Simulated Environmental Mapping And Analysis Program Research Articles

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.

Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data

Spaceborne imaging spectrometers are expected to facilitate regional-scale vegetation analyses with multi-season hyperspectral imagery. However, we still lack a better understanding on both whether multi-season hyperspectral approaches are favorable over single-season approaches, as well as on the benefits of hyperspectral compared to multispectral data. Our study investigates the potential of multi-season unmixing of simulated Environmental Mapping and Analysis Program (EnMAP) data for vegetation class fraction mapping across diverse natural and semi-natural ecoregions in California, USA. We utilized spring, summer and fall 2013 simulated EnMAP imagery derived from Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) data covering study sites in the San Francisco Bay Area, Lake Tahoe and Santa Barbara. Regression-based unmixing with synthetic training datasets from spectral libraries was implemented for mapping needleleaf tree, broadleaf tree, shrub, herbaceous and non-vegetation fractions, and independent reference data was used for validation. Multi-season unmixing of simulated EnMAP had average Mean Absolute Errors (MAE) over all classes of 8.7% for the Bay Area, 8.5% for Lake Tahoe and 9.6% for Santa Barbara. However, larger errors in the low and high end of the fraction range remained, particularly in open-canopy woodlands and xeric shrub-dominated regions. Single-season unmixing of simulated EnMAP revealed large seasonal and regional variations within individual vegetation classes. In most cases, the best performing single-season unmixing had similar errors as the multi-season unmixing, i.e., ∆MAEs within ±1.0%. This points to the advantage of the multi-season integration strategy for more robust and generalized mapping independent from season and study site. Relative to EnMAP analyses, multi-season unmixing of multispectral Landsat composites for the same seasons yielded increases in average MAEs of +1.7%, +2.3% and +1.4% for the three study sites. This indicates that the higher spectral resolution of simulated EnMAP provides more relevant discriminative information when comparing contemporary image pairs. Unmixing of seasonal spectral-temporal metrics (STMs) from all available Landsat images for an entire year took advantage of the full temporal detail provided by these ongoing missions. We found Landsat STMs to effectively map vegetation class fractions, with average MAEs of 9.9%, 10.0% and 9.7% for the three study sites. Still, improvements particularly for mapping fractions of the woody vegetation classes through multi-season unmixing of simulated EnMAP point to the benefit of high spectral resolution data, and we assume that a comparable higher temporal resolution of hyperspectral satellites will further positively influence results. Overall, we conclude that multi-season unmixing of spaceborne imaging spectroscopy data holds great potential for advancing vegetation class fraction mapping across natural and semi-natural ecosystems.

Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

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.

Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution.Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas.

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.

SMILE CORRECTION IN THE ENMAP GROUND SEGMENT PROCESSOR: A QUALITATIVE ANALYSIS

Abstract. The Environmental Mapping and Analysis Program (EnMAP) is an upcoming German hyperspectral satellite mission aiming to observe and characterize the Earth’s environment on a global scale. In course of a the development of the EnMAP ground segment processing chain, measures were taken to deal with probable spectral and spatial distortion effects of the sensor, that can only be addressed by post-processing once the satellite platform is launched into orbit. One of these effects is a pixel- and band-wise spectral distortion, the so called sensor smile effect. This paper describes the methods and performance of the smile correction process as implemented for the EnMAP ground segment processing chain, explaining design choices and difficulties currently encountered with the proposed approach and evaluating the results of the smile correction on basis of simulated EnMAP hyperspectral data.

Evaluation of Unmixing Methods for Impervious Surface Area Extraction From Simulated EnMAP Imagery

Distribution of impervious surface area (ISA) is an important input in a wide range of urban ecosystem studies. The future launch of the German hyperspectral satellite environmental mapping and analysis program (EnMAP) in 2019 provides new opportunities for timely and global ISA extraction. The previously proposed EnMAP applications heavily relied on existing reference endmembers, which may be impractical on a global scale. To overcome this defect, we suggest to use the nonnegative matrix factorization (NMF) method to extract the endmember directly from EnMAP imagery. Three traditional unmixing method (e.g., N-Findr, pixel purity index, and independent component analysis) and four NMF-based methods with different constraints (e.g., sparseness, convex volume, and nonlinearity) were used to obtain series of endmember sets, ISA fraction, and classification maps. In results, the NMF-based methods outperformed the three traditional unmixing method, by achieving 0.5–0.6 R-squared values in the linear regression models between predicted and reference ISA percentages, and over 85% overall accuracy in ISA classification maps. We found that the NMF-based spectral unmixing methods are suitable to work with the EnMAP image, when reference endmember data are unavailable. In addition, we processed the widely used Hydice urban test image with the same methods and compared the resulting ISA percentage/classification maps with the EnMAP results, considering the different features of the Hydice and EnMAP sensors. In the results, it is proved that the EnMAP image has great potential in ISA mapping on a global scale, with reasonable overall accuracy and economical efficiency.

A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska

Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor.

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.

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fed), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.

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.

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA) and simulated Environmental Mapping and Analysis Program (EnMAP) and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%.

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.

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.

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.

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.