Enhanced Global Messenger Multispectral Mosaics using GSA Pansharpening

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However, these global products inherit the spatially inconsistent and significantly low spatial resolution of the global MDIS datasets due to the MESSENGER spacecraft orbit, especially in the southern hemisphere. This creates a critical resolution gap compared to the higher-resolution monochromatic data. This study addresses this limitation by applying the Gram–Schmidt Adaptive (GSA) pansharpening technique to enhance the spatial resolution of global multispectral mosaics. The method has been used to process two key datasets, the Map Projected Multispectral Reduced Data Record mosaic and the Basemap Enhanced Color Global Mosaic, successfully increasing their resolution fourfold, from 665 m/px to a globally consistent 166 m/px. Objective validation confirms that this enhancement achieved high colour fidelity and introduced minimal negligible alterations to the spectral components of the images. The resulting high-resolution datasets reveal the spectroscopic properties of numerous previously unresolved, smaller-scale surface features, such as structural features, hollows, slope lineae, and volcanic vents, essential for detailed geological interpretation. This work provides a robust new reference dataset for comprehensive global studies of Mercury's surface composition and geology, while also demonstrating a method suitable for handling large planetary datasets and preparing for the exploitation of future data from the BepiColombo mission. Mercury Pansharpening MESSENGER Gram-Schmidt Adaptive Multispectral Global basemap Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission provided unprecedented monochrome and multispectral images of Mercury, primarily through its Mercury Dual Imaging System (MDIS). The MDIS was a primary instrument within the low-mass science payload and played a critical role in fulfilling the mission objective to globally characterize the geological history of Mercury (Hawkins et al., 2007 ; Bedini et al., 2017). The MDIS consisted of two cameras: the monochromatic Narrow-Angle Camera (NAC), which enabled targeted high-resolution imaging down to meter-scale spatial resolution, and the Wide-Angle Camera (WAC), a multispectral imager that observed across the visible and near-infrared spectrum (430 nm to 1020 nm) through 8 to 11 filters. MDIS WAC and NAC data provided the first global image sets of the planet’s surface (Denevi et al., 2018 ). It enabled regional-to-local-scale mapping of landforms, surface spectral variability, and topographic relief through stereo imaging. However, the resolution of this dataset varies significantly and is influenced by the acquisition conditions. The spacecraft's polar orbit was, in fact, carefully designed to manage its thermal health and is highly elliptical (Solomon et al., 2007 ), with a closest approach altitude of ~ 200 km over the northern hemisphere (Wright et al., 2021 ). Multispectral mosaics and derived products were therefore distributed after resampling at approximately 64–128 pixels per degree (ppd - about 332–665 m/px), i.e., at the maximum resolution achieved. The effective resolution in the southern hemisphere was reduced due to the greater altitude of the flyover reaching up to 2700 m/pixel (Denevi et al., 2018 ). However, the panchromatic data, leveraging the much higher resolution of the NAC, partially compensated for this distance, so that the monochromatic MESSENGER mosaics are distributed after resampling at a four times greater resolution, approximately 256 ppd (166 m/px), consistently over the whole globe, including the southern hemisphere. This study focuses on resolving the gap in resolution between MESSENGER multispectral and monochromatic data through data fusion techniques. In particular, we apply pansharpening to improve the spatial resolution of the available multispectral dataset by 4× or more, leveraging the image structural properties of the panchromatic image, thus providing a novel reference dataset for future global studies of Mercury. 2 Data This study focuses on two different global datasets: the Map Projected Multispectral Reduced Data Record (MDR) mosaic and the 3-bands Basemap Enhanced Color Global Mosaic (BECGM), both at 665 m/px (Denevi et al., 2017). The MDR is an averaged mosaic of WAC multispectral cubes providing near-global coverage across eight WAC narrow band filters, centered at 430, 480, 559, 629, 750, 829, 898, and 1000 nm. The WAC images used for the mosaic were selected to minimize the solar incidence angle and the emission angle, conditions most suitable for exploiting surface spectral properties. The positioning of the images, extrapolated from the SPICE kernels, was implemented using Bundle Adjustment and a global control network (Denevi et al., 2017). The BECGM is an RGB colour composite of derived products based on the 430, 750, and 1000 nm bands to emphasize color differences on Mercury's surface. It is composed by the second principal component (PC2), the PC1, and the ratio of the 430- to 1000-nm in the red, green, and blue channels respectively. Both cubes are resampled to a resolution of 665 m/px, with an effective resolution ranging from approximately 665 to up to 2700 m/px in the South Pole area. These were released in 2017 in the MESSENGER Release 16, as delivery of final extended mission products (Denevi et al., 2017). The Map-projected low-incidence angle basemap reduced data record (LOI), composed of WAC and NAC images captured at low solar incidence angles (up to ∼45°), was used as the reference panchromatic data (Hawkins et al., 2007 ). This panchromatic data offers a resolution of 166 m/px (256 ppd) and the low incidence helps to emphasise variations in surface albedo. Although previous studies (Tullo et al., 2024 ) have shown that illumination has a relatively limited influence on the spectral preservation of the final results, similar lighting conditions to those of the multispectral data help prevent the inheritance of shadows that could affect the contrast and colour rendering of the images. 3 Method Due to physical limitations, optical sensors in remote sensing balance spatial resolution, spectral resolution, and signal-to-noise ratio (SNR). Consequently, they provide either high spatial resolution with few spectral bands or high spectral resolution at the expense of ground sampling. Pansharpening data fusion techniques aim to address these limitations by leveraging information from a reference panchromatic image to enhance the spatial resolution of a given multi- or hyperspectral image, thereby combining the qualities of both instruments. Among the many methods that have emerged in recent years, the Gram–Schmidt Adaptive (GSA) method quickly gained popularity for its reliability, often serving as the standard by which other methods are compared (Dalla Mura et al., 2015 ; Vivone et al., 2021 ). Its application in recent benchmarks such as Ciotola et al. (2024) and Guarino et al. ( 2025 ) shows that GSA remains one of the most efficient and stable methods, even when compared to the innovative deep learning (DL) applications that have emerged in recent years. The GSA method builds upon the previous GS pansharpening method, which shares with it the principles of orthogonalisation, panchromatic substitution, and inverse transformation. Unlike the original, however, it introduces an adaptive mechanism to adjust the synthetic intensity component relative to the reference panchromatic component. The adapted panchromatic is derived as a weighted average of the spectral bands, with weights dynamically calculated through regression coefficients between the spectral bands and the reference image (Aiazzi et al., 2007). This enables it to successfully handle minor registration and alignment errors, making it suitable for managing even large sets of non-orthorectified data. Furthermore, the method's flexibility allows the pseudo-synthetic to be adapted to bands that do not directly correlate with the optical spectrum, as in the case of the BECGM. The GSA version used is implemented in Python as part of the open-source PANCO suite (PAnsharpening and CO-Registration - Tullo et al., 2024 ), v.2.0 ( 10.5281/zenodo.17512026 ). Due to computational constraints, both datasets were processed in tiles and then mosaicked back into a single dataset. The datasets were divided into tiles measuring approximately 30° per side (6x12), but were densified in the polar regions (over ± 70 °) to approximately 10° per side in order to limit the influence of a statistically significant number of null pixels on the results. 4 Results and evaluation Figure 1 provides a global overview of the two datasets, showing high color fidelity relative to the original cubes at the same contrast stretch, thereby preserving the same value ranges. The fourfold and more increase in spatial resolution allows the observation of numerous smaller-scale morphologies that were previously unresolved, such as structural features and medium- to large-scale hollows and vents (Fig. 2 and Fig. 3 ). Furthermore, the method also tends to mitigate artifacts caused by stitching between different resolutions, which is common in multispectral mosaics (Fig. 4 ). However, local artifacts of the panchromatic LOI, such as small misalignment and abrupt brightness transitions, are inevitably inherited in the processed data and are clearly visible in several areas. To provide an objective evaluation of the results, several standard performance indicators were adopted to assess both spectral and structural aspects of the images. The preservation of spectral information has been investigated by comparing results subsampled to the starting resolution with the original multispectral data (MS). The following numerical indicators were used: the Spectral Angle Mapper (SAM), the Root Mean Square Error (RMSE), the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), the Universal Image Quality Index (UQI), and the Peak Signal-to-Noise Ratio with blocking factor (PSNR-B). Furthermore, the development of structural characteristics was evaluated by comparing them with the panchromatic reference image using the Structural Similarity Index Measure (SSIM), and single-band panchromatic images were synthesized from the results. A summary of the parameters used is shown in Table 1 . Table 1 Summary of the performance indexes used to evaluate the results. Range Ideal Considered aspect Reference SAM 0–180° 0 Spectral slope alteration Yuhas et al. ( 1992 ) RMSE 0 – ထ 0 Spectral values distortion Martens & Meesters ( 1998 ) ERGAS 0 – ထ 0 Cumulative normalised dissimilarity Wald ( 2002 ) UQI -1–1 1 Spectral and structural properties dissimilarities Wang & Bovik ( 2002 ) PSNR-B 0 - ထ ထ SNR analysis and image “blockiness” Yim & Bovik (2011) SSIM 0–1 1 Structural properties and image perception Wang et al. ( 2004 ) The null pixels and the immediately surrounding regions (in a buffer of approximately 100 pixels) were avoided in the calculations and ignored as a contribution to the statistics, so as not to distort the results. The tools for evaluating the results were programmed in Python and validated using the dataset and results provided in Vivone et al. ( 2023 ). Figures 5 and 6 show the numerical results of the validation of the two mosaics, respectively for the MDR and BECGM. The numerical indicators relating to spectral information show extremely limited alterations for both datasets, both in terms of spectral shape (SAM less than 0.3° for MDR and less than 3.3° for BECGM) and range of values (ERGAS around 2 for both). At the same time, structural indicators confirm the acquisition of higher-resolution details, with structural properties very close to the reference panchromatic, achieving SSIM values close to 1 for both datasets. Given its spectral nature, which is closer to panchromatic, the processed MDR image shows slightly higher values than the panchromatic image, reflecting values close to ideal across all the numerical indicators tested. However, the differences in the indicators, particularly in SAM and RMSE, are mostly given by the different numerical range: the MDR data is expressed in I/F in decimal values (in a range between 0.0 and 0.2), while BECGM is a derivative product normalized to the range of integer values 0-255. Finally, while the GSA-enhanced MDRs closely match the original dataset in terms of absolute calibration, the enhanced spatial resolution enables more accurate spectral analysis. As shown in Fig. 7 , spectra extracted from the same ROIs within Matisse LRM (Fig. 3 a, b) and Castiglione crater pyroclastic deposits (Fig. 3 e, f) have comparable averages (a vertical shift is necessary to distinguish them) in terms of absolute reflectance, but GSA-enhanced spectra show smaller 1σ standard errors than original MDRs. This is a direct benefit of the spatial resolution improvements, which allow averaging more pixels than in the original data. Given the preservation of the original range of values, the processing by separate tiles shows no visible seamlines or stitching artifacts in the final combined product. 5 Example of case studies The enhanced global multispectral mosaics produced in this work enable not only improved regional analyses but also the exploration of entirely new case studies focusing on small-scale surface features, previously limited by the coarse resolution and noise of MESSENGER’s colour datasets. One particularly promising application concerns the stratification observed in the blue central peaks, both within the Caloris basin (Fig. 8 a-b) and in several other regions of Mercury. A number of impact craters show orange rims, representing optically mature material, while their central peaks appear strikingly blue or dark blue. Beyond the well-known cases in which hollows contribute to this fresh, blue signature, these blue central peaks offer a valuable opportunity to study Mercury’s deep stratigraphy (Ernst et al., 2010 ), as they expose material excavated from depth during the impact event. Equally interesting could be the opposite cases, where the central peak appears orange while the crater rim is blue. Understanding these contrasting patterns may provide new insights into Mercury’s geological evolution, including variations in volatile content and the mechanical or compositional differences between crustal layers across the planet. Hokusai crater could also be an interesting target, since it is considered a candidate source for delivering water ice to Mercury’s north pole through a relatively recent impact event (Ernst et al., 2018 ). The improved multispectral resolution (Fig. 8 c-d) provides the level of detail needed to refine geological mapping of Hokusai, potentially constraining its age, compositional heterogeneity, and its role in volatile transport. In fact, we can distinguish subtle surface features, including probable hollows on the crater floor, localized darker patches within the interior, and deposits with contrasting reflectance along the crater walls. These features were either blurred or difficult to separate in the original multispectral products. The pansharpened mosaic enables the extraction of more localized and accurate multispectral signatures, reducing spectral mixing and allowing smaller-scale units to be analysed independently. Degas crater provides another strong example of the scientific potential of the enhanced multispectral mosaic. At the improved spatial resolution, the extensive network of floor fractures becomes clearly resolved (Fig. 8 e-f) and displays a distinctive light-blue spectral signature that is difficult to recognize in the original multispectral products. The improved definition allows the geometry and continuity of individual fractures to be mapped with much greater confidence, facilitating a direct comparison between fractured and non-fractured floor materials. Another scientific application for this methodology is the analysis of fresh materials ejected by impact craters. Several fresh craters on Mercury are small-scale (i.e., Fig. 9 a), rendering the characterization of potential spectral features within them or of potential spectral variation within their ejecta challenging. Thanks to the improved spatial resolution of the pansharpened multispectral products and basemaps presented in this analysis (Fig. 9 b), future studies of these surface features may provide new insights into their spectral properties and the composition of fresh materials on Mercury. The improved basemaps also allow novel spectroscopic studies of bright streaks (Bickel et al., 2026 ), potential indicators of recent volatile-driven mass wasting on Mercury. In particular, Fig. 9 shows that, with this novel basemap, the bright streaks of Degas (panels c and d) and Martins (panels e and f) are now fully resolved and can be investigated spectroscopically. In all the above case studies, increased spatial resolution also improves the accuracy of spectroscopic analyses. In particular, the higher spatial resolution of GSA-enhanced MDRs implies a higher number of pixels that can be averaged for a given ROI (i.e., Fig. 7 ), resulting in a more robust estimate of mean spectra and smaller standard errors. In turn, this allows for a more accurate spectral discrimination, like e.g. from clustering techniques (Lucchetti et al., 2021; Pajola et al., 2021 ; Vergara Sassarini et al., 2025), or band identifications (Galiano et al., 2026). 6 Conclusions The application of the GSA method has enabled the spatial resolution of multispectral global mosaics of Mercury to be increased from 665 to 166 m/px, also for the southern hemisphere, where it was originally lower due to Messenger's orbit (up to 2700 km/px). Thanks to the LOI mosaic used as the reference panchromatic, the effective ground resolution is consistent across the southern hemisphere, which originally had lower resolution due to Messenger's orbit. The two new datasets enable spectroscopic analysis of previously unresolved, medium- to small-scale geological features, including hollows, structural features, fresh craters, bright lineae, and pyroclastic vents. Future analyses of these and other small-scale Mercury could leverage the novel basemap presented in this study to further investigate their compositional properties. The validation of the results, both visually and through performance indicators, shows minimal and negligible alterations in the spectral component while gaining the structural properties of the panchromatic mosaic. This implies that the processing does not compromise the applicability of the results, even in spectral signature analysis, whose potential remains equivalent to that of the original data. These results also demonstrate the method's validity, even for large datasets that require asynchronous processing of tiles due to computational constraints, with no visible joints between adjacent tiles. This also suggests the possibility of expanding PANCO's uses and flexibility by enabling asynchronous processing and parallelization. This study is preparatory for use with other similar large datasets, such as the future exploitation of BepiColombo's global data, in particular for the integration of the three channels of the SIMBIO-SYS instrument: HRIC, STC, and VIHI (Cremonese et al., 2020). 7 Data availability Version 1 of both processed datasets is available in open access in the INAF archive, at the link: https://owncloud.ia2.inaf.it/index.php/s/kvkwuIlDWxUN69J . Both datasets are distributed as single files and as quadrangle subsets to simplify their use. The Enhanced color map is released in two numerical formats: Integer (Byte) like the original, and floating 32bit as produced by the tool, which better preserves surface variations at the cost of greater computational and storage. PANCO tool is released open-source under AGPL-3.0 license at github.com/adritullo/PANCO (Version v.2.0– 10.5281/zenodo.17512026 ) and in INAF Open Access Archive. The original MESSENGER images are credited to NASA / Johns Hopkins University Applied Physics Laboratory / Carnegie Institution of Washington and are available at Johns Hopkins University Applied Physics Laboratory LLC archives ( https://messenger.jhuapl.edu/Explore/Images.html#global-mosaics ). Abbreviations BECGM Basemap Enhanced Color Global Mosaic ERGAS Erreur Relative Globale Adimensionnelle de Synthèse GSA Gram-Schmidt Adaptive LOI Map-projected low-incidence angle basemap reduced data record MDIS Mercury Dual Imaging System MDR Map Projected Multispectral Reduced Data Record MESSENGER MErcury Surface, Space ENvironment, GEochemistry, and Ranging MS multispectral data NAC Narrow-Angle Camera PAN panchromatic data PANCO PAnsharpening and CO-Registration PCn Principal Component PSNR B Peak Signal-to-Noise Ratio with blocking factor RGB Red, Green, Blue RMSE Root Mean Square Error SAM Spectral Angle Mapper SNR signal-to-noise ratio SSIM Structural Similarity Index Measure UQI Universal Image Quality Index WAC Wide-Angle Camera Declarations The authors must provide the following sections under the heading “Declarations”. Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Competing interests No applicable competing interests have been identified. Funding The study has been supported by the Italian Space Agency (ASI-INAF agreement no. 2024-40-HH.0) and INAF (INAF MiniGrant “Combined implementation of CaSSIS and HiRISE data through pansharpening experiments” - CUP C93C23008430001). Authors' contributions Adriano Tullo: Conceptualization, Methodology, Software, Validation, Writing, Data Curation Acknowledgements The study has been supported by the Italian Space Agency (ASI-INAF agreement no. 2024-40-HH.0) and INAF (INAF MiniGrant “Combined implementation of CaSSIS and HiRISE data through pansharpening experiments” - CUP C93C23008430001). Availability of data and materials Both the software and the dataset (original and processed) are available open source. More details and links in the Data availability paragraph. 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IEEE Trans Image Process 20(1):88–98. 10.1109/TIP.2010.2061859 Yuhas RH, Goetz AF, Boardman JW (1992) Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop, vol 1. AVIRIS Supplementary Files GraphicalAbstract.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 02 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 03 Feb, 2026 First submitted to journal 21 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8661573","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590773680,"identity":"e9acac9e-b9ea-448b-b071-41d4c750a3f0","order_by":0,"name":"Adriano Tullo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3QoQoCMRjA8e840DKxrni+gjbDga/iEK5psVwQmWWX7ApyvoIWMW4MzrIHsOkVTcIZDYLj5NS0Mxr2D9sY/PjGAGy2v6wH/HVwpxxCrHde3JQQBA7loDSp6Buz6b22nDgMcmIcU48GKb/vfOhiQvkt7gzrDByZGQhWl5aYqQCQJmKxxSOcQMnDDgHwGpM5kbWtXo/UTJqaiMebLDFZlU1paSI/Uygm6zLSVmeQDRYgpFIq5gkmm0R/nTIQbx+4tyvzvWrUF6dsPCFx4sosNJAi9HV26A/AZrPZbKaeK/BY1vo3moIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4292-7093","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":true,"prefix":"","firstName":"Adriano","middleName":"","lastName":"Tullo","suffix":""},{"id":590773681,"identity":"1992e984-cead-446b-aa82-0f07b9e30ecf","order_by":1,"name":"Cristina Re","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Re","suffix":""},{"id":590773682,"identity":"f3b13ef7-bdc5-4424-bf0f-22d787cd1c48","order_by":2,"name":"Silvia Bertoli","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Bertoli","suffix":""},{"id":590773683,"identity":"734dafd9-c2e4-423e-8e73-9090d2b13f8b","order_by":3,"name":"Giovanni Munaretto","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Munaretto","suffix":""},{"id":590773684,"identity":"596bc477-12cb-4275-8009-203d8bc576bc","order_by":4,"name":"Pamela Cambianica","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Cambianica","suffix":""},{"id":590773685,"identity":"4b7c4345-8a91-495b-b589-73a82521c76a","order_by":5,"name":"Natalia Amanda Vergara Sassarini","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"Amanda Vergara","lastName":"Sassarini","suffix":""},{"id":590773686,"identity":"51771ab8-dd85-470b-9328-d4e6e8cd2861","order_by":6,"name":"Riccardo La Grassa","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"La","lastName":"Grassa","suffix":""},{"id":590773687,"identity":"b06582f1-fbf9-4233-879e-210babdba456","order_by":7,"name":"Gabriele Cremonese","email":"","orcid":"","institution":"INAF OaPD: Osservatorio Astronomico di Padova","correspondingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Cremonese","suffix":""}],"badges":[],"createdAt":"2026-01-21 15:35:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8661573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8661573/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049682,"identity":"08a26bfb-83e2-447e-92ff-1a4f7c7339a9","added_by":"auto","created_at":"2026-02-20 07:44:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18738627,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the two new datasets; \u003cem\u003eThe MDR 8-bands mosaic a) and the BECGM b) enhanced the original 665 m/px to 166 m/px (c, d) using the LOI mosaic as panchromatic reference data and the GSA pansharpening algorithm. Both datasets maintain high color fidelity with the original data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/d14c166da9e4c0df79c23bcd.jpg"},{"id":102982192,"identity":"14706053-b9ed-4ab3-b3aa-289da2aa47e9","added_by":"auto","created_at":"2026-02-19 09:12:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3355095,"visible":true,"origin":"","legend":"\u003cp\u003eThe GSA MDR Mosaic details; \u003cem\u003eComparison of details of the MDR mosaic at original resolution (a, c, e) and pansharpened at 166 m/px (b, d, f). The higher resolution allows minor features that were not previously visible to be observed, such as: b) hollows at the edge and near the Hitomaro crater, d) the central peak and the ejecta of the Mena Crater, and f) lobate scarps at the southern edge of the Caloris Basin.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/831f7ec9c38a33af4c18d8db.jpg"},{"id":102982200,"identity":"f7c6a070-2b7e-4a0a-b0b4-6b2ecaef26ea","added_by":"auto","created_at":"2026-02-19 09:12:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":641152,"visible":true,"origin":"","legend":"\u003cp\u003eThe GSA Enhanced Color Mosaic details: \u003cem\u003eComparison of details of the BECGM at original resolution (a, c, e) and pansharpened at 166 m/px (b, d, f). The improvement allows for greater discrimination of surface features as for example: b) the LRMs and the hollows at the edge of the Matisse crater, d) the graben systems inside the Rachmaninoff Crater, and f) the central peak, vents and pyroclastic deposits inside the Castiglione Crater White outlines represents ROI to extract average spectra in Fig. 4\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/3124a2e5f15dc038ac01590c.jpg"},{"id":102982196,"identity":"123b8a69-8470-4dc4-ba22-5531bba12b23","added_by":"auto","created_at":"2026-02-19 09:12:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9356548,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of local artifacts mitigation. \u003cem\u003ea) Detail of the craters Bach, Yun Sǒn-Do, Ōkyoin the southern polar region, and d) the Matabei Crater in the southern hemisphere. The artifacts caused by stitching images with different resolutions and illumination are clearly observable in the original mosaic (a, d) but are mostly mitigated in the processed data, respectively b) and e), although some artifacts inherited from the LOI panchromatic mosaic (c and f) are still inherited by the derived product.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/1bb16353344c464aee65d18c.png"},{"id":102982191,"identity":"af5e92fe-a638-407a-a379-83cbef2da135","added_by":"auto","created_at":"2026-02-19 09:12:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":561448,"visible":true,"origin":"","legend":"\u003cp\u003eGSA MDR spatial and spectral indexes. \u003cem\u003eResults of the comparison between the original MDR 8-bands mosaic and the processed mosaic using the GSA pansharpening algorithm.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/666c96638063636731c1a1aa.png"},{"id":103049866,"identity":"193aba61-5b9f-450d-838b-4dc206535b74","added_by":"auto","created_at":"2026-02-20 07:46:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":673364,"visible":true,"origin":"","legend":"\u003cp\u003eGSA ECM spatial and spectral indexes. \u003cem\u003eResults of the comparison between the original MDR 8-bands mosaic and the processed mosaic using the GSA pansharpening algorithm.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/cbe64c42e4706e2b0da72da3.png"},{"id":102982198,"identity":"c7882f24-dd1f-4fd1-a5d2-56a3ea9750e1","added_by":"auto","created_at":"2026-02-19 09:12:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":250527,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of the enhancement of spectral characterization. \u003cem\u003eComparison of a) the LRM at the edge of Matisse crater and b) the Castiglione crater pyroclastic deposits average spectra extracted from ROIs in Fig.3a-b and Fig,3e-f, respectively. In light blue the 8-band spectra from the MDR basemap and in orange from the pansharpened version. A vertical shift has been added for better visibility. Error-bars represent 1σ standard errors. The closeup on the right of the panels highlights the smaller error bars in the pansharpened version.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/5b576f605a8942a14d561771.jpg"},{"id":102982199,"identity":"75f80bc6-522f-4203-a966-5784c88cc019","added_by":"auto","created_at":"2026-02-19 09:12:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3664762,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of crater floor details resolvable with the GSA enhanced datasets. \u003cem\u003eComparison of resolved crater features between the BECGM (665 m/px, left) and the GSA enhanced BECGM (166 m/px, right). In detail panels a) and b) depicts the central peak of a crater located in Caloris Planitia, panels c) and d) the floor of Hokusai crater, e) and f) the floor and the fractures of Degas crater.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/d6142b2b0fbe8bcef59261d1.jpg"},{"id":102982201,"identity":"1bfa1e9f-e061-45f8-93d8-b3a56442fa6d","added_by":"auto","created_at":"2026-02-19 09:12:36","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3515070,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of small-scale geological feature observable with the GSA enhanced datasets. \u003cem\u003eComparison of small-scale surface features between the original BECGM (665 m/px, left) and the GSA pansharpened version (166 m/px, right). The panels a) and b) show a fresh crater in Caloris Planitia, c) and d) bright streaks on the northern rim of the Degas crater and, e) and f) bright streaks on the walls of Martins crater\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig.9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/16945646ed87ec83af67e121.jpg"},{"id":103050689,"identity":"f95cea8a-b90d-4b2c-b431-121a8ccba7f8","added_by":"auto","created_at":"2026-02-20 07:53:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42127989,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/f9e6ca3a-4f14-4635-a186-60ab11e46420.pdf"},{"id":102982194,"identity":"77ef0016-5f72-4b39-b1de-16559a4d27c3","added_by":"auto","created_at":"2026-02-19 09:12:35","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":71243,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8661573/v1/88a0ee33729e96a28e2072c7.jpg"}],"financialInterests":"","formattedTitle":"Enhanced Global Messenger Multispectral Mosaics using GSA Pansharpening","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission provided unprecedented monochrome and multispectral images of Mercury, primarily through its Mercury Dual Imaging System (MDIS). The MDIS was a primary instrument within the low-mass science payload and played a critical role in fulfilling the mission objective to globally characterize the geological history of Mercury (Hawkins et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bedini et al., 2017).\u003c/p\u003e \u003cp\u003eThe MDIS consisted of two cameras: the monochromatic Narrow-Angle Camera (NAC), which enabled targeted high-resolution imaging down to meter-scale spatial resolution, and the Wide-Angle Camera (WAC), a multispectral imager that observed across the visible and near-infrared spectrum (430 nm to 1020 nm) through 8 to 11 filters. MDIS WAC and NAC data provided the first global image sets of the planet\u0026rsquo;s surface (Denevi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It enabled regional-to-local-scale mapping of landforms, surface spectral variability, and topographic relief through stereo imaging. However, the resolution of this dataset varies significantly and is influenced by the acquisition conditions. The spacecraft's polar orbit was, in fact, carefully designed to manage its thermal health and is highly elliptical (Solomon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), with a closest approach altitude of ~\u0026thinsp;200 km over the northern hemisphere (Wright et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Multispectral mosaics and derived products were therefore distributed after resampling at approximately 64\u0026ndash;128 pixels per degree (ppd - about 332\u0026ndash;665 m/px), i.e., at the maximum resolution achieved. The effective resolution in the southern hemisphere was reduced due to the greater altitude of the flyover reaching up to 2700 m/pixel (Denevi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the panchromatic data, leveraging the much higher resolution of the NAC, partially compensated for this distance, so that the monochromatic MESSENGER mosaics are distributed after resampling at a four times greater resolution, approximately 256 ppd (166 m/px), consistently over the whole globe, including the southern hemisphere.\u003c/p\u003e \u003cp\u003eThis study focuses on resolving the gap in resolution between MESSENGER multispectral and monochromatic data through data fusion techniques. In particular, we apply pansharpening to improve the spatial resolution of the available multispectral dataset by 4\u0026times; or more, leveraging the image structural properties of the panchromatic image, thus providing a novel reference dataset for future global studies of Mercury.\u003c/p\u003e "},{"header":"2 Data","content":"\u003cp\u003eThis study focuses on two different global datasets: the Map Projected Multispectral Reduced Data Record (MDR) mosaic and the 3-bands Basemap Enhanced Color Global Mosaic (BECGM), both at 665 m/px (Denevi et al., 2017). The MDR is an averaged mosaic of WAC multispectral cubes providing near-global coverage across eight WAC narrow band filters, centered at 430, 480, 559, 629, 750, 829, 898, and 1000 nm. The WAC images used for the mosaic were selected to minimize the solar incidence angle and the emission angle, conditions most suitable for exploiting surface spectral properties. The positioning of the images, extrapolated from the SPICE kernels, was implemented using Bundle Adjustment and a global control network (Denevi et al., 2017).\u003c/p\u003e \u003cp\u003eThe BECGM is an RGB colour composite of derived products based on the 430, 750, and 1000 nm bands to emphasize color differences on Mercury's surface. It is composed by the second principal component (PC2), the PC1, and the ratio of the 430- to 1000-nm in the red, green, and blue channels respectively. Both cubes are resampled to a resolution of 665 m/px, with an effective resolution ranging from approximately 665 to up to 2700 m/px in the South Pole area. These were released in 2017 in the MESSENGER Release 16, as delivery of final extended mission products (Denevi et al., 2017).\u003c/p\u003e \u003cp\u003eThe Map-projected low-incidence angle basemap reduced data record (LOI), composed of WAC and NAC images captured at low solar incidence angles (up to \u0026sim;45\u0026deg;), was used as the reference panchromatic data (Hawkins et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This panchromatic data offers a resolution of 166 m/px (256 ppd) and the low incidence helps to emphasise variations in surface albedo. Although previous studies (Tullo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have shown that illumination has a relatively limited influence on the spectral preservation of the final results, similar lighting conditions to those of the multispectral data help prevent the inheritance of shadows that could affect the contrast and colour rendering of the images.\u003c/p\u003e"},{"header":"3 Method","content":"\u003cp\u003eDue to physical limitations, optical sensors in remote sensing balance spatial resolution, spectral resolution, and signal-to-noise ratio (SNR). Consequently, they provide either high spatial resolution with few spectral bands or high spectral resolution at the expense of ground sampling. Pansharpening data fusion techniques aim to address these limitations by leveraging information from a reference panchromatic image to enhance the spatial resolution of a given multi- or hyperspectral image, thereby combining the qualities of both instruments.\u003c/p\u003e \u003cp\u003eAmong the many methods that have emerged in recent years, the Gram\u0026ndash;Schmidt Adaptive (GSA) method quickly gained popularity for its reliability, often serving as the standard by which other methods are compared (Dalla Mura et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vivone et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its application in recent benchmarks such as Ciotola et al. (2024) and Guarino et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) shows that GSA remains one of the most efficient and stable methods, even when compared to the innovative deep learning (DL) applications that have emerged in recent years.\u003c/p\u003e \u003cp\u003eThe GSA method builds upon the previous GS pansharpening method, which shares with it the principles of orthogonalisation, panchromatic substitution, and inverse transformation. Unlike the original, however, it introduces an adaptive mechanism to adjust the synthetic intensity component relative to the reference panchromatic component. The adapted panchromatic is derived as a weighted average of the spectral bands, with weights dynamically calculated through regression coefficients between the spectral bands and the reference image (Aiazzi et al., 2007). This enables it to successfully handle minor registration and alignment errors, making it suitable for managing even large sets of non-orthorectified data. Furthermore, the method's flexibility allows the pseudo-synthetic to be adapted to bands that do not directly correlate with the optical spectrum, as in the case of the BECGM.\u003c/p\u003e \u003cp\u003eThe GSA version used is implemented in Python as part of the open-source PANCO suite (PAnsharpening and CO-Registration - Tullo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), v.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.17512026\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.17512026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Due to computational constraints, both datasets were processed in tiles and then mosaicked back into a single dataset. The datasets were divided into tiles measuring approximately 30\u0026deg; per side (6x12), but were densified in the polar regions (over \u0026plusmn;\u0026thinsp;70 \u0026deg;) to approximately 10\u0026deg; per side in order to limit the influence of a statistically significant number of null pixels on the results.\u003c/p\u003e"},{"header":"4 Results and evaluation","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a global overview of the two datasets, showing high color fidelity relative to the original cubes at the same contrast stretch, thereby preserving the same value ranges. The fourfold and more increase in spatial resolution allows the observation of numerous smaller-scale morphologies that were previously unresolved, such as structural features and medium- to large-scale hollows and vents (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the method also tends to mitigate artifacts caused by stitching between different resolutions, which is common in multispectral mosaics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, local artifacts of the panchromatic LOI, such as small misalignment and abrupt brightness transitions, are inevitably inherited in the processed data and are clearly visible in several areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo provide an objective evaluation of the results, several standard performance indicators were adopted to assess both spectral and structural aspects of the images. The preservation of spectral information has been investigated by comparing results subsampled to the starting resolution with the original multispectral data (MS). The following numerical indicators were used: the Spectral Angle Mapper (SAM), the Root Mean Square Error (RMSE), the Erreur Relative Globale Adimensionnelle de Synth\u0026egrave;se (ERGAS), the Universal Image Quality Index (UQI), and the Peak Signal-to-Noise Ratio with blocking factor (PSNR-B). Furthermore, the development of structural characteristics was evaluated by comparing them with the panchromatic reference image using the Structural Similarity Index Measure (SSIM), and single-band panchromatic images were synthesized from the results. A summary of the parameters used is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the performance indexes used to evaluate the results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdeal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsidered aspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;180\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectral slope alteration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYuhas et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1992\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 \u0026ndash; ထ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectral values distortion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMartens \u0026amp; Meesters (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eERGAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 \u0026ndash; ထ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative normalised dissimilarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWald (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUQI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectral and structural properties dissimilarities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWang \u0026amp; Bovik (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSNR-B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 - ထ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eထ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNR analysis and image \u0026ldquo;blockiness\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYim \u0026amp; Bovik (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSSIM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural properties and image perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe null pixels and the immediately surrounding regions (in a buffer of approximately 100 pixels) were avoided in the calculations and ignored as a contribution to the statistics, so as not to distort the results. The tools for evaluating the results were programmed in Python and validated using the dataset and results provided in Vivone et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the numerical results of the validation of the two mosaics, respectively for the MDR and BECGM. The numerical indicators relating to spectral information show extremely limited alterations for both datasets, both in terms of spectral shape (SAM less than 0.3\u0026deg; for MDR and less than 3.3\u0026deg; for BECGM) and range of values (ERGAS around 2 for both). At the same time, structural indicators confirm the acquisition of higher-resolution details, with structural properties very close to the reference panchromatic, achieving SSIM values close to 1 for both datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven its spectral nature, which is closer to panchromatic, the processed MDR image shows slightly higher values than the panchromatic image, reflecting values close to ideal across all the numerical indicators tested. However, the differences in the indicators, particularly in SAM and RMSE, are mostly given by the different numerical range: the MDR data is expressed in I/F in decimal values (in a range between 0.0 and 0.2), while BECGM is a derivative product normalized to the range of integer values 0-255.\u003c/p\u003e \u003cp\u003eFinally, while the GSA-enhanced MDRs closely match the original dataset in terms of absolute calibration, the enhanced spatial resolution enables more accurate spectral analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, spectra extracted from the same ROIs within Matisse LRM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b) and Castiglione crater pyroclastic deposits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f) have comparable averages (a vertical shift is necessary to distinguish them) in terms of absolute reflectance, but GSA-enhanced spectra show smaller 1σ standard errors than original MDRs. This is a direct benefit of the spatial resolution improvements, which allow averaging more pixels than in the original data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the preservation of the original range of values, the processing by separate tiles shows no visible seamlines or stitching artifacts in the final combined product.\u003c/p\u003e"},{"header":"5 Example of case studies","content":"\u003cp\u003eThe enhanced global multispectral mosaics produced in this work enable not only improved regional analyses but also the exploration of entirely new case studies focusing on small-scale surface features, previously limited by the coarse resolution and noise of MESSENGER\u0026rsquo;s colour datasets. One particularly promising application concerns the stratification observed in the blue central peaks, both within the Caloris basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-b) and in several other regions of Mercury. A number of impact craters show orange rims, representing optically mature material, while their central peaks appear strikingly blue or dark blue. Beyond the well-known cases in which hollows contribute to this fresh, blue signature, these blue central peaks offer a valuable opportunity to study Mercury\u0026rsquo;s deep stratigraphy (Ernst et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as they expose material excavated from depth during the impact event. Equally interesting could be the opposite cases, where the central peak appears orange while the crater rim is blue. Understanding these contrasting patterns may provide new insights into Mercury\u0026rsquo;s geological evolution, including variations in volatile content and the mechanical or compositional differences between crustal layers across the planet.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHokusai crater could also be an interesting target, since it is considered a candidate source for delivering water ice to Mercury\u0026rsquo;s north pole through a relatively recent impact event (Ernst et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The improved multispectral resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec-d) provides the level of detail needed to refine geological mapping of Hokusai, potentially constraining its age, compositional heterogeneity, and its role in volatile transport. In fact, we can distinguish subtle surface features, including probable hollows on the crater floor, localized darker patches within the interior, and deposits with contrasting reflectance along the crater walls. These features were either blurred or difficult to separate in the original multispectral products. The pansharpened mosaic enables the extraction of more localized and accurate multispectral signatures, reducing spectral mixing and allowing smaller-scale units to be analysed independently. Degas crater provides another strong example of the scientific potential of the enhanced multispectral mosaic. At the improved spatial resolution, the extensive network of floor fractures becomes clearly resolved (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee-f) and displays a distinctive light-blue spectral signature that is difficult to recognize in the original multispectral products. The improved definition allows the geometry and continuity of individual fractures to be mapped with much greater confidence, facilitating a direct comparison between fractured and non-fractured floor materials.\u003c/p\u003e \u003cp\u003eAnother scientific application for this methodology is the analysis of fresh materials ejected by impact craters. Several fresh craters on Mercury are small-scale (i.e., Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), rendering the characterization of potential spectral features within them or of potential spectral variation within their ejecta challenging. Thanks to the improved spatial resolution of the pansharpened multispectral products and basemaps presented in this analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), future studies of these surface features may provide new insights into their spectral properties and the composition of fresh materials on Mercury. The improved basemaps also allow novel spectroscopic studies of bright streaks (Bickel et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), potential indicators of recent volatile-driven mass wasting on Mercury. In particular, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows that, with this novel basemap, the bright streaks of Degas (panels c and d) and Martins (panels e and f) are now fully resolved and can be investigated spectroscopically.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn all the above case studies, increased spatial resolution also improves the accuracy of spectroscopic analyses. In particular, the higher spatial resolution of GSA-enhanced MDRs implies a higher number of pixels that can be averaged for a given ROI (i.e., Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), resulting in a more robust estimate of mean spectra and smaller standard errors. In turn, this allows for a more accurate spectral discrimination, like e.g. from clustering techniques (Lucchetti et al., 2021; Pajola et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vergara Sassarini et al., 2025), or band identifications (Galiano et al., 2026).\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThe application of the GSA method has enabled the spatial resolution of multispectral global mosaics of Mercury to be increased from 665 to 166 m/px, also for the southern hemisphere, where it was originally lower due to Messenger's orbit (up to 2700 km/px). Thanks to the LOI mosaic used as the reference panchromatic, the effective ground resolution is consistent across the southern hemisphere, which originally had lower resolution due to Messenger's orbit. The two new datasets enable spectroscopic analysis of previously unresolved, medium- to small-scale geological features, including hollows, structural features, fresh craters, bright lineae, and pyroclastic vents. Future analyses of these and other small-scale Mercury could leverage the novel basemap presented in this study to further investigate their compositional properties.\u003c/p\u003e \u003cp\u003eThe validation of the results, both visually and through performance indicators, shows minimal and negligible alterations in the spectral component while gaining the structural properties of the panchromatic mosaic. This implies that the processing does not compromise the applicability of the results, even in spectral signature analysis, whose potential remains equivalent to that of the original data.\u003c/p\u003e \u003cp\u003eThese results also demonstrate the method's validity, even for large datasets that require asynchronous processing of tiles due to computational constraints, with no visible joints between adjacent tiles. This also suggests the possibility of expanding PANCO's uses and flexibility by enabling asynchronous processing and parallelization. This study is preparatory for use with other similar large datasets, such as the future exploitation of BepiColombo's global data, in particular for the integration of the three channels of the SIMBIO-SYS instrument: HRIC, STC, and VIHI (Cremonese et al., 2020).\u003c/p\u003e"},{"header":"7 Data availability","content":"\u003cp\u003eVersion 1 of both processed datasets is available in open access in the INAF archive, at the link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://owncloud.ia2.inaf.it/index.php/s/kvkwuIlDWxUN69J\u003c/span\u003e\u003cspan address=\"https://owncloud.ia2.inaf.it/index.php/s/kvkwuIlDWxUN69J\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Both datasets are distributed as single files and as quadrangle subsets to simplify their use. The Enhanced color map is released in two numerical formats: Integer (Byte) like the original, and floating 32bit as produced by the tool, which better preserves surface variations at the cost of greater computational and storage.\u003c/p\u003e \u003cp\u003ePANCO tool is released open-source under AGPL-3.0 license at github.com/adritullo/PANCO (Version v.2.0\u0026ndash;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.17512026\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.17512026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and in INAF Open Access Archive. The original MESSENGER images are credited to NASA / Johns Hopkins University Applied Physics Laboratory / Carnegie Institution of Washington and are available at Johns Hopkins University Applied Physics Laboratory LLC archives (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://messenger.jhuapl.edu/Explore/Images.html#global-mosaics\u003c/span\u003e\u003cspan address=\"https://messenger.jhuapl.edu/Explore/Images.html#global-mosaics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBECGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBasemap Enhanced Color Global Mosaic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERGAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eErreur Relative Globale Adimensionnelle de Synth\u0026egrave;se\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGram-Schmidt Adaptive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMap-projected low-incidence angle basemap reduced data record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMercury Dual Imaging System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMap Projected Multispectral Reduced Data Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMESSENGER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMErcury Surface, Space ENvironment, GEochemistry, and Ranging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultispectral data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNarrow-Angle Camera\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epanchromatic data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePANCO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePAnsharpening and CO-Registration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCn\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSNR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eB Peak Signal-to-Noise Ratio with blocking factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRGB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed, Green, Blue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpectral Angle Mapper\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esignal-to-noise ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural Similarity Index Measure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversal Image Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWide-Angle Camera\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eThe authors\u003c/span\u003e \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003emust\u003c/span\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eprovide the following sections under the heading \u0026ldquo;Declarations\u0026rdquo;.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eNo applicable competing interests have been identified.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study has been supported by the Italian Space Agency (ASI-INAF agreement no. 2024-40-HH.0) and INAF (INAF MiniGrant \u0026ldquo;Combined implementation of CaSSIS and HiRISE data through pansharpening experiments\u0026rdquo; - CUP C93C23008430001).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eAdriano Tullo: Conceptualization, Methodology, Software, Validation, Writing, Data Curation\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe study has been supported by the Italian Space Agency (ASI-INAF agreement no. 2024-40-HH.0) and INAF (INAF MiniGrant \u0026ldquo;Combined implementation of CaSSIS and HiRISE data through pansharpening experiments\u0026rdquo; - CUP C93C23008430001).\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eBoth the software and the dataset (original and processed) are available open source. More details and links in the Data availability paragraph.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBecker KJ, Robinson MS, Becker TL, Weller LA, Turner S, Nguyen L, Selby C, Denevi BW, Murchie SL, McNutt RL, Solomon SC (2009) Near Global Mosaic of Mercury. Presented at the AGU Fall Meeting Abstracts, pp. P21A-1189\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedini PD (2017) MESSENGER Mission Overview. Johns Hopkins APL Tech Digest 34(1):5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBickel VT, Munaretto G, Bertoli S, Cremonese G, Cambianica P, Vergara Sassarini NA (2026) Slope Lineae as Potential Indicators of Recent Volatile Loss on Mercury. 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AVIRIS\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mercury, Pansharpening, MESSENGER, Gram-Schmidt Adaptive, Multispectral, Global basemap","lastPublishedDoi":"10.21203/rs.3.rs-8661573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8661573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission delivered multispectral global image mosaics of Mercury representing the reference for the geomorphological and spectroscopic characterization of the hermean surface at regional to local (i.e., hundreds of meters) scales. However, these global products inherit the spatially inconsistent and significantly low spatial resolution of the global MDIS datasets due to the MESSENGER spacecraft orbit, especially in the southern hemisphere. This creates a critical resolution gap compared to the higher-resolution monochromatic data. This study addresses this limitation by applying the Gram\u0026ndash;Schmidt Adaptive (GSA) pansharpening technique to enhance the spatial resolution of global multispectral mosaics. The method has been used to process two key datasets, the Map Projected Multispectral Reduced Data Record mosaic and the Basemap Enhanced Color Global Mosaic, successfully increasing their resolution fourfold, from 665 m/px to a globally consistent 166 m/px.\u003c/p\u003e \u003cp\u003eObjective validation confirms that this enhancement achieved high colour fidelity and introduced minimal negligible alterations to the spectral components of the images. The resulting high-resolution datasets reveal the spectroscopic properties of numerous previously unresolved, smaller-scale surface features, such as structural features, hollows, slope lineae, and volcanic vents, essential for detailed geological interpretation.\u003c/p\u003e \u003cp\u003eThis work provides a robust new reference dataset for comprehensive global studies of Mercury's surface composition and geology, while also demonstrating a method suitable for handling large planetary datasets and preparing for the exploitation of future data from the BepiColombo mission.\u003c/p\u003e","manuscriptTitle":"Enhanced Global Messenger Multispectral Mosaics using GSA Pansharpening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 09:12:30","doi":"10.21203/rs.3.rs-8661573/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2026-03-02T13:08:56+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-02-18T14:55:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T09:34:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T09:55:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth, Planets and Space","date":"2026-01-21T10:34:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f1469f9-bf90-4044-9b66-63a5cf7a5f51","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T09:30:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 09:12:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8661573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8661573","identity":"rs-8661573","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}