ALBERT

Description: Porphyry copper deposits represent very large differentiated hydrothermal alteration footprints resulting from convective fluid circulation. Hyperspectral imaging spectroscopy can provide a rapid and effective tool for systematic mapping of mineralogical footprints of many mineral systems including the porphyry copper deposits at local to regional scales. To demonstrate the capability of airborne hyperspectral imagery in deposit scale exploration, a portion of the HyMap data collected over a porphyry copper deposit named Shadan in eastern Iran was selected. By using a combination of spectral unmixing techniques and feature extraction methods, a large suite of alteration minerals was detected over this deposit. This includes white mica abundance, composition, and crystallinity, kaolinite abundance and crystallinity, ferric oxide content and composition, together with jarosite, chlorite-epidote, amphiboles, and tourmaline abundances and distribution. The results of this study indicate that airborne hyperspectral imagery and their mineral map products can be used to delineate porphyry copper systems and target the most promising areas for further exploration activities.

Description: The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP core themes are environmental changes, ecosystem responses to human activities, and management of natural resources. In 2021 major milestones were achieved in the sensor and satellite preparation which is by end-2021 in the final acceptance review and pre-launch phase, with a launch window opening April 2022 (Fischer et al., ESA LPS 2022). Accordingly, the mission science support shifted from science development to pre-launch and launch support. The EnMAP science preparation program has been run for more than a decade to support industrial and mission development, and scientific exploitation of the data by the user community. The program is led by the German Research Center for Geosciences (GFZ) Potsdam supported by several partners and is funded within the German Earth observation program by the DLR Space Agency with resources from the German Federal Ministry for Economic Affairs and Energy (BMWi). In 2020 a new 3+1-year project phase started during which specific activities are performed at the GFZ Potsdam together with the four project partners Humboldt-University (HU) Berlin, Alfred-Wegener Institute (AWI) Bremerhaven, Ludwig Maximilian University (LMU) Munich, and University Greifswald. These activities focus on the preparation for the scientific exploitation of the data by the user community as well as mission support during the commissioning phase and the start of the nominal phase, supported by the EnMAP Science Advisory Group. In this presentation, we aim at providing an update of the current science preparation activities performed at GFZ. This includes an update of the data product validation activities focusing on an independent validation of the EnMAP radiance and reflectance products. For smooth and efficient validation especially during the commissioning phase, a semi-automatic processing chain is being developed (EnVAL), which streamlines the validation sites and in-situ data management as well as the validation tasks and report generation. Also, an update on new resources in the online learning initiative HYPERedu will be presented. In particular, the first Massive Open Online Course (MOOC) on the basics of imaging spectroscopy titled ‘Beyond the Visible – Introduction to Hyperspectral Remote Sensing’ was successfully opened in November 2021. An update will be further provided on the status of algorithms included in the EnMAP-Box related to data pre-processing and derivation of geological and soil mapping. It includes the EnMAP processing tool (EnPT) that is developed as an alternative to the processing chain of the EnMAP ground segment and provides free and open-source features to process EnMAP Level-1B data to Level-2A bottom-of-atmosphere (BOA) reflectance, and the EnMAP geological Mapper (EnGeoMap) and Soil Mapper (EnSoMap) for users in bare Earth and Geosciences applications. Finally, a background mission plan is developed as mission internal to fully exploit the resources of the satellite in terms of functionalities and/or capacities when there are resources available after all user requests have been processed. It can be used to generate time series databases interesting for the user community and anticipate future user needs, or to prototype and validate new mission strategies, such as large mosaicking demonstrations and/or synergies with other hyperspectral missions.

Description: A physically-based image processing approach, based on a single-source surface energy balance framework, is developed here to model the land surface temperature (LST) over complex/rugged geologic terrains at medium to high spatial resolution (〈102 m). This approach combines atmospheric parameters with a bulk-layer soil model and remote-sensing-based parameterization schemes to simulate surface temperature over bare surfaces. The model’s inputs comprise a digital elevation model, surface temperature data, and a set of land surface parameters including albedo, emissivity, roughness length, thermal conductivity, soil porosity, and soil moisture content, which are adjusted for elevation, solar time, and moisture contents when necessary. High-quality weather data were acquired from a nearby weather station. By solving the energy balance, heat, and water flow equations per pixel and subsequently integrating the surface and subsurface energy fluxes over time, a model-simulated temperature map/dataset is generated. The resulting map can then be contrasted with concurrent remote sensing LST (typically nighttime) data aiming to remove the diurnal effects and constrain the contribution of the subsurface heating component. The model’s performance and sensitivity were assessed across two distinct test sites in China and Iran, using point-scale observational data and regional-scale ASTER imagery, respectively. The model, known as the Surface Kinetic Temperature Simulator (SkinTES), has direct applications in resource exploration and geological studies in arid to semi-arid regions of the world.

Description: The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

Description: 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.

Description: Petroleum accumulations may coincide with either positive or negative temperature anomalies, which are conventionally detected using in situ temperature measurements made in shallow boreholes 1-3 m deep. Data gathered in this way, however, can be sparse and costly, and may require intensive fieldwork over a long time period. This article explores the possibility of detecting thermal anomalies associated with petroleum entrapment using satellite-derived land surface temperature data. For this aim, a robust correction scheme based on a physically-based land surface model was applied to night-time kinetic temperature data derived from NASA's ASTER instrument. The numerical model, known as SKinTES, attempts to simulate diurnal effects and to remove them from the measured temperature data to yield a residual temperature anomaly map. The performance of this methodology was tested over the Alborz oilfield located on an anticline of the same name in the Qom region of Central Iran. The study area has an arid to semi-arid climate and the surface geology is dominated by outcrops of the Lower Miocene Upper Red Formation. The modelling approach used successfully highlighted several negative temperature anomalies over the oil-bearing parts of the Alborz structure. In comparison to the uncorrected data, the anomalies were shown to be highly enhanced in both spatial and magnitude terms. In addition, time series analysis indicated that the temperature anomalies were consistent over time. The authenticity of the anomalies was confirmed by a suite of in situ temperature measurements made at shallow boreholes. In conclusion, a unifying framework is proposed to explain the occurrence of both negative and positive temperature anomalies over petroleum accumulations. The new modelling and correction scheme is expected to broaden the application of remote sensing land surface temperature data not only in petroleum exploration but also in other types of geothermic investigations including geothermal exploration.

Description: Porosity is a significant property of soil, yet an elusive parameter to retrieve from remote sensing data. Here, an empirical relationship between soil water contents and infrared emissivity is employed for porosity mapping. The assumption is that by deriving the soil water content at saturation point, soil porosity could be indirectly measured. Through time series analysis of daytime ASTER thermal data acquired during the wet seasons, a porosity map was generated over a test site in the Qom area (Iran) and qualitatively verified using field observations. The approach was shown to yield reasonable results over exposed soil and lithologic outcrops. This approach can potentially provide an effective tool for topsoil porosity mapping at local to regional scales.