Environmental Mapping And Analysis Program Data Research Articles

Full mission evaluation of EnMAP water leaving reflectance products using three atmospheric correction processors

This study presents what we believe is the first extensive assessment of the water reflectance products from the German hyperspectral Environmental Mapping and Analysis Program (EnMAP). We evaluate EnMAP’s standard normalized water leaving reflectance [ρ W ] N over 17 water sites in the first two years of the mission. The EnMAP [ρ W ] N standard product is generated by a dedicated water atmospheric correction (AC) called the Modular Inversion Program (MIP). The quality of the [ρ W ] N retrievals was assessed using in situ hyperspectral measurements and Aerosol Robotic Network - Ocean Colour (AERONET-OC) multispectral measurements. The results showed very good agreement between in situ hyperspectral match-ups and EnMAP [ρ W ] N , with an underestimation of EnMAP of −17.37% (bias, β) and an error (ϵ) of 23.75% at 418 – 797 nm. Two other AC processors were also investigated: the polynomial based algorithm applied to MERIS (Polymer) and the atmospheric correction for OLI lite (Acolite). The intercomparison exercise between the three AC methods applied to EnMAP data using the hyperspectral match-up dataset showed better statistical metrics for MIP (ϵ=23%,β=−17.37%) compared to Polymer (ϵ=42.20%,β=−2.43%) and Acolite (ϵ=97%,β=97%). The superior performance of MIP was further confirmed by the validation results obtained with the multispectral match-up dataset; MIP retrievals show good agreement with in situ measurements at the majority of study sites. Conversely, Polymer and Acolite retrievals tended to overestimate, especially in clearer waters as the Lampedusa study site.

Evaluation of EnMAP hyperspectral satellite data for epithermal alteration mapping at Cuprite-Goldfield, southwestern Nevada, USA

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission launched into orbit on April 1, 2022. The EnMAP records 246 hyperspectral channels in the 0.42-2.45 μm wavelength range, with a spatial resolution of 30 m. This wavelength range is useful for environmental and geological mapping. This study evaluates the application of the EnMAP data to the mapping of epithermal alteration systems at Cuprite and Goldfield in southwestern Nevada, USA. The Cuprite has served since 1980s as a test site of the U.S. National Aeronautics and Space Administration (NASA) for the evaluation of airborne and satellite remote sensing sensors. The surface reflectance Cuprite-Goldfield EnMAP scene analyzed in this study was recorded on April 9, 2023. The EnMAP data were analyzed using the spectral mixture analysis technique. The results obtained from the EnMAP moderate spatial resolution satellite hyperspectral data show accurate detection of the alteration zones in the Cuprite-Goldfield study area. The study indicates the potential of the EnMAP data for various geological and environmental applications.

Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters

Our study investigates the capability of the environmental mapping and analysis program (EnMAP) scenes simulated using the EnMAP end-to-end simulator software (EeteS) based on the AISA Eagle airborne data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches (principal component regression(PCR), partial least square regression (PLSR) and random forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several inland water reservoirs within the southern part of the Czech Republic. Airborne hyperspectral images were also used to develop a model to compare its performance with models developed based on the simulated EnMAP data. Adequate prediction accuracy was obtained for both Chl-a (R2 = 0.89, RMSE = 43.06 μg/L, and Lin’s concordance correlation coefficient (LCCC) = 0.91) and TSS (R2 = 0.91, RMSE = 17.53 mg/L, and LCCC = 0.94), which were close enough to those obtained from the airborne hyperspectral images. Chl-a and TSS correlated with the wavelengths around 550 nm and 700 to 750 nm of the red and near-infrared (NIR) regions. In addition, the spatial distribution maps derived from the simulated EnMAP were comparable to those obtained from the AISA Eagle airborne data. Overall, it can be concluded that the simulated EnMAP image successfully and reliably predicted and spatially mapped the selected biophysical properties of the small inland water bodies.

Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas

Spaceborne imaging spectroscopy is a highly promising data source for all agricultural management and research disciplines that require spatio-temporal information on crop properties. Recently launched science-driven missions, such as the Environmental Mapping and Analysis Program (EnMAP), deliver unprecedented data from the Earth’s surface. This new kind of data should be explored to develop robust retrieval schemes for deriving crucial variables from future routine missions. Therefore, we present a workflow for inferring crop carbon content (Carea), and aboveground dry and wet biomass (AGBdry, AGBfresh) from EnMAP data. To achieve this, a hybrid workflow was generated, combining radiative transfer modeling (RTM) with machine learning regression algorithms. The key concept involves: (1) coupling the RTMs PROSPECT-PRO and 4SAIL for simulation of a wide range of vegetation states, (2) using dimensionality reduction to deal with collinearity, (3) applying a semi-supervised active learning technique against a 4-years campaign dataset, followed by (4) training of a Gaussian process regression (GPR) machine learning algorithm and (5) validation with an independent in situ dataset acquired during the ESA Hypersense experiment campaign at a German test site. Internal validation of the GPR-Careaand GPR-AGB models achieved coefficients of determination (R2) of 0.80 for Carea and 0.80, 0.71 for AGBdry and AGBfresh, respectively. The mapping capability of these models was successfully demonstrated using airborne AVIRIS-NG hyperspectral imagery, which was spectrally resampled to EnMAP spectral properties. Plausible estimates were achieved over both bare and green fields after adding bare soil spectra to the training data. Validation over green winter wheat fields generated reliable estimates as suggested by low associated model uncertainties (<40%). These results suggest a high degree of model reliability for cultivated areas during active growth phases at canopy closure. Overall, our proposed carbon and biomass models based on EnMAP spectral sampling demonstrate a promising path toward the inference of these crucial variables over cultivated areas from future spaceborne operational hyperspectral missions.

Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR

Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.

Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data

Tools that enhance the management of the soil resource are vital for sustainability and continued productivity. The assessment and monitoring of surface-soil condition provide key information required to effectively manage this resource. This research used a simulated Environmental Mapping and Analysis Program (EnMAP) hyperspectral image cube of an agricultural region in semi-arid Mediterranean Spain to classify soil erosion states. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to derive within-pixel fractions of eroded and accumulated soil states. Soil states (eroded, intermediate, accumulated) were classified with an overall accuracy of 70.3% using the unmixing fraction maps and terrain derivatives. The results generated in this research provided an outlook for the applicability of the future EnMAP sensor for soil erosion investigations in semi-arid Mediterranean environments.

Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of predicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragon, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as predictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few predictors (10 to 58 of 244) and demonstrated good fit (Rcal2>0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.

Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression

Hyperspectral remote sensing data offer the opportunity to map urban characteristics in detail. Though, adequate algorithms need to cope with increasing data dimensionality, high redundancy between individual bands, and often spectrally complex urban landscapes. The study focuses on subpixel quantification of urban land cover compositions using simulated environmental mapping and analysis program (EnMAP) data acquired over the city of Berlin, utilizing both machine learning regression and classification algorithms, i.e., multioutput support vector regression (MSVR), standard support vector regression (SVR), import vector machine classifier (IVM), and support vector classifier (SVC). The experimental setup incorporates a spectral library and a reference land cover fraction map used for validation purposes. The library spectra were synthetically mixed to derive quantitative training data for the classes vegetation, impervious surface, soil, and water. MSVR and SVR models were trained directly using the synthetic mixtures. For IVM and SVC, a modified hyperparameter selection approach is conducted to improve the description of urban land cover fractions by means of probability outputs. Validation results demonstrate the high potential of the MSVR for subpixel mapping in the urban context. MSVR outperforms SVR in terms of both accuracy and computational time. IVM and SVC work similarly well, yet with lower accuracies of subpixel fraction estimates compared to both regression approaches.

Subalpine and alpine vegetation classification based on hyperspectral APEX and simulated EnMAP images

ABSTRACTThe characterization of vegetation is a very important ecological task, especially in sensitive mountain areas, as alpine regions often respond to small short-term variations of abiotic and biotic components as well as long-term global changes. Spatial techniques, such as imaging spectroscopy, allow for detailed classification of different syntaxonomic categories of vegetation and their status. Based on the Airborne Prism Experiment (APEX) and simulated Environmental Mapping and Analysis Program (EnMAP) data, this study focused on subalpine and alpine vegetation mapping in the eastern part of the Polish Karkonosze National Park (KPN). The spatial resolution of APEX (3.12 m) enabled the classification of 21 vegetation communities, which was generalized into eight vegetation types. These types were identified on scaled-up APEX data, as both 252 bands from most of the spectral range and a spectrally reduced dataset of 30 minimum noise fraction (MNF) transforms, and compared to the simulated (30 m spatial resolution) EnMAP data using test areas extracted from the field survey derived reference non-forest vegetation map. After preprocessing, a pixel purity index (PPI) was calculated using the MNF image and then the training and validation pixels were selected with Support Vector Machine classification of vegetation communities carried out using different kernel functions: linear, polynomial, radial basis function, and sigmoid. The classification accuracy was obtained for 21 base classes, and the best result was achieved by using the linear function and 252 bands (overall accuracy (OA) of 74.39%). The next step was to classify the eight generalized vegetation types, and the OA for the APEX data reached 90.72% while EnMAP reached 78.25%. The results show the potential use of APEX and EnMAP imagery in mapping subalpine and alpine vegetation on a community and vegetation-type scales, within a diverse ecosystem such as the Karkonosze National Park.

Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.

EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission

Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information.

Restoration of Simulated EnMAP Data through Sparse Spectral Unmixing

This paper proposes the use of spectral unmixing and sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. The described method independently decomposes each image element into a set of representative spectra, which come directly from the image and have previously undergone a low-pass filtering in noisy bands. The residual vector from the unmixing process is considered as mostly composed of noise and ignored in the reconstruction process. The first assessment of the results is encouraging, as the original bands taken into account are reconstructed with a high signal-to-noise ratio and low overall distortions. Furthermore, the same method could be applied for the inpainting of dead pixels, which could affect EnMAP data, especially at the end of the satellite’s life cycle.

Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP

In times of global environmental change, the sustainability of human–environment systems is only possible through a better understanding of ecosystem processes. An assessment of anthropogenic environmental impacts depends upon monitoring natural ecosystems. These systems are intrinsically complex and dynamic, and are characterized by ecological gradients. Remote sensing data repeatedly collected in a systematic manner are suitable for describing such gradual changes over time and landscape gradients, e.g., through information on the vegetation’s phenology. Specifically, imaging spectroscopy is capable of describing ecosystem processes, such as primary productivity or leaf water content of vegetation. Future spaceborne imaging spectroscopy missions like the Environmental Mapping and Analysis Program (EnMAP) will repeatedly acquire high-quality data of the Earth’s surface, and will thus be extremely useful for describing natural ecosystems and the services they provide. In this conceptual paper, we present some of the preparatory research of the EnMAP Scientific Advisory Group (EnSAG) on natural ecosystems and ecosystem transitions. Through two case studies we illustrate the usage of spectral indices derived from multi-date imaging spectroscopy data at EnMAP scale, for mapping vegetation gradients. We thus demonstrate the benefit of future EnMAP data for monitoring ecological gradients and natural ecosystems.

The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI

In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (αspec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R2cv = 0.87) followed by the models built with the fused datasets (EnMAP–aisaEAGLE and EnMAP–Sentinel-2 fusion each with a R2cv of 0.75) and the simulated EnMAP data (R2cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications.

The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing. The software is freely available and offers a range of tools and applications for the processing of spectral imagery, including classical processing tools for imaging spectroscopy data as well as powerful machine learning approaches or interfaces for the integration of methods available in scripting languages. A special developer version includes the full open source code, an application programming interface and an application wizard for easy integration and documentation of new developments. This paper gives an overview of the EnMAP-Box for users and developers, explains typical workflows along an application example and exemplifies the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications.

Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of natural environments by means of high quality hyperspectral data becomes possible. However, the amplified spectral mixing due to the sensor’s ground sampling distance of 30 m on the one hand and the patterns of natural landscapes in the form of gradual transitions between different land cover types on the other require special attention. Based on simulated EnMAP data, this study focuses on mapping shrub vegetation along a landscape gradient of shrub encroachment in a semi-arid, natural environment in Portugal. We demonstrate how probability outputs from a support vector classification (SVC) model can be used to extend a hard classification by information on shrub cover fractions. This results in a more realistic representation of gradual transitions in shrub vegetation maps. We suggest a new, adapted approach for SVC parameter selection: During the grid search, parameter pairs are evaluated with regard to the prediction of synthetically mixed test data, representing shrub to non-shrub transitions, instead of the hard classification of original, discrete test data. Validation with an unbiased, equalized random sampling shows that the resulting shrub-class probabilities from adapted SVC more accurately represent shrub cover fractions (mean absolute error/root mean squared error of 16.3%/23.2%) compared to standard SVC (17.1%/29.5%). Simultaneously, the discrete classification output was considerably improved by incorporating synthetic mixtures into parameter selection (averaged F1 accuracies increased from 72.4% to 81.3%). Based on our findings, the integration of synthetic mixtures into SVC parameterization allows the use of SVC for sub-pixel cover fraction estimation and, this way, can be recommended for deriving improved qualitative and quantitative descriptions of gradual transitions in shrub vegetation. The approach is therefore of high relevance for mapping natural ecosystems from future EnMAP data.

Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning

The upcoming hyperspectral satellite mission Environmental Mapping and Analysis Program (EnMAP) will provide timely and globally sampled imaging spectrometer data on a frequent basis. This will create unprecedented opportunities for a variety of environmental research fields and lead to manifold novel applications. These opportunities specifically apply to challenging environments, including heterogeneous urban landscapes. In this paper, we explored the potential of EnMAP data for quantifying land cover along the urban–rural gradient of Berlin, Germany. Land cover fraction maps from a simulated EnMAP scene at 30m spatial resolution were derived based on support vector regression (SVR) combined with synthetically mixed training data. Results demonstrate that EnMAP imagery will be well suited for mapping impervious, vegetation and soil surface types according to the VIS framework. Moreover, EnMAP data will allow extending the VIS framework by more detailed sub-categories such as roof and pavement, or low vegetation and tree. However, we advise caution that spaceborne imaging spectrometer data of improved quality will not completely help to overcome well known phenomena of spectral similarity between materials and spectral confusion caused by the presence of shaded areas. To identify possible benefits and limitations of EnMAP data, comparisons to fraction maps derived from a higher resolution Hyperspectral Mapper (HyMap) image at 9m spatial resolution and a multispectral Landsat ETM+-like image at 30m spatial resolution were drawn. First, we demonstrate that both VIS and extended VIS mapping reveal similar accuracies compared to maps from spatially higher resolution data. Second, we illustrate the superiority of the higher spectral information content for improved and extended urban land cover mapping compared to multispectral data. Overall, this study provides important insights into the potential of spaceborne imaging spectrometer and specifically future EnMAP data for urban remote sensing.

EeteS—The EnMAP End-to-End Simulation Tool

The design of future Earth imaging systems, the optimization of fundamental instrument parameters, and the development and evaluation of data pre-processing and scientific-exploitation algorithms require an accurate end-to-end simulation of the entire image generation and processing chain. For this purpose, the end-to-end simulation software EeteS has been developed within the framework of the Environmental Mapping and Analysis Program (EnMAP) mission. This paper presents the EeteS simulation approach and software implementation focusing on calibration and pre-processing. The sequential processing chain of the EnMAP scene simulator consists of four independent parts-the atmospheric, spatial, spectral and radiometric modules. This forward simulator is coupled with a backward simulation branch consisting of calibration modules (non-linearity, dark current and absolute radiometric calibration) and a series of pre-processing modules (radiometric calibration, co-registration, atmospheric correction and orthorectification) forming the complete end-to-end simulation tool. In the result 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. 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. EeteS has also been used to estimate the SNR characteristics of potential EnMAP products after calibration and pre-processing. Comparing the results to SNR characteristics achieved by the already existing EO-1 Hyperion system has shown a significantly improved SNR which can be expected from future EnMAP data products.