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.

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 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.

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.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. CHIME will globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. The mission shall provide Level 1B, 1C and 2A products, as well a set of downstream products related to the different environmental applications, such as the quantification of vegetation traits. In this context, this work presents the first hybrid retrieval models for the operational delivery of vegetation properties products. Within ESA's CHIME end-to-end (E2E) simulator study, 13 leaf and canopy trait models were developed as part of the L2B vegetation (L2BV) module. The E2E framework functions as a simulated reality that enabled to test and improve the algorithms. The models were further tuned and validated against campaign data using active learning methods. As a proof of concept, the prototype retrieval models were applied to both hyperspectral airborne (HyPlant) and spaceborne (PRISMA) imagery that were first resampled to CHIME band settings. Among the provided vegetation products, it led to a first space-based canopy nitrogen content map over a heterogeneous landscape. The obtained CHIME-like L2BV traits maps demonstrate the feasibility to routinely deliver a collection of next-generation vegetation products across the globe.

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. After several years delay due to a major design issue to meet the mission requirements, the mission is now back on track and planned for launch in 2021. This paper presents an update of the mission status with recent activities and developments from the space and the ground segment. Furthermore, a draft plan for the independent validation of EnMAP radiance and reflectance products was developed and will be introduced, along with highlights of the science preparatory activities in 2019 including airborne campaigns, algorithm consolidations, and HYPERedu education initiative.

EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. The data products will cover the spectral range from 420 nm to 2450 nm with a spectral sampling distance between 5 and 12 nm with an expected signal-to-noise-ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave infrared parts of the electro-magnetic spectrum. The resulting images will cover an area of 30 km in the across- track direction with a ground sampling distance of 30 m. The across-track tilt-capability of 30° enables revisit times of less than four days. The resulting data products will be freely available to the scientific user community for measuring, deriving, and analyzing diagnostic parameters, which describe vital processes on the Earth's surface comprising agriculture, forestry, soil and geological environments, as well as coastal zones and inland waters. This work concentrates on the description of activities performed and facilities involved for the preparation of these products. It starts out by the description of the User Portals for observation requests and acquisition planning, touches the aspects of creating the time-lines, the commanding and controlling of the satellite, the downlink of the telemetry and payload data, the design of the processing chain and the archiving of data plus a set of activities flanking the above for the provision of high-quality data products.

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 Environmental Mapping and Analysis Program (En-MAP) is an spaceborne imaging spectroscopy mission under development by a consortium of German Earth Observation research institutions. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to 4 days at Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide. In this talk we will provide an overview of the mission status including both technical developments and preparatory activities for the implementation of the EnMAP scientific program.

The German Environmental Mapping and Analysis Program (EnMAP) will provide hyperspectral spaceborne data to the global geoscientific community. Here we present EnGeoMAP the EnMAP Geological Mapper and EnSoMAP the EnMAP Soil Mapper which are two geomapping tool provided in the EnMAP Box. They offer geologists and soil scientists worldwide a unique toolset for the analysis of future EnMAP data. The structure, workflow and results of EnGeoMAP and EnSoMAP are shown and discussed together with a brief overview on further developments. Synergies between EnMAP and other sensors are furthermore demonstrated.

EnMAP (Environmental Mapping and Analysis Program) is a German spaceborne imaging spectrometer Earth observing mission planned for launch in 2019. This paper reflects the status of the mission with an focus to changes of the Ground Segment based on a major review conducted in 2016 and the EnMAP Data Exploitation and Application Development Program and recent activities.

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.

The Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill the current gap in space-based imaging spectroscopy data. An overview of the main characteristics and current status of the mission will be provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to 4 days at Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

The “High resolution temperature and spectral emissivity mapping” (HiTeSEM) initiative aims at developing a conceptual instrument design for a hyperspectral thermal satellite to find answers for the most pressing research and data requirements within the scope of Food Security and Human Health. The satellite is proposed to consist of two long-wave infrared (LWIR) sensors, (1) a hyperspectral system with ~ 75 bands at 7.2 - 12.5 μm (NEΔT of <; 0.05 K) and a ground sampling distance (GSD) of 60 m and (2) a panchromatic (PAN) LWIR high resolution imager with two bands (8.0 - 10.25 μm and 10.25 - 12.5 μm, NEΔT of ~0.06 K) but a three times higher GSD of 20 m to extend the system to regional applications where higher spatial accuracy is required. For an accurate water vapor content (CWV) estimation, which is needed for accurate atmospheric correction and temperature-emissivity separation (TES), three wavelengths within the range 7.2-7.3 μm are used. Based on the science case, key regions of interest were identified in India, Asia, Andes mountains, Mediterranean ecosystems and densely-populated as well as growing regions.

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.

This study reports the calibration and validation activities for the Environmental Mapping and Analysis Program (EnMAP; www.enmap.org). EnMAP is a German imaging spectroscopy satellite mission with the declared goal to investigate the Earth's surface with a so far surpassing quality. The key scientific questions to which EnMAP will contribute are related to climate change impacts, land cover changes and processes, natural resources, biodiversity and ecosystems, water availability and quality, geohazards and risk management. The satellite operates in a sun synchronous orbit in 650 km height with a local time of the descending node set to 11:00 and an across tilt opportunity to improve the local revisit time. Two pushbroom spectrometers with 242 channels in total cover the spectral range from 420 nm to 2450 nm with a mean resolution of 6.5 nm in the visible and 10 nm in the shortwave-infrared. The ground nadir pixel size is 30 m and 1000 spatial pixels generate a swath with of 30 km. For the CalVal activities, the routine calibration is conducted within the ground segment of DLR, while the independent validation activities are lead by GFZ. Data is operationally processed on-ground to standardized calibrated products and delivered to the international user community [1]. Standardized data products will comprise radiance and reflectance products that make use of calibration information gained pre- and inflight. To ensure high quality standards, additional independent product validation activities are planned.

In the upcoming years, many new remote sensing sensors will start operating in space. Sentinel-2 is certainly one of the most outstanding systems that will deliver a flood of detailed and continuous data from the Earth's surface during the next years. However, the heterogeneity of remote sensing data recorded using different sensors demands prelaunch activities to develop the synergies for efficient multisensor data analysis. In this context, accurate sensor simulations are a valuable tool that enables a meaningful intersensor comparison. This paper addresses the simulation of the future Sentinel-2 data and products. The presented Sentinel-2 end-to-end simulation (S2eteS) software models Sentinel-2 data acquisition, sensor calibration, and data preprocessing, which are strongly oriented on the real system. Several tests were performed to prove the software capability to generate accurate Sentinel-2 products, with regard to the quality of the radiance and reflectance products. As an example for a large variety of possible applications, the effects of unknown spectral band shifts, sensor noise, and radiometric accuracy on the accuracy of different Sentinel-2 vegetation indexes (VIs) were investigated. The software also holds the possibility to simulate other similar multispectral sensors because of its generic design.

In this paper we evaluate the impact of different soil algorithms and sensor characteristics for the quantitative determination of surface soil properties based on hyperspectral images. Two types of methodologies-physical analyses of spectral features and statistical multivariate procedures-implemented in different algorithms are considered against ground truth measurements. Parameters of interest are soil organic carbon (SOC), clay and iron oxide content. The soil algorithms were tested on hyperspectral imagery for different study sites based on two types of data sets: airborne data (GSD <;5m) and associated EnMAP satellite simulated data (GSD 30m). The study sites represent a wide range of soil types and properties associated with arid/semiarid through temperate environments in resp. southern and northern Europe. The results show that no simple best-practice rule can be observed that relates methodology used, with sensor resolution and key soil parameter of interest. Further studies must demonstrate for each sensor and each soil parameter which soil algorithm delivers the best performance.

This study investigates the use of AISA optical airborne hyperspectral (2 m spatial resolution) and simulated satellite EnMAP (30 m) to produce detailed lithologic maps in a subarctic region (Nunavik, Canada) where ultramafic rock units associated with Ni-Cu-(PGE) mineralization are exposed in the presence of lichen coatings. Spectral mixture analysis methods were used to first derive image endmembers and subsequently unmix the image pixels to generate abundance maps that can be used to map the geological materials of interest, specifcally mafic, ultramafic and gossan units. Results demonstrates the feasibility of both AISA and EnMAP to provide large scale reconnaissance mapping capability of geologic materials over vast subarctic and arctic regions where field access is limited.

EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2017. In order to ensure data product standards during the complete mission lifetime operational workflows are established. These cover all activities for pre- and in-flight spectral, radiometric, and geometric characterization and calibration as well as for the independent product validation of the quality controlled images. Spectral and radiometric calibration of the hyperspectral imager covering the wavelength range from 420 nm to 2450 nm is especially based on satellite onboard sources and a full aperture diffuser. Geometric calibration and validation is based on acquisitions of selected reference sites, but also compared to further ground-truth, air-, and spaceborne missions. Standardized products including geometric and/or atmospheric corrections are generated by a fully-automatic hyperspectral image processing chain.

The influence of plant and canopy architecture on canopy bidirectional reflectance and the bidirectional reflectance distribution function (BRDF) is the subject of this paper. To understand BRDF-influenced reflectance signals, this influence must be identified and quantified, which requires detailed knowledge concerning the structure and BRDF of the observed canopies. In situ BRDF measurements of canopies are time consuming and depend on the availability of a field goniometer. In contrast to field measurements, computer-based simulations of the canopy BRDF offer an alternative approach that considers parameter-driven setups of virtual canopies under constant illumination conditions. This paper presents the hyperspectral simulation of canopy reflectance (HySimCaR) system, which has been developed in the context of the EnMAP mission. This spectral, spatial, and temporal simulation system consists of detailed virtual 3-D cereal canopies of different phenological stages, whose geometries are linked to the corresponding spectral information. The system enables the simulation of realistic bidirectional reflectance spectra on the basis of virtual 3-D scenarios by incorporating any possible viewing position with ray-tracing techniques. The parameterization of a number of canopy structure parameters, such as phenological stage, row distance, and row orientation, enables the modeling of the bidirectional reflectance and, based on them, the approximation of the BRDF for many structurally different cereal canopies. HySimCaR has been validated with respect to structural and spectral accuracy using three cereal types, namely, wheat, rye, and barley, at 13 different phenological stages. The results show that the virtual cereal canopies are re-created in a realistic way, and it is possible to model their detailed canopy bidirectional reflectance and their BRDF using HySimCaR.

In this investigation we focused on the development of a new water detection algorithm based on VIS-NIR imaging spectroscopy data. By analyzing different images containing inland and ocean water types we found the slopes of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR spectral region to be diagnostic features for surface water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. This aspect has been considered in the development of a knowledge-based classifier. The results (probability of detection generally lies above 90%) indicate the great potential of the developed algorithm for surface water body detection and delineation within urban, rural and coastal scenes.

The sensor end-to-end simulation software EeteS has been developed within the framework of the Environmental Mapping and Analysis Program (EnMAP) mission. EeteS is capable of simulating EnMAP-like raw image scenes (L0) taking into account a variety of instrumental and environmental configurations. Furthermore, EeteS allows simulations of EnMAP reflectance images carrying out the complete L1 and L2 processing chains and necessary sensor calibration steps. The sequential processing chain of the simulator consists of several independent modules that are briefly described in this paper. Analysis of the intermediate and final EeteS simulation products has shown the accurate, reliable and consistent performance of the developed modules enabling the system to support technical decision-making processes required for the development of the EnMAP sensor. Besides the optimization of fundamental instrument parameters, data simulated by EeteS is already used for the development and evaluation of data pre-processing and scientific-exploitation algorithms.

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability.

The simulation of remote sensing images is a valuable tool for defining future Earth observation systems, optimizing instrument parameters, and developing and validating data-processing algorithms. A scene simulator for optical Earth observation data has been developed within the Environmental Mapping and Analysis Program (EnMAP) hyperspectral mission. It produces EnMAP-like data following a sequential processing approach consisting of five independent modules referred to as reflectance, atmospheric, spatial, spectral, and radiometric modules. From a modeling viewpoint, the spatial module is the most complex. The spatial simulation process considers the satellite-target geometry, which is adapted to the EnMAP orbit and operating characteristics, the instrument spatial response, and the sources of spatial nonuniformity (keystone, telescope distortion and smile, and detector coregistration). The spatial module of the EnMAP scene simulator is presented in this paper. The EnMAP spatial and geometric characteristics will be described, the simulation methodology will be presented in detail, and the capability of the EnMAP simulator will be shown by illustrative examples.

The simulation of remote-sensing images is a useful tool for a variety of tasks, such as the definition of future Earth Observation systems, the optimization of instrument specifications, and the development and validation of data processing algorithms. A scene simulator for optical hyperspectral and multispectral data has been implemented in the frame of the Environmental Mapping and Analysis Program (EnMAP) mission. EnMAP is a German-built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure in the 420-2450-nm spectral range at a varying spectral sampling of 6.5-10 nm. Images will cover 30 times 30 km areas at an approximate ground sampling distance of 30 m. The EnMAP scene simulator presented in this paper is able to generate realistic EnMAP-like data in an automatic way under a set of user-driven instrumental and scene parameters. Radiance and digital numbers data are generated by five sequential processing modules which are able to produce data over a range of natural environments, acquisition and illumination geometries, cloud covers, and instrument configurations. The latter include the simulation of data nonuniformity in the spatial and spectral domains, spatially coherent and noncoherent instrumental noise, and instrument's modulation transfer function. Realistic surface patterns for the simulated data are provided by existing remote-sensing data in different environments, from dry geological sites to green vegetation areas. A flexible radiative transfer simulation scheme enables the generation of different illumination, observation, and atmospheric conditions. The methodology applied to the complete scene simulation and some sample results are presented and analyzed in this paper.

We classified 922 urban biotopes from 11 different biotope types in a 50.6 km2study area in Berlin, Germany. As input advanced data products were derived from hyperspectral and simulated multispectral data. Urban surface materials were derived from the hyperspectral data by classification and linear spectral unmixing. Multispectral data was classified using four different per-pixel and object-oriented classifiers. The results show that our developed method for biotope classification works well with hyperspectral and with multispectral input data yielding comparable overall accuracies of 88.1 and 91.3 percent.

FLEX (FLuorescence EXperiment) is a candidate mission for the European Space Agency (ESA) Earth Explorer program. The main objective of the mission is the measurement the chlorophyll fluorescence signal emitted by vegetation at the red and far-red spectral regions (roughly 630-770 nm). The current FLEX mission design includes different instruments intended to provide the appropriate characterization of those atmospheric and surface parameters necessary for the retrieval and interpretation of the fluorescence signal. The complete processing chain for the derivation of fluorescence and reflectance products from the radiance data acquired by the different instruments included in the FLEX pay-load is described in this paper. Six processing modules have been implemented: cloud screening, aerosol optical thickness (AOT) retrieval, automatic spectral characterisation, columnar water vapor (CWV) retrieval, fluorescence retrieval and reflectance retrieval. The processing chain has been tested against a scene-based simulated data set which reproduces FLEX instruments and realistic atmospheric conditions.

The Environmental Mapping and Analysis Program (EnMAP) is a German built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure over the 420-2450 nm spectral range at a varying spectral sampling of 5-10 nm. Images will covered 30 kmtimes30 km areas at approximate pixel sizes of 30 m. The primary goal of EnMAP is the exploitation of hyperspectral data for the derivation of high-spectral resolution observations of biophysical, biochemical and geochemical variables from a range of surface covers, such as vegetation canopies, rock and soil targets and coastal waters, on a global scale. General descriptions of the EnMAP instrument, the satellite operation concept, the data processing and archiving structures and current project development activities are provided in this paper.

The environmental mapping and analysis program (EnMAP) is a joint response of German Earth observation research institutions, value-adding (VA) resellers and space industry to the increasing demand on accurate, quantitative information about the evolution of terrestrial ecosystems. With its hyperspectral capabilities covering the visible, near- and short-wave infrared wavelengths, EnMAP will provide high quality, standardized, and consistent data on a timely and frequent basis. Its primary focus will be on the considerable improvement of already standardized products and the development of new quantitative and subsequently highly informative data and its derivatives. Only an imaging spectrometer, such as the EnMAP, can resolve and detect biophysical, biochemical, and geochemical variables in distinct detail. This will tremendously increase our understanding of coupled biospheric and geospheric processes and thus, enable the management and ensure the sustainability of our vital resources. After a competitive and successfully accomplished phase A, EnMAP has been approved by the German Aerospace Agency in the beginning of 2006. The instrument performance allows for a detailed monitoring, characterisation and parameter extraction of vegetation targets, rock/soils, and inland and coastal waters on a global scale. By the scientific lead of the GeoForschungsZentrum Potsdam (GFZ) and the industrial prime ship of Kayser-Threde, the ongoing planning aims towards an internationalisation of the mission approach. The EnMAP instrument provides information based on 218 contiguous spectral bands in the wavelength range from 420 nm to 2450 nm. It is characterized by a SNR of > 500:1 in the VNIR and >150:1 in the SWIR range at a ground resolution of 30 m times 30 m. A national and international broad science and application community can draw from an extensive and highly resolved pool of information, supporting the modeling and optimization process on their results. Operation of an airborne system (ARES - a joint venture of DLR and GFZ), starting in 2006, and the evolution of data handling and extraction procedures will further support this process.

The Airborne Reflective/Emissive Spectrometer is specified as a whisk-broom imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.47-2.45 μm and 8-12 μm with 160 spectral bands. The instrument is being built by Integrated Spectronics, financed by the German Aerospace Agency (DLR) and the GeoResearch Centre Potsdam (GFZ) and will be available to the scientific community from end 2005 on. The spectroradiometric design is based on scientific requirements derived from three main application scenarios comprising vegetation, soil, and mineral sciences. Two of these are described in this letter. Measured or modeled reflectance spectra are input to a simulation model that calculates at-sensor radiance spectra, resamples them with the channel-specific response functions, adds different amounts of noise in the radiance domain, and performs a retrieval to get the corresponding noisy surface reflectance spectra. The retrieval results as a function of the sensor noise level are compared with the accuracy requirements imposed by the different application fields taking into account the technical boundary conditions. The final specifications account for the most demanding requirements of the three application fields: a spectral sampling distance of 13-14 nm in the 470-1800 nm region, and 12 nm in the 2000-2450-nm region. The required noise-equivalent radiances are 5, 3, and 2 nW/spl middot/cm/sup -2//spl middot/sr/sup -1//spl middot/nm/sup -1/ for the spectral regions 470-1000, 1000-1800, and 2000-2450 nm, respectively.

The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.

A new concept for the detection of small objects from modular optoelectronic multispectral scanner (MOMS-02) high spatial resolution panchromatic satellite imagery is presented. The authors combine supervised shape classification with unsupervised image segmentation in an iterative procedure which allows a target-oriented search for specific object shapes.