Joachim Hill

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.

Radiometric correction of multitemporal Thematic Mapper data for use in agricultural land-cover classification and vegetation monitoring

Abstract Many remote sensing applications, especially multitemporal approaches, require radiometric corrections of image data in which radiometric normalization to standard conditions and modelistic atmospheric corrections are often considered as alternative solutions. Successful radiometric normalization depends on the availability of suitable reference targets within the scenes under considerations, which may be critical. It is demonstrated that even simplified atmospheric correction modelling can provide a valuable alternative solution. We present an atmospheric correction approach for Thematic Mapper data, which is based solely on the evaluation of scene information and may therefore be considered operational. The method is based on the determination of aerosol optical thickness from histogram minima and clear water targets. Atmospheric conditions are assumed constant over the scene, but their variation with the Sunto-satellite scattering angle is accounted for. The environment effect is approximately...

Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands

The development of vegetation cover is one of the primary indicators for land degradation, stability, or regeneration in regions threatened by overgrazing. This paper addresses the problem how spatially explicit information about degradation processes in European Mediterranean rangelands can be derived from long time series of satellite data. The selected test site in central Crete, Greece, is considered to be representative for the highly heterogeneous character of such landscapes. The monitoring approach comprises the time period between 1977 and 1996, covered by nine Landsat TM and four Landsat MSS images. Special emphasis has hence been put on the evaluation of potentials and drawbacks when coupling Landsat TM and MSS based results. The data sets were geometrically and radiometrically pre-processed in a rigorous fashion, followed by a linear spectral unmixing approach and a time series analysis of vegetation fraction images. Based on the resulting map, the spatio-temporal patterns of vegetation cover changes are explained. Even a test site such as central Crete, with its limited spatial extend, exhibits heterogeneous patterns of change, supporting the hypothesis that long time series of EOS data from Landsat-like sensors are mandatory to identify the relevant changes at landscape level.

Land degradation, soil erosion and desertification monitoring in Mediterranean ecosystems

Abstract Under the European Commissionss DG XII “Research & Development Programme in the Field of the Environment” emphasis is given to identify, map and control desertification phenomena in the Mediterranean area. Based upon previous experiences in Mediterranean land cover mapping, the “Environmental Mapping and Modelling Unit” of the Institute for Remote Sensing Applications is actively participating in this programme. A semi‐operational approach to land degradation mapping has recently been developed which requires radiometric rectification of the multi‐spectral images and the availability of spectral libraries. Linear spectral mixture modelling is then used to decompose image spectra into their spectrally distinct components, the fractional abundance of which then provides a largely unbiased measure for direct mapping of vegetation abundance and the identification of soil conditions and erosion hazards. Ground verification in a well‐controlled test site in the south of France proved an excellent accu...

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.

Mapping Complex Patterns of Erosion and Stability in Dry Mediterranean Ecosystems

Parametrizing soil reflectance spectra with variables related to specific shape characteristics of the spectral profile permits organic carbon concentrations in soils to be estimated on the basis of regionally validated regression models. An important feature of the approach is that it can not only be applied to continuous spectra but, without notable loss in accuracy, also to the spectral resolution of operational earth observation satellites such as the Landsat-TM or -ETM systems. Using this type of imagery, it can also be shown that soil organic matter is positively correlated to growth conditions for cereal crops in dryland agriculture. Strong correlations with qualitative erosion indicators that can be derived through spectral unmixing approaches demonstrate that soil organic matter is an important indicator for assessing land degradation processes in dry ecosystems from space.

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.

Imaging spectrometry : a tool for environmental observations

Preface. 1. Imaging spectrometry -- its present and future role in environmental research P.J. Curran. 2. Scientific issues and instrumental opportunities in remote sensing and high resolution spectrometry M.M. Verstraete. 3. Remote sensing and the estimation of ecosystem parameters and functions C.A. Wessman. 4. Estimating canopy biochemistry through imaging spectrometry C.A. Wessman. 5. Soil spectral properties and their relationships with environmental parameters -- examples from arid regions R. Escadafal. 6. Data analysis -- processing requirements and available software tools W. Mehl. 7. Retrieving canopy properties from remote sensing measurements M.M. Verstraete. 8. Spectral mixture analysis -- new strategies for the analysis of multispectral data M.O. Smith, J.B. Adams, D.E. Sabol. 9. Modeling canopy spectral properties to retrieve biophysical and biochemical characteristics F. Baret, S. Jacquemoud. 10. Optical properties of leaves: modeling and experimental studies J. Verdebout, S. Jacquemoud, G. Schmuck. 11. Imaging spectrometry in agriculture -- plant vitality and yield indicators J.P.G.W. Clevers. 12. Mapping sparse vegetation canopies M.O. Smith, J.B. Adams, D.E. Sabol. 13. Land degradation and soil erosion mapping in a Mediterranean ecosystem J. Hill, W. Mehl, M. Altherr. 14. Imaging spectroscopy in hydrology and agriculture -- determination of model parameters W. Mauser, H. Bach. 15. Alpine and subalpine land use and ecosystems mapping K.I. Itten, P. Meyer, T. Kellenberger, M. Schaepman, S. Sandmeier, I. Leiss, S. Erdos. 16. Imaging spectrometry as a research tool for inland water resources analysis A.G. Dekker, M. Donze. 17. Future applications, sensor developments and research programmes int he field of imaging spectrometry J. Bodechtel, S. Sommer. Index.

Assessment of rainfall and NDVI anomalies in Spain (1989-1999) using distributed lag models

In this study a link was established between anomalies in climatic and Advanced Very High Resolution Radiometer (AVHRR)/Normalized Difference Vegetation Index (NDVI) data in Spain for the period from 1989 to 1999 on a monthly and annual basis using multivariate distributed lag (DL) models and generalized least‐square (GLS) parameter estimation. In most areas significant time‐delayed correlation between anomalies of monthly rainfall and NDVI data was confined to an interval of 1 month. Locally higher lag orders of up to 3 months were found. By contrast, relationships between surface temperature and the NDVI were insignificant in the multivariate context at most locations. The multiple correlation coefficients of the DL models achieved 0.6 in the maximum. Regions characterized by the most significant NDVI–rainfall correlations include the southern forelands of the Pyrenees in Catalũna, rainfed agricultural areas in Extremadura, Andalusia, and the western parts of Castilla y Leon. Average ratios of rainfall to potential evapotranspiration (PET) in the sensitive areas ranged between 0.5 and 2, with annual rainfall amounts less than 700 mm. For each land‐cover class a linear discriminant analysis (LDA) was carried out to assess the environmental factors that might explain the differences in the NDVI–rainfall relationships. The highest discriminant coefficients and factor loadings were recorded for those factors that recurrently trigger water deficit in the sensitive regions, such as low total annual rainfall, large seasonal rainfall variability, high average PET and surface temperature. On the annual basis the lagged correlation of the NDVI and rainfall data was confined to natural vegetation (grassland and scrubland) areas in western Spain. This region suffered from a severe drought in the early 1990s, after which biomass production lagged several years behind improved rainfall conditions. The approach presented is useful for assessing the influence of climatic variables on the pattern of temporal anomalies in the NDVI or related vegetation parameters.

Comparative Analysis of LANDSAT-5 TM and SPOT HRV-1 Data for Use in Multiple Sensor Approaches

Abstract In view of multiple sensor approaches in remote sensing, we have analyzed the geometric and, in particular, radiometric accuracy of multispectral SPOT HRV (high resolution visible) data in comparison to Landsat-5 Thematic Mapper images. We used concurrent system corrected TM and SPOT images which were recorded almost simultaneously over the southern Ardeche region (France). The TM and SPOT scenes were registered to map projection with subpixel accuracy, but the mountainous character of the study region required the use of digital elevation data for the compensation of relief induced distortions. Our radiometric comparison of the TM and SPOT sensors involved an analysis of the calibration accuracy and a comparison of spectral greenness and brightness features which can be obtained from the two systems. The calibration assessments were primarily based upon the apparent reflectances of bare soil reference targets, and the in-flight calibration of both sensor systems was found in accordance with the respective specifications. For the comparative analysis of spectral features we used an extended set of targets, representing a wide range of vegetated and nonvegetated cover types. In order to directly relate the image-based relations to those obtained from field radiometry data, we had to correct the TM and SPOT data for atmospheric absorption, scattering and pixel adjacency effects. Both systems provide slightly different brightness and greenness information (Tasseled Cap equivalent brightness and greenness, normalized difference vegetation index). But these indices appear to be linearly related, and the respective transfer functions permit mutual data adjustments. Therefore, a wide range of applications in agricultural monitoring and vegetation observation can be approached by using multiple sensor data sets which involve TM and SPOT imagery.