The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping.

A majority of hyperspectral super-resolution methods aim to enhance the spatial resolution of hyperspectral imaging data (HSI) by integrating high-resolution multispectral imaging data (MSI), leveraging rich spectral information for various geospatial applications. Key challenges include spectral distortions from high-frequency spatial data, high computational complexity, and limited training data, particularly for new-generation sensors with unique noise patterns. In this contribution, we propose a novel parallel patch-wise sparse residual learning (P²SR) algorithm for resolution enhancement based on fusion of HSI and MSI. The proposed method uses multi-decomposition techniques (i.e., Independent component analysis, Non-negative matrix factorization, and 3D wavelet transforms) to extract spatial and spectral features to form a sparse dictionary. The spectral and spatial characteristics of the scene encoded in the dictionary enable reconstruction through a first-order optimization algorithm to ensure an efficient sparse representation. The final spatially enhanced HSI is reconstructed by combining the learned features from low-resolution HSI and applying an MSI-regulated guided filter to enhance spatial fidelity while minimizing artifacts. P²SR is deployable on a high-performance computing (HPC) system with parallel processing, ensuring scalability and computational efficiency for large HSI datasets. Extensive evaluations on three diverse study sites demonstrate that P²SR consistently outperforms traditional and state-of-the-art (SOA) methods in both quantitative metrics and qualitative spatial assessments. Specifically, P²SR achieved the best average PSNR (25.2100) and SAM (12.4542) scores, indicating superior spatio-spectral reconstruction contributing to sharper spatial features, reduced mixed pixels, and enhanced geological features. P²SR also achieved the best average ERGAS (8.9295) and Q2n (0.5156), which suggests better overall fidelity across all bands and perceptual accuracy with the least spectral distortions. Importantly, we show that P²SR preserves critical spectral signatures, such as Fe²⁺ absorption, and improves the detection of fine-scale environmental and geological structures. P²SR’s ability to maintain spectral fidelity while enhancing spatial detail makes it a powerful tool for high-precision remote sensing applications, including mineral mapping, land-use analysis, and environmental monitoring.

In this contribution, we propose a novel parallel patch-wise sparse residual learning (P2SR) algorithm for resolution enhancement based on fusion of hyperspectral imaging data (HSI) and multispectral imaging data (MSI). The proposed method uses multi-decomposition techniques to extract spatial and spectral features to form a sparse dictionary. The spectral and spatial characteristics of the scene encoded as a dictionary enables reconstruction through a first order optimization algorithm to ensure an efficient sparse representation. The final spatially enhanced HSI is reconstructed using sparse-dictionary features from low resolution HSI and applying a MSI regulated guided filter to enhance spatial fidelity while minimizing artifacts. P2SR is deployable on a high-performance computing (HPC) system with parallel processing, ensuring scalability and computational efficiency for large HSI datasets. Extensive evaluations on three diverse study sites demonstrate that P2SR consistently outperforms traditional and state-of-the-art (SOA) methods in both quantitative metrics and qualitative spatial assessments. P2SR displays superior spatio-spectral reconstruction contributing to sharper spatial features, reduced mixed pixels, and enhanced geological features. Importantly, we show that P2SR preserves critical spectral signatures such as Fe²⁺ absorption and improves the detection of fine-scale environmental and geological structures. P2SR’s ability to maintain spectral fidelity while enhancing spatial detail makes it a powerful tool for high-precision remote sensing applications, including mineral mapping, land-use analysis, and environmental monitoring.

Mining generates more than 14 billion tonnes of waste each year, which must be safely stored and managed over decades. In circular-economy frameworks, mine waste is increasingly recognised not only as an environmental liability but also as a potential secondary resource, and industrial-scale valorisation initiatives are already being implemented by major mining companies and specialised start-ups. Using mining waste for CO2 sequestration presents a particularly promising valorisation approach, as it promotes both the “zero waste” as well as the “net zero CO2” principles. In this contribution, we explore integrated Earth-observation (EO) workflows to support the assessment of CO2 sequestration potential at mine waste sites through two complementary pathways: (i) passive mineral carbonation, where atmospheric CO2 is bound through natural weathering of reactive waste materials, and (ii) carbon sequestration through revegetation and improved stewardship of post-mining landscapes. Mafic and ultramafic waste materials rich in Mg-Fe-Ca-bearing silicates, including olivine, serpentine, pyroxenes, amphiboles and smectites, exhibit high carbonation potential and occur widely in waste derived from mining of e.g. asbestos, diamonds, Ni-Cr, PGM, and Pb-Zn ores. Despite their abundance in active and legacy waste facilities, identifying and quantifying these minerals at scale remains challenging, as mine waste is intrinsically heterogeneous and existing mine-waste inventories rarely include spatially explicit mineralogical information. Using resolution-enhanced satellite hyperspectral data, we derive spatially continuous maps of reactive mineral assemblages in mine waste deposits through band-ratio analysis, minimum-wavelength mapping, and spectral unmixing based on dedicated mineral libraries. These products support the screening, targeting, and design of mineral-based CO₂ sequestration strategies such as enhanced weathering or ex-situ carbonation. In parallel, we monitor revegetation dynamics on mine waste deposits as a proxy for vegetation-based carbon sequestration. Reclamation commonly involves the addition of topsoil or the construction of technosols from waste materials to enable plant growth and soil development. However, revegetation success is often spatially variable due to hydrology, nutrient limitations, toxicity, and surface instability. We apply decomposition and trend analysis to long-term Sentinel-2 and Landsat vegetation-index time series to quantify revegetation trajectories across multiple closed mine sites. This approach reveals fine-scale patterns in rehabilitation success, identifies erosion-affected or delayed-recovery zones and provides objective indicators for optimising reclamation strategies. Where available, LiDAR and hyperspectral data are integrated to characterise vertical vegetation structure and species composition, providing a basis for above-ground biomass estimation, for carbon accounting and crediting assessments.The presented EO-based monitoring framework, developed within the EU-funded MOSMIN project, enables consistent comparison, prioritisation, and tracking of CO2 sequestration potential across heterogeneous waste deposits, supporting more sustainable land-use and waste-management strategies.

Rare Earth Elements (REEs) have become critical for global technological advancements and, consequently, economic growth. Ensuring supply requires significant future exploration, potentially including the use of space-borne hyperspectral data for direct mapping of REEs. While space-borne detection of REEs has been demonstrated (e.g., Asadzadeh et al., 2024), this approach has limited application. Low concentrations of these valuable resources in most carbonatite host rocks and small sizes of ore zones represent a major hurdle and complicate reliable detection and mapping efforts. We propose a comprehensive approach to remotely characterise carbonatites, which are known to host REEs, with the aim of improving our overall understanding of these unusual rocks and better identifying potentially fertile systems. Carbonatites are typically classified into three types: calcio-carbonatites, magnesio-carbonatites, and ferro-carbonatites. However, recent studies, such as Mitchell & Gittins (2022), suggest additional variants that don't fit these categories, indicating the current classification system may require further refinement. Regardless of classification complexities, the composite mineralogical phases of carbonatites are spectrally active and exhibit distinctive absorption features in hyperspectral data. Furthermore, the presence of alteration halos and the structural controls commonly associated with carbonatite structures make these sites well-suited for optical remote sensing studies by both hyperspectral and multispectral datasets. This paves the way for the development of a global carbonatite atlas based on remote sensing data.We demonstrate the feasibility of the approach using two REE-bearing carbonatite complexes in Namibia—Lofdal and Marinkas-Quellen. We selected EnMAP provided by the German Aerospace Center (DLR) hyperspectral data as they are the most accurate to this date (Chakraborty et,al., 2024). We employed different processing techniques such as minimum wavelength mapping and spectral abundance analysis to map the carbonatite lithologies in each of the two sites individually. We then streamlined the workflow to identify common parameters and trained a decision tree to map the broader carbonatite footprints across both sites. In parallel, Sentinel-2 multispectral data was used to map geological structures (e.g., dykes, faults, and bedding) aiming to understand controls on carbonatite emplacement. A fusion-based resolution enhancement algorithm was also applied to integrate EnMAP with Sentinel-2 data, providing a more spatially detailed understanding of the targets. We aim to expand this study to include a wider range of carbonatite complexes, with the goal of creating a global carbonatite atlas. By covering diverse geological settings and ages, this atlas will capture the full spectrum of mineralogical variation and structural features, enhancing our understanding of carbonatite bodies. This atlas not only will promote the applications of remote sensing techniques in carbonatite studies but also provide a valuable basis for future exploration of REEs in carbonatite settings. 1. Asadzadeh, S., Koellner, N., & Chabrillat, S. (2024). Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California. Scientific Reports2. Mitchell, R. H., & Gittins, J. (2022). Carbonatites and carbothermalites: A revised classification. Lithos3. Chakraborty, R., Rachdi, I., Thiele, S., Booysen, R., Kirsch, M., Lorenz, S., ... & Sebari, I. (2024). A Spectral and Spatial Comparison of Satellite-Based Hyperspectral Data for Geological Mapping. Remote Sensing