EnMAP, the Environmental Mapping and Analysis Program, is the spaceborne German hyperspectral satellite mission. On behalf of the Geman Space Agency at DLR, the satellite is being developed by OHB system AG and the ground segment is being provided by DLR. Science lead institute is GFZ Potsdam.

As of writing, the satellite has now successfully finished its environmental test campaign and is being prepared to be launched in April 2022 with a Falcon 9 from Florida. In this talk, we give an overview on the current status of the mission, its capabilites and hopefully deliver first results of the satellite's first days in orbit. We will also give an outlook on the planning for the commissioning phase, the operational phase and collaborations.

The launch of the spaceborne imaging spectroscopy mission EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is scheduled for April 2022.

The presentation will detail the status and planning of EnMAP operations. The status covers on the one hand the realized system to perform operations and on the other hand the results of the Launch and Early Orbit Phase (LEOP) (0.5 months) and, in particular, the first insights of the Commissioning Phase (CP). The planning covers the complete activities of the CP (5.5 months) and the subsequent routine phase (54 months) with the provision of quantitative imaging spectroscopic measurements substantially improving remote sensing standard products and allowing advantageous user-driven information products to be established.

The objective of EnMAP is to measure, derive, and analyze quantitative diagnostic parameters describing key processes on the Earth’s surface focusing on issues related to soil and geology, agriculture, forestry, urban areas, aquatic systems, ecosystem transitions and associated science.

The spectral range of EnMAP covers 420 nm to 2450 nm based on a prism-based dual-spectrometer with a spectral sampling distance between 4.8 nm and 8.2 nm for the VNIR (Visible and Near Infrared; 450 nm to 1000 nm) and between 7.4 nm and 12.0 nm for the SWIR (Shortwave Infrared; 900 nm to 2450 nm). An on-board doped Spectralon sphere enables a spectral accuracy of better than 0.5 nm in VNIR and 1.0 nm in SWIR. The target signal-to-noise ratio (SNR) is 500:1 at 495 nm and 150:1 at 2200 nm (at reference radiance level representing 30% surface albedo, 30° Sun zenith angle, ground at sea level, and 40 km visibility with rural atmosphere). The signal is fed into two parallel amplifiers with different gains for each of the two detectors to have a large dynamic range. Sun calibration measurements with an on-board full-aperture diffuser enable a radiometric accuracy of better than 5%. Additional measurements, e.g. for non-linearity and closed shutter measurements for subtraction of dark signal, complement the calibration. Each detector array has 1000 valid pixels in spatial direction and, with a geometric resolution 30 m x 30 m, a swath width (across-track) of 30 km is realized. A swath length (along-track) of 5000 km, split to several observations, is reached per day. The repeat cycle of 398 revolutions in 27 days combined with an across-track tilt capability of 30° enables a target revisit time of less than 4 days. And each region is viewable under an out-of-nadir angle of at most 5°. The local time of descending node is 11:00.

The satellite, which is realized by OHB System AG, will be operated by the ground segment. DLR’s Earth Observation Center (EOC) together with the German Space Operations Center (GSOC) are responsible for operations. Mission management is covered by DLR’s Space Agency. Control and command of the satellite based on flight operations procedures using real-time and dumped data is performed via S-band ground stations for telemetry and telecommand data in Weilheim (Neustrelitz as backup) and in addition Inuvik, O’Higgins, and Svalbard for non-nominal operations. Proposals, observations, and associated research are presented by an interactive map supporting the establishment of a world-wide user network. In case of tasking conflicts, issued observations are prioritized not only according to static information like the underlying priority of the request, but also based on historical and predicted cloud coverage information taking satellite constraints such as power and storage into account. All information is incorporated into the mission timeline immediately on reception and feedback to users on planned observations is provided whenever the planning state of an observation changes. Required orbit maneuvers, for orbit maintenance and collision avoidance, and contacts with X-band ground stations for instrument data reception in Neustrelitz (Inuvik as backup) are also considered in the mission timeline and as such transmitted to the satellite during S-band passes. Together with orbit and attitude data of the satellite, image products from received instrument data during X-band passes are fully-automatically generated at three processing levels, and long-term archived in tiles of 30 km x 30 km. A catalogue allows users to search and browse all products based on the standardized protocols CSW (catalogue service for the web) and WMS (web mapping service). Because of the necessary various processing options, each product is specifically generated for each order and delivered using SFTP (secure file transfer protocol) to the scientists.

Level 1B products are corrected to Top-of-Atmosphere (TOA) radiances including defective pixel flagging, non-linearity correction, dark signal (and digital offset) correction, gain matching, straylight correction, radiometric/spectral referencing, radiometric calibration, and defective pixel interpolation. Level 1C products are orthorectified to a user selected map projection and resampling model. The physical sensor model is applied by the method of direct georeferencing with a correction of sensor interior orientation, satellite motion, light aberration and refraction, and terrain related distortions from raw imagery. The products have a geolocation accuracy of 30 m with respect to a reference image based on selected Sentinel-2 Level 1C products having an absolute geolocation accuracy of 12.5 m. Level 2A products are compensated for atmospheric effects with separate algorithms for land and water applications. For the land case the units are expressed as remote sensing, namely Bottom-of-Atmosphere (BOA), reflectances. For the water case the units are normalized water leaving remote sensing reflectance or subsurface irradiance reflectance based on user selection. A pixel classification (e.g. land-water-background, cloud) is performed and aerosol optical thickness, columnar water vapor, and adjacency corrections are treated accordingly. At all processing levels per-pixel quality information and rich metadata are appended online.

Offline quality control, e.g. based on pseudo invariant calibration sites (PICS), maintenance and fine tuning of the image processing chain, and calibrations of the instrument during operations complete the ground segment. The independent validation of products, e.g. based on already established calibration/validation procedures, sites, networks, and products of other missions, is performed by the science segment led by the GFZ German Research Centre for Geosciences. All elements of the mission are characterized and calibrated, technically verified and operationally validated until launch.

EnMAP will be launched by a Falcon 9 of SpaceX from Florida, USA. The subsequent LEOP covers the first contact with satellite after separation, setting up telemetry and telecommand communications, continuous monitoring of health status, checkout and configuration of all platform functions, e.g. achievement of nominal power and thermal configuration, activation and calibration of sensors and actuators, e.g. for attitude and orbit determination and control, and acquisition of required orbital parameters. The subsequent CP covers the activation of the instrument data storage and all payload functions including the first image acquisition, downlink, and processing. The focus is first on radiometric, second on spectral in-flight calibration using all on-board equipment and taking pre-flight characterization and calibration into account, and in parallel on geometric characterization using Earth observations. The results lead to the optimization of the radiometric and geometric processors at first and then to the atmospheric processors. These activities are iterated and complemented by continuous instrument monitoring and quality control. Finally, based on end-to-end experiences the user interfaces from observation planning to product delivery and the complete processing chains are fine-tuned. The objective of the CP is to put space and ground segment into nominal routine operations with detailed in-orbit performance analyses of on-board and on-ground functionalities resulting in product approval for users which is expected for October 2022. The subsequent routine phase keeps the mission working in nominal operations based on ground procedures and by appropriately handling non-nominal operations, if required. All elements are supervised, the satellite is kept in the required orbit, data are acquired and dumped according to the requests and following the planned mission timeline, and products are processed and delivered to users. The offered operational services are complemented by a service team offering expert advice on the exploitation of EnMAP.

EnMAP operations are planned to be continued until April 2027.

We present the measurement concepts and results of the pre-flight characterization and calibration measurements of the EnMAP HyperSpectral Imager (HSI). The final measurement campaign of the instrument was performed in 2020 prior to the integration of the instrument onto the platform. Based on specific design of the HSI the measurement concept was designed to be performed in air with subsequent corrections applied for in vacuum performance estimation. The measurement concepts and setups are demonstrated to be of excellent quality with the obtained accuracies exceeding the required values significantly. Thus, the choice of ambient conditions for calibration and the design of the optical setups and equipment are confirmed. We show that the as built HSI exceeds the expectations in almost all important parameters such as SNR, NEdL, MTF and distortions indicating that we can expect very good data quality in flight. Calibration coefficients for radiometry, geometry and spectral registration were obtained at high quality allowing for a very good reference point for the initial in orbit calibrations during the commissioning phase. The respective on-board devices and concepts for spectral (spectral signature-based multipoint registration) and radiometric calibration (full aperture diffuser) have been validated and characterized and will allow to transfer and maintain the calibration of the HSI to the final mission environment. With a planned launch date for EnMAP in April 2022 we hope to be able to present first light from the mission to confirm the on-ground results.

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.

The quantification of agricultural traits is essential to evaluate seasonal dynamics of crops, in particular to assess the efficiency of management measures or the effects of changing climatic conditions. Spaceborne imaging spectroscopy data from recently launched or upcoming missions allows the spatiotemporally-explicit monitoring of crop traits, such as leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter content (Cm), leaf carotenoid content (Cxc) or leaf water content (Cw), as well as upscaled canopy-level variables and average leaf inclination angle (ALIA). The unprecedented high-dimensional data streams call for efficient, fast and easy-to-use evaluation tools to obtain key agricultural information over large and heterogeneous cultivated areas. In this respect, the Agricultural Applications (Agri-Apps) have been developed within the freely available EnMAP-box 3.9, which is provided within in the framework of the German Environmental Mapping Analysis Program (EnMAP) mission. The Agri-Apps specifically provide three main retrieval tools, which employ different estimation strategies: (1) empirical algorithms based on parametric regressions, such as the Analyze Spectral Integral (ASI), (2) physically based models, using radiative transfer models (RTM), such as the Plant Water Retrieval (PWR) tool, and (3) hybrid tools, which combine machine learning (ML) regression algorithms with RTMs, such as the hybrid ANN tool. The ASI tool (1) computes integral indices from continuum removed spectra and combines these into three-band rasters of Cxc, Cab and Cw. These three different leaf biochemical constituents then are quantified applying specific calibration functions and are displayed as RGB-images. The PWR tool (2) uses an efficient algorithm to quantitatively extract canopy water content information directly from single hyperspectral signatures or from spectroscopic images applying the Beer-Lambert law. The hybrid ANN tool (3) uses an artificial neural network (ANN) algorithm, which is trained over a simulated data base generated by the PROSAIL model.
The objective of the present study was to test the three tools regarding their suitability, retrieval performance and practical applicability for the upcoming EnMAP mission. To obtain reliable product evaluations, in situ data of crop traits (winter wheat, winter barley and corn) were collected at the Munich-North-Isar (MNI) and Irlbach test sites in Bavaria, Germany, during the growing seasons of 2017, 2018, 2020 and 2021. To demonstrate the dynamics of the crop growth cycle, a series of crop trait simulations was generated with the ASI, PWR and the hybrid ANN tools over both test sites. Thereby, none of the tools was specifically calibrated for the sites, so that the transferability and general applicability of the retrieval algorithms could also be evaluated. Accuracy of the algorithms was evaluated against in situ reference data, leading to best root mean square errors (RMSE) of 8 µg/cm² for Cab and RMSE = 1.0 m²/m² for LAI retrieval (hybrid ANN tool), RMSE = 0.03 cm for Cw using the PWR tool, and RMSE = 0.01 cm for canopy Cw using the ASI tool. Though the latter failed for Cm and Cab retrievals, most of the results suggest a good to very high retrieval performance. In summary, the hybrid ANN tool outperformed the two others which may be attributed to its property to combine physics-awareness with the capabilities of learning algorithms. Second, mapping capabilities of the three tools are demonstrated on a time-series from 2021 consisting of five images from the spaceborne DLR Earth Sensing Imaging Spectrometer (DESIS), of three acquisitions by the PRecursore IperSpettrale della Missione Applicativa) (PRISMA) of the Italian Space Agency (ASI) and of one airborne Acquisition by the The Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS_NG). By combining data from all three instruments, it was possible to compile one of the first adequately dense time-series of (predominantly) spaceborne hyperspectral scenes over agricultural land. Mapping provided high fidelity of the tools. The hybrid ANN tool is specifically demonstrated on the AVIRIS-NG scene acquired in the context of the ESA CHIME campaigns 2021 over the Irlbach site. Figure 1 shows the resulting maps over the agricultural area on 30 May, 2021. In general, the map shows plausible estimates for the observed phase of the season, with crop growth patterns well reflected by the estimated traits. The intra-field distributions are relatively narrow and spatially consistent, which can be seen as an indirect measure for the accuracy of the retrieval tool. Overall, our results show that the Agri-Apps of the EnMAP-Box and in particular the hybrid ANN tool will be suited to efficiently process data from the EnMAP satellite mission in a quick and automated, user-friendly, transferable and generally applicable way. The Agri-Apps of the EnMAP-Box are ready to provide highly relevant agricultural products from spaceborne hyperspectral data as soon as EnMAP data becomes available from 2022 onwards.

Figure 1: Estimates of LAI, Cm, Cab and ALIA from AVIRIS-NG airborne data using the hybrid ANN tool of the EnMAP-Box Agri-Apps, Irlbach test site, Germany.

The German Environmental Mapping and Analysis Program (EnMAP) is an imaging spectroscopy satellite mission aiming at monitoring and characterizing the Earth’s environment on a global scale. As part of the EnMAP mission preparation the EnMAP-Box 3 has been developed as a free and open source (FOSS) plug-in for QGIS that is designed to process imaging spectroscopy data in a GIS environment. The main development goals are (i) advanced processing of hyperspectral remote sensing data by offering state-of-the-art applications and (ii) enhanced visualization and exploration of multi-band remote sensing data and spectral libraries in a GIS environment. Therefore, the algorithms provided in the EnMAP-Box 3 will also be of high value for other multi- and hyperspectral EO missions. The Python-based plug-in bridges and combines efficiently all advantages of QGIS (e.g. for visualization, vector data processing), packages like GDAL (for data IO or working with virtual raster files) and abundant libraries for Python (e.g. scikit-learn for EO data classification and or PyQtGraph for fast and interactive chart drawing). It consists of a (i) graphical user interface (GUI) for hyperspectral data visualization and spectral library management, (ii) a set of advanced general and application-oriented algorithms, and (iii) a high-level application programming interface (EnMAP API). The EnMAP-Box can be started from QGIS or stand-alone, and is registered in the QGIS plug-in repository.
The EnMAP-Box is a QGIS plugin with a separate GUI. Typical tasks like raster or vector file management and layer styling follow the same principles and look-and-feel, known from QGIS. They are extended by more specific requirements for imaging spectroscopy data such as spectral library integration. In contrast to QGIS and other GIS software that have a single map view, the EnMAP-Box follows the concept of having multiple, linkable views on the data sources at the same time next to each other. Currently, we offer map views for visualizing raster and vector data as well as spectral views for visualizing image pixel profiles and existing spectral libraries entries, for building new spectral libraries, or for performing spectral processing with spectra. Views can interact in various ways with each other, for example (i) the spatial position and the scale of map views can be synchronized, (ii) the selected image pixel profile of a map view can be visualized in a spectral view together with other image and library spectra, or (iii) the location of previously collected image profiles can be visualized as a point layer inside a map view. Besides such data management and visualization functionality, the EnMAP-Box provides the typical yet often more elaborated interactive tools for data exploration and preparation. Here, easy-to-use workflows for machine learning classification and regression and a raster image calculator, which offers the full flexibility of NumPy operations, shall be mentioned. Moreover, domain specific interactive applications (for agriculture, geology, soil or forest science and hydrology) have been contributed by several project partners as part of the mission preparation activities. These include e.g. radiative transfer models for forward/backward simulation of leaf and vegetation canopy reflectance, suites for analyzing soil and geology spectra, or for mapping water constituents.
EnMAP-Box algorithms are developed based on the QGIS processing framework and can be used from the GUI, stand-alone Python scripts and the command line. The EnMAP-Box API enables convenient raster data IO for memory efficient block-wise image processing. The available algorithms can be used inside the QGIS Model Designer to build complex workflows covering i) machine learning (i.e image classification, regression and unmixing) and accuracy assessment, ii) various spectral resampling options, iii) spatial and spectral filtering and more.
In summary, the EnMAP-Box 3 is a powerful FOSS plugin for QGIS with a long list of available image processing and analysis tools and applications. Its full strength will be needed, when more spaceborne imaging spectroscopy data becomes available over the coming years. The Toolbox is constantly improved and extended and is a key element of the HYPERedu learning platform for imaging spectroscopy.
We present the concept of the EnMAP-Box 3 together with two typical application examples that highlight its user-friendly yet powerful algorithms.