiRISE stereo observations. The independent HRSC validation confirms that the reconstructed topography is consistent with external reference data across the gap region.
References: [1] McEwen, A.S., et al., 2007. Journal of Geophysical Research: Planets, 112, E05S02. doi:10.1029/2005JE002605. [2] Hepburn, A.J., Holt, T., Hubbard, B. and Ng, F., 2019. Geoscientific Instrumentation, Methods and Data Systems, 8, 293–313. doi:10.5194/gi-8-293-2019. [3] Kirk, R.L., et al., 2008. Journal of Geophysical Research: Planets, 113, E00A24. doi:10.1029/2007JE003000. [4] Beyer, R.A., Alexandrov, O. and McMichael, S., 2018. Earth and Space Science, 5, 537–548. doi:10.1029/2018EA000409. [5] Sutton, S.S., et al., 2022. Remote Sensing, 14, 2403. doi:10.3390/rs14102403. [6] Sutton, S.S., et al., 2025. 56th Lunar and Planetary Science Conference, Abstract #2463. [7] HiRISE Operations Center, 2024. PDS Release: September 2024 Images. University of Arizona HiRISE.
How to cite: Mancini, F., Pondrelli, M., Pacifici, A., Aboudan, A., Salese, F., and Ori, G. G.: Mitigating the HiRISE RED4 CCD data gap in stereo DTM production: an adaptive workflow for high-resolution Martian DTM generation, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1270, https://doi.org/10.5194/epsc2026-1270, 2026.
Introduction
The Colour and Stereo Surface Imaging System (CaSSIS [1]) onboard ESA’s Trace Gas Orbiter (TGO) is a multispectral camera that collects images of the surface at 4–5 m/pixel, in blue (BLU, ~480 nm), panchromatic (PAN, ~677 nm), red (RED, ~836 nm) and near-infrared (NIR, ~939 nm) filters across Visible/Near-Infrared wavelengths. CaSSIS has been operational since 2016, and in that time has collated an image database [2] with ~4.4% unique coverage of the surface in all four bands. Although not a spectrometer, CaSSIS has been shown to be capable of differentiating between ferrous and ferric materials and has become an invaluable mapping and context tool in Mars planetary science. CaSSIS and High Resolution Imaging Science Experiment (HiRISE [3]) operational targeting currently relies primarily on panchromatic Context Camera (CTX) or Thermal Emission Imaging System (THEMIS) basemaps, leaving the colour and compositional character of the surface unknown prior to acquisition.
The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM [4]) onboard the Mars Reconnaissance Orbiter has provided near-global visible-to-near-infrared (VNIR) coverage through its mapping-mode Visible/Near-Infrared Reduced Data Records (VRDRs): mosaicked 5°×5° tiles spanning >99% of the surface at ~90 m/pixel across 92 spectral channels [5-7]. By resampling each VRDR pixel spectrum with the CaSSIS filter response functions, we have produced the CaSSIS Resampled Data Record (CaRDR): a simulated four-band CaSSIS-style view of the martian surface at ~90 m/pixel with near-global coverage (Figure 1), providing instrument planners with foreknowledge of surface colour prior to high-resolution image acquisition, and unlocking new global-regional compositional investigative opportunities [8].
Figure 1: a) The Global CaRDR Dataset in NIR-PAN-BLU, simulating CaSSIS colours at 90 m/pixel. b) cbtnpb_t1249 (NPB, stretched) in Nili Fossae, showing colorful rugged terrains with diverse mineralogy. c) cbtnpb_t0792 (NPB, stretched) in NW Noachis Terra, showing colourful plains south of Coprates Chasma. Footprints of HiRISE colour (red) and CaSSIS (yellow) are shown to highlight lack of high-resolution colour images in these areas.
Methods
Each of the 1764 VRDR 5°×5° Lambert Albedo tiles were resampled to produce a corresponding CaRDR tile. For each spatial pixel and each CaSSIS channel, the resampled CaRDR value is the normalised dot product of the per-pixel VRDR spectrum with the CaSSIS spectral response function interpolated to the CRISM VNIR spectral domain.
Results
Output CaRDR tiles are 32-bit floating-point four-band GeoTIFFs with ENVI-compatible headers, inheriting the coordinate reference system of the source VRDR tile. For each tile we also compute a Colour Band Ratio Composite (CBRC) spectral parameter cube containing the ratios RED/PAN (RPR), PAN/BLU (PBR), PAN/NIR (PNR), an atmospheric ice index (ICE-ATM), and a surface water ice index (ICE-H₂O), following [9]. A group of 8-bit unsigned integer browse products is provided for each tile: various three-band colour composites available from CaSSIS (NPB, NRB, NRP, RPB), a PAN browse, a synthetic true-colour product, and two CBRC products (CBRC1: RPR-PBR-PNR; CBRC2: RPR-PBR-ICE-ATM). The complete dataset has a total uncompressed volume of ~0.81 TB.
Comparing the t1250 CaRDR tile to a co-registered, Lambert Albedo-corrected CaSSIS image (MY37_024813_159_0) over the spectrally diverse Nili Fossae region demonstrates that four-band surface colour is well reproduced at the hectometre scale. CaRDR-pixel to downsampled CaSSIS-pixel comparison yields a mean signed percentage error of −0.7% across all bands, with per-band means of −8.9% (BLU), +8.1% (PAN), −0.6% (RED), and −1.7% (NIR). Error distributions are approximately Gaussian. Qualitative comparison with the standard VRDR RGB browse product (R: 600nm, G: 530nm, B: 440 nm) confirms that CaRDR NPB composites reveal improved local contrast in bedrock exposures and sun-facing slopes, deeper blue tints in ferrous-bearing units, and smoother apparent inter-strip residuals as a result of the effective ~23-channel averaging inherent in the resampling operation.
New target identification and improvements to instrument operational efficiencies
The CaRDR dataset reveals striking colour diversity across vast areas of the martian surface that have received no prior high-resolution colour coverage from CaSSIS or HiRISE. These include, but are not limited to: rugged, mineralogically diverse regions surrounding Nili Fossae (Figure 1b), colourful plains south of Coprates Chasma containing potential chloride deposits (Figure 1c) [10], and ejecta deposits north of Hellas Planitia exhibiting red-toned outcrops of potentially shock-metamorphosed plagioclase-bearing material [11]. The CaRDR dataset identifies these regions amongst others, as compelling new high-priority targets for CaSSIS, HiRISE, and next-generation orbital imagers.
Additionally, by providing instrument planners with foreknowledge of the approximate CaSSIS-band colour and spectral character of surfaces at the hectometre scale, CaRDRs enable colour-informed footprint placement prior to acquisition of high-resolution CaSSIS or HiRISE images. The dataset will help instrument operational efficiency by helping image planners adjust footprints onto the most spectrally interesting sub-regions of a target, reducing the need for repeat observations.
Conclusions
The CaRDR dataset covers >99% of the martian surface at ~90 m/pixel with a total volume of ~0.81 TB across 1,764 tiles. Lambert Albedo values are reproducible to within ~10% of true CaSSIS observations. The dataset [8] is publicly available at the Natural History Museum Data Portal, and the resampling pipeline is available at github.com/rbstabbins/vrdr_resampler. The CaRDR dataset provides a global spectral baseline against which all future high-resolution colour observations can be contextualised.
References
[1] Thomas et al., Space Sci. Rev., 212, 1897, 2017. [2] Thomas, ESA PSA, doi:10.5270/esa-da0ic0t, 2021. [3] McEwen et al., J. Geophys. Res., 112, E05S02, 2007. [4] Murchie et al., J. Geophys. Res., 112, E05S03, 2007. [5] Murchie et al., NASA PDS, doi:10.17189/QGXH-3R49, 2025. [6] Seelos et al., Icarus, 419, 115612, 2024. [7] Murchie et al., EPSC-DPS2025-136, 2025. [8] McNeil & Stabbins, NHM Data Portal, doi:10.5519/TBYP7PU2, 2026. [9] Tornabene et al., Space Sci. Rev., 214, 18, 2018. [10] Bickel et al., Sci. Data, 11, 845, 2024. [11] McNeil et al., EPSC-DPS2025-617, 2025.
How to cite: McNeil, J. and Stabbins, R.: CaSSIS Goes Global: a Multispectral Colour View of Mars From CRISM-CaSSIS Resampling, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-443, https://doi.org/10.5194/epsc2026-443, 2026.
Introduction
The surface albedo and thermal inertia are key parameters to understand the climate and geology of Mars. Albedo controls the amount of solar energy that is absorbed by the surface, playing an important role in the Surface Energy Budget (SEB), which on Mars is controlled by the radiative terms [1]. The SEB and the surface thermal inertia determine the temporal evolution of the ground temperature, which in turn drives the near-surface atmospheric processes. In addition, surface albedo and thermal inertia allow the geological characterization of the terrain, as they depend on rock abundance, particle size or porosity.
The MEDA instrument [2] onboard the Perseverance rover of the Mars 2020 mission includes the Radiation and Dust Sensor (RDS) [3] and the Thermal InfraRed Sensor (TIRS) [4], among other sensors, which allow the direct determination of these parameters.
Methodology and Calibration Improvements
Surface albedo is obtained from downward and upward shortwave fluxes, which are obtained respectively from the RDS TOP-7 and TIRS-IR3 channels. The retrieval of the thermal inertia uses also the surface brightness temperature measured by the TIRS-IR5 and the surface energy budget, in which the downward and upward longwave fluxes are obtained respectively from the TIRS-IR1 and TIRS-IR4 channels.
This work expands and improves the results presented in [1] covering the first 350 sols of the mission. In order to accurately perform longer-term analyses, such as this one, it becomes necessary to correct RDS and TIRS measurements for the effect of dust accumulation. This is done by means of a Dust Correction Factor (DCF). In this work we use recent correction factors for dust deposition on the RDS [5] and TIRS [6] channels, which improve the accuracy of the radiative terms, which in turn allow for a refinement in the calculation of the surface albedo and thermal inertia.
Figure 1 shows the DCF on the RDS TOP-7 channel. The temporal evolution shows two periods of strong net dust accumulation, between sols 400-600 and 1000-1200, covering slightly more than the first half of the perihelion season. During the remaining periods, dust accumulation experiences little variability. Finally, net dust removal occurs at the end of the analyzed period.
Figure 1. Dust Correction Factor for the RDS TOP-7 channel.
Figure 2 shows the DCF on the TIRS channels. Dust accumulation is small for the last three channels because they are looking downwards. The behavior of IR1 and IR2 is similar to that of the RDS TOP-7, but with a smaller effect of dust accumulation, particularly on the IR1 channel.
Figure 2. Dust Correction Factor for the TIRS channels.
Results
Figure 3 shows the surface albedo during the first 1,600 sols of the mission. Surface albedo strongly depends on illumination and viewing geometry, leading to a significant diurnal variation [1]. For this reason, we show the values at noon, which represent the diurnal minimum. The temporal evolution appears to show a seasonal cycle, with minimum values at around Ls = 180º and maximum values at around Ls = 270º. The values range from 0.10 to 0.23, and the surface albedo in MY 37 appears to be systematically larger than in MY 36.
Figure 3. Surface albedo at Noon for the first 1,600 sols. Vertical lines indicate Ls = 180º and Ls = 270º for MY 36 and 37.
Figure 4 shows the surface thermal inertia during the first 1,600 sols of the mission. Contrary to the albedo, the temporal evolution is now controlled by the portion of the terrain observed by TIRS, and a seasonal cycle is not clearly identified. The strong fluctuations can be attributed to the relatively small field of view of TIRS, which observes an area of approximately 3-4 m2, and therefore it is very sensitive to surface heterogeneities. Values range from below 200 to above 600 SI units, indicating a variety of terrain features.
Figure 4. Surface thermal inertia for the first 1,600 sols.
Discussion
These results can be compared to those obtained from satellite observations. In the case of thermal inertia, THEMIS retrievals [7] around the rover traverse are tipically between 200 and 400 SI units. The smaller range of values can be attributed to the different spatial resolutions of both instruments.
Mean values from OMEGA albedo observations [8] for the region where Perseverance is located are around 0.16, in very good agreement with MEDA retrievals. OMEGA observations (Figure 5) also show higher albedo values as the rover moved towards the West, in agreement with MEDA retrievals. Furthermore, the seasonal cycle observed from MEDA is consistent with that observed at Syrtis Major from MARCI observations for MY 28 to 33, with annual minima around Ls = 180º and maxima around Ls = 270º [9]. The increase in brightness is approximately coincident with the strong decreases of the RDS DCF, suggesting that it can be partially attributed to dust deposition.
Figure 5. OMEGA albedo values, with the rover traverse shown in black.
The presented results are important to validate numerical models and provide ground-truth for satellite observations of albedo and thermal inertia. In addition, these results include the area named Three Forks, where ten sample tubes have been deposited. Finally, they allow exploring the role of dust storms and dust lifting and deposition on these parameters throughout the mission.
Acknowledgements
This research is funded by the Spanish Ministry of Science, Innovation and Universities (MICIU)/State Agency of Research (AEI) project PID2024-161247OB-C31, funded by MICIU/AEI /10.13039/501100011033 / FEDER, UE
References
[1] Martínez, G. M. et al. (2023). Journal of Geophysical Research: Planets, 128, e2022JE007537. [2] Rodríguez-Manfredi, J. A. et al. (2021). Space Science Reviews, 217(3), 1–86. [3] Apéstigue, V. et al. (2022). Sensors, 22(8), 2907. [4] Sebastián, E. et al. (2020). Measurement, 164, 107968. [5] Vicente-Retortillo, A. et al. (2024). Geophysical Research Letters, 51, e2023GL107975. [6] Sebastián, E. et al. (2025). [7] Fergason R. L. et al. (2006), JGR 111, E12004. [8] Vincendon M. et al. (2015), Icarus 251, 145-163. [9] Wellington, D. F., and Bell III, J. F. (2020). Icarus, 349, 113766.
How to cite: Vicente-Retortillo, A., Martínez, G., Sebastián, E., Lemmon, M., Rodríguez-Veloso, R., Toledo, D., Apéstigue, V., Arruego, I., and Rodríguez-Manfredi, J. A.: Albedo and Thermal Inertia at Jezero Crater from MEDA over the First 1600 Sols: Extended Analysis and Calibration Improvements, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1140, https://doi.org/10.5194/epsc2026-1140, 2026.