Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California—a mining area known to host bastnaesite-Ce ore in sövite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth’s surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.

Porphyry copper deposits are associated with large alteration footprints, and alteration mapping plays a key role in the exploration of these deposits. Imaging spectroscopy is commonly deployed for exploration targeting, yet it has rarely been used to map deposit-scale alteration patterns before initiating drilling. To close this gap, the Shadan porphyry Cu-Au deposit was thoroughly studied using the HyMap hyperspectral data (visible near-infrared–short-wave infrared) at 5-m resolution corroborated by rock geochemistry, magnetometry, and laboratory spectroscopy. Shadan is a well-exposed deposit with near-perfect zonation located in the volcanic belts of eastern Iran containing >135 Mt of ore at 0.3% Cu and 0.4 g/t Au. Thirteen minerals, including white mica, Al smectite, kaolinite, ferric/ferrous minerals, biotite, actinolite, epidote, chlorite, tourmaline, and jarosite, were mapped by applying the multifeature extraction methodology. The propylitic zone was partitioned into actinolite, epidote, and chlorite subfacies. The compositions of biotite and white mica were observed to become Fe and Al rich, respectively, toward the mineralized zones. The chemistry of actinolite was observed to change from Fe to Mg rich inward, providing a new vectoring tool for porphyry copper exploration. The study provided significant information about fluid-rock interactions and the chemistry of the circulating fluids including the oxidation-reduction states and acidity. By integrating the mineral maps with other data sets using the fuzzy logic method, the promising (ore) zones were identified and used to plan the next-stage drilling. This work demonstrated that imaging spectroscopy can be effectively used to better understand porphyry systems and provide deposit-scale vectors toward the mineralized centers, facilitating drilling.

Hyperspectral imaging technology holds great potential for various stages of the mining life cycle, both in active and abandoned mines, from exploration to reclamation. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite imagery yields high spectral resolution, a high signal-to-noise ratio (SNR), and global availability with breakthrough systems like EnMAP, EMIT, GaoFen-5, PRISMA, and Tanager-1, limited spatial and temporal resolution poses challenges for the mining sectors, which require decimetre-to-centimetre-scale spatial resolution for applications such as reconciliation and environmental monitoring and daily temporal revisit times, such as for ore/waste estimates and geotechnical assessments. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high-spatial-resolution data relevant to the pit/mine scale, with the capability for frequent, user-defined re-visit times for areas of limited extent. Areas of interest can be defined by the user and targeted explicitly. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers the detection of different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. This is related to but not exclusive to detecting deleterious minerals for different processes (e.g., clays, iron oxides, talc), secondary iron oxides indicating the leakage of acid mine drainage for rehabilitation efforts, swelling clays potentially affecting rock integrity and stability, and alteration minerals used to vector toward economic mineralisation (e.g., dickite, jarosite, alunite). In this paper, we review applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging datasets in or appropriate to the mining sector, with a particular focus on hyperspectral VNIR-SWIR UASs. Complementarily, we draw on previous insights from airborne, satellite, and ground-based imaging systems. We also discuss common practises for UAS survey planning and ground sampling considerations to aid in data interpretation.