of Environment 113 (2009) S123–S137

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Remote Sensing of Environment

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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 b Remote Sensing Laboratories, Dept. Geography, Univ. of Zurich, Switzerland c 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 article info abstract

Article history: The science of spectroscopy has existed for more than three centuries, and imaging spectroscopy for the Received 8 October 2008 Earth system for three decades. We first discuss the historical background of spectroscopy, followed by Received in revised form 9 March 2009 imaging spectroscopy, introducing a common definition for the latter. The relevance of imaging spectroscopy Accepted 9 March 2009 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 Keywords: advancements. We demonstrate these efforts using four examples that represent progress due to imaging Imaging spectroscopy Imaging spectrometry 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, Remote sensing and iv) assessing interaction of light with the Earth surface. Recent advances of imaging spectroscopy Earth System science 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 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 from the 1960s to the 1980s led to the development of first analytical methods (e.g. Arcybashev & Belov,1958; Lyon,1962); subsequently to the Three centuries ago Sir Isaac Newton published in his ‘Treatise of inclusion of ‘additional’ bands in multispectral imagers (e.g. the 2.09– Light’ the concept of dispersion of light (Newton, 1704), indicating that 2.35 µm band in Landsat for the detection of hydrothermal alteration white light can be dispersed in continuous ‘colors’ using prisms. The minerals as proposed by A.F.H. Goetz); first imaging profilers (e.g. Chiu & corpuscular theory proposed and developed by Newton was gradually Collins, 1978; Collins et al., 1982)andfinally to initial research imaging succeeded over time by the wave theory, resulting in Maxwell's equations spectrometers (e.g. Goetz et al., 1982; Vane et al., 1983). Significant of electromagnetic waves (Maxwell, 1873).Butitwasonlyintheearly progress was then achieved when, in particular, airborne imaging 19th century that quantitative measurement of dispersed light was spectrometers became available on a wider basis (e.g. Gower et al., recognized and standardized by the discovery of dark lines in the solar 1987; Kruse et al.,1990; Rowlands et al.,1994; Green et al., 1998), some of spectrum by Joseph von Fraunhofer (1817) and their interpretation as them helping to prepare for spaceborne imaging spectrometer activities absorption lines on the basis of experiments by Bunsen and Kirchhoff (Goetz & Herring, 1989; Rast et al., 2001). However, true imaging (1863).Thetermspectroscopywasfirst used in the late 19th century and spectrometers in space, satisfying a strict definition of a criterion of provided the empirical foundations for atomic and molecular physics contiguity over an extended spectral range, either the visible near- (Born & Wolf, 1999). Following this, astronomers began to use spectro- infrared or through the shortwave infrared—as discussed later—are still scopy for determining radial velocities of stars, clusters, and galaxies and very few. Recent developments indicate soon a larger availability of space stellar compositions (Hearnshaw, 1986). Advances in technology based imaging spectrometers given the recommendation and/or combined with increased awareness of the potential of spectroscopy 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). ⁎ Corresponding author. Remote Sensing Laboratories, Dept. Geography, Univ. of Zurich, Switzerland. Tel.: +41 43 635 51 60. Apart from technological advances, global modelers recognize that E-mail address: [email protected] (M.E. Schaepman). many of the key processes that control climate sensitivity or abrupt

0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2009.03.001 S124 M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137 climate changes (e.g., clouds, vegetation, oceanic convection) depend amount of available bands—even down to the level of multispectral on very small spatial scales. They cannot be represented in full detail systems (Chang & Du, 1999; Chang, 1999; Guo et al., 2006). In addition in the context of global models, and scientific understanding of them signal-to-noise ratios are still low in certain systems and even fractions is still notably incomplete (Le Treut et al., 2007), and are asking for of minimal noise might introduce considerable errors (Nischan et al., more holistic approaches at local scales. Airborne and spaceborne 1999; Okin et al., 2001). Datavolume is another tradeoff to be considered imaging spectrometers are currently targeted at providing solutions when designing imaging spectrometers, mainly due to limited downlink for the above issues. Nevertheless, imaging spectrometers may capacities from satellites to ground stations. A detailed discussion on represent ‘the ultimate optical remote sensing technology’‘that is advanced data reduction possibilities is presented in the pre-processing very clearly not yet anywhere near complete’ (MacDonald et al., 2009- section of this paper. Tradeoff discussions of advanced multiband in this issue). 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 spectro- metry, hyperspectral imaging, and occasionally ultraspectral imaging Imaging spectroscopy has had exponential growth over the past two are often used interchangeably. Even though semantic differences might decades in terms of referenced publications and associated citations (cf., exist, a common framework for such definitions is the simultaneous Fig. 1). There is a good indication of the increasing relevance of imaging acquisition of spatially coregistered images,inmanynarrow, spectrally spectroscopy to Earth observations and its related research. We contiguous bands, measured in calibrated radiance units,fromaremotely performed searches in altavista.com, and citations in scopus.com using operated platform (Schaepman, 2007). Consequently, imaging spectro- combinations of keywords (e.g., hyperspectral, imaging spectroscopy, meter collected data facilitates quantitative and qualitative character- and imaging spectrometry) to illustrate this exponential growth. ization of both the surface and the atmosphere, using geometrically A thematic separation of the search terms used in the above coherent spectral measurements. This result can then be used for the overview will be increasingly difficult in the future, since methods mostly unambiguous direct and indirect identification of surface based on imaging spectroscopy are not exclusively applied in Earth materials and atmospheric trace gases, the measurement of their observation, but also in space research (Clark et al., 2005), exobiology relative concentrations, and subsequently the assignment of the (Arnold et al., 2002; Kiang et al., 2007a,b), neurosciences (Devonshire proportional contribution of mixed pixel signals (e.g. spectral unmix- et al., 2004), chemometrics (Fernández Pierna et al., 2004), amongst ing), the derivation of their spatial distribution (e.g. mapping), and others (Gessner et al., 2006; Miskelly & Wagner, 2005). finally their evolution over time (multi-temporal analysis). A recent analysis using Scopus (Scopus, 2007)forremotesensing and imaging spectroscopy related articles (n=74801, using search 3. Limitations and tradeoffs when using imaging spectrometer data terms ‘imaging spectroscopy’, ‘imaging spectrometry’, ‘hyperspectral’,or ‘remote sensing’) resulted in an h-index (Hirsch, 2005)forremote In many cases, the availability of a larger number of spectral bands sensing of h=159 (indicating that 159 scientific contributions in remote has created important competitive advantages with regards to techni- sensing were cited at least 159 times), whereas imaging spectroscopy ques based on multispectral imaging. In multispectral data, the related articles contributed to h=61 of these. Apart from textbooks and detectable number of pure spectral signatures (endmembers) is often infrastructure articles being the most cited overall in remote sensing less than the effective number of endmembers present. In imaging (e.g. Hapke, 1993; Holben et al., 1998; Jensen, 1996; Lillesand & Kiefer, spectrometer data, the effective number of spectral signatures generally 1979), imaging spectroscopy related topics are widely spread (n=5810, exceeds the number of endmembers present in a pixel. This results in a using search terms ‘imaging spectroscopy’, ‘imaging spectrometry’, tradeoff between these technology, a fact that introduces important ‘hyperspectral’) and highly cited in a large variety of domains (e.g. Goetz limitations in the inversion process required to estimate the fractional et al., 1985; Green et al., 1998; Harsanyi & Chang 1994; Keshava & abundances of spectral endmembers (Adams et al., 1995). In addition, Mustard 2002; Kruse 1993; Thenkabail et al., 2000). the fine spectral resolution available in imaging spectrometer data allows a more subtle characterization of the spatial heterogeneity using 5. Instruments spectral mixture analysis techniques (Plaza et al., 2004). However, spectrometers still suffer from significant band-to-band correlation, Earth observation based on imaging spectroscopy has been resulting in dimensionality issues and consequently reducing the total 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 S125 research tool into a commodity product available to a broad user Finally, some promising new developments in instrument design community. In the latter half of the 20th century scientists developed for planetary exploration have potential application for Earth spaceborne instruments that view the Earth in a few spectral bands, observations. A relevant ongoing effort in this context is the Moon capturing a portion of the spectral information in reflected light. Mineralogy Mapper (M3)(Green et al., 2008), selected as a NASA However, the few spectral bands of multispectral satellites fail to Discovery Mission of Opportunity in early 2005 and launched on capture the complete diversity of the compositional information October 21, 2008. M3 is the first imaging spectrometer designed to present in the solar reflected spectrum of the Earth. In the 1970s, provide complete coverage of a planetary sized body in our solar realization of the limitations of the multispectral approach when faced system at high spatial resolution. with the diversity and complexity of spectral signatures found on the surface of the Earth, led to the concept of an imaging spectrometer. The 6. Technological advancement use of an imaging spectrometer was also understood to be valid for scientific missions to other planets and objects in our solar system. Only Imaging spectrometer instrument technology will continue to in the late 1970s did the detector array, electronics, computer and benefit from various developments. Recently, true spectroscopy focal optical technology reach significant maturity to allow creation of an plane arrays have become available with improved quantum effi- imaging spectrometer. With the arrival of these technologies and ciency, increased readout ports, rectangular design and consistent scientific impetus, first generation instruments were proposed (e.g. readout in the spectral domain (Chorier et al., 2004; Chuh, 2004), also Collins et al., 1982), with the Airborne Imaging Spectrometer (AIS) of being expanded to the emissive part of the spectrum. To achieve high the Jet Propulsion Laboratory (Vane et al., 1983)havingmostinfluence spectral–spatial uniformity and high precision measurements on the structural development of future imaging spectrometers. Even as advanced optical designs require enabling components (curved, a demonstration experiment, the success of AIS led to formulation and high-efficiency dispersive elements (Lobb, 1994; Mouroulis, 1998) development of the proposal for the Airborne Visible/Infrared Imaging and ultra-straight slits). Opto-mechanical designs must focus on Spectrometer (AVIRIS), a system that measures the total upwelling spectral and radiometric stability (Xiong et al., 2005). With stability, spectral radiance in the spectral range from 380 to 2510 nm at ~10 nm spectral, radiometric and spatial calibration (Green, 1998; Green et al., sampling interval and an equivalent spectral response function (Green 2003) can be readily established from the spectral features of the et al., 1998). The AVIRIS system has been upgraded and improved in a atmosphere as well as uniform/measured calibration targets on the continuous effort since then to meet the requirements of investigators Earth (Fox et al., 2003; Kneubuehler et al., 2004). using hyperspectral data for science research and applications. Complementing these instrument advances, new developments in Following the path initiated by AVIRIS, several new imaging spectro- high-performance computing will help overcome the limitations that meters have been successfully employed in airborne (e.g., HyMap, AISA, current processing capabilities impose on certain imaging spectro- Hydice, etc.) and spaceborne Earth observation based exploration scopy applications. In particular, the price paid for the wealth of missions (for a full listing of instruments see Schaepman, 2009). As a spectral information available from imaging spectrometers is the result, imaging spectrometer data today are widespread, but still not yet enormous amounts of data that these instruments generate. Several in common use. However, the lack of spatial and temporal continuity of applications exist, however, where having the desired information airborne and spaceborne imaging spectrometer missions and data calculated in (near) real-time is highly desirable (Plaza et al., 2004). remains a recurring challenge to the user community. There is an Such is the case of military applications, where a commonly pursued emerging need to converge from exploratory mission concepts (e.g. goal is detection of full or sub-pixel targets, often associated to hostile former ESA's Earth Explorer Core Mission proposals such as SPECTRA; weaponry, camouflage, concealment, and decoys. Other relevant Rast et al., 2004), technology demonstrators (e.g. NASA's Hyperion on examples can be found in applications aimed at detecting and/or EO-1; Ungar et al., 2003), and operational precursor missions (e.g. ESA's tracking natural disasters such as forest fires, oil spills, and other types CHRIS on PROBA; Cutter et al., 2004), towards systematic measurement of chemical contamination, where a timely response of algorithm and current operational missions (e.g. ESA's MERIS on ; Bezy analysis is highly desirable. In this regard, the emergence of et al., 1999), NASA's MODIS (Salomonson et al., 1989) on /). programmable onboard hardware devices such as field programmable Despite the naming of MODIS (MODerate Resolution Imaging Spectro- gate arrays (FPGAs) already allow real-time imaging spectroscopy radiometer) and MERIS (MEdium Resolution Imaging Spectrometer), data processing and lossless data compression in real-time (Mielikai- these instruments follow the classical definition of “imaging spectro- nen, 2006; Penna et al., 2006). On the other hand, the emergence of meters”, namely having more than 10 (narrow) spectral bands. general purpose graphic processing units (GPUs),1 driven by the ever- Technically MERIS was built using a contiguity criterion, but end growing demands of the video-game industry, have allowed these users do not receive contiguous spectral data products, whereas MODIS systems to evolve from expensive application-specific units into is built as an instrument with many (but not contiguous) spectral highly parallel and programmable commercial-off-the-shelf compo- bands. Occasionally the term ‘hyperspectral’ has been suggested as an nents which may eventually replace (more expensive) FPGAs as a tool alternative for many band instruments, leaving “imaging spectrometer” of choice for onboard data processing and compression in many for contiguous spectral bands instruments, but a literature survey on application domains with real-time requirements (Montrym & the use of these terms indicates a strong need for a proper terminology Moreton, 2005). Current GPUs can deliver a peak performance in definition in this domain. the order of 360 Gigaflops, more than seven times the performance of Several initiatives proposing space based Earth Observation instru- the fastest ×86 dual-core processor (around 50 Gigaflops). The ever- ments in these categories have been submitted for evaluation and growing computational demands of remote sensing applications will approval (e.g. HERO (Hyperspectral Environment and Resource Obser- fully benefit from compact hardware components and take advantage ver; Hollinger et al., 2006), EnMAP (Environmental Mapping and of the small size and relatively low cost of these units as compared to Analysis Program; Kaufmann et al., 2006), Flora (Asner & Green, 2004), FPGAs (Setoain et al., 2007). Despite the appealing perspectives FLEX (Moreno et al., 2006), SpectraSat (Full Spectral Landsat proposal), introduced by specialized data processing components, current MEOS (Trishchenko et al., 2007), amongst others). However for the time hardware architectures including FPGAs (which allow on-the-fly being, airborne imaging spectrometer initiatives (e.g. Asner et al., reconfiguration) and GPUs (fostering very high performance at low 2008a; Fournier et al., 2007; Kuester et al., 2007; Oppelt & Mauser, 2007; cost) still present limitations that must be carefully analyzed when 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. 1 http://www.gpgpu.org. S126 M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137 considering their incorporation to remote sensing missions (El- data without incorporating the spatial component, several single- Ghazawi et al., 2008; Woolston et al., 2008). In particular, the very pixel analytical techniques have been proposed. Principal component fine granularity of FPGAs is still inefficient, with extreme situations related techniques (Landgrebe, 2003) including mixture tuned in which only a very small portion of the chip is available for logic matched filtering (MTMF), a method that estimates the relative and effective algorithm implementations while the majority of degree of match of a given pixel to a reference spectrum and resources need to be used for interconnects and configuration. This approximates the sub-pixel abundance, or partial least squares (PLS) usually results in a relevant penalty in terms of speed and power. On regression models to characterize the variance in sample pixel spectra the other hand, both FPGAs and GPUs are still difficult to radiation- are widely used and have powerful applications in Earth System harden (currently-available radiation-tolerant FPGA devices have Science, including their use as pre-processing steps for further two orders of magnitude fewer equivalent gates than commercial spatially-oriented processing. Additional efforts include modified FPGAs). Gaussian methods (Berge & Solberg, 2006) or wavelet-based methods Additional reduction in downlink volume may be realized by (Kaewpijit et al., 2003). It is worth noting, however, that such spectral- combining onboard processing capability with non-traditional data based techniques would yield the same result for the data if the spatial reduction techniques such as cloud screening, band aggregation positions were randomly permuted. In fact, one of the distinguishing (when appropriate for example in Case 1 water (ocean) applications), properties of imaging spectrometer data is the multivariate informa- and employing smartly combined lossy and lossless compression tion coupled with a two-dimensional (2-D) pictorial representation (Abousleman et al., 1995; Aiazzi et al., 2001; Qian et al., 2006; Roger & amenable to image interpretation. Subsequently, there is a need to Cavenor, 1996)over“stable” targets some of the time. With focus on incorporate the spatial component of the data in the development of these technological areas, spaceborne imaging spectrometers may be techniques for imaging spectrometer data exploitation (Schaepman, developed with user defined instrument performance (Schläpfer & 2007). Classification approaches are also changing from hard Schaepman, 2002). Multiple-sensor approaches (Moreno et al., 2006), classifiers towards approaches of soft classifiers based on expert small satellites (Kramer & Cracknell, 2008), as well as imaging systems (Goodenough et al., 2000), Support Vector Machines (Camps- spectrometers sensitive in the multiangular (Lee et al., 2007), thermal Valls & Bruzzone, 2005), Markov Random Fields (MRF) for sub-pixel domains (Venus et al., 2006), and combinations thereof, will further mapping (Kasetkasem et al., 2005), contextual classifiers, and image significantly broaden the field of applications. change detection and fusion (Zurita-Milla et al., 2008). Further, morphological approaches for joint exploitation of the spatial and 7. (Pre-)processing spectral information available in the input data have been recently explored (Clark et al., 2003; Dell'Acqua et al., 2004; Plaza et al., 2005; A diverse array of techniques has been applied to extract Soille, 2003). information from hyperspectral data during the last decade (Chang, While integrated spatial/spectral developments hold great pro- 2003). Pre-processing imaging spectrometer data is now adopting mise for hyperspectral image analysis, they also introduce new multi-instrumented approaches, including improved estimation of processing challenges, especially from the viewpoint of their efficient the composition of the atmosphere which allows retrieval of surface implementation in (high-performance) computing platforms (Plaza & reflectance and ultimately the derivation of highly accurate Albedo Chang, 2007). In particular, the price paid for the wealth of spatial and products (e.g., blue/white-sky Albedo (BHR); black-sky Albedo spectral information available from hyperspectral sensors is the (DHR)) (Govaerts et al., 2008; Schaepman-Strub et al., 2006). On enormous amounts of data that they generate. Several applications the other hand, processing techniques are inherently either full pixel exist, however, where having the desired information in near real- techniques or mixed pixel techniques (Keshava & Mustard, 2002), time is critical. Such is the case of automatic target recognition for where each pixel vector in a hyperspectral scene provides a “spectral military and defense/security deployment (Chang, 2003). Other signature” that characterizes the underlying materials at each site in a relevant examples include environmental monitoring and assessment scene. The underlying assumption governing full pixel techniques is (Phinn et al., 2008), urban planning and management studies that each pixel vector measures the response of a single material (Roessner et al., 2001), risk/hazard prevention (Chabrillat et al., (Landgrebe, 2003). By contrast, the underlying assumption governing 2002), assessment of health hazards (Kutser et al., 2006; Park et al., mixed pixel techniques (also called “spectral unmixing” approaches) 2004; Thompson, 2002), detection of wildfire related events (Denni- is that each pixel vector measures the response of multiple underlying son et al., 2006; Jia et al., 2006; Rahman and Gamon, 2004; Robichaud materials at each site (Adams et al., 1986). Mixed pixels are a mixture et al., 2007), biological threat detection (Russell et al., 2006), of multiple substances, and may exist for one of two reasons. Firstly, if monitoring of oil spills and other types of chemical contamination the spatial resolution of the sensor given by its point spread function is (Flanders et al., 2006; Lopez-Pena & Duro, 2004; Salem et al., 2005). not high enough to separate different materials, these can jointly With the recent dramatic increase in the amount and complexity of occupy a single pixel, and the resulting spectral measurement will be a imaging spectrometer data, parallel processing has become a tool of composite of the individual spectra. Secondly, mixed pixels can also choice for many remote sensing missions, especially with the advent result when distinct materials are combined into a homogeneous, of low-cost systems such as commodity clusters. Further, the new intimate mixture. This situation is independent of the spatial processing power offered by grid computing environments can be resolution of the sensor and its point spread function and therefore employed to tackle extremely large remotely sensed data sets and to the issue of mixed pixels exists even at microscopic scales. An imaging get reasonable response times in complex image analysis scenarios by spectrometer acquired scene (sometimes referred to as “data cube”)is taking advantage of local (user) computing resources (Brazile et al., therefore approximated using a combination of the two situations, 2008; Plaza et al., 2006). These advances will increase efficiency in where a few sites in a scene can be regarded as macroscopically pure processing data and meet timeliness needs (Wolfe et al., 2008). materials, while most others are mixtures of materials. Finally, there is a trend toward establishment of integrated systems An important aspect in the design of advanced imaging spectro- solutions and supporting data assimilation (Dorigo et al., 2007; Fang meter image analysis techniques is how to efficiently integrate both et al., 2008) as well as data fusion based approaches (Gomez et al., the spatial and the spectral information present in the data. Most 2001; Robinson et al., 2000; Zurita-Milla et al., 2008). These solutions available techniques for imaging spectrometer data processing focus are expected to provide scalable approaches, allowing the integration on analyzing the data without incorporating information on the of multiple data sources. Data assimilation will further advance solid spatially adjacent data, i.e., the data is treated not as an image but as coupling of physical models, which link soil-vegetation-atmosphere- an unordered listing of spectral measurements. In order to process the transfer (SVAT) models to state space estimation algorithms (Olioso M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137 S127 et al., 2005). Spectroscopy will be increasingly integrated into a state assessment (Fig. 2) (e.g. Huang et al., 2007; Kobayashi & multidisciplinary research environment, complemented by in situ Iwabuchi, 2008; Malenovsky et al., 2008; Verhoef & Bach, 2007). sensing. Networks of in situ sensors exist already (e.g. FLUXNET), and In the future particular attention will be paid to integrating plant with telecommunication technologies it is increasingly feasible for physiological approaches from leaf to molecular level (Gierlinger & these networks to achieve (near) real-time integration of hetero- Schwanninger, 2007; Ustin et al., 2009-in this issue), and optimizing geneous sensor webs into the information infrastructure (Andreadis & the coupling of canopy scale RTM with atmospheric RTM, supporting Lettenmaier, 2006; Schaepman, 2007). large region (semi-) operational retrieval of relevant surface para- meters (Gobron et al., 2008; Pinty et al., 2007). 8. Increased understanding of relevant processes through the use of spectroscopy 8.2. Assessing surface heterogeneity

In this section, we discuss four main advances in remote sensing The assessment of spatial heterogeneity—or canopy vertical and that have been achieved through the improved availability of air- horizontal structure—has seen many approaches and today researchers and spaceborne imaging spectrometer data and related sciences. are becoming increasingly interesting in combined instrument These are (i) bridging scaling gaps from molecules to ecosystems approaches using LIDAR, multiangular instruments and spectrometers by using coupled radiative transfer models, (ii) assessing surface to better address the structural complexity. Significant advances in heterogeneity including clumping, (iii) physically-based (inversion) assessing canopy heterogeneity using imaging spectrometers only have modeling, and iv) interaction of light with the Earth's surface, been achieved using the angular dimension of the data (Chopping et al., respectively. 2008) in addition to the spectral component. Even though model inversion and variable retrieval over closed canopies can be performed 8.1. Bridging scaling gaps from molecules to ecosystems and biomes with reasonable complexity and good quality (Donoghue et al., 2007), sparse and clumped canopies are currently the greater challenge Bridging temporal, spatial and spectral scaling gaps has always (Duthoit et al., 2008) (c.f., Fig. 3). Much of the spectral mixture analysis been a predominant topic in remote sensing (Chen et al., 1999; literature using imaging spectroscopy addresses sparse vegetation Marceau & Hay, 1999; Wessman, 1992), and their application to conditions (Asner & Lobell, 2000; Asner et al., 2008b; Elmore et al., imaging spectroscopy has remained underexplored for a long time, 2000; Garcia and Ustin, 2001; Mustard, 1993; Okin et al., 2001; Roberts but it now receives increasingly focused attention (Lewis & Disney, et al., 1997), scattering component separation (Roberts et al., 1993, 2007; Roberts et al., 2004; Schaepman, 2007). While Malenovsky et al. 1998), and vegetation structure at the scale of functional type differ- (2007) discuss the topic in more detail, several approaches to bridge ences (Gamon et al., 1993; Pignatti et al., 2009). various scaling levels exist. Classical gaps can be identified in photon- Significant improvements have been made in identifying back- vegetation interaction, when scaling from leaf to canopy level and ground (or understory) signals in sparse and clumped canopies coupled (or linked) radiative transfer models (RTM) are used (c.f., (Bunting & Lucas, 2006) as well as refining the canopy composition at Jacquemoud et al., 2009-in this issue). Similarly, when scaling from various levels of detail (Malenovsky et al., 2008). Additionally canopy to ecosystem (or biome) level, coupled RTM including a full approaches using the advantages of full spectrum contiguous cover- consideration of the atmosphere are needed (Baret et al., 2007). Other age have allowed the assessment of sparse and clumped forest scaling approaches are based on using inverted geometrical optical canopies, given that some spectral separability is possible, either models, where an estimate of the shadow and background fraction is through dominant species occurrence (Schaepman et al., 2007)or needed for MODIS type spatial resolution (Zeng et al., 2008a,b). A unique biochemical species composition (Asner et al., 2008b). more systematic coupling of these models will be needed to support In the future, we will increasingly see developing methods that scaling approaches ranging from leaves to ecosystems or even biome 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). S128 M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137

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 Old-growth forests characterized by a structural heterogeneity and a scattering properties (e.g., photosynthetic vs. non-photosynthetic large amount of woody compounds, may act as important carbon sinks vegetation; Verrelst et al., 2008, in revision), assessing canopy hetero- (Knohl et al., 2003; Luyssaert et al., 2008). Despite the relevance of old- geneity from spectrometer or using multiangular measurements growth, however, a comprehensive review in Remote Sensing of (Gobron et al., 2002; Koetz et al., 2007b; Pinty et al., 2002)forpre- Environment revealed that the majority of physically-based approaches constraining RTM inversion procedures. Recent findings indicate the have been applied on young to mature forests, rather than the more need for canopy representation at the shoot (needle) level, allowing structurally complex canopies of old-growth forests. Fig. 5 displays proper upscaling approaches from leaves to canopies and biomes studies of applications of RTM in the last decade. Radiative transfer (Kokaly et al., 2009-in this issue; Zeng et al., 2008b). models can be categorized as (i) 1D or turbid-medium models; or (ii) when the canopy space consists of crown architectural elements, as 8.3. Physical modeling 3D model. In Fig. 5, the symbols indicate whether biophysical (open symbols) or biochemical (closed symbols) variables are quantified. At a Quantitative, physical models have been widely used in remote glance, the suggestion by Huang et al. (2008) that relatively simple 1D sensing for all Earth system relevant spheres (e.g. soil (Pinty et al., models are still preferred is not confirmed by this figure. Some studies 1989), vegetation (Koetz et al., 2004), snow (Painter et al., 2003), have evidently relied on the turbid-medium type of models such as the water (Hoge et al., 2003); atmosphere (Lyapustin, 2005), etc.). SAIL family (1D in space), yet the majority of the encountered studies Imaging spectroscopy has significantly contributed to the accelerated effectively applied canopy models in 3D space. Mostly RTMs are usually use of physical models in remote sensing, fostered through the linked with optical remote sensing data through numerical inversion inherently high dimensionality of imaging spectrometer data (Board- methods with the purpose of inferring one or more model variable. For man, 1989; Boardman, 1990; Hoffbeck & Landgrebe, 1996). Compre- other applications RTMs are used to generate synthetic data e.g. for hensive comparisons of radiative transfer models used in remote unmixing or classification purposes. sensing have been performed on a regular basis and document well A model-driven motivation for selecting younger, structurally more the increased complexity incorporated in the modeling approaches homogenous stands may be the smaller degrees of uncertainty; hence (Pinty et al., 2001, 2004; Widlowski et al., 2007, 2008). these stands are better suited for model parameterization and validation. We further demonstrate the progress achieved in physical Nonetheless, validation of models on forests of great structural complex- modeling on the retrieval of canopy characteristics using existing ity has not been demonstrated. Finally, due to the ill-posed nature of the literature. The structure of the canopy determines the pattern of light inversion problem (Atzberger, 2004), a simplified approximation of the attenuation and the distribution of photosynthesis, respiration, canopy representation may be required simply because larger numbers transpiration and nutrient cycling in the canopy (Baldocchi et al., of variables will increase the uncertainty of the inversion. 2004). Physically-based approaches to infer estimates of canopy Very few contributions discuss characteristics of old-growth characteristics from remote sensing data are increasingly used and forests (Song et al., 2002, 2007). In these studies, canopy structural have been shown to be useful, for example, in driving ecosystem variables and leaf optical properties were entered in a hybrid models to assess spatial estimates of carbon fluxes and pools (Quaife geometric-optical radiative transfer model (GORT) to mimic struc- et al., 2008), or biodiversity (Kooistra et al., 2008; Schaepman, 2007). tural canopy changes characteristic of successional change. Fig. 5 also Increasingly, systematic approaches using spectral fingerprinting of suggests a significant imbalance towards canopy biophysical proper- dominant species (Abbasi et al., 2008; Lucas et al., 2008) allow ties compared to canopy (or even leaf) chemistry is observed. The spectral databases (Bojinski et al., 2003) to serve as modeling baseline retrieval of leaf chemistry is usually resolved by coupling a relatively for inversion approaches. Fig. 4 shows the reflectance variability of simple leaf-level model (PROSPECT) with a canopy-level model (SAIL 21,000 leaf measurements of dominant tree species performed in a DART, FLIGHT, GeoSAIL). The woody component is typically fixed in heavily clouded area of the Elburz mountains region in Iran. these retrieval studies, an assumption that does not necessarily match ..Shemne l eoeSnigo niomn 1 20)S123 (2009) 113 Environment of Sensing Remote / al. et Schaepman M.E. – 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. S129 S130 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: Gastellu- Etchegory 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 Ifarraguerri & Chang, 1999; Lee et al., 2004; Rast et al., 2004), but also elements are subject to vertical and horizontal variability over time, significantly contributed to the mapping of its ecotones (Lucas & we expect that crown and understory compositional variability will be Carter, 2008) and other transitional zones (e.g. urban-green vegeta- explicitly integrated in modeling experiments in the future (Verrelst tion; Wania & Weber, 2007; Xiao et al., 2004). et al., 2008, in revision). In particular in unmanaged ecotones (e.g. ecosystem, communities, or habitat boundaries) like the tundra—boreal forest and forest— 8.4. From the interaction of light with the Earth surface to photon-matter heathland transitions, are where pressure for change in terms of interactions climate-related disturbance and human impacts are identified (Chapin et al., 2005). In managed (agricultural) ecosystems the improved Early classical remote sensing approaches were dominated by the precision is a key economical factor, contributing to better yield qualitative interpretation of the interaction of light with matter (Gates & estimates (Zarco-Tejada et al., 2005) as well as use of high spectral Tantraporn, 1952; Gates et al., 1965; Minnaert, 1940; Wakeling, 1980) and spatial resolution for species identification and mapping (Castro- and their interpretation of observations of the variety of colors and Esau et al., 2006; Malenovsky et al., 2008). optical phenomena present in the atmosphere, water and the landscape. In both managed and unmanaged ecosystems the spectroscopy This interpretation of ‘color’ was gradually refined by using spectro- focus is on detection and identification of plant succession, phenology, scopic techniques, finally resulting in the assessment of photon- plant health, plant functional types (Schmidtlein & Sassin, 2004), and vegetation interactions (Myneni & Ross, 1991). This interaction on monitoring invasive species (Asner et al., 2008c; Hestir et al., in approach has been continuously developed and modern imaging press; Noujdina & Ustin, 2008; Underwood et al., 2003). Biochemical spectrometers currently provide observational validation of these applications will concentrate on the (simultaneous) retrieval of early theories (Knyazikhin et al., 1992; Panferov et al., 2001; Shabanov moisture content (Cheng et al., 2006, 2008; Li et al., 2007), carbon et al., 2007; Widlowski et al., 2006). Also, single molecule spectroscopy (C) (Grace et al., 2007), nitrogen (N) (Martin et al., 2008; Smith et al., is an emerging field (Michalet et al., 2007), with photon spectroscopy 2002), and potentially phosphorus (P) (Mutanga & Kumar, 2007), and approaches being a realistic development in the near future (Bed- connecting soil, leaf, and plant functioning with atmospheric fluxes narkiewicz & Whelan, 2007). using quantitative approaches. The pigment and photosynthetic system of vegetation is of increasing interest, which will finally 9. Contributions of imaging spectroscopy to the Earth system science allow coupling models from molecules to leaf, plant and canopy scales. The sound retrieval of combined atmospheric and vegetation Imaging spectroscopy has not only enabled mapping parts of the properties will further allow refining 3D radiative transfer approaches Earth system with unprecedented accuracy (Adams et al., 1989; in particular in partly cloudy atmospheres (Lyapustin & Wang, 2005). M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137 S131

Quantitative physically-based soil models remain to be developed spectroscopy plays an important role will increase in frequency and taking into account the full spectral coverage (e.g. reflective and impact. Environmental systems analysis with the support of imaging emissive) currently available, although many of the basic spectral spectrometers can be found in various domains (Kokaly et al., 2007; interactions have long been a focus of interest (Stoner & Baumgardner, Phan et al., in review)), however true integrated approaches of Earth 1981). system science using imaging spectrometer data are still infrequent Within the hydrosphere, imaging spectroscopy has supported the today. development of applications in aquatic environments (Schott, 2003), in particular within Case 1 (open ocean) (Morel & Belanger, 2006)and 10. Conclusions and outlook 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) Over nearly three decades, imaging spectroscopy has evolved from and bathymetry (Lesser & Mobley, 2007). Glacial surfaces, however, experimental approaches in their infancy to becoming a commodity have been analyzed sparsely using imaging spectrometers so far, apart product that is used in a multitude of Earth Science and Planetary from instruments dedicated to outer space research (Doute et al., 2007). Science applications. Within the cryosphere, snow cover represents a critical component Practically, to achieve new success requires improved data quality in Earth's regional and global climate and hydrologic cycle. While the and wider availability of systematic and consistent remote sensing snow-albedo feedback is perhaps the most sensitive in controlling the observations to the user community. Secondly, broader availability of Earth system response to changing climate, the albedo of snow across high-performance distributed computing resources is also needed in the globe is poorly understood and characterized (Dozier & Painter, order to run quantitative, physically-based models at various scales. 2004; Molotch et al., 2004). Recent advances in instrumentation, Particular advances in the understanding of the interaction of light modeling, and scientific inquiry have substantially improved our with the Earth surface and atmosphere have been achieved through understanding of the spatial and temporal forcing of and by snow coupled RTM bridging spatial, temporal and spectral scaling gaps from cover. In particular, field and imaging spectroscopy have facilitated molecules to ecosystems and biomes. Spectrodirectional measure- inference of the influences on broadband and hyperspectral directional ments incrementally improved the understanding and assessment of reflectance of snow grain size (and in turn albedo), grain morphology, surface heterogeneity vertically and horizontally including clumping snow impurities, surface liquid water content, and biological content effects. The vast number of spectral bands in these instruments has (Dozier et al., 2009-in this issue; Green et al., 2006; Matzl and allowed significantly improved physically-based (inversion) model- Schneebeli, 2006; Nolin & Dozier, 2000; Painter et al., 2003, 2007a,b). ing. Ultimately our understanding of the interaction of light with the On the other hand, imaging spectroscopy of the lithosphere is very Earth surface has moved away from the classical color theory towards well established and in particular the interest in assessing the mineral a more comprehensive understanding of photon-matter interaction. composition of surfaces and materials advanced the development of Our quantitative understanding of photon-matter interactions has spectroscopy most significantly. Mineral mapping can be identified as been significantly enriched by the opportunity to look at simultaneous the most popular and successful application achieved using imaging acquisition of many, contiguous spectral bands. Data fusion and spectroscopy, allowing various approaches to be implemented either assimilation methods will continue to increase the potential of these focusing on the Earth (Clark et al., 1990; Cloutis, 1996; Kruse et al., approaches, allowing the continuous assessment of the Earth system 2003; Sabins, 1999) or other planets (Bibring et al., 2005). Models of in the spectral, spatial and temporal domains. Summarizing all of the minerals were developed allowing key progress to be made in mixture above, imaging spectroscopy currently enables spatially continuous analysis (Lucey, 1998) and topography (Feng et al., 2003). Beyond mapping of physical, chemical, biological and artificial variables of the mineralogy, lithospheric approaches include mapping of volcanoes Earth's surface and atmospheric composition with unprecedented and their plume composition (Carn et al., 2005; Guinness et al., 2007; accuracy. Spinetti et al., 2008) as well as indoor and outdoor dust analysis In this paper, we have demonstrated the development and (Chudnovsky et al., 2007) and petrology (Edwards et al., 2007). progress of significant new fields of remote sensing technology and Within the pedosphere, assessing soil related variables receives applications and identified potential near-term advancements of significant attention. Topics such as mapping biological soil crusts (Ustin imaging spectroscopy in the Earth sciences. However, the imaging et al., 2009; Weber et al., 2008), mapping desert surface grain size (Okin spectroscopy community must increase its efforts to inform relevant & Painter, 2004), assessing soil carbon (Bartholomeus et al., 2008; Okin stakeholders of the value of these data and the urgent need to acquire et al., 2001; Stevens et al., 2006), separating soil and vegetation continuous high quality imaging spectrometer data of the Earth. The components (Bartholomeus et al., 2007), and land degradation observed research trends indicate that this need is becoming more (Chabrillat, 2006) have become common. A detailed overview is widely understood and seen as essential for the sustainable develop- discussed in Ben-Dor et al. (2008; this issue). However, physical ment of our resources and the protection of our environment. Various based, quantitative models of soils beyond Hapke's theory of the single position papers underline the urgency of the above requirements, scattering albedo remain sparse (e.g., Cierniewski & Karnieli, 2002; namely the requirement for consistent observations for long periods Liang & Townshend, 1996) and there is an urgent need for the and more holistic instrumented approaches (Simon et al., 2006) that development of such models, allowing soils to be treated in similar are capable of performing true multidisciplinary approaches to solving spectroscopy fashion as other Earth components. problems in the Earth sciences. The atmosphere has received significant attention with the use of advanced imaging spectrometers, either in measuring atmospheric Acknowledgements composition (Bovensmann et al., 1999), or—related more to the observation of the Earth surface—the compensation of the atmospheric The authors wish to thank the organizers of the IEEE Geoscience influence when deriving spectral surface properties from air- or and Remote Sensing Society's IGARSS conference for hosting the spaceborne imaging spectrometers (Gao et al., 2009-in this issue; special session at IGARSS 2006 (Denver, USA) entitled ‘State of Science Liang & Fang, 2004). Atmospheric compensation or constituent retrieval of Environmental Applications of Imaging Spectroscopy (in honor of will remain an ongoing challenge when separating surface and atmo- Dr. Alexander F.H. Goetz)’. The authors would like to acknowledge the spheric scattering components (Gao et al., 2009-in this issue; Liu et al., support of their employing institutions; THP acknowledges funding 2006; Neville et al., 2008; Sanders et al., 2001; Qu et al., 2003). from NSF contract ATM-0432327 and NASA CA NNG04GC52A. We Finally, within the Earth system sciences, integrated, multidisci- thank the anonymous reviewers for constructive comments and plinary (or even transdisciplinary) approaches where imaging helpful remarks. S132 M.E. Schaepman et al. / Remote Sensing of Environment 113 (2009) S123–S137

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