The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission designed to characterise and monitor the Earth’s environment and its changes. The EnMAP satellite was launched on April 1st, 2022 and has been in the operational phase since November of that year. This presentation provides an update on the mission status two years after launch, and on the activities of the science segment, including data quality and mission performance, new developments in open source algorithms and educational tools (EnMAP-Box, HYPERedu), current status of EnMAP's background and foreground missions, as well as selected demonstration examples of geo- and bio-EnMAP products. This reflects the scientific achievements for various cutting-edge and environmental applications that are of benefit to society today.

Methane mitigation from anthropogenic sources such as in the production and transport of fossil fuels has been found as one of the most promising strategies to curb global warming in the near future. Satellite-based imaging spectrometers have demonstrated to be well-suited to detect and quantify these emissions at high spatial resolution, which allows the attribution of plumes to sources. The PRISMA satellite mission (ASI, Italy) has been successfully used for this application and the recently-launched EnMAP mission (DLR/GFZ, Germany) presents similar spatial and spectral characteristics (30 m spatial resolution, 30 km swath, about 8 nm spectral sampling at 2300 nm). In this work, we investigate the potential and limitations of EnMAP for methane remote sensing, using PRISMA as a benchmark to deduce its added-value. We analyze the spectral and radiometric performance of EnMAP in the 2300 nm region used for methane retrievals acquired using the matched-filter method. Our results show that in arid areas, EnMAP spectral resolution is about 2.7 nm finer and the signal-to-noise-ratio values are approximately twice as large, which leads to an improvement in retrieval performance. Several EnMAP examples of plumes from different sources around the world with flux rate values ranging from 1 to 20 t/h are illustrated. We show plumes from sectors such as onshore oil and gas and coal mining, but also from more challenging sectors such as landfills and offshore oil and gas. We detect two plumes in a close-to-sunglint configuration dataset with unprecedented flux rates of about 1 t/h, which suggests that the detection limit in offshore areas can be considerably lower under favorable conditions.

Spaceborne imaging spectroscopy is undergoing a rapid expansion with a new generation of missions in recent years. Following the Hyperion (2000) and HICO (2009) missions, new spaceborne imaging spectroscopy missions have recently started operating: DESIS (2018), PRISMA (2019), HISUI (2019) and more recently EnMAP (2022) and EMIT (2022). These missions face the common challenge of providing accurate spectral and radiometric results over a wide spectral range. This requires accurate instrument calibration and the validation of the results obtained. In this contribution, we provide an overview of the calibration and validation (CalVal) activities in the EnMAP mission, and we present the CalVal results that were obtained as part of the commissioning phase (April - October 2022).

The hyperspectral EnMAP (Environmental Mapping and Analysis Program) satellite was successfully launched in April 2022, passed its commissioning phase, and entered the nominal phase of operational data acquisition in November 2022. Since then, users may submit data acquisition proposals and download the data in three processing levels: Level-1B (radiometrically-corrected and spectrally-characterized top-of-atmosphere (TOA) radiance), Level-1C (geometrically-corrected L1B data), and Level-2A (atmospherically-corrected Level-1C data, i.e., bottom-of-atmosphere (BOA) reflectance). The official product generation is usually done by the ground segment processing chain. Alternatively, the EnMAP processing tool (EnPT) provides a highly customizable free and open-source pre-processing chain enabling additional functionalities and options to fulfill individual user requirements and quality expectations. Here, we provide an overview of the implemented pre-processing chain and its modular design with a specific focus on the additional functionalities of EnPT to obtain highly accurate and customizable hyperspectral EnMAP Level-2A data.

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. The mission is now ready to start with the sensor being by end of 2021 in Flight Acceptance Review, ready to be shipped to the launch pad in early 2022. This paper presents first an update of the mission status with recent activities and developments from the space and the ground segment. Then, an update of selected highlights of the science segment activities at launch phase are presented including preparation and if possible early results for the validation of EnMAP products, updates on EnMAP science algorithms (EnMAP-Box) developed at GFZ, online education initiative (HYPERedu), and further mission support activities such as background mission.

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. After several years delay, the instrument is finished and in the final stage of environmental characterization and assembly for a launch early 2022. This paper presents an update of the mission status and activities in the frame of the science preparation and mission support project led by the German Research Center for Geosciences (GFZ) Potsdam. Further, this paper presents a specific focus on the planning for the independent EnMAP data product validation.

Combining airborne LiDAR (ALS) and hyperspectral data refers to utilize the LiDAR based Digital Elevation Model (DEM) and the spectral information of the hyperspectral imaging (HSI) sensor. The separation of both discretized data entities leads to a substantial loss of information and does not exhaust the full capabilities of the contrasting sensors. A physically based in-flight fusion of HSI and ALS sensor characteristics is presented. Based on their respective intensity information overlaps, ray tracing and radiative transfer procedures utilize geometric and radiometric synergies. In a first step a rigorous parametric co-alignment procedure is realized using an automated and adjustable tie point detection algorithm. It ensures sub-pixel co-alignment of the contrasting sensors. In a second step we present a rigorous illumination correction of HSI data based on the radiometric cross-calibrated return intensity information of ALS data. This radiometric fusion corrects cloud and cast shadowing effects, across track illumination, partly anisotropy effects and illumination changes over time for the entire HSI wavelength domain. The presented fundamental fusion of the passive and active sensor characteristics is aimed at improving and developing the complete, sensor inherent data density to ensure highest spectral and geometric information content for a variety of applications.

The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimensionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications.

Hyperspectral rare earth elements detection in space borne and near-field acquired images becomes more and more important for global exploration. In comparison to classic exploration methods, the benefit of hyperspectral surveys is the fast and in-situ generation of spatial information. Current hyperspectral investigations do more and more include rare earth element mappings - one new tool for hyperspectral rare earth mapping is the REEMAP algorithm. So far it is trained for five rare earth elements (erbium, dysprosium, holmium, neodymium and thulium). Previous versions of REEMAP did not map samarium. The here presented study focusses on the extension of REEMAP to identify samarium and presents a detailed mapping of the samarium and dysprosium occurrences of a two-carbonatite units containing outcrop (rauhaugites - dolomitic carbonatites and rødbergites - hematitic carbonatites) at Fen Complex, Norway. Four absorption bands of samarium were scrutinized for their shape characteristics in order to extend REEMAP for the detection of samarium. REEMAP was extended with these newly defined filter parameters. The mapping result for the investigated outcrop show that two absorption bands proved to be robust enough to be used in the REEMAP algorithm. The two remaining absorption bands are superimposed by H2O absorptions and are therefore not recommended for space borne or near-field hyperspectral analyses. However, the resulting samarium map shows the two-rock units represented by different samarium concentration levels and revealed a gradual increase of samarium towards the top of the rauhaugites rock unit. This study shows that REEMAP can be trained for the detection of samarium, especially for two of the investigated absorption bands (1250 and 1567 nm), and that REEMAP helps for in-situ interpretations of REE ore distributions.

Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration.