Remote Sensing of Environment 113 (2009) S123–S137
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Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Earth system science related imaging spectroscopy—An assessment
Michael E. Schaepman a,b,⁎, Susan L. Ustin c, Antonio J. Plaza d, Thomas H. Painter e,
Jochem Verrelst a, Shunlin Liang f
a
Centre for Geo-Information, Wageningen Univ., Wageningen, The Netherlands
Remote Sensing Laboratories, Dept. Geography, Univ. of Zurich, Switzerland
Dept. LAWR, University of California Davis 95616, USA
d
Dept. Technology of Computers and Communications, Univ. of Extremadura, Caceres, Spain
e
Snow Optics Laboratory, Dept. Geography, Univ. of Utah, USA
f
Dept. Geography, Univ. of Maryland, USA
b
c
a r t i c l e
i n f o
Article history:
Received 8 October 2008
Received in revised form 9 March 2009
Accepted 9 March 2009
Keywords:
Imaging spectroscopy
Imaging spectrometry
Hyperspectral imaging
Remote sensing
Earth System science
a b s t r a c t
The science of spectroscopy has existed for more than three centuries, and imaging spectroscopy for the
Earth system for three decades. We first discuss the historical background of spectroscopy, followed by
imaging spectroscopy, introducing a common definition for the latter. The relevance of imaging spectroscopy
is then assessed using a comprehensive review of the cited literature. Instruments, technological
advancements and (pre-)processing approaches are discussed to set the scene for application related
advancements. We demonstrate these efforts using four examples that represent progress due to imaging
spectroscopy, namely (i) bridging scaling gaps from molecules to ecosystems using coupled radiative transfer
models (ii) assessing surface heterogeneity including clumping, (iii) physical based (inversion) modeling,
and iv) assessing interaction of light with the Earth surface. Recent advances of imaging spectroscopy
contributions to the Earth system sciences are discussed. We conclude by summarizing the achievements of
thirty years of imaging spectroscopy and strongly recommend this community to increase its efforts to
convince relevant stakeholders of the urgency to acquire the highest quality imaging spectrometer data for
Earth observation from operational satellites capable of collecting consistent data for climatically-relevant
periods of time.
© 2009 Elsevier Inc. All rights reserved.
1. Introduction/historical background
Three centuries ago Sir Isaac Newton published in his ‘Treatise of
Light’ the concept of dispersion of light (Newton, 1704), indicating that
white light can be dispersed in continuous ‘colors’ using prisms. The
corpuscular theory proposed and developed by Newton was gradually
succeeded over time by the wave theory, resulting in Maxwell's equations
of electromagnetic waves (Maxwell, 1873). But it was only in the early
19th century that quantitative measurement of dispersed light was
recognized and standardized by the discovery of dark lines in the solar
spectrum by Joseph von 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 19th century and
provided the empirical foundations for atomic and molecular physics
(Born & Wolf, 1999). Following this, astronomers began to use spectroscopy for determining radial velocities of stars, clusters, and galaxies and
stellar compositions (Hearnshaw, 1986). Advances in technology
combined with increased awareness of the potential of spectroscopy
⁎ Corresponding author. Remote Sensing Laboratories, Dept. Geography, Univ. of
Zurich, Switzerland. Tel.: +41 43 635 51 60.
E-mail address: michael.schaepman@geo.uzh.ch (M.E. Schaepman).
0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2009.03.001
from the 1960s to the 1980s led to the development of first analytical
methods (e.g. Arcybashev & Belov,1958; Lyon,1962); subsequently to the
inclusion of ‘additional’ bands in multispectral imagers (e.g. the 2.09–
2.35 µm band in Landsat for the detection of hydrothermal alteration
minerals as proposed by A.F.H. Goetz); first imaging profilers (e.g. Chiu &
Collins, 1978; Collins et al., 1982) and finally to initial research imaging
spectrometers (e.g. Goetz et al., 1982; Vane et al., 1983). Significant
progress was then achieved when, in particular, airborne imaging
spectrometers became available on a wider basis (e.g. Gower et al.,
1987; Kruse et al., 1990; Rowlands et al., 1994; Green et al., 1998), some of
them helping to prepare for spaceborne imaging spectrometer activities
(Goetz & Herring, 1989; Rast et al., 2001). However, true imaging
spectrometers in space, satisfying a strict definition of a criterion of
contiguity over an extended spectral range, either the visible nearinfrared or through the shortwave infrared—as discussed later—are still
very few. Recent developments indicate soon a larger availability of space
based imaging spectrometers given the recommendation and/or
approval of missions such as EnMap (Kaufmann et al., 2006) and the
U.S. National Research Council's decadal survey, which recommended
implementing the HyspIRI mission (NRC, 2007).
Apart from technological advances, global modelers recognize that
many of the key processes that control climate sensitivity or abrupt
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climate changes (e.g., clouds, vegetation, oceanic convection) depend
on very small spatial scales. They cannot be represented in full detail
in the context of global models, and scientific understanding of them
is still notably incomplete (Le Treut et al., 2007), and are asking for
more holistic approaches at local scales. Airborne and spaceborne
imaging spectrometers are currently targeted at providing solutions
for the above issues. Nevertheless, imaging spectrometers may
represent ‘the ultimate optical remote sensing technology’ ‘that is
very clearly not yet anywhere near complete’ (MacDonald et al., 2009in this issue).
amount of available bands—even down to the level of multispectral
systems (Chang & Du, 1999; Chang, 1999; Guo et al., 2006). In addition
signal-to-noise ratios are still low in certain systems and even fractions
of minimal noise might introduce considerable errors (Nischan et al.,
1999; Okin et al., 2001). Data volume is another tradeoff to be considered
when designing imaging spectrometers, mainly due to limited downlink
capacities from satellites to ground stations. A detailed discussion on
advanced data reduction possibilities is presented in the pre-processing
section of this paper. Tradeoff discussions of advanced multiband
imagers versus contiguous imaging spectrometers will persist to exist
and will mainly be dominated by SNR and spectral fidelity issues.
2. Definition
4. Relevance and impact derived from the scientific literature
In the literature, the terms imaging spectroscopy, imaging spectrometry, hyperspectral imaging, and occasionally ultraspectral imaging
are often used interchangeably. Even though semantic differences might
exist, a common framework for such definitions is the simultaneous
acquisition of spatially coregistered images, in many narrow, spectrally
contiguous bands, measured in calibrated radiance units, from a remotely
operated platform (Schaepman, 2007). Consequently, imaging spectrometer collected data facilitates quantitative and qualitative characterization of both the surface and the atmosphere, using geometrically
coherent spectral measurements. This result can then be used for the
mostly unambiguous direct and indirect identification of surface
materials and atmospheric trace gases, the measurement of their
relative concentrations, and subsequently the assignment of the
proportional contribution of mixed pixel signals (e.g. spectral unmixing), the derivation of their spatial distribution (e.g. mapping), and
finally their evolution over time (multi-temporal analysis).
3. Limitations and tradeoffs when using imaging spectrometer data
In many cases, the availability of a larger number of spectral bands
has created important competitive advantages with regards to techniques based on multispectral imaging. In multispectral data, the
detectable number of pure spectral signatures (endmembers) is often
less than the effective number of endmembers present. In imaging
spectrometer data, the effective number of spectral signatures generally
exceeds the number of endmembers present in a pixel. This results in a
tradeoff between these technology, a fact that introduces important
limitations in the inversion process required to estimate the fractional
abundances of spectral endmembers (Adams et al., 1995). In addition,
the fine spectral resolution available in imaging spectrometer data
allows a more subtle characterization of the spatial heterogeneity using
spectral mixture analysis techniques (Plaza et al., 2004). However,
spectrometers still suffer from significant band-to-band correlation,
resulting in dimensionality issues and consequently reducing the total
Imaging spectroscopy has had exponential growth over the past two
decades in terms of referenced publications and associated citations (cf.,
Fig. 1). There is a good indication of the increasing relevance of imaging
spectroscopy to Earth observations and its related research. We
performed searches in altavista.com, and citations in scopus.com using
combinations of keywords (e.g., hyperspectral, imaging spectroscopy,
and imaging spectrometry) to illustrate this exponential growth.
A thematic separation of the search terms used in the above
overview will be increasingly difficult in the future, since methods
based on imaging spectroscopy are not exclusively applied in Earth
observation, but also in space research (Clark et al., 2005), exobiology
(Arnold et al., 2002; Kiang et al., 2007a,b), neurosciences (Devonshire
et al., 2004), chemometrics (Fernández Pierna et al., 2004), amongst
others (Gessner et al., 2006; Miskelly & Wagner, 2005).
A recent analysis using Scopus (Scopus, 2007) for remote sensing
and imaging spectroscopy related articles (n = 74801, using search
terms ‘imaging spectroscopy’, ‘imaging spectrometry’, ‘hyperspectral’, or
‘remote sensing’) resulted in an h-index (Hirsch, 2005) for remote
sensing of h = 159 (indicating that 159 scientific contributions in remote
sensing were cited at least 159 times), whereas imaging spectroscopy
related articles contributed to h = 61 of these. Apart from textbooks and
infrastructure articles being the most cited overall in remote sensing
(e.g. Hapke, 1993; Holben et al., 1998; Jensen, 1996; Lillesand & Kiefer,
1979), imaging spectroscopy related topics are widely spread (n = 5810,
using search terms ‘imaging spectroscopy’, ‘imaging spectrometry’,
‘hyperspectral’) and highly cited in a large variety of domains (e.g. Goetz
et al., 1985; Green et al., 1998; Harsanyi & Chang 1994; Keshava &
Mustard 2002; Kruse 1993; Thenkabail et al., 2000).
5. Instruments
Earth observation based on imaging spectroscopy has been
transformed in little more than two decades from a sparsely available
Fig. 1. Internet based and citation database search for the terms ‘imaging spectroscopy’ ‘imaging spectrometry’ and ‘hyperspectral’ (1990–2007). Registered listings and hits extracted
from the internet and from a citation database. Listings, hits and regression model are in an exponential scale.
M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137
research tool into a commodity product available to a broad user
community. In the latter half of the 20th century scientists developed
spaceborne instruments that view the Earth in a few spectral bands,
capturing a portion of the spectral information in reflected light.
However, the few spectral bands of multispectral satellites fail to
capture the complete diversity of the compositional information
present in the solar reflected spectrum of the Earth. In the 1970s,
realization of the limitations of the multispectral approach when faced
with the diversity and complexity of spectral signatures found on the
surface of the Earth, led to the concept of an imaging spectrometer. The
use of an imaging spectrometer was also understood to be valid for
scientific missions to other planets and objects in our solar system. Only
in the late 1970s did the detector array, electronics, computer and
optical technology reach significant maturity to allow creation of an
imaging spectrometer. With the arrival of these technologies and
scientific impetus, first generation instruments were proposed (e.g.
Collins et al., 1982), with the Airborne Imaging Spectrometer (AIS) of
the Jet Propulsion Laboratory (Vane et al., 1983) having most influence
on the structural development of future imaging spectrometers. Even as
a demonstration experiment, the success of AIS led to formulation and
development of the proposal for the Airborne Visible/Infrared Imaging
Spectrometer (AVIRIS), a system that measures the total upwelling
spectral radiance in the spectral range from 380 to 2510 nm at ~10 nm
sampling interval and an equivalent spectral response function (Green
et al., 1998). The AVIRIS system has been upgraded and improved in a
continuous effort since then to meet the requirements of investigators
using hyperspectral data for science research and applications.
Following the path initiated by AVIRIS, several new imaging spectrometers have been successfully employed in airborne (e.g., HyMap, AISA,
Hydice, etc.) and spaceborne Earth observation based exploration
missions (for a full listing of instruments see Schaepman, 2009). As a
result, imaging spectrometer data today are widespread, but still not yet
in common use. However, the lack of spatial and temporal continuity of
airborne and spaceborne imaging spectrometer missions and data
remains a recurring challenge to the user community. There is an
emerging need to converge from exploratory mission concepts (e.g.
former ESA's Earth Explorer Core Mission proposals such as SPECTRA;
Rast et al., 2004), technology demonstrators (e.g. NASA's Hyperion on
EO-1; Ungar et al., 2003), and operational precursor missions (e.g. ESA's
CHRIS on PROBA; Cutter et al., 2004), towards systematic measurement
and current operational missions (e.g. ESA's MERIS on ENVISAT; Bezy
et al., 1999), NASA's MODIS (Salomonson et al., 1989) on Terra/Aqua).
Despite the naming of MODIS (MODerate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer),
these instruments follow the classical definition of “imaging spectrometers”, namely having more than 10 (narrow) spectral bands.
Technically MERIS was built using a contiguity criterion, but end
users do not receive contiguous spectral data products, whereas MODIS
is built as an instrument with many (but not contiguous) spectral
bands. Occasionally the term ‘hyperspectral’ has been suggested as an
alternative for many band instruments, leaving “imaging spectrometer”
for contiguous spectral bands instruments, but a literature survey on
the use of these terms indicates a strong need for a proper terminology
definition in this domain.
Several initiatives proposing space based Earth Observation instruments in these categories have been submitted for evaluation and
approval (e.g. HERO (Hyperspectral Environment and Resource Observer; Hollinger et al., 2006), EnMAP (Environmental Mapping and
Analysis Program; Kaufmann et al., 2006), Flora (Asner & Green, 2004),
FLEX (Moreno et al., 2006), SpectraSat (Full Spectral Landsat proposal),
MEOS (Trishchenko et al., 2007), amongst others). However for the time
being, airborne imaging spectrometer initiatives (e.g. Asner et al.,
2008a; Fournier et al., 2007; Kuester et al., 2007; Oppelt & Mauser, 2007;
Richter et al., 2005; Schaepman et al., 2004) will continue to provide the
majority of new instruments, before successor missions for Hyperion,
CHRIS/PROBA and others are realized.
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Finally, some promising new developments in instrument design
for planetary exploration have potential application for Earth
observations. A relevant ongoing effort in this context is the Moon
Mineralogy Mapper (M3) (Green et al., 2008), selected as a NASA
Discovery Mission of Opportunity in early 2005 and launched on
October 21, 2008. M3 is the first imaging spectrometer designed to
provide complete coverage of a planetary sized body in our solar
system at high spatial resolution.
6. Technological advancement
Imaging spectrometer instrument technology will continue to
benefit from various developments. Recently, true spectroscopy focal
plane arrays have become available with improved quantum efficiency, increased readout ports, rectangular design and consistent
readout in the spectral domain (Chorier et al., 2004; Chuh, 2004), also
being expanded to the emissive part of the spectrum. To achieve high
spectral–spatial uniformity and high precision measurements
advanced optical designs require enabling components (curved,
high-efficiency dispersive elements (Lobb, 1994; Mouroulis, 1998)
and ultra-straight slits). Opto-mechanical designs must focus on
spectral and radiometric stability (Xiong et al., 2005). With stability,
spectral, radiometric and spatial calibration (Green, 1998; Green et al.,
2003) can be readily established from the spectral features of the
atmosphere as well as uniform/measured calibration targets on the
Earth (Fox et al., 2003; Kneubuehler et al., 2004).
Complementing these instrument advances, new developments in
high-performance computing will help overcome the limitations that
current processing capabilities impose on certain imaging spectroscopy applications. In particular, the price paid for the wealth of
spectral information available from imaging spectrometers is the
enormous amounts of data that these instruments generate. Several
applications exist, however, where having the desired information
calculated in (near) real-time is highly desirable (Plaza et al., 2004).
Such is the case of military applications, where a commonly pursued
goal is detection of full or sub-pixel targets, often associated to hostile
weaponry, camouflage, concealment, and decoys. Other relevant
examples can be found in applications aimed at detecting and/or
tracking natural disasters such as forest fires, oil spills, and other types
of chemical contamination, where a timely response of algorithm
analysis is highly desirable. In this regard, the emergence of
programmable onboard hardware devices such as field programmable
gate arrays (FPGAs) already allow real-time imaging spectroscopy
data processing and lossless data compression in real-time (Mielikainen, 2006; Penna et al., 2006). On the other hand, the emergence of
general purpose graphic processing units (GPUs),1 driven by the evergrowing demands of the video-game industry, have allowed these
systems to evolve from expensive application-specific units into
highly parallel and programmable commercial-off-the-shelf components which may eventually replace (more expensive) FPGAs as a tool
of choice for onboard data processing and compression in many
application domains with real-time requirements (Montrym &
Moreton, 2005). Current GPUs can deliver a peak performance in
the order of 360 Gigaflops, more than seven times the performance of
the fastest ×86 dual-core processor (around 50 Gigaflops). The evergrowing computational demands of remote sensing applications will
fully benefit from compact hardware components and take advantage
of the small size and relatively low cost of these units as compared to
FPGAs (Setoain et al., 2007). Despite the appealing perspectives
introduced by specialized data processing components, current
hardware architectures including FPGAs (which allow on-the-fly
reconfiguration) and GPUs (fostering very high performance at low
cost) still present limitations that must be carefully analyzed when
1
http://www.gpgpu.org.
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considering their incorporation to remote sensing missions (ElGhazawi et al., 2008; Woolston et al., 2008). In particular, the very
fine granularity of FPGAs is still inefficient, with extreme situations
in which only a very small portion of the chip is available for logic
and effective algorithm implementations while the majority of
resources need to be used for interconnects and configuration. This
usually results in a relevant penalty in terms of speed and power. On
the other hand, both FPGAs and GPUs are still difficult to radiationharden (currently-available radiation-tolerant FPGA devices have
two orders of magnitude fewer equivalent gates than commercial
FPGAs).
Additional reduction in downlink volume may be realized by
combining onboard processing capability with non-traditional data
reduction techniques such as cloud screening, band aggregation
(when appropriate for example in Case 1 water (ocean) applications),
and employing smartly combined lossy and lossless compression
(Abousleman et al., 1995; Aiazzi et al., 2001; Qian et al., 2006; Roger &
Cavenor, 1996) over “stable” targets some of the time. With focus on
these technological areas, spaceborne imaging spectrometers may be
developed with user defined instrument performance (Schläpfer &
Schaepman, 2002). Multiple-sensor approaches (Moreno et al., 2006),
small satellites (Kramer & Cracknell, 2008), as well as imaging
spectrometers sensitive in the multiangular (Lee et al., 2007), thermal
domains (Venus et al., 2006), and combinations thereof, will further
significantly broaden the field of applications.
7. (Pre-)processing
A diverse array of techniques has been applied to extract
information from hyperspectral data during the last decade (Chang,
2003). Pre-processing imaging spectrometer data is now adopting
multi-instrumented approaches, including improved estimation of
the composition of the atmosphere which allows retrieval of surface
reflectance and ultimately the derivation of highly accurate Albedo
products (e.g., blue/white-sky Albedo (BHR); black-sky Albedo
(DHR)) (Govaerts et al., 2008; Schaepman-Strub et al., 2006). On
the other hand, processing techniques are inherently either full pixel
techniques or mixed pixel techniques (Keshava & Mustard, 2002),
where each pixel vector in a hyperspectral scene provides a “spectral
signature” that characterizes the underlying materials at each site in a
scene. The underlying assumption governing full pixel techniques is
that each pixel vector measures the response of a single material
(Landgrebe, 2003). By contrast, the underlying assumption governing
mixed pixel techniques (also called “spectral unmixing” approaches)
is that each pixel vector measures the response of multiple underlying
materials at each site (Adams et al., 1986). Mixed pixels are a mixture
of multiple substances, and may exist for one of two reasons. Firstly, if
the spatial resolution of the sensor given by its point spread function is
not high enough to separate different materials, these can jointly
occupy a single pixel, and the resulting spectral measurement will be a
composite of the individual spectra. Secondly, mixed pixels can also
result when distinct materials are combined into a homogeneous,
intimate mixture. This situation is independent of the spatial
resolution of the sensor and its point spread function and therefore
the issue of mixed pixels exists even at microscopic scales. An imaging
spectrometer acquired scene (sometimes referred to as “data cube”) is
therefore approximated using a combination of the two situations,
where a few sites in a scene can be regarded as macroscopically pure
materials, while most others are mixtures of materials.
An important aspect in the design of advanced imaging spectrometer image analysis techniques is how to efficiently integrate both
the spatial and the spectral information present in the data. Most
available techniques for imaging spectrometer data processing focus
on analyzing the data without incorporating information on the
spatially adjacent data, i.e., the data is treated not as an image but as
an unordered listing of spectral measurements. In order to process the
data without incorporating the spatial component, several singlepixel analytical techniques have been proposed. Principal component
related techniques (Landgrebe, 2003) including mixture tuned
matched filtering (MTMF), a method that estimates the relative
degree of match of a given pixel to a reference spectrum and
approximates the sub-pixel abundance, or partial least squares (PLS)
regression models to characterize the variance in sample pixel spectra
are widely used and have powerful applications in Earth System
Science, including their use as pre-processing steps for further
spatially-oriented processing. Additional efforts include modified
Gaussian methods (Berge & Solberg, 2006) or wavelet-based methods
(Kaewpijit et al., 2003). It is worth noting, however, that such spectralbased techniques would yield the same result for the data if the spatial
positions were randomly permuted. In fact, one of the distinguishing
properties of imaging spectrometer data is the multivariate information coupled with a two-dimensional (2-D) pictorial representation
amenable to image interpretation. Subsequently, there is a need to
incorporate the spatial component of the data in the development of
techniques for imaging spectrometer data exploitation (Schaepman,
2007). Classification approaches are also changing from hard
classifiers towards approaches of soft classifiers based on expert
systems (Goodenough et al., 2000), Support Vector Machines (CampsValls & Bruzzone, 2005), Markov Random Fields (MRF) for sub-pixel
mapping (Kasetkasem et al., 2005), contextual classifiers, and image
change detection and fusion (Zurita-Milla et al., 2008). Further,
morphological approaches for joint exploitation of the spatial and
spectral information available in the input data have been recently
explored (Clark et al., 2003; Dell'Acqua et al., 2004; Plaza et al., 2005;
Soille, 2003).
While integrated spatial/spectral developments hold great promise for hyperspectral image analysis, they also introduce new
processing challenges, especially from the viewpoint of their efficient
implementation in (high-performance) computing platforms (Plaza &
Chang, 2007). In particular, the price paid for the wealth of spatial and
spectral information available from hyperspectral sensors is the
enormous amounts of data that they generate. Several applications
exist, however, where having the desired information in near realtime is critical. Such is the case of automatic target recognition for
military and defense/security deployment (Chang, 2003). Other
relevant examples include environmental monitoring and assessment
(Phinn et al., 2008), urban planning and management studies
(Roessner et al., 2001), risk/hazard prevention (Chabrillat et al.,
2002), assessment of health hazards (Kutser et al., 2006; Park et al.,
2004; Thompson, 2002), detection of wildfire related events (Dennison et al., 2006; Jia et al., 2006; Rahman and Gamon, 2004; Robichaud
et al., 2007), biological threat detection (Russell et al., 2006),
monitoring of oil spills and other types of chemical contamination
(Flanders et al., 2006; Lopez-Pena & Duro, 2004; Salem et al., 2005).
With the recent dramatic increase in the amount and complexity of
imaging spectrometer data, parallel processing has become a tool of
choice for many remote sensing missions, especially with the advent
of low-cost systems such as commodity clusters. Further, the new
processing power offered by grid computing environments can be
employed to tackle extremely large remotely sensed data sets and to
get reasonable response times in complex image analysis scenarios by
taking advantage of local (user) computing resources (Brazile et al.,
2008; Plaza et al., 2006). These advances will increase efficiency in
processing data and meet timeliness needs (Wolfe et al., 2008).
Finally, there is a trend toward establishment of integrated systems
solutions and supporting data assimilation (Dorigo et al., 2007; Fang
et al., 2008) as well as data fusion based approaches (Gomez et al.,
2001; Robinson et al., 2000; Zurita-Milla et al., 2008). These solutions
are expected to provide scalable approaches, allowing the integration
of multiple data sources. Data assimilation will further advance solid
coupling of physical models, which link soil-vegetation-atmospheretransfer (SVAT) models to state space estimation algorithms (Olioso
M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137
et al., 2005). Spectroscopy will be increasingly integrated into a
multidisciplinary research environment, complemented by in situ
sensing. Networks of in situ sensors exist already (e.g. FLUXNET), and
with telecommunication technologies it is increasingly feasible for
these networks to achieve (near) real-time integration of heterogeneous sensor webs into the information infrastructure (Andreadis &
Lettenmaier, 2006; Schaepman, 2007).
8. Increased understanding of relevant processes through the use
of spectroscopy
In this section, we discuss four main advances in remote sensing
that have been achieved through the improved availability of airand spaceborne imaging spectrometer data and related sciences.
These are (i) bridging scaling gaps from molecules to ecosystems
by using coupled radiative transfer models, (ii) assessing surface
heterogeneity including clumping, (iii) physically-based (inversion)
modeling, and iv) interaction of light with the Earth's surface,
respectively.
8.1. Bridging scaling gaps from molecules to ecosystems and biomes
Bridging temporal, spatial and spectral scaling gaps has always
been a predominant topic in remote sensing (Chen et al., 1999;
Marceau & Hay, 1999; Wessman, 1992), and their application to
imaging spectroscopy has remained underexplored for a long time,
but it now receives increasingly focused attention (Lewis & Disney,
2007; Roberts et al., 2004; Schaepman, 2007). While Malenovsky et al.
(2007) discuss the topic in more detail, several approaches to bridge
various scaling levels exist. Classical gaps can be identified in photonvegetation interaction, when scaling from leaf to canopy level and
coupled (or linked) radiative transfer models (RTM) are used (c.f.,
Jacquemoud et al., 2009-in this issue). Similarly, when scaling from
canopy to ecosystem (or biome) level, coupled RTM including a full
consideration of the atmosphere are needed (Baret et al., 2007). Other
scaling approaches are based on using inverted geometrical optical
models, where an estimate of the shadow and background fraction is
needed for MODIS type spatial resolution (Zeng et al., 2008a,b). A
more systematic coupling of these models will be needed to support
scaling approaches ranging from leaves to ecosystems or even biome
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state assessment (Fig. 2) (e.g. Huang et al., 2007; Kobayashi &
Iwabuchi, 2008; Malenovsky et al., 2008; Verhoef & Bach, 2007).
In the future particular attention will be paid to integrating plant
physiological approaches from leaf to molecular level (Gierlinger &
Schwanninger, 2007; Ustin et al., 2009-in this issue), and optimizing
the coupling of canopy scale RTM with atmospheric RTM, supporting
large region (semi-) operational retrieval of relevant surface parameters (Gobron et al., 2008; Pinty et al., 2007).
8.2. Assessing surface heterogeneity
The assessment of spatial heterogeneity—or canopy vertical and
horizontal structure—has seen many approaches and today researchers
are becoming increasingly interesting in combined instrument
approaches using LIDAR, multiangular instruments and spectrometers
to better address the structural complexity. Significant advances in
assessing canopy heterogeneity using imaging spectrometers only have
been achieved using the angular dimension of the data (Chopping et al.,
2008) in addition to the spectral component. Even though model
inversion and variable retrieval over closed canopies can be performed
with reasonable complexity and good quality (Donoghue et al., 2007),
sparse and clumped canopies are currently the greater challenge
(Duthoit et al., 2008) (c.f., Fig. 3). Much of the spectral mixture analysis
literature using imaging spectroscopy addresses sparse vegetation
conditions (Asner & Lobell, 2000; Asner et al., 2008b; Elmore et al.,
2000; Garcia and Ustin, 2001; Mustard, 1993; Okin et al., 2001; Roberts
et al., 1997), scattering component separation (Roberts et al., 1993,
1998), and vegetation structure at the scale of functional type differences (Gamon et al., 1993; Pignatti et al., 2009).
Significant improvements have been made in identifying background (or understory) signals in sparse and clumped canopies
(Bunting & Lucas, 2006) as well as refining the canopy composition at
various levels of detail (Malenovsky et al., 2008). Additionally
approaches using the advantages of full spectrum contiguous coverage have allowed the assessment of sparse and clumped forest
canopies, given that some spectral separability is possible, either
through dominant species occurrence (Schaepman et al., 2007) or
unique biochemical species composition (Asner et al., 2008b).
In the future, we will increasingly see developing methods that
either combine spectroscopic approaches with structural measurements
Fig. 2. Coupled states, processes and scales ranging from cellular architecture to global biogeochemical cycles. The contribution of linked radiative transfer models in down-and
upscaling ranges from leaves to biomes (modified following Miller et al., 2006).
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Fig. 3. Assessing landscape heterogeneity of forest and agricultural canopies indicating the level of difficulty of the assessment (modified following Miller et al., 2006).
(Asner et al., 2008c; Koetz et al., 2007a), advanced partitioning of various
scattering properties (e.g., photosynthetic vs. non-photosynthetic
vegetation; Verrelst et al., 2008, in revision), assessing canopy heterogeneity from spectrometer or using multiangular measurements
(Gobron et al., 2002; Koetz et al., 2007b; Pinty et al., 2002) for preconstraining RTM inversion procedures. Recent findings indicate the
need for canopy representation at the shoot (needle) level, allowing
proper upscaling approaches from leaves to canopies and biomes
(Kokaly et al., 2009-in this issue; Zeng et al., 2008b).
8.3. Physical modeling
Quantitative, physical models have been widely used in remote
sensing for all Earth system relevant spheres (e.g. soil (Pinty et al.,
1989), vegetation (Koetz et al., 2004), snow (Painter et al., 2003),
water (Hoge et al., 2003); atmosphere (Lyapustin, 2005), etc.).
Imaging spectroscopy has significantly contributed to the accelerated
use of physical models in remote sensing, fostered through the
inherently high dimensionality of imaging spectrometer data (Boardman, 1989; Boardman, 1990; Hoffbeck & Landgrebe, 1996). Comprehensive comparisons of radiative transfer models used in remote
sensing have been performed on a regular basis and document well
the increased complexity incorporated in the modeling approaches
(Pinty et al., 2001, 2004; Widlowski et al., 2007, 2008).
We further demonstrate the progress achieved in physical
modeling on the retrieval of canopy characteristics using existing
literature. The structure of the canopy determines the pattern of light
attenuation and the distribution of photosynthesis, respiration,
transpiration and nutrient cycling in the canopy (Baldocchi et al.,
2004). Physically-based approaches to infer estimates of canopy
characteristics from remote sensing data are increasingly used and
have been shown to be useful, for example, in driving ecosystem
models to assess spatial estimates of carbon fluxes and pools (Quaife
et al., 2008), or biodiversity (Kooistra et al., 2008; Schaepman, 2007).
Increasingly, systematic approaches using spectral fingerprinting of
dominant species (Abbasi et al., 2008; Lucas et al., 2008) allow
spectral databases (Bojinski et al., 2003) to serve as modeling baseline
for inversion approaches. Fig. 4 shows the reflectance variability of
21,000 leaf measurements of dominant tree species performed in a
heavily clouded area of the Elburz mountains region in Iran.
Old-growth forests characterized by a structural heterogeneity and a
large amount of woody compounds, may act as important carbon sinks
(Knohl et al., 2003; Luyssaert et al., 2008). Despite the relevance of oldgrowth, however, a comprehensive review in Remote Sensing of
Environment revealed that the majority of physically-based approaches
have been applied on young to mature forests, rather than the more
structurally complex canopies of old-growth forests. Fig. 5 displays
studies of applications of RTM in the last decade. Radiative transfer
models can be categorized as (i) 1D or turbid-medium models; or
(ii) when the canopy space consists of crown architectural elements, as
3D model. In Fig. 5, the symbols indicate whether biophysical (open
symbols) or biochemical (closed symbols) variables are quantified. At a
glance, the suggestion by Huang et al. (2008) that relatively simple 1D
models are still preferred is not confirmed by this figure. Some studies
have evidently relied on the turbid-medium type of models such as the
SAIL family (1D in space), yet the majority of the encountered studies
effectively applied canopy models in 3D space. Mostly RTMs are usually
linked with optical remote sensing data through numerical inversion
methods with the purpose of inferring one or more model variable. For
other applications RTMs are used to generate synthetic data e.g. for
unmixing or classification purposes.
A model-driven motivation for selecting younger, structurally more
homogenous stands may be the smaller degrees of uncertainty; hence
these stands are better suited for model parameterization and validation.
Nonetheless, validation of models on forests of great structural complexity has not been demonstrated. Finally, due to the ill-posed nature of the
inversion problem (Atzberger, 2004), a simplified approximation of the
canopy representation may be required simply because larger numbers
of variables will increase the uncertainty of the inversion.
Very few contributions discuss characteristics of old-growth
forests (Song et al., 2002, 2007). In these studies, canopy structural
variables and leaf optical properties were entered in a hybrid
geometric-optical radiative transfer model (GORT) to mimic structural canopy changes characteristic of successional change. Fig. 5 also
suggests a significant imbalance towards canopy biophysical properties compared to canopy (or even leaf) chemistry is observed. The
retrieval of leaf chemistry is usually resolved by coupling a relatively
simple leaf-level model (PROSPECT) with a canopy-level model (SAIL
DART, FLIGHT, GeoSAIL). The woody component is typically fixed in
these retrieval studies, an assumption that does not necessarily match
M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137
Fig. 4. Leaf spectral fingerprints of four dominant species in the Elburz mountains (Iran). Sunlit and shaded leaf measurements performed adaxial and abaxial along altitude gradients are plotted with their standard deviation.
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M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137
Fig. 5. Stand age study site compared for year of publication per radiative transfer model used. The open symbols represent biophysical retrievals (e.g. fraction cover, LAI), while the
closed symbols represent biochemical retrievals (e.g. chlorophyll). Symbols are plotted on the averaged age. The grey lines represent the full range the stand age of the used study
site. (Referenced legend a: Bruniquel-Pinel & Gastellu-Etchegory, 1998; b: Gemmell, 1998; c: Gemmell and Varjo, 1999; d: Gastellu-Etchegory et al., 1999; e: Gemmell, 1999; f: Brown
et al., 2000; g: Demarez & Gastellu-Etchegory, 2000; h: Kuusk & Nilson, 2000; i: Hu et al., 2000; j: Gao et al., 2000; k: Huemmrich, 2001; l: Gastellu-Etchegory & Bruniquel-Pinel,
2001 m: Lacaze & Roujean, 2001; n: Gemmell et al., 2001; o: Kimes et al., 2002; p: Gemmell et al., 2002; q: Song et al., 2002; r: Wang et al., 2003; s: Shabanov et al., 2003; t: GastelluEtchegory et al., 2003; u: Rautiainen et al., 2004; v: Zarco-Tejeda et al., 2004; w: Peddle et al., 2004; x: Fernandes et al., 2004; y: Meroni et al., 2004; z, aa: Koetz et al., 2004; ab, ac: Fang et al.,
2003; ad: Zhang et al., 2005; ae: Rautiainen & Stenberg, 2005; af: Rautiainen, 2005; ag: Disney et al., 2006; ah: Schlerf & Atzberger, 2006; ai: Eriksson et al., 2006; aj: Soudani et al., 2006;
ak, al: Cheng et al., 2006; am: Zhang et al., 2006; an Song et al., 2007; ao: Koetz et al., 2007a,b; ap: Colombo et al., 2008; aq: Lang et al., 2007; ar: Malenovsky et al., 2008; as, at: Kuusk et al.,
2008; au: Huang et al., 2008; av: Suarez et al., 2008; aw: Verrelst et al., 2008; ax: Quaife et al., 2008; ay: Moorthy et al., 2008).
reality (Roberts et al., 2004). Given that foliage elements and woody
elements are subject to vertical and horizontal variability over time,
we expect that crown and understory compositional variability will be
explicitly integrated in modeling experiments in the future (Verrelst
et al., 2008, in revision).
8.4. From the interaction of light with the Earth surface to photon-matter
interactions
Early classical remote sensing approaches were dominated by the
qualitative interpretation of the interaction of light with matter (Gates &
Tantraporn, 1952; Gates et al., 1965; Minnaert, 1940; Wakeling, 1980)
and their interpretation of observations of the variety of colors and
optical phenomena present in the atmosphere, water and the landscape.
This interpretation of ‘color’ was gradually refined by using spectroscopic techniques, finally resulting in the assessment of photonvegetation interactions (Myneni & Ross, 1991). This interaction
approach has been continuously developed and modern imaging
spectrometers currently provide observational validation of these
early theories (Knyazikhin et al., 1992; Panferov et al., 2001; Shabanov
et al., 2007; Widlowski et al., 2006). Also, single molecule spectroscopy
is an emerging field (Michalet et al., 2007), with photon spectroscopy
approaches being a realistic development in the near future (Bednarkiewicz & Whelan, 2007).
9. Contributions of imaging spectroscopy to the Earth system science
Imaging spectroscopy has not only enabled mapping parts of the
Earth system with unprecedented accuracy (Adams et al., 1989;
Ifarraguerri & Chang, 1999; Lee et al., 2004; Rast et al., 2004), but also
significantly contributed to the mapping of its ecotones (Lucas &
Carter, 2008) and other transitional zones (e.g. urban-green vegetation; Wania & Weber, 2007; Xiao et al., 2004).
In particular in unmanaged ecotones (e.g. ecosystem, communities,
or habitat boundaries) like the tundra—boreal forest and forest—
heathland transitions, are where pressure for change in terms of
climate-related disturbance and human impacts are identified (Chapin
et al., 2005). In managed (agricultural) ecosystems the improved
precision is a key economical factor, contributing to better yield
estimates (Zarco-Tejada et al., 2005) as well as use of high spectral
and spatial resolution for species identification and mapping (CastroEsau et al., 2006; Malenovsky et al., 2008).
In both managed and unmanaged ecosystems the spectroscopy
focus is on detection and identification of plant succession, phenology,
plant health, plant functional types (Schmidtlein & Sassin, 2004), and
on monitoring invasive species (Asner et al., 2008c; Hestir et al., in
press; Noujdina & Ustin, 2008; Underwood et al., 2003). Biochemical
applications will concentrate on the (simultaneous) retrieval of
moisture content (Cheng et al., 2006, 2008; Li et al., 2007), carbon
(C) (Grace et al., 2007), nitrogen (N) (Martin et al., 2008; Smith et al.,
2002), and potentially phosphorus (P) (Mutanga & Kumar, 2007), and
connecting soil, leaf, and plant functioning with atmospheric fluxes
using quantitative approaches. The pigment and photosynthetic
system of vegetation is of increasing interest, which will finally
allow coupling models from molecules to leaf, plant and canopy scales.
The sound retrieval of combined atmospheric and vegetation
properties will further allow refining 3D radiative transfer approaches
in particular in partly cloudy atmospheres (Lyapustin & Wang, 2005).
M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137
Quantitative physically-based soil models remain to be developed
taking into account the full spectral coverage (e.g. reflective and
emissive) currently available, although many of the basic spectral
interactions have long been a focus of interest (Stoner & Baumgardner,
1981).
Within the hydrosphere, imaging spectroscopy has supported the
development of applications in aquatic environments (Schott, 2003), in
particular within Case 1 (open ocean) (Morel & Belanger, 2006) and
Case 2 (coastal) (Goodman & Ustin, 2007; Gitelson et al., 2008; Ruddick
et al., 2001) waters, as well as inland water quality (Kallio et al., 2001)
and bathymetry (Lesser & Mobley, 2007). Glacial surfaces, however,
have been analyzed sparsely using imaging spectrometers so far, apart
from instruments dedicated to outer space research (Doute et al., 2007).
Within the cryosphere, snow cover represents a critical component
in Earth's regional and global climate and hydrologic cycle. While the
snow-albedo feedback is perhaps the most sensitive in controlling the
Earth system response to changing climate, the albedo of snow across
the globe is poorly understood and characterized (Dozier & Painter,
2004; Molotch et al., 2004). Recent advances in instrumentation,
modeling, and scientific inquiry have substantially improved our
understanding of the spatial and temporal forcing of and by snow
cover. In particular, field and imaging spectroscopy have facilitated
inference of the influences on broadband and hyperspectral directional
reflectance of snow grain size (and in turn albedo), grain morphology,
snow impurities, surface liquid water content, and biological content
(Dozier et al., 2009-in this issue; Green et al., 2006; Matzl and
Schneebeli, 2006; Nolin & Dozier, 2000; Painter et al., 2003, 2007a,b).
On the other hand, imaging spectroscopy of the lithosphere is very
well established and in particular the interest in assessing the mineral
composition of surfaces and materials advanced the development of
spectroscopy most significantly. Mineral mapping can be identified as
the most popular and successful application achieved using imaging
spectroscopy, allowing various approaches to be implemented either
focusing on the Earth (Clark et al., 1990; Cloutis, 1996; Kruse et al.,
2003; Sabins, 1999) or other planets (Bibring et al., 2005). Models of
minerals were developed allowing key progress to be made in mixture
analysis (Lucey, 1998) and topography (Feng et al., 2003). Beyond
mineralogy, lithospheric approaches include mapping of volcanoes
and their plume composition (Carn et al., 2005; Guinness et al., 2007;
Spinetti et al., 2008) as well as indoor and outdoor dust analysis
(Chudnovsky et al., 2007) and petrology (Edwards et al., 2007).
Within the pedosphere, assessing soil related variables receives
significant attention. Topics such as mapping biological soil crusts (Ustin
et al., 2009; Weber et al., 2008), mapping desert surface grain size (Okin
& Painter, 2004), assessing soil carbon (Bartholomeus et al., 2008; Okin
et al., 2001; Stevens et al., 2006), separating soil and vegetation
components (Bartholomeus et al., 2007), and land degradation
(Chabrillat, 2006) have become common. A detailed overview is
discussed in Ben-Dor et al. (2008; this issue). However, physical
based, quantitative models of soils beyond Hapke's theory of the single
scattering albedo remain sparse (e.g., Cierniewski & Karnieli, 2002;
Liang & Townshend, 1996) and there is an urgent need for the
development of such models, allowing soils to be treated in similar
spectroscopy fashion as other Earth components.
The atmosphere has received significant attention with the use of
advanced imaging spectrometers, either in measuring atmospheric
composition (Bovensmann et al., 1999), or—related more to the
observation of the Earth surface—the compensation of the atmospheric
influence when deriving spectral surface properties from air- or
spaceborne imaging spectrometers (Gao et al., 2009-in this issue;
Liang & Fang, 2004). Atmospheric compensation or constituent retrieval
will remain an ongoing challenge when separating surface and atmospheric scattering components (Gao et al., 2009-in this issue; Liu et al.,
2006; Neville et al., 2008; Sanders et al., 2001; Qu et al., 2003).
Finally, within the Earth system sciences, integrated, multidisciplinary (or even transdisciplinary) approaches where imaging
S131
spectroscopy plays an important role will increase in frequency and
impact. Environmental systems analysis with the support of imaging
spectrometers can be found in various domains (Kokaly et al., 2007;
Phan et al., in review)), however true integrated approaches of Earth
system science using imaging spectrometer data are still infrequent
today.
10. Conclusions and outlook
Over nearly three decades, imaging spectroscopy has evolved from
experimental approaches in their infancy to becoming a commodity
product that is used in a multitude of Earth Science and Planetary
Science applications.
Practically, to achieve new success requires improved data quality
and wider availability of systematic and consistent remote sensing
observations to the user community. Secondly, broader availability of
high-performance distributed computing resources is also needed in
order to run quantitative, physically-based models at various scales.
Particular advances in the understanding of the interaction of light
with the Earth surface and atmosphere have been achieved through
coupled RTM bridging spatial, temporal and spectral scaling gaps from
molecules to ecosystems and biomes. Spectrodirectional measurements incrementally improved the understanding and assessment of
surface heterogeneity vertically and horizontally including clumping
effects. The vast number of spectral bands in these instruments has
allowed significantly improved physically-based (inversion) modeling. Ultimately our understanding of the interaction of light with the
Earth surface has moved away from the classical color theory towards
a more comprehensive understanding of photon-matter interaction.
Our quantitative understanding of photon-matter interactions has
been significantly enriched by the opportunity to look at simultaneous
acquisition of many, contiguous spectral bands. Data fusion and
assimilation methods will continue to increase the potential of these
approaches, allowing the continuous assessment of the Earth system
in the spectral, spatial and temporal domains. Summarizing all of the
above, imaging spectroscopy currently enables spatially continuous
mapping of physical, chemical, biological and artificial variables of the
Earth's surface and atmospheric composition with unprecedented
accuracy.
In this paper, we have demonstrated the development and
progress of significant new fields of remote sensing technology and
applications and identified potential near-term advancements of
imaging spectroscopy in the Earth sciences. However, the imaging
spectroscopy community must increase its efforts to inform relevant
stakeholders of the value of these data and the urgent need to acquire
continuous high quality imaging spectrometer data of the Earth. The
observed research trends indicate that this need is becoming more
widely understood and seen as essential for the sustainable development of our resources and the protection of our environment. Various
position papers underline the urgency of the above requirements,
namely the requirement for consistent observations for long periods
and more holistic instrumented approaches (Simon et al., 2006) that
are capable of performing true multidisciplinary approaches to solving
problems in the Earth sciences.
Acknowledgements
The authors wish to thank the organizers of the IEEE Geoscience
and Remote Sensing Society's IGARSS conference for hosting the
special session at IGARSS 2006 (Denver, USA) entitled ‘State of Science
of Environmental Applications of Imaging Spectroscopy (in honor of
Dr. Alexander F.H. Goetz)’. The authors would like to acknowledge the
support of their employing institutions; THP acknowledges funding
from NSF contract ATM-0432327 and NASA CA NNG04GC52A. We
thank the anonymous reviewers for constructive comments and
helpful remarks.
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