Michael E. Schaepman - UZH · 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

[17:24 2/3/2009 5270-Warner-Ch12.tex] Paper Size: a4 paper Job No: 5270 Warner: The SAGE Handbook of Remote Sensing Page: 166 166–178

12Imaging Spectrometers

M i c h a e l E . S c h a e p m a n

Keywords: imaging spectroscopy, imaging spectrometry,hyperspectral, airbone, spaceborne.

INTRODUCTION

Imaging spectrometers have significantly im-proved the understanding of interactions of photonswith the surface and atmosphere. Spectroscopy hasexisted since the eighteenth century; the imagingpart of this term became technically possible in theearly 1980s. The first part of this chapter is devotedto a short historical background of this evolution.In subsequent sections, imaging spectroscopy isdefined and the main acquisition principles are dis-cussed. The main imaging spectrometers used forEarth observation are presented, as well as emerg-ing concepts which give an insight in a broadrange of air to spaceborne associated technology.Imaging spectroscopy has expanded to many otherdisciplines, and the approach is used in medicine,extraterrestrial research, process, and manufactur-ing industries, just to name a few areas. In addition,much development is currently seen in other wave-length domains such as the ultraviolet and thethermal. However, this chapter focuses on Earthobservation based imaging spectrometers in thesolar reflective wavelength range.

HISTORICAL BACKGROUND

Three centuries ago Sir Isaac Newton publishedthe concept of dispersion of light in his ‘Treatise ofLight,’ and the concept of a spectrometer was born(Figure 12.1).

The corpuscular theory by Newton was grad-ually succeeded over time by the wave theory,

resulting in Maxwell’s equations of electromag-netic waves (Maxwell 1873). But it was only inthe early nineteenth century that quantitative mea-surement of dispersed light was recognized andstandardized by Joseph von Fraunhofer’s discoveryof the dark lines in the solar spectrum (Fraunhofer1817) and their interpretation as absorption lines onthe basis of experiments by Bunsen and Kirchhoff(1863). The term spectroscopy was first used in thelate nineteenth century and provides the empiri-cal foundations for atomic and molecular physics(Born and Wolf 1999). Following this, astronomersbegan to use spectroscopy for determining radialvelocities of stars, clusters, and galaxies and stel-lar compositions (Hearnshaw 1986). A historicalexample of an astronomical spectrometer is GeorgeE. Hale’s spectroheliograph (Figure 12.2) of theearly twentieth century. The spectroheliograph wasdesigned by this American astronomer to col-lect spectral images of the sun by simultaneouslyscanning the sun’s image across the entrance slitand a film plate past the exit slit of a two-prismmonochromator.

Advances in technology and increased aware-ness of the potential of spectroscopy in the 1960sto 1980s led to the development of the first analyti-cal methods used in remote sensing (Arcybashevand 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 thedetection of hydrothermal alteration minerals), aswell as first airborne and later spaceborne imagingspectrometer concepts and instruments (Collinset al. 1982, Goetz et al. 1982, Vane et al. 1984, Vane1986). Significant recent progress was achieved


IMAGING SPECTROMETERS 167

when in particular airborne imaging spectrometersbecame available on a wider basis (Goetz et al.1985, Gower et al. 1987, Kruse et al. 1990, Rickardet al. 1993, Birk and McCord 1994, Rowlands et al.1994, Green et al. 1998) helping to prepare forspaceborne imaging spectrometer activities (Goetzand Herring 1989). This initial phase of develop-ment lasted until the late 1990s, when the first imag-ing spectrometers were launched in space (e.g.,MODIS (Salomonson et al. 1989), MERIS (Rastet al. 1999)). Nevertheless, true imaging spectrom-eters in space, satisfying a strict definition of a con-tiguity 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 focalplane (detector) development (Chorier and Tribolet2001), readout electronics, storage devices, andoptical design (Mouroulis and Green 2003) areleading to a significantly better sensing of theEarth’s surface. Improvements in optical design(Mouroulis et al. 2000) signal-to-noise, finer andbetter defined bandwidths as well as contiguousspectral sampling combined with the goal of better

understanding of the modeled interaction of pho-tons with matter (Schläpfer and Schaepman 2002)will allow for more quantitative, direct and indirectidentification of surface materials, and atmospherictransmittance based on spectral properties fromground, air, and space.

DEFINITIONS OF IMAGINGSPECTROSCOPY TERMS

Spectroscopy is defined as the study of light asa function of wavelength that has been emit-ted, reflected,or scattered from a solid, liquid, orgas. In remote sensing, the quantity most usedis (surface) reflectance (expressed as a percent-age). Spectroradiometry is the technology for mea-suring the power of optical radiation in narrow,contiguous wavelength intervals. The quantitiesmeasured are usually expressed as spectral irra-diance (commonly measured in W m−2 nm−1)and spectral radiance (commonly measured inW sr−1 m−2 nm−1).

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Figure 12.1 Sir Isaac Newton’s ‘Treatise of Light’ discusses the concept of dispersion of lightin 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).


168 THE SAGE HANDBOOK OF REMOTE SENSING

Spectroradiometric measurements remain oneof the least reliable of all physical measurementsdue to the multidimensionality of the problem,the instability of the measuring instruments andstandards used, and sparse dissemination of theprinciples and techniques used for eliminatingor reducing the measurement errors (Kostkowski1997).

The term hyperspectral (alternatively also ultra-spectral) is used most often for spectroscopy andspectrometry interchangeably and denotes usuallythe presence of a wealth of spectral bands withoutfurther specification. The variable use of the aboveterms expresses a variation in flavors, but usuallynot a fundamental physical difference. Hyperspec-tral denotes many spectral bands, which potentiallycan be used to solve an n−1 dimensional prob-lem, where n represents the number of spectralbands. An imaging spectrometer with 200 spectralbands (i.e., dimensions = 200) can theoreticallysolve a spectral unmixing based problem with 199end members, or can be used in a model inversionapproach with 199 unknowns. Practically, there areinstrument performance limitations (e.g., signal-to-noise ratio (SNR)), or strong correlations betweenadjacent bands, as well as ill-posed problems inmodel inversion, which reduce this dimensionalitysignificantly.

The original definition for imaging spectrome-try was coined by Goetz et al. (1985) as being‘the acquisition of images in hundreds of con-tiguous, registered, spectral bands such that foreach pixel a radiance spectrum can be derived’(Figure 12.3). A more detailed definition is that

imaging spectrometry (imaging spectroscopy, oralso hyperspectral imaging) is a passive remotesensing technology for the simultaneous acqui-sition of spatially coregistered images, in many,spectrally contiguous bands, measured in cali-brated radiance units, from a remotely operatedplatform (Schaepman et al. 2006).

In the specific case of imaging spectrometry, thefocus of the refined definition is on many, spectrallycontiguous bands, de-emphasizing the need of‘hundreds of contiguous bands’ (Goetz, 2007). Thecontiguity criteria or the proximity requirement ofspectral bands is usually poorly defined, in particu-lar since all imaging spectrometers in remote sens-ing undersample the Earth. The Nyquist–Shannontheorem requires that a perfect reconstruction ofthe signal is possible when the sampling frequencyis greater than twice the maximum frequency ofthe signal being sampled, which is not the casein space based imaging spectrometers. The rate ofundersampling requires compromises to be madein the resolution-acquisition-time domains, whichin turn has fostered the development of deconvolu-tion theories. Initially, instruments having at least10 adjacent spectral bands with a spectral resolu-tion (or full width at half the maximum (FWHM))of 10 nm were considered as imaging spectrome-ters, however, nowadays the understanding is thatimaging spectrometers must be able to sample indi-vidual relevant features (absorption, reflectance,transmittance, and emittance) with at least threeor more contiguous spectral bands at a spectralresolution smaller than the spectral width of thefeature itself.

Figure 12.3 Original imaging spectrometry concept drawing as used by G. Vane and A. Goetz(courtesy of NASA JPL).



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 con-cept, called the data cube, is used to visualizethe relation between the spatial and the spec-tral domains present. The spatial data is acquiredby imaging a scene using techniques such asstaring filter wheels, pushbroom, or whiskbroomscanner, amongst others. When acquiring data inonly one spectral band (monochromatic acquisi-tion), each individual element may be referredto as a pixel with a spatial extent and a sin-gle wavelength. By adding many spectral bandsall pixels can be represented conceptually asvoxels. A voxel (‘volume element’) is a three-dimensional equivalent of a pixel, representingindividual radiometers having 3D units of length(x, y) and wavelength (z). All these individualradiometers represent the data cube as a 3D discreteregular grid (Figure 12.4).

Imaging spectrometer data is often visualized asa data cube formed by a series of image layers,each layer of which is an individual wavelengthinterval. The sides of this cube are color-codedspectra by intensity, and the top is a three-bandcolor composite (Figure 12.5).

IMAGING SPECTROMETER DATACUBE ACQUISITION

The acquisition of the data cube is performed differ-ently by different imaging spectrometer technolo-gies. In general, whiskbroom imaging spectrome-ters collect series of pixels, pushbroom scanners

Figure 12.5 Imaging spectrometer datacube acquired by an airborne HyMap systemon 26 August 1998 in Switzerland (LimpbachValley). The sides are color-coded spectra byintensity. (See the color plate section of thisvolume for a color version of this figure).

series of lines, and staring systems, filter wheelsystems, or snap shot cameras series of monochro-matic images at different spectral wavelengths(Figure 12.6). The data acquisition process is usu-ally performed until a complete data cube is filledwith voxels. In the following sections, each of themajor acquisition approaches is discussed in moredetail.

Whiskbroom scanners

Whiskbroom scanners are usually opto- or elec-tromechanical sensors that cover the field-of-view(FOV) by a mechanized angular movement usinga scanning mirror sweeping from one edge of the



swath to the other, or by the mechanical rota-tion of the sensor system. The inherent sensorinstantaneous field-of-view (IFOV) is therefore asingle spatial pixel and its associated spectral com-ponent. The image lines are collected using anacross-track scanning mechanism and the imageis acquired by the forward movement of the plat-form used (Figure 12.7). The particular advantagesof the whiskbroom scanning principle for imagingspectrometers include a higher spectral uniformitysince all pixels are recorded using the same detec-tor line array and allowing the optical design toaccommodate a larger detector pixel size.

Because the whiskbroom system can rely onsingle detectors, the calibration effort is usually

Pixel

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y Spatial dimension(along track)

Spectraldimensionl

Figure 12.6 Data cube schematic depictingthe three major acquisition principles ofimaging spectrometers: Pixels are acquiredby whiskbroom systems, lines by pushbroomsystems, and images by filter wheel systems(or staring cameras).

much simpler than with other systems. In addi-tion, this technology supports in-flight calibrationwith scanning reference sources located at the endor the beginning of each scan line. The disad-vantages of this design include the presence ofa mechanical scanning system, the shorter inte-gration time that is available than in pushbroombased systems, and the image forming geometrywhich is dependent on the scanning speed, scanmirror arrangement and the orthogonal platformmovement. Imaging spectrometers based on thewhiskbroom scanning principle include the air-borne AVIRIS (Green et al. 1998), DAIS (Changet al. 1993), and HyMap (Cocks et al. 1998)instruments, as well as the spaceborne MODISinstrument (Salomonson et al. 1989).

Pushbroom scanners

A pushbroom scanner is a sensor typically with-out mechanical scanning components for the dataacquisition. The image formation is solely basedon the (forward) movement of the sensor. A push-broom sensor is an imaging system which acquiresa series of one-dimensional samples orthogonal tothe platform line of flight with the along-track spa-tial dimension constructed by the forward motionof the platform. The spectral component is acquiredby dispersing the incoming radiation onto an areaarray. Translated to the concept of the data cube, apushbroom scanner records the across track dimen-sion x and the spectral dimension λ, representinglines, simultaneously (Figure 12.8) and the alongtrack (y) component is acquired with the platformmovement.

Pushbroom scanners have the advantage thatthey allow a longer integration time for eachindividual detector element in comparison withwhiskbroom based instruments (e.g., the inverse ofthe line frequency is equal to the pixel dwell time).

Figure 12.7 Whiskbroom scanning and its representation in the data cube (left singlespectrum (one pixel), middle one scan line, right full data cube).



x

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Figure 12.8 Pushbroom scanning and its representation in the data cube (a single scan line iscomposed out of the across track pixels x and the spectral bands λ (left), resulting in a fulldata cube (right)).

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Figure 12.9 Filter wheel acquisition and its representation in the data cube (left singlemonochromatic image, right full data cube).

In addition, there are distinct and fixed geometricrelations between the pixels within a scan line.

Since area arrays are used as focal planes inthese systems, the uniform calibration of the detec-tor response is critical. In a combined analysis ofSNR, uniformity, and stability, pushbroom scan-ners might not necessarily outperform whiskbroomsystems even though they have a longer integra-tion time.

Examples of pushbroom based imaging spec-trometers include the airborne CASI (Babey andAnger 1989) and ROSIS (Kunkel et al. 1991)instruments and the spaceborne MERIS (Rast et al.1999) and Hyperion (Pearlman et al. 2003).

Filter wheel cameras

The filter wheel camera is an opto-mechanical sen-sor that changes the spectral sensitivity of variouschannels using a turnable filter wheel in the opticalpath. The field-of-view (FOV) therefore representsa full monochromatic frame, represented in thedata cube by the x and y axis. The spectral com-ponent is collected by rotating the filter wheelto different band pass filters, which have differ-ent transmissions for different spectral regions.The data cube is filled by ‘stacking’ individual(monochromatic) images on top of each other(Figure 12.9).



The particular advantages of the filter wheelcamera consist in the coherent spatial coregistra-tion if not used on a moving platform. This makesthis technology very suitable for staring telescopeapplications in astronomy. Most filter wheel cam-eras use area arrays for the simultaneous coverageof the spatial extent. If operated from a movingplatform, mosaicing the individual frames is themost important feature. The calibration of the filterwheel camera can be performed by using a cali-brated spectrometer or band pass filter/detector totest the sensitivity and non-uniformity of the detec-tor elements. The nonuniformity calibration of thedetector array is the most challenging issue withthis technology. The disadvantages of this designinclude the presence of the mechanical turning fil-ter wheel, which necessitates a fast change of theindividual filters on moving platforms. Even so,individual spectral images may not be aligned sat-isfactorily. A major advantage is that it is easyto change the spectral bands by replacing the fil-ter wheel for different applications. In general,very few airborne or spaceborne imaging spec-trometers are based on the filter wheel cameraapproach, mostly due to its limitation in simultane-ously collecting many spectral bands. However, theconcept has been demonstrated in airborne instru-ments (e.g., Airborne POLDER (Leroy and Bréon1996)), spaceborne (e.g., STRV-2 MWIR imager(Cawley 2003) and in astronomy staring telescopesusing a filter wheel approach.

Other, less frequently used imagingspectrometer concepts

Wedge spectrometers (Figure 12.10) are based ona linear spectral wedge filter, which can be mated

directly to an area array, avoiding the use of oftenbulky and complex optics required for imagingspectrometers that use gratings or prisms. Thisapproach acquires a 2D FOV, consisting of x · ylines (corresponding to the number of x · y pix-els of the area array). The difference comparedto a filter wheel is that here the y pixels (in thealong-track direction) record y different spectralchannels but for different adjacent spatial swaths.With the movement of the platform along-track, theacross-track ground images are sequentially sam-pled at the range of wavelengths supported by thewedge filter.

The major advantage of a wedge spectrometeris the compact design because many optical ele-ments can be avoided. The major disadvantage isaccommodating the Earth rotation (or the platformmovement), which can generate spectral smearing(or spectral mis-registration).

The calibration of wedge spectrometers is com-parable to pushbroom imagers in static environ-ments, although the wider FOV in the along-trackdirection may introduce different challenges. Anexample of a wedge spectrometer flow in spaceis the LAC instrument onboard of EO-1 (Reuteret al. 2001), others are in planning (Puschell et al.2001), but the concept has not yet seen significantdata distribution and use.

Other interesting imaging spectrometer conceptsinclude the computed tomography imaging spec-trometer (CTIS) (Descour et al. 1997). CTIS isa non-scanning instrument capable of simulta-neously acquiring full spectral information fromevery position within its FOV. The raw image col-lected by the CTIS consists of 49 diffraction orders.The 0th diffraction order is located at the center ofthe image. This order represents a direct view ofthe spatial radiance distribution in the field stop

x

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Figure 12.10 Wedge spectrometer and its representation in the data cube (left single scan,right optimal filled cube).



and exhibits no dispersion. The remaining diffrac-tion orders exhibit dispersion increasing with ordernumber. Reconstruction of the data cube from theraw data requires knowledge of how individualvoxels map to the imaging array. Each voxel corre-sponds to an object volume, measuring �x�y�λ,where �x, �y, and �λ are the spatial and spectralsampling intervals, respectively.

Another emerging technology for imaging spec-trometers is the use of acousto-optical tunablefilters (AOTF) allowing a rapid change of spectralbands (Calpe-Maravilla et al. 2004). ConceptuallyAOTF based systems are similar to filter wheelinstruments.

EVOLUTION FROM AIRBORNE TOSPACEBORNE IMAGING SPECTROMETERS

This section presents an overview of selectedinstruments which have had an impact on the evo-lution and development of imaging spectrometers.Comprehensive and detailed overviews are diffi-cult to generate; however, Kramer (2002) presentsa very complete list of existing and plannedinstruments.

Early concepts of acquiring spectral (and direc-tional) information from natural targets were dis-cussed already in 1958 in the former Soviet Union(Arcybashev and Belov, 1958). The idea was toacquire a scene – a forest in this case – undervarious view angles by using a complex flight pat-tern (Figure 12.11). In addition, the camera – a

spectrograph at this time named Spectrovisor – wastilted to different view directions to increase theangle of observation.

Inherent limitations of computer I/O perfor-mance and – at these times – tape or band recordercapacity, resulted in first designs of spectrome-ters that were not capable of imaging the fullswath width continuously. Consequently they werecalled profiling systems, having across-track gapsin the spatial coverage. One of the first profil-ing instruments that was deployed on an aircraftwas the GER (Geophysical and EnvironmentalResearch Corp., of Millbrook, NY, USA, a com-pany that is no longer in business) MARK IIAirborne Infrared Spectroradiometer (Chiu andCollins 1978, Figure 12.12).

Much of the technology development for imag-ing spectrometers took place in the 1970s and 1980sat the NASA Jet Propulsion Laboratory (JPL) inPasadena (USA). At that time, Alex Goetz andGregg Vane proposed successfully to use NASAinternal funds to use a hybrid focal plane arraywith 32×32 elements, allowing the constructionof an imaging spectrometer that covered the spec-tral region beyond the 1100 nm cutoff of siliconarrays (Goetz 2007). These efforts resulted in theAirborne Imaging Spectrometer (AIS) (Vane et al.1984) (Figure 12.12).

Following the successful deployment of AIS,the spectroscopists at JPL proposed a fully fledgedimaging spectrometer program that would rangefrom the airborne AIS1 and AIS2, the AirborneVisible/Infrared Imaging Spectrometer (AVIRIS),as well as two orbiting sensors, the Shuttle Imaging

Figure 12.11 Spectro-directional airborne acquisition pattern assessing forest angularspectral reflectance (left) and the Spectrovisor imaging spectrograph (right) (Arcybashev andBelov 1958, Kol’cov 1959) (Reprinted with permission of Juris Druck + Verlag AG).



Figure 12.12 Airborne imaging spectrometers. From left, top: GER MARK II Airborne InfraredSpectroradiometer, Airborne Imaging Spectrometer (AIS) instrument assembly, AirborneVisible/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 FullSpectrum Imager (SFSI), Hyperspectral Digital Imagery Collection Experiment (HYDICE)detector assembly, and Hyperspectral Mapper (HyMap). (See the color plate section of thisvolume 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 satellite-borne instrument, the High Resolution ImagingSpectrometer (HIRIS).

The development of AVIRIS started in 1984 andthe imager first flew aboard a NASA ER-2 aircraftat 20 km altitude in 1987 (Vane 1987). Since thenit has gone through major upgrades as technologychanged in detectors, electronics, and computing.AVIRIS can be seen as the major driver for thedevelopment of imaging spectrometry.

The AVIRIS instrument (Figure 12.12) con-tains 224 different detectors, each with a wave-length sensitivity range (also known as spectralbandwidth) of approximately 10 nanometers (nm),allowing it to cover the entire range between

380 nm and 2,500 nm. AVIRIS uses a scanningmirror to sweep back and forth (whiskbroom fash-ion), producing 614 pixels for the 224 detectorseach scan. The pixel size and swath width of theAVIRIS data depend on the altitude from whichthe data is collected. When collected by the ER-2from 20 km above the ground, the so-called ‘highaltitude option,’ each pixel produced by the instru-ment covers an area approximately 20 × 20 mon the ground (with some overlap between pix-els), thus yielding a ground swath about 11 kmwide. When collected by the lower flying TwinOtter at a 4 km altitude, the ‘low altitude option,’each ground pixel is 4 × 4 m, and the swath is2 km wide.



In Canada, the development of imaging spec-trometers has been intensive and G. Borstad propo-sed the Fluorescence Line Imager/ProgrammableMultispectral Imager (FLI/PMI) instrument with288 spectral bands (‘spectral mode’) and 512pixels swath width (‘spatial mode’). The first datawere acquired with this instrument around 1985(Figure 12.12) (Borstad et al. 1985).

An interesting further development of theFLI/PMI instrument is considered to be the CASI(CompactAirborne Spectrographic Imager), devel-oped as a combination of a spectral or spatialimager similar to the FLI/PMI but subsequentlyenhanced to an operational and commercially flowninstrument for many years (Babey and Anger 1989,Gower et al. 1992). ITRES Corp., the manufacturerof the CASI line of instruments, can be seen as anoffspring of Moniteq, who produced the FLI/PMIinstrument.

In Europe, airborne imaging spectrometers werefirst primarily flown by leasing instruments fromthe US or Canada. However, thanks to the efforts ofthe German Aerospace Centre (DLR, Oberpfaffen-hofen (GER)) two instruments became availableon a broader basis for the user community. First,a European Commission supported purchase of aGER imaging spectrometer (Collins and Chang,1990) with particular features such as the inclusionof a thermal range (Chang et al. 1993) prompted aEuropean proposal for anAirborne Remote SensingCapability (EARSEC) (Carrère et al. 1995). Theinstrument was named GER DAIS 7915 (DigitalAirborne Imaging Spectrometer) and incorporated72 solar reflective and 7 mid-infrared/thermalbands (79 in total). Its operation was eventually dis-continued in 2005, after having served 10 years inEurope fostering the use of imaging spectrometers(Figure 12.12).

The second development by DLR can be con-sidered an airborne forerunner instrument for thespaceborne MERIS on ENVISAT and was namedROSIS (Kunkel et al. 1991). The Reflective OpticsSystem Imaging Spectrometer was tested first in1989 (van der Piepen et al. 1989) and featureda CCD–based pushbroom design. The instrumentincluded a choice of selectable bands (32 out of128) covering the wavelength range significant forcoastal zones and oceans (400–1100 nm). A totallyrevised version of ROSIS was presented underthe name ROSIS-03 in 1998 (Gege et al. 1998)(Figure 12.12).

Two other interesting airborne instruments werealso developed. One was the SWIR Full Spec-trum Imager (SFSI) (Neville and Powell 1992).SFSI employs a two-dimensional platinum sili-cide Schottky barrier CCD array with 488 rowsof 512 detector elements. In operation, a regionof 480 lines by 496 columns is used; two adja-cent lines are summed together on the detectorarray to yield an effective array of 240 by 496

detector elements. This gives 240 spectral bandsfor each of 496 pixels in the across-track dimen-sion for each integration period, which is hardwareselectable by various clock speeds (40–67 ms). Thedata are digitized to 13 bits and recorded as 16 bits(Figure 12.12).

The HYDICE instrument is also worth mention-ing. HYDICE (Rickard et al. 1993) was a programto develop a state-of-the-art imaging spectrometerto support utility studies of high spectral resolu-tion measurements in the 400–2500 nm range. Theprogram was initiated by the U.S. Congress toinvestigate the application of hyperspectral data tothe needs of federal agencies (forest assessment forthe U.S. Department of Agriculture, mineral explo-ration for the U.S. Geological Survey, and so forth).The sensor was built by Hughes Danbury OpticalSystems, Inc., and integrated into a Convair 580aircraft operated by the Environmental ResearchInstitute of Michigan (ERIM). The HYDICE sen-sor made its first data collection flight on 26 January1995. The HYDICE sensor is a pushbroom imag-ing spectrometer that uses a biprism dispersingelement and a two-dimensional focal plane arrayto enable a single optical path design. The arrayis a 320 × 210 element InSb array fabricated byHughes Santa Barbara Research Center, with mul-tiple gain regions to support operation over thefull 400–2500 nm spectral range. The array iselectronically shuttered with a fixed read time of7.3 ms. The frame rate can be adjusted from 8.3to 50 ms, allowing one to use nearly the full rangeof velocity to height (V /H) ratios within the flightenvelope of the CV 580. In particular, the altituderange from 5,000 to 25,000 feet can be used toachieve spatial resolutions from 0.8 to 4 meters(Figure 12.12).

A widely available instrument is the HyMap.Manufactured by Integrated Spectronics ofAustralia, and operated by HyVista Corp., alsoof Australia, HyMap (and its predecessor calledProbe1) became operational in 1996 (Cocks et al.1998). The HyMap series of airborne hyperspectralscanners have been deployed in a large numberof countries, undertaking hyperspectral remotesensing surveys in support of a wide variety ofapplications ranging from mineral explorationto defense research to satellite simulation. Theevolution of the HyMap series continues withthe development of a system providing hyper-spectral 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 currentlystill only sparsely available. Following a strictinterpretation of the definition of an imaging spec-trometer, only Hyperion on EO1 (Pearlman et al.2003) and CHRIS on PROBA (Barnsley et al.2004) can be considered true imaging spectrom-eters. MERIS on ENVISAT (Rast et al. 1999) is



designed like a true imaging spectrometer using apushbroom concept with continuous dispersion onthe CCD array, but does not read out all contiguousspectral bands for the use in the ground process-ing and archiving facility. MODIS (Salomonsonet al. 1989) on Terra and Aqua satisfies the classical10 spectral bands and 10 nm spectral resolutiondefinitions. However, MODIS does not satisfy thecontinuity criterion and is technologically built asan advanced multiband spectrometer using whiskb-room acquisition.

EMERGING INSTRUMENTSAND CONCEPTS

Earth observation based on imaging spectroscopyhas been transformed in less than 30 years froma sparsely available research tool into a com-modity product available to a broad user com-munity. Currently, imaging spectrometer data arewidespread and they prove, for example, that dis-tributed models of biosphere processes can assimi-late these observations to improve estimates of NetPrimary Production, and that in combination withdata assimilation methods, can estimate complexvariables such as soil respiration, at various spa-tial scales (Schaepman 2007). However, a lack ofdata continuity of airborne and spaceborne imag-ing spectrometer missions remain a continuingchallenge to the user community.

In addition, imaging spectrometers do not onlycover the solar reflective part of the electromag-netic spectrum of land surfaces, they increasinglyalso cover atmospheric sounding (e.g., SCIA-MACHY (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 con-verge from exploratory mission concepts (e.g.,former ESA’s Earth Explorer Core Mission pro-posal SPECTRA (Rast et al. 2004)) and technologydemonstrators (e.g., NASA’s Hyperion on EO-1),and operational precursor missions (e.g., SSTL’sCHRIS on the ESA PROBA mission), towardsystematic measurement and operational mis-sions (e.g., ESA’s MERIS on ENVISAT, NASA’sMODIS on Terra/Aqua).

Several initiatives proposing space operatedEarth Observation imaging spectrometers in theabove categories have been submitted for eval-uation and approval (e.g., EnMAP (Hofer et al.2006)) or Flora (Asner et al. 2005)). However forthe time being, existing and future airborne imag-ing spectrometer initiatives (e.g., ARES (Richteret al. 2005), APEX (Schaepman et al. 2004)) willcontinue to provide regular access to imaging spec-trometer data, before routine collection at regionaland global scales will be available.

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Michael E. Schaepman - UZH · 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