Imaging Spectrometers

Transcription

12
Imaging Spectrometers
Michael E. Schaepman
Keywords: imaging spectroscopy, imaging spectrometry,
hyperspectral, airbone, spaceborne.
INTRODUCTION
Imaging spectrometers have significantly improved the understanding of interactions of photons
with the surface and atmosphere. Spectroscopy has
existed since the eighteenth century; the imaging
part of this term became technically possible in the
early 1980s. The first part of this chapter is devoted
to a short historical background of this evolution.
In subsequent sections, imaging spectroscopy is
defined and the main acquisition principles are discussed. The main imaging spectrometers used for
Earth observation are presented, as well as emerging concepts which give an insight in a broad
range of air to spaceborne associated technology.
Imaging spectroscopy has expanded to many other
disciplines, and the approach is used in medicine,
extraterrestrial research, process, and manufacturing industries, just to name a few areas. In addition,
much development is currently seen in other wavelength domains such as the ultraviolet and the
thermal. However, this chapter focuses on Earth
observation based imaging spectrometers in the
solar reflective wavelength range.
HISTORICAL BACKGROUND
Three centuries ago Sir Isaac Newton published
the concept of dispersion of light in his ‘Treatise of
Light,’ and the concept of a spectrometer was born
(Figure 12.1).
The corpuscular theory by Newton was gradually succeeded over time by the wave theory,
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resulting in Maxwell’s equations of electromagnetic waves (Maxwell 1873). But it was only in
the early nineteenth century that quantitative measurement of dispersed light was recognized and
standardized by Joseph von Fraunhofer’s discovery
of the dark lines in the solar spectrum (Fraunhofer
1817) and their interpretation as absorption lines on
the basis of experiments by Bunsen and Kirchhoff
(1863). The term spectroscopy was first used in the
late nineteenth century and provides the empirical foundations for atomic and molecular physics
(Born and Wolf 1999). Following this, astronomers
began to use spectroscopy for determining radial
velocities of stars, clusters, and galaxies and stellar compositions (Hearnshaw 1986). A historical
example of an astronomical spectrometer is George
E. Hale’s spectroheliograph (Figure 12.2) of the
early twentieth century. The spectroheliograph was
designed by this American astronomer to collect spectral images of the sun by simultaneously
scanning the sun’s image across the entrance slit
and a film plate past the exit slit of a two-prism
monochromator.
Advances in technology and increased awareness of the potential of spectroscopy in the 1960s
to 1980s led to the development of the first analytical methods used in remote sensing (Arcybashev
and Belov 1958, Lyon 1962), the inclusion of
‘additional’spectral bands in multispectral imagers
(e.g., the 2.09–2.35 µm band in Landsat for the
detection of hydrothermal alteration minerals), as
well as first airborne and later spaceborne imaging
spectrometer concepts and instruments (Collins
et al. 1982, Goetz et al. 1982, Vane et al. 1984, Vane
1986). Significant recent progress was achieved
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IMAGING SPECTROMETERS
when in particular airborne imaging spectrometers
became available on a wider basis (Goetz et al.
1985, Gower et al. 1987, Kruse et al. 1990, Rickard
et al. 1993, Birk and McCord 1994, Rowlands et al.
1994, Green et al. 1998) helping to prepare for
spaceborne imaging spectrometer activities (Goetz
and Herring 1989). This initial phase of development lasted until the late 1990s, when the first imaging spectrometers were launched in space (e.g.,
MODIS (Salomonson et al. 1989), MERIS (Rast
et al. 1999)). Nevertheless, true imaging spectrometers in space, satisfying a strict definition of a contiguity criterion, are still sparse (CHRIS/PROBA
(Barnsley et al. 2004, Cutter 2006), Hyperion/EO-1
(Pearlman et al. 2003)).
Technological advances in the domain of focal
plane (detector) development (Chorier and Tribolet
2001), readout electronics, storage devices, and
optical design (Mouroulis and Green 2003) are
leading to a significantly better sensing of the
Earth’s surface. Improvements in optical design
(Mouroulis et al. 2000) signal-to-noise, finer and
better defined bandwidths as well as contiguous
spectral sampling combined with the goal of better
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understanding of the modeled interaction of photons with matter (Schläpfer and Schaepman 2002)
will allow for more quantitative, direct and indirect
identification of surface materials, and atmospheric
transmittance based on spectral properties from
ground, air, and space.
DEFINITIONS OF IMAGING
SPECTROSCOPY TERMS
Spectroscopy is defined as the study of light as
a function of wavelength that has been emitted, reflected,or scattered from a solid, liquid, or
gas. In remote sensing, the quantity most used
is (surface) reflectance (expressed as a percentage). Spectroradiometry is the technology for measuring the power of optical radiation in narrow,
contiguous wavelength intervals. The quantities
measured are usually expressed as spectral irradiance (commonly measured in W m−2 nm−1 )
and spectral radiance (commonly measured in
W sr−1 m−2 nm−1 ).
N
M
S
d
a
Fig 18.
D
b
A
B
G
F
g
C
c
E
e
Figure 12.1 Sir Isaac Newton’s ‘Treatise of Light’ discusses the concept of dispersion of light
in 1704. He demonstrated that white light could be split up into component colors using prisms,
and found that each pure color is characterized by a specific refrangibility (Newton 1704).
Figure 12.2 Schematic drawing of Hale’s spectroheliograph, which was used to image the sun
(Wright et al. 1972).
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THE SAGE HANDBOOK OF REMOTE SENSING
Spectroradiometric measurements remain one
of the least reliable of all physical measurements
due to the multidimensionality of the problem,
the instability of the measuring instruments and
standards used, and sparse dissemination of the
principles and techniques used for eliminating
or reducing the measurement errors (Kostkowski
1997).
The term hyperspectral (alternatively also ultraspectral) is used most often for spectroscopy and
spectrometry interchangeably and denotes usually
the presence of a wealth of spectral bands without
further specification. The variable use of the above
terms expresses a variation in flavors, but usually
not a fundamental physical difference. Hyperspectral denotes many spectral bands, which potentially
can be used to solve an n−1 dimensional problem, where n represents the number of spectral
bands. An imaging spectrometer with 200 spectral
bands (i.e., dimensions = 200) can theoretically
solve a spectral unmixing based problem with 199
end members, or can be used in a model inversion
approach with 199 unknowns. Practically, there are
instrument performance limitations (e.g., signal-tonoise ratio (SNR)), or strong correlations between
adjacent bands, as well as ill-posed problems in
model inversion, which reduce this dimensionality
significantly.
The original definition for imaging spectrometry was coined by Goetz et al. (1985) as being
‘the acquisition of images in hundreds of contiguous, registered, spectral bands such that for
each pixel a radiance spectrum can be derived’
(Figure 12.3). A more detailed definition is that
imaging spectrometry (imaging spectroscopy, or
also hyperspectral imaging) is a passive remote
sensing technology for the simultaneous acquisition of spatially coregistered images, in many,
spectrally contiguous bands, measured in calibrated radiance units, from a remotely operated
platform (Schaepman et al. 2006).
In the specific case of imaging spectrometry, the
focus of the refined definition is on many, spectrally
contiguous bands, de-emphasizing the need of
‘hundreds of contiguous bands’ (Goetz, 2007). The
contiguity criteria or the proximity requirement of
spectral bands is usually poorly defined, in particular since all imaging spectrometers in remote sensing undersample the Earth. The Nyquist–Shannon
theorem requires that a perfect reconstruction of
the signal is possible when the sampling frequency
is greater than twice the maximum frequency of
the signal being sampled, which is not the case
in space based imaging spectrometers. The rate of
undersampling requires compromises to be made
in the resolution-acquisition-time domains, which
in turn has fostered the development of deconvolution theories. Initially, instruments having at least
10 adjacent spectral bands with a spectral resolution (or full width at half the maximum (FWHM))
of 10 nm were considered as imaging spectrometers, however, nowadays the understanding is that
imaging spectrometers must be able to sample individual relevant features (absorption, reflectance,
transmittance, and emittance) with at least three
or more contiguous spectral bands at a spectral
resolution smaller than the spectral width of the
feature itself.
Figure 12.3 Original imaging spectrometry concept drawing as used by G. Vane and A. Goetz
(courtesy of NASA JPL).
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Figure 12.4 Conceptual imaging spectrometer data cube with two spatial domains (x and y )
and the spectral domain (λ). Randomly distributed voxels each represent individual
‘radiometers’ (left) and a fully acquired data cube (right).
IMAGING SPECTROMETER PRINCIPLES
In imaging spectrometry a generalized data concept, called the data cube, is used to visualize
the relation between the spatial and the spectral domains present. The spatial data is acquired
by imaging a scene using techniques such as
staring filter wheels, pushbroom, or whiskbroom
scanner, amongst others. When acquiring data in
only one spectral band (monochromatic acquisition), each individual element may be referred
to as a pixel with a spatial extent and a single wavelength. By adding many spectral bands
all pixels can be represented conceptually as
voxels. A voxel (‘volume element’) is a threedimensional equivalent of a pixel, representing
individual radiometers having 3D units of length
(x, y) and wavelength (z). All these individual
radiometers represent the data cube as a 3D discrete
regular grid (Figure 12.4).
Imaging spectrometer data is often visualized as
a data cube formed by a series of image layers,
each layer of which is an individual wavelength
interval. The sides of this cube are color-coded
spectra by intensity, and the top is a three-band
color composite (Figure 12.5).
IMAGING SPECTROMETER DATA
CUBE ACQUISITION
series of lines, and staring systems, filter wheel
systems, or snap shot cameras series of monochromatic images at different spectral wavelengths
(Figure 12.6). The data acquisition process is usually performed until a complete data cube is filled
with voxels. In the following sections, each of the
major acquisition approaches is discussed in more
detail.
Whiskbroom scanners
The acquisition of the data cube is performed differently by different imaging spectrometer technologies. In general, whiskbroom imaging spectrometers collect series of pixels, pushbroom scanners
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Figure 12.5 Imaging spectrometer data
cube acquired by an airborne HyMap system
on 26 August 1998 in Switzerland (Limpbach
Valley). The sides are color-coded spectra by
intensity. (See the color plate section of this
volume for a color version of this figure).
Whiskbroom scanners are usually opto- or electromechanical sensors that cover the field-of-view
(FOV) by a mechanized angular movement using
a scanning mirror sweeping from one edge of the
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swath to the other, or by the mechanical rotation of the sensor system. The inherent sensor
instantaneous field-of-view (IFOV) is therefore a
single spatial pixel and its associated spectral component. The image lines are collected using an
across-track scanning mechanism and the image
is acquired by the forward movement of the platform used (Figure 12.7). The particular advantages
of the whiskbroom scanning principle for imaging
spectrometers include a higher spectral uniformity
since all pixels are recorded using the same detector line array and allowing the optical design to
accommodate a larger detector pixel size.
Because the whiskbroom system can rely on
single detectors, the calibration effort is usually
much simpler than with other systems. In addition, this technology supports in-flight calibration
with scanning reference sources located at the end
or the beginning of each scan line. The disadvantages of this design include the presence of
a mechanical scanning system, the shorter integration time that is available than in pushbroom
based systems, and the image forming geometry
which is dependent on the scanning speed, scan
mirror arrangement and the orthogonal platform
movement. Imaging spectrometers based on the
whiskbroom scanning principle include the airborne AVIRIS (Green et al. 1998), DAIS (Chang
et al. 1993), and HyMap (Cocks et al. 1998)
instruments, as well as the spaceborne MODIS
instrument (Salomonson et al. 1989).
Pushbroom scanners
Line
Pixel
l
Spectral
dimension
Image
x
Spatial dimension
(across track)
y Spatial dimension
(along track)
Figure 12.6 Data cube schematic depicting
the three major acquisition principles of
imaging spectrometers: Pixels are acquired
by whiskbroom systems, lines by pushbroom
systems, and images by filter wheel systems
(or staring cameras).
A pushbroom scanner is a sensor typically without mechanical scanning components for the data
acquisition. The image formation is solely based
on the (forward) movement of the sensor. A pushbroom sensor is an imaging system which acquires
a series of one-dimensional samples orthogonal to
the platform line of flight with the along-track spatial dimension constructed by the forward motion
of the platform. The spectral component is acquired
by dispersing the incoming radiation onto an area
array. Translated to the concept of the data cube, a
pushbroom scanner records the across track dimension x and the spectral dimension λ, representing
lines, simultaneously (Figure 12.8) and the along
track (y) component is acquired with the platform
movement.
Pushbroom scanners have the advantage that
they allow a longer integration time for each
individual detector element in comparison with
whiskbroom based instruments (e.g., the inverse of
the line frequency is equal to the pixel dwell time).
Figure 12.7 Whiskbroom scanning and its representation in the data cube (left single
spectrum (one pixel), middle one scan line, right full data cube).
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l
l
y
y
x
x
Figure 12.8 Pushbroom scanning and its representation in the data cube (a single scan line is
composed out of the across track pixels x and the spectral bands λ (left), resulting in a full
data cube (right)).
l
l
y
y
x
x
Figure 12.9 Filter wheel acquisition and its representation in the data cube (left single
monochromatic image, right full data cube).
In addition, there are distinct and fixed geometric
relations between the pixels within a scan line.
Since area arrays are used as focal planes in
these systems, the uniform calibration of the detector response is critical. In a combined analysis of
SNR, uniformity, and stability, pushbroom scanners might not necessarily outperform whiskbroom
systems even though they have a longer integration time.
Examples of pushbroom based imaging spectrometers include the airborne CASI (Babey and
Anger 1989) and ROSIS (Kunkel et al. 1991)
instruments and the spaceborne MERIS (Rast et al.
1999) and Hyperion (Pearlman et al. 2003).
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Filter wheel cameras
The filter wheel camera is an opto-mechanical sensor that changes the spectral sensitivity of various
channels using a turnable filter wheel in the optical
path. The field-of-view (FOV) therefore represents
a full monochromatic frame, represented in the
data cube by the x and y axis. The spectral component is collected by rotating the filter wheel
to different band pass filters, which have different transmissions for different spectral regions.
The data cube is filled by ‘stacking’ individual
(monochromatic) images on top of each other
(Figure 12.9).
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directly to an area array, avoiding the use of often
bulky and complex optics required for imaging
spectrometers that use gratings or prisms. This
approach acquires a 2D FOV, consisting of x · y
lines (corresponding to the number of x · y pixels of the area array). The difference compared
to a filter wheel is that here the y pixels (in the
along-track direction) record y different spectral
channels but for different adjacent spatial swaths.
With the movement of the platform along-track, the
across-track ground images are sequentially sampled at the range of wavelengths supported by the
wedge filter.
The major advantage of a wedge spectrometer
is the compact design because many optical elements can be avoided. The major disadvantage is
accommodating the Earth rotation (or the platform
movement), which can generate spectral smearing
(or spectral mis-registration).
The calibration of wedge spectrometers is comparable to pushbroom imagers in static environments, although the wider FOV in the along-track
direction may introduce different challenges. An
example of a wedge spectrometer flow in space
is the LAC instrument onboard of EO-1 (Reuter
et al. 2001), others are in planning (Puschell et al.
2001), but the concept has not yet seen significant
data distribution and use.
Other interesting imaging spectrometer concepts
include the computed tomography imaging spectrometer (CTIS) (Descour et al. 1997). CTIS is
a non-scanning instrument capable of simultaneously acquiring full spectral information from
every position within its FOV. The raw image collected by the CTIS consists of 49 diffraction orders.
The 0th diffraction order is located at the center of
the image. This order represents a direct view of
the spatial radiance distribution in the field stop
The particular advantages of the filter wheel
camera consist in the coherent spatial coregistration if not used on a moving platform. This makes
this technology very suitable for staring telescope
applications in astronomy. Most filter wheel cameras use area arrays for the simultaneous coverage
of the spatial extent. If operated from a moving
platform, mosaicing the individual frames is the
most important feature. The calibration of the filter
wheel camera can be performed by using a calibrated spectrometer or band pass filter/detector to
test the sensitivity and non-uniformity of the detector elements. The nonuniformity calibration of the
detector array is the most challenging issue with
this technology. The disadvantages of this design
include the presence of the mechanical turning filter wheel, which necessitates a fast change of the
individual filters on moving platforms. Even so,
individual spectral images may not be aligned satisfactorily. A major advantage is that it is easy
to change the spectral bands by replacing the filter wheel for different applications. In general,
very few airborne or spaceborne imaging spectrometers are based on the filter wheel camera
approach, mostly due to its limitation in simultaneously collecting many spectral bands. However, the
concept has been demonstrated in airborne instruments (e.g., Airborne POLDER (Leroy and Bréon
1996)), spaceborne (e.g., STRV-2 MWIR imager
(Cawley 2003) and in astronomy staring telescopes
using a filter wheel approach.
Other, less frequently used imaging
spectrometer concepts
Wedge spectrometers (Figure 12.10) are based on
a linear spectral wedge filter, which can be mated
l
l
y
x
y
x
Figure 12.10 Wedge spectrometer and its representation in the data cube (left single scan,
right optimal filled cube).
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and exhibits no dispersion. The remaining diffraction orders exhibit dispersion increasing with order
number. Reconstruction of the data cube from the
raw data requires knowledge of how individual
voxels map to the imaging array. Each voxel corresponds to an object volume, measuring xyλ,
where x, y, and λ are the spatial and spectral
sampling intervals, respectively.
Another emerging technology for imaging spectrometers is the use of acousto-optical tunable
filters (AOTF) allowing a rapid change of spectral
bands (Calpe-Maravilla et al. 2004). Conceptually
AOTF based systems are similar to filter wheel
instruments.
EVOLUTION FROM AIRBORNE TO
SPACEBORNE IMAGING SPECTROMETERS
This section presents an overview of selected
instruments which have had an impact on the evolution and development of imaging spectrometers.
Comprehensive and detailed overviews are difficult to generate; however, Kramer (2002) presents
a very complete list of existing and planned
instruments.
Early concepts of acquiring spectral (and directional) information from natural targets were discussed already in 1958 in the former Soviet Union
(Arcybashev and Belov, 1958). The idea was to
acquire a scene – a forest in this case – under
various view angles by using a complex flight pattern (Figure 12.11). In addition, the camera – a
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spectrograph at this time named Spectrovisor – was
tilted to different view directions to increase the
angle of observation.
Inherent limitations of computer I/O performance and – at these times – tape or band recorder
capacity, resulted in first designs of spectrometers that were not capable of imaging the full
swath width continuously. Consequently they were
called profiling systems, having across-track gaps
in the spatial coverage. One of the first profiling instruments that was deployed on an aircraft
was the GER (Geophysical and Environmental
Research Corp., of Millbrook, NY, USA, a company that is no longer in business) MARK II
Airborne Infrared Spectroradiometer (Chiu and
Collins 1978, Figure 12.12).
Much of the technology development for imaging spectrometers took place in the 1970s and 1980s
at the NASA Jet Propulsion Laboratory (JPL) in
Pasadena (USA). At that time, Alex Goetz and
Gregg Vane proposed successfully to use NASA
internal funds to use a hybrid focal plane array
with 32×32 elements, allowing the construction
of an imaging spectrometer that covered the spectral region beyond the 1100 nm cutoff of silicon
arrays (Goetz 2007). These efforts resulted in the
Airborne Imaging Spectrometer (AIS) (Vane et al.
1984) (Figure 12.12).
Following the successful deployment of AIS,
the spectroscopists at JPL proposed a fully fledged
imaging spectrometer program that would range
from the airborne AIS1 and AIS2, the Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS),
as well as two orbiting sensors, the Shuttle Imaging
Figure 12.11 Spectro-directional airborne acquisition pattern assessing forest angular
spectral reflectance (left) and the Spectrovisor imaging spectrograph (right) (Arcybashev and
Belov 1958, Kol’cov 1959) (Reprinted with permission of Juris Druck + Verlag AG).
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Figure 12.12 Airborne imaging spectrometers. From left, top: GER MARK II Airborne Infrared
Spectroradiometer, Airborne Imaging Spectrometer (AIS) instrument assembly, Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS); Middle: Fluorescence Line Imager/
Programmable Multispectral Imager (FLI/PMI), Digital Airborne Imaging Spectrometer
(DAIS7915), Reflective Optics Imaging Spectrometer (ROSIS); Bottom: Shortwave Infrared Full
Spectrum Imager (SFSI), Hyperspectral Digital Imagery Collection Experiment (HYDICE)
detector assembly, and Hyperspectral Mapper (HyMap). (See the color plate section of this
volume for a color version of this figure).
Source: Photos courtesy of: S.-H. Chang, G. Vane, R. Green, R. Baxton, A. Müller, H. van der Piepen,
B. Neville, M. Landers, and M. Schaepman.
Spectrometer Experiment (SISEX) and a satelliteborne instrument, the High Resolution Imaging
Spectrometer (HIRIS).
The development of AVIRIS started in 1984 and
the imager first flew aboard a NASA ER-2 aircraft
at 20 km altitude in 1987 (Vane 1987). Since then
it has gone through major upgrades as technology
changed in detectors, electronics, and computing.
AVIRIS can be seen as the major driver for the
development of imaging spectrometry.
The AVIRIS instrument (Figure 12.12) contains 224 different detectors, each with a wavelength sensitivity range (also known as spectral
bandwidth) of approximately 10 nanometers (nm),
allowing it to cover the entire range between
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380 nm and 2,500 nm. AVIRIS uses a scanning
mirror to sweep back and forth (whiskbroom fashion), producing 614 pixels for the 224 detectors
each scan. The pixel size and swath width of the
AVIRIS data depend on the altitude from which
the data is collected. When collected by the ER-2
from 20 km above the ground, the so-called ‘high
altitude option,’ each pixel produced by the instrument covers an area approximately 20 × 20 m
on the ground (with some overlap between pixels), thus yielding a ground swath about 11 km
wide. When collected by the lower flying Twin
Otter at a 4 km altitude, the ‘low altitude option,’
each ground pixel is 4 × 4 m, and the swath is
2 km wide.
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In Canada, the development of imaging spectrometers has been intensive and G. Borstad proposed the Fluorescence Line Imager/Programmable
Multispectral Imager (FLI/PMI) instrument with
288 spectral bands (‘spectral mode’) and 512
pixels swath width (‘spatial mode’). The first data
were acquired with this instrument around 1985
(Figure 12.12) (Borstad et al. 1985).
An interesting further development of the
FLI/PMI instrument is considered to be the CASI
(CompactAirborne Spectrographic Imager), developed as a combination of a spectral or spatial
imager similar to the FLI/PMI but subsequently
enhanced to an operational and commercially flown
instrument for many years (Babey and Anger 1989,
Gower et al. 1992). ITRES Corp., the manufacturer
of the CASI line of instruments, can be seen as an
offspring of Moniteq, who produced the FLI/PMI
instrument.
In Europe, airborne imaging spectrometers were
first primarily flown by leasing instruments from
the US or Canada. However, thanks to the efforts of
the German Aerospace Centre (DLR, Oberpfaffenhofen (GER)) two instruments became available
on a broader basis for the user community. First,
a European Commission supported purchase of a
GER imaging spectrometer (Collins and Chang,
1990) with particular features such as the inclusion
of a thermal range (Chang et al. 1993) prompted a
European proposal for anAirborne Remote Sensing
Capability (EARSEC) (Carrère et al. 1995). The
instrument was named GER DAIS 7915 (Digital
Airborne Imaging Spectrometer) and incorporated
72 solar reflective and 7 mid-infrared/thermal
bands (79 in total). Its operation was eventually discontinued in 2005, after having served 10 years in
Europe fostering the use of imaging spectrometers
(Figure 12.12).
The second development by DLR can be considered an airborne forerunner instrument for the
spaceborne MERIS on ENVISAT and was named
ROSIS (Kunkel et al. 1991). The Reflective Optics
System Imaging Spectrometer was tested first in
1989 (van der Piepen et al. 1989) and featured
a CCD–based pushbroom design. The instrument
included a choice of selectable bands (32 out of
128) covering the wavelength range significant for
coastal zones and oceans (400–1100 nm). A totally
revised version of ROSIS was presented under
the name ROSIS-03 in 1998 (Gege et al. 1998)
(Figure 12.12).
Two other interesting airborne instruments were
also developed. One was the SWIR Full Spectrum Imager (SFSI) (Neville and Powell 1992).
SFSI employs a two-dimensional platinum silicide Schottky barrier CCD array with 488 rows
of 512 detector elements. In operation, a region
of 480 lines by 496 columns is used; two adjacent lines are summed together on the detector
array to yield an effective array of 240 by 496
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detector elements. This gives 240 spectral bands
for each of 496 pixels in the across-track dimension for each integration period, which is hardware
selectable by various clock speeds (40–67 ms). The
data are digitized to 13 bits and recorded as 16 bits
(Figure 12.12).
The HYDICE instrument is also worth mentioning. HYDICE (Rickard et al. 1993) was a program
to develop a state-of-the-art imaging spectrometer
to support utility studies of high spectral resolution measurements in the 400–2500 nm range. The
program was initiated by the U.S. Congress to
investigate the application of hyperspectral data to
the needs of federal agencies (forest assessment for
the U.S. Department of Agriculture, mineral exploration for the U.S. Geological Survey, and so forth).
The sensor was built by Hughes Danbury Optical
Systems, Inc., and integrated into a Convair 580
aircraft operated by the Environmental Research
Institute of Michigan (ERIM). The HYDICE sensor made its first data collection flight on 26 January
1995. The HYDICE sensor is a pushbroom imaging spectrometer that uses a biprism dispersing
element and a two-dimensional focal plane array
to enable a single optical path design. The array
is a 320 × 210 element InSb array fabricated by
Hughes Santa Barbara Research Center, with multiple gain regions to support operation over the
full 400–2500 nm spectral range. The array is
electronically shuttered with a fixed read time of
7.3 ms. The frame rate can be adjusted from 8.3
to 50 ms, allowing one to use nearly the full range
of velocity to height (V /H) ratios within the flight
envelope of the CV 580. In particular, the altitude
range from 5,000 to 25,000 feet can be used to
achieve spatial resolutions from 0.8 to 4 meters
(Figure 12.12).
A widely available instrument is the HyMap.
Manufactured by Integrated Spectronics of
Australia, and operated by HyVista Corp., also
of Australia, HyMap (and its predecessor called
Probe1) became operational in 1996 (Cocks et al.
1998). The HyMap series of airborne hyperspectral
scanners have been deployed in a large number
of countries, undertaking hyperspectral remote
sensing surveys in support of a wide variety of
applications ranging from mineral exploration
to defense research to satellite simulation. The
evolution of the HyMap series continues with
the development of a system providing hyperspectral coverage across the solar wavelengths
(0.4–2.5 µm) and 32 bands in the thermal infrared
(8–12 µm) (Figure 12.12).
Spaceborne imaging spectrometers are currently
still only sparsely available. Following a strict
interpretation of the definition of an imaging spectrometer, only Hyperion on EO1 (Pearlman et al.
2003) and CHRIS on PROBA (Barnsley et al.
2004) can be considered true imaging spectrometers. MERIS on ENVISAT (Rast et al. 1999) is
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designed like a true imaging spectrometer using a
pushbroom concept with continuous dispersion on
the CCD array, but does not read out all contiguous
spectral bands for the use in the ground processing and archiving facility. MODIS (Salomonson
et al. 1989) on Terra and Aqua satisfies the classical
10 spectral bands and 10 nm spectral resolution
definitions. However, MODIS does not satisfy the
continuity criterion and is technologically built as
an advanced multiband spectrometer using whiskbroom acquisition.
EMERGING INSTRUMENTS
AND CONCEPTS
Earth observation based on imaging spectroscopy
has been transformed in less than 30 years from
a sparsely available research tool into a commodity product available to a broad user community. Currently, imaging spectrometer data are
widespread and they prove, for example, that distributed models of biosphere processes can assimilate these observations to improve estimates of Net
Primary Production, and that in combination with
data assimilation methods, can estimate complex
variables such as soil respiration, at various spatial scales (Schaepman 2007). However, a lack of
data continuity of airborne and spaceborne imaging spectrometer missions remain a continuing
challenge to the user community.
In addition, imaging spectrometers do not only
cover the solar reflective part of the electromagnetic spectrum of land surfaces, they increasingly
also cover atmospheric sounding (e.g., SCIAMACHY (Bovensmann et al. 1999), and GIFTS
(Elwell et al. 2006)) and the thermal region
(SEBASS (Hackwell et al. 1996)).
In any case, there is an emerging need to converge from exploratory mission concepts (e.g.,
former ESA’s Earth Explorer Core Mission proposal SPECTRA (Rast et al. 2004)) and technology
demonstrators (e.g., NASA’s Hyperion on EO-1),
and operational precursor missions (e.g., SSTL’s
CHRIS on the ESA PROBA mission), toward
systematic measurement and operational missions (e.g., ESA’s MERIS on ENVISAT, NASA’s
MODIS on Terra/Aqua).
Several initiatives proposing space operated
Earth Observation imaging spectrometers in the
above categories have been submitted for evaluation and approval (e.g., EnMAP (Hofer et al.
2006)) or Flora (Asner et al. 2005)). However for
the time being, existing and future airborne imaging spectrometer initiatives (e.g., ARES (Richter
et al. 2005), APEX (Schaepman et al. 2004)) will
continue to provide regular access to imaging spectrometer data, before routine collection at regional
and global scales will be available.
[17:24 2/3/2009 5270-Warner-Ch12.tex]
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Imaging Spectrometers

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