Karl Staenz

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.

Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS

Hyperspectral image data sets acquired near Cuprite, Nevada, in 1995 with the Short-Wave Infrared (SWIR) Full Spectrum Imager (SFSI) and in 1996 with the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) are analysed with a spectral unmixing procedure and the results compared. The nominal pixel centre spacings are 1.0 by 1.5 m for SFSI and 16.2 by 18.1 m for AVIRIS across track and along track, respectively; the region imaged by SFSI is a small portion of the full AVIRIS scene. Both data cubes have nominal spectral band centre spacings of approximately 10 nm. The image data, converted to radiance units, are atmospherically corrected and converted to surface reflectances. Spectral end members are extracted automatically from the two data sets; those representing mineral species common to both are compared to each other and to reference spectra obtained with a field instrument, the Portable Infrared Mineral Analyser (PIMA). The full sets of end members are used in a constrained linear unmixing of the respective hyperspectral image cubes. The resulting unmixing fraction images derived from the AVIRIS and SFSI data sets for the minerals alunite, buddingtonite, kaolinite, and opal correlate well, with correlation coefficients ranging from 0.75 to 0.91, after compensation for shadowing and misregistration effects.

Spectral unmixing of airborne hyperspectral data for baseline mapping of mine tailings areas

The Kam Kotia mine tailings areas near Timmins in Ontario, Canada have been generating and discharging acidic mine drainage (AMD) into the surrounding areas for more than 35 years, killing large areas of forest and polluting the local water system. This paper presents results from the remote sensing monitoring programme in the Kam Kotia mine. Hyperspectral TRW (Thompson Ramo Wooldridge Inc.) Imaging Spectrometer III data were acquired over the Kam Kotia mine and tailings areas. This paper describes (1) the data pre‐processing (noise removal, atmospheric correction, spectral smile correction, scene‐based calibration) needed to radiometrically calibrate the images and (2) a novel procedure which combines constrained spectral mixture analysis and threshold‐based classification. With this developed procedure one can retrieve fraction maps of major mine tailings‐related surface materials and hence generate a surface map separating green vegetation, transition zones, dead vegetation, and oxidized tailings, and calculate the extent (surficial area) of each of the zones. The four zones are correlated with the extent and degree of vegetation cover affected by tailings material and are interpreted to span respectively from very low to medium, high, and very high AMD pollution. This procedure can be used to monitor changes in the course of the boundary between affected zones and finally quantify the rehabilitation process in mine tailings areas with high vegetation cover.

Recent Developments in the Hyperspectral Environment and Resource Observer (HERO) Mission

In 1997, the Canadian Space Agency (CSA) and Canadian industry began developing enabling technologies for hyperspectral satellites. Since then, the CSA has conducted mission and payload concept studies in preparation for launch of the first Canadian hyperspectral earth observation satellite. This Canadian hyperspectral remote sensing project is now named the Hyperspectral Environment and Resource Observer (HERO) Mission. In 2005, the Preliminary System Requirement Review (PSRR) and the Phase A (Preliminary Mission Definition) were concluded. Recent developments regarding the payload include an extensive comparison of potential optical designs. The payload uses separate grating spectrometers for the visible near-infrared and short-wave infrared portions of the spectrum. The instrument covers a swath of >30 km, has a ground sampling distance of 30 m, a spectral range of 400-2500 nm, and a spectral sampling interval of 10 nm. Smile and keystone are minimized. Recent developments regarding the mission include requirements simplification, data compression studies, and hyperspectral data simulation capability. In addition, a Prototype Data Processing Chain (PDPC) has been defined for 3 key hyperspectral applications. These are: geological mapping in the arctic environment, dominant species identification for forestry, and leaf area index for estimating foliage cover as well as forecasting crop growth and yield in agriculture.

Retrieval of Forest Canopy Parameters by Inversion of the PROFLAIR Leaf-Canopy Reflectance Model Using the LUT Approach

The potential of simulating broad leaf forest canopy spectral reflectance using a canopy-leaf PROFLAIR (PROSPECT + FLAIR) model was investigated in this study. The model was inverted with hyperspectral Hyperion data using a look up table (LUT) approach to retrieve canopy leaf area index (LAI), leaf chlorophyll content (Ca+b) and canopy integrated chlorophyll content (LAI × Ca+b). The LUT was populated by simulating the model in forward mode using a space of realization generated based on the specific distribution of the input parameters and based on a priori information from the field. The estimated variables were then compared to ground measurements collected in the field. The results showed the ability of the PROFLAIR model to realistically simulate canopy spectral reflectance. When compared to ground measurements, the model showed a reasonable performance to retrieve canopy LAI with an RMSE of 0.47 and leaf chlorophyll content with an RMSE of 4.46 μg/cm2.

Potential of Hyperion EO-1 hyperspectral data for wheat crop chlorophyll content estimation

Chlorophyll content is an essential biochemical parameter to track the main developmental stages and yield of cereals relevant for agriculture. In this perspective, several spectral chlorophyll indices have been developed to estimate chlorophyll content at both the leaf and canopy levels using remote sensing data and considering different crop types. The application of these chlorophyll indices under agricultural field conditions has not been rigorously tested and validated for wheat crops. The objective of this study is to investigate the relationship between a wide range of spectral chlorophyll indices and chlorophyll content of wheat crop using hyperspectral data acquired with the Hyperion Earth Observing 1 (EO-1) sensor and ground and laboratory measurements for validation purposes. The Hyperion data and ground measurements were acquired on 30 June 2002 over two agricultural fields near Indian Head, Saskatchewan, Canada. The image data were corrected for a spatial shift between the visible near-infrared (VNIR) and short-wave infrared (SWIR) detectors, destriped, and noise reduced. The data were then transformed to surface reflectance, corrected for sensor smile (1–3 nm shift in the VNIR and SWIR), and postprocessed to remove residual errors. The ground measurements included the leaf chlorophyll in arbitrary units using the soil-plant analyses development 502 (SPAD-502) meter and leaf chlorophyll content estimated from chemical laboratory analysis. The SPAD-502 measurements were correlated with laboratory-extracted leaf chlorophyll content to establish two calibration equations for the computation of chlorophyll ab (Chl ab) and chlorophyll a (Chl a) content, with a coefficient of determination (R2) of 0.72 and 0.69 and a root mean square error (RMSE) of 3.53 and 1.94 µg/cm2, respectively. The chlorophyll indices were derived from the Hyperion data and validated against those derived from a subset of the converted SPAD-502 measurements. The normalized difference pigment index (NDPI) showed the best results for wheat chlorophyll content estimation using the Hyperion data against those derived from the converted SPAD-502 measurements, with an index of agreement (D) of 0.66 and an RMSE of 2.89 µg/cm2. The performance of the NDPI at the wheat canopy level makes it a suitable tool to calibrate biophysical process models used in agriculture.

Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features

Initial steps are proposed and tested in the development of a method for retrieving and (or) refining instrument spectral characteristics for dispersive hyperspectral imagers such as the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (casi), HyMap, Hyperion, and compact high-resolution imaging spectrometer (CHRIS) based on data acquired by the instrument in operation using statistical spectrum matching with moderate-resolution transmittance code (MODTRAN) modelled instrument results in the vicinity of reference Fraunhofer feature windows. Until now, such scene-based retrieval has focused primarily on refining spectral band-centre shifts while assuming that spectral response function (SRF) parameters remain static. In particular, most methods assume that the SRF is of a Gaussian shape. As a consequence of recent investigations showing that scene-based discernment of SRF shape should be feasible given current typical instrument performance, this paper explores algorithmic components deemed necessary for the development of a look-up table (LUT) based retrieval method for obtaining SRF parameters on a band-by-band basis, even in the presence of minor band-centre or bandwidth deviations from nominal instrument specifications. The proposed method employing these components is appropriate for dispersive hyperspectral imagers but not for others, for example Fourier transform hyperspectral imagers. In experiments using nominal implementations of the proposed components, reference spectra match expected LUT spectra in nearly all cases, even when band-centre and bandwidth deviations are considered. This holds true for all three modelled instruments and nearly all of the six selected Fraunhofer windows. Expected signal-to-noise requirements are in many cases challenging, yet feasible using signal-enhancement techniques such as along-track averaging.

Overview of terrestrial imaging spectroscopy missions

This paper provides a brief overview of current civilian imaging spectroscopy (hyperspectral) missions currently operating in space or ready for launch for imaging the Earth. This overview is followed by a list of missions currently under development, and the paper concludes with a survey of missions in a planning stage. The latter is probably not a complete list of missions, but provides a good cross-section of sensors, which might be in space around the 2020 time frame.

Summary of current and future terrestrial civilian hyperspectral spaceborne systems

This paper provides an overview of current and future civilian hyperspectral spaceborne systems for terrestrial applications. For this purpose, a brief history of hyperspectral mission initiatives is given together with the spectral and spatial characteristic of current systems in orbit today. Future sensor systems are divided into missions, which are under development and in a planning stage. The latter category provides a good cross section of sensor systems to come, but is probably not a complete list of hyperspectral instruments considered by the various space agencies.

Remote sensing of crop residue using hyperion (EO-1) data

The goal of this research was to investigate the potential of hyperspectral Hyperion (EO-1) data and constrained linear spectral unmixing analysis (CLSMA) for percent crop residue cover estimation and mapping. The Hyperion image data was acquired at the beginning of the agricultural season, May 20 2002, as well as ground reference measurements for validation purposes. In this period, there is mainly only the presence of bare soil and crop residue before any crop cover development. The image data were corrected for the sensor artifacts: a spatial misregistration between the VNIR and SWIR data, and striping, and, in addition, the noise was reduced. The data were atmospherically corrected and then transformed to surface reflectance and, subsequently, corrected for sensor smile/frown and post-processed to remove residual errors. In order to extract the crop residue fraction (percent cover), the image was unmixed using the pure spectra (endmember) collected in the field simultaneously with Hyperion data from different targets (dry and wet wheat residue, and bright and dark soil) with a GER-3700 spectroradiometer. In order to represent the existing trees and all the photosynthetically targets in the scene, a vegetation endmember was selected from our spectral library. Correlation between ground references measurements and extracted fractions from Hyperion data using CLSMA showed that the method satisfactorily predicts crop residues percent cover (D of 0.94, R2 of 0.73 and RMSE of 8.7%) and soil percent cover (D of 0.91, R2 of 0.70 and RMSE of 10.03%).