Martin Bachmann

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is 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 and 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 four days at the 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.

Final TerraSAR-X Calibration Results Based on Novel Efficient Methods

TerraSAR-X is a satellite mission for scientific and commercial applications operating a highly flexible X-band synthetic aperture radar (SAR) instrument with a multitude of different operation modes. As product quality is of crucial importance, the success or failure of the mission depends essentially on the method of calibrating TerraSAR-X in an efficient way during commissioning the entire system in a restricted time. Only then, product quality and the correct in-orbit operation of the entire SAR system can be ensured. This paper describes the in-orbit calibration method for TerraSAR-X and dedicated activities performed during the commissioning phase as well as final results derived from all calibration procedures.

Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes. This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.

Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing

The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R2 = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R2 = 0.39 to 0.78), but poor accuracies for the second axis (R2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R2 LiD = 0.3, R2 MNF = 0.08, R2 MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R2 LiD = 0.75, R2 MNF = 0.76, R2 MNF+LiD = 0.78). The improvement in R2 was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs.

Influence of the Adjacency Effect on Ground Reflectance Measurements

It is well known that the adjacency effect has to be taken into account during the retrieval of surface reflectance from high spatial resolution satellite imagery. The effect results from atmospheric scattering, depends on the reflectance contrast between a target pixel and its large-scale neighborhood, and decreases with wavelength. Recently, ground reflectance field measurements were published, claiming a substantial influence of the adjacency effect at short distance measurements (< 2 m), and an increase of the effect with wavelength. The authors repeated similar field measurements and found that the adjacency effect usually has a negligible influence at short distances, decreasing with wavelength in agreement with theory, but can have a small influence in high-reflectance contrast environments. Radiative transfer calculations were performed to quantify the influence at short and long distances for cases of practical interest (vegetation and soil in a low-reflectance background). For situations with large reflectance contrasts, the atmospheric backscatter component of the adjacency effect can influence ground measurements over small-area targets, and should therefore be taken into account. However, it is not possible to draw a general conclusion, since some of the considered surfaces are known for exhibiting strong directional effects

A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem

The accurate assessment of selected soil constituents can provide valuable indicators to identify and monitor land changes coupled with degradation which are frequent phenomena in semiarid regions. Two approaches for the quantification of soil organic carbon, iron oxides, and clay content based on field and laboratory spectroscopy of natural surfaces are tested. (1) A physical approach which is based on spectral absorption feature analysis is applied. For every soil constituent, a set of diagnostic spectral features is selected and linked with chemical reference data by multiple linear regression (MLR) techniques. (2) Partial least squares regression (PLS) as an exclusively statistical multivariate method is applied for comparison. Regression models are developed based on extensive ground reference data of 163 sampled sites collected in the Thicket Biome, South Africa, where land changes are observed due to intensive overgrazing. The approaches are assessed upon their prediction performance and significance in regard to a future quantification of soil constituents over large areas using imaging spectroscopy.

Correction of cirrus effects in Sentinel-2 type of imagery

Optical satellite images are often contaminated with cirrus clouds. Thin cirrus can be detected with a channel at 1.38 μm, and an established cirrus removal method exists for visible/near-infrared (VNIR) channels in atmospheric window regions, which was demonstrated with Moderate Resolution Imaging Spectrometer (MODIS) data. This contribution addresses open issues of cirrus correction for Sentinel-2 type of instruments, that is, future spaceborne sensors such as Sentinel-2 or similar instruments. These issues are (i) an extension of the existing technique to account for cirrus during the water vapour retrieval (channel at 0.94 μm) and surface reflectance calculation to avoid reflectance artefacts at 0.94 μm, (ii) a discussion of options concerning cirrus removal in the short-wave infrared (SWIR, channels at 1.6 and 2.2 μm) region and (iii) an analysis of channel parallax (view angle) requirements to achieve a high-quality cirrus removal.

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.

The processing chain and Cal/Val operations of the future hyperspectral satellite mission EnMAP

The German Aerospace Center DLR - namely the Applied Remote Sensing Cluster CAF and the German Space Operations Center GSOC - is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Applied Remote Sensing Cluster has long lasting experiences with air- and spaceborne acquisition, processing, and analysis of hyperspectral image data. This paper mainly addresses the concept of the operational and automatic processing chain and the calibration/data quality to generate high quality data products.

EnMAP Ground Segment Design: An Overview and Its Hyperspectral Image Processing Chain

EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is the first German hyperspectral remote sensing satellite mission. This chapter focuses on the challenges on the design of the ground segment as a whole and in particular of its image processing chain. In the context of the system response time we investigate the ability of tilting the satellite which allows for frequent revisits and enables meaningful downstream change detection activities on a global scale. In the context of comparable and high-quality controlled products we investigate in detail the processing steps to radiometrically calibrate, spectrally characterize, geometrically and atmospherically correct the data. The status corresponds to the baseline for the production activities of the ground segment, namely only minor changes are expected. The launch is planned for 2016. The establishment and operation of the ground segment is under responsibility of the Earth Observation Center (EOC) and the German Space Operations Center (GSOC) at the German Aerospace Center (DLR).