Henning Buddenbaum

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.

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (CAB) and nitrogen (CN) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that CAB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate CAB was achieved from laboratory spectra using continuum-removal transformed data (R2cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R2cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R2cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate CAB were typically located in the red edge region and near the green reflectance peak. For CN, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.

Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods

Classifications of coniferous forest stands regarding tree species and age classes were performed using hyperspectral remote sensing data (HyMap) of a forest in western Germany. Spectral angle mapper (SAM) and maximum likelihood (ML) classifications were used to classify the images. Classification was performed using (i) spectral information alone, (ii) spectral information and stem density, (iii) spectral and textural information, (iv) all data together, and results were compared. Geostatistical and grey level co‐occurrence matrix based texture channels were derived from the HyMap data. Variograms, cross variograms, pseudo‐cross variograms, madograms, and pseudo‐cross madograms were tested as geostatistical texture measures. Pseudo‐cross madograms, a newly introduced geostatistical texture measure, performed best. The classification accuracy (kappa) using hyperspectral data alone was 0.66. Application of pseudo‐cross madograms increased it to 0.74, a result comparable to that obtained with stem density information derived from high spatial resolution imagery.

Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites

Tree species information is a basic variable for forest inventories. Knowledge on tree species is relevant for biomass estimation, habitat quality assessment, and biodiversity characterization. Hyperspectral data have been proven to have a high potential for the mapping of tree species composition. However, open questions remain concerning the robustness of existing classification approaches. Here, a number of classification approaches were compared to classify tree species from airborne hyperspectral data across three forest sites to identify a single approach which continuously delivers high classification performances over all test sites. Examined approaches included three feature selection methods [genetic algorithm (GA), support vector machines (SVM) wrapper, and sparse generalized partial least squares selection (PLS)] each combined with two nonparametric classifiers (SVM and Random Forest). Two further setups included classifications applied to the full hyperspectral dataset and to an image transformed with a minimum noise fraction (MNF) transformation. Results showed that SVM wrapper and the GA slightly outperformed the PLS-based algorithm. In most cases, the best classification runs involving a feature selection algorithm outperformed those incorporating the full hyperspectral dataset. However, the best overall results were obtained when using the first 10-20 components of the MNF-transformed image. Selected bands were frequently located in the visual region close to the green peak, at the chlorophyll absorption feature and the red edge rise as well as in three parts of the short-wave infrared region close to water absorption features. These findings are relevant for improving the robustness of tree species classifications from airborne hyperspectral data incorporating feature reduction techniques.

The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy

Laboratory imaging spectroscopy can be used to explore physical and chemical variations in soil profiles on a submillimetre scale. We used a hyperspectral scanner in the 400 to 1000 nm spectral range mounted in a laboratory frame to record images of two soil cores. Samples from these cores were chemically analyzed, and spectra of the sampled regions were used to train chemometric PLS regression models. With these models detailed maps of the elemental concentrations in the soil cores could be produced. Eight different spectral pretreatments were applied to the sample spectra and to the resulting images in order to explore the influence of these pre-treatments on the estimation of elemental concentrations. We found that spectral preprocessing has a minor influence on chemometry results when powerful regression algorithms like PLSR are used.

Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS)

The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe) and laboratory soil reflectance spectra resampled to Landsat sensor’s resolution. The models used were partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R2 = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), which performed better than the PLSR model (R2 = 0.70, RMSE = 6.95, and RPD = 1.82). The models were subsequently applied on a pixel-by-pixel basis to the reflectance values derived from two Landsat images (2006 and 2012) to generate quantitative maps of the soil salinity. The resulting maps were validated successfully for 37 and 26 sampling points for 2006 and 2012, respectively, with R2 = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R2 = 0.71 and 0.73 for 2006 and 2012, respectively, for the PLSR model. The results indicated that MARS is a more suitable technique than PLSR for the estimation and mapping of soil salinity, especially in areas with high levels of salinity. The method developed in this paper can be used for other satellite data, like those provided by Landsat 8, and can be applied in other arid and semi-arid environments.

Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands

Full-waveform small-footprint laser scanning and airborne hyperspectral image data of a forest area in Germany were fused to get a detailed characterization of forest reflective properties and structure. Combining active laser scanning data with passive hyperspectral data increases the information content without adding much redundancy. The small-footprint light detection and ranging (lidar) waveforms on the area of each 5 m × 5 m HyMap pixel were combined into quasi-large-footprint waveforms of 0.5 m vertical resolution by calculating the mean laser intensity in each voxel. As exemplary applications for this data set, we present the estimation of crown base heights and the ease of displaying vertical and horizontal slices through the three-dimensional data set. As a consequence of the identical geometry of the voxel bases and the hyperspectral image, they could be joined as a multi-band image. The combined spectra are well suited for interpretations of pixel content. In a test classification of tree species and age classes, the joint image performed better than the hyperspectral image alone and also better than the hyperspectral image combined with lidar percentile images.

The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities

Given the importance of forest ecosystems, the availability of reliable, spatially explicit information about the site-specific climate sensitivity of tree species is essential for implementing suitable adaptation strategies. In this study, airborne hyperspectral data were used to assess the response of deciduous species (dominated by European beech and Sessile and Pedunculate oak) to water stress during a summery dry spell. After masking canopy gaps, shaded crown areas and non-deciduous species, potentially indicative spectral indices, the Photochemical Reflectance Index (PRI), Moisture Stress Index (MSI), Normalized Difference Water Index (NDWI), and Chlorophyll Index (CI), were analyzed with respect to available maps of site-specific soil moisture regimes. PRI provided an important indication of site-specific photosynthetic stress on leaf level in relation to limitations in soil water availability. The CI, MSI and NDWI revealed statistically significant differences in total chlorophyll and water concentration at the canopy level. However, after reducing the canopy effects by normalizing these indices with respect to the structure-sensitive simple ratio (SR) vegetation index, it was not yet possible to identify site-specific concentration differences in leaf level at this early stage of the drought. The selected indicators were also tested with simulated EnMAP and Sentinel-2 data (derived from the original airborne data set). While PRI proved to be useful also in the spatial resolution of EnMAP (GSD = 30 m), this was not the case with Sentinel-2, owing to the lack of adequate spectral bands; the remaining indicators (MSI, CI, SR) were also successfully produced with Sentinel-2 data at superior spatial resolution (GSD = 10 m). The study confirms the importance of using earth observation systems for supplementing traditional ecological site classification maps, particularly during dry spells and heat waves when ecological gradients are increasingly reflected in the spectral response at the tree crown level. It also underlined the importance of using Sentinel-2 and EnMAP in synergy, as soon as both systems become available.

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R2 of 0.85 and 0.80 for ECe, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of ECe, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R2 = 0.81, 0.89 and 0.73, respectively, compared to R2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (ECe), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments.

Field Imaging Spectroscopy of Beech Seedlings under Dryness Stress

In order to monitor dryness stress under controlled conditions, we set up an experiment with beech seedlings in plant pots and built a platform for observing the seedlings with field imaging spectroscopy. This serves as a preparation for multi-temporal hyperspectral air- and space-borne data expected to be available in coming years. Half of the trees were watered throughout the year; the other half were cut off from water supply for a five-week period in late summer. Plant health and soil, as well as leaf water status, were monitored. Moreover, hyperspectral images of the trees were acquired four times during the experiment. Results show that the experimental imaging setup is well suited for recording hyperspectral images of objects, like the beech pots, under natural illumination conditions. The high spatial resolution makes it feasible to discern between background, soil, wood, green leaves and brown leaves. Furthermore, it could be shown that dryness stress is detectable from an early stage even in the limited spectral range considered. The decline of leaf chlorophyll over time was also well monitored using imaging spectroscopy data.