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In the lower set of curves, the local minimum in the curve shifts to shorter wavelengths with increasing slope. The top set of curves are offset for clarity. In the continuum-removed spectra, we can see there is no real shift in the absorption-band center. The continuum-removal process isolates spectral features and puts them on a level playing field so they may be intercompared.

Continuum removal and feature comparison, is in this author's opinion, the key to successful spectral identification. If we isolate the spectral features, remove the continuum, and scale the band depth or band area to be equal, we can see subtle band shifts and shapes Figure 24a.


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Now compare a harder case: halloysite and kaolinite Figure 10c. You might note that halloysite has a different absorption feature at 1. However, if you were obtaining the spectrum through the Earth's atmosphere, you would have virtually no data in that wavelength region because atmospheric water absorbs too much of the signal.

The diagnostic feature is the 2. The continua removed 2. Figure 24a. Comparison of calcite and dolomite continuum-removed features. The dolomite absorption occurs at a shorter wavelength than the calcite absorption. Figure 24b. Comparison of kaolinite and halloysite spectral features. Both mineral spectra have the same band position at 2. However, the kaolinite spectrum shows a stronger feature at 2. One of the most challenging spectral features to distinguish between are those in spectra of various plant species.

Figure 25a shows four plant spectra the spectra are offset for clarity. The overall shapes are quite similar. If we remove the continuum according to Figure 25b, we see the detailed chlorophyll absorption spectral variations for these as well as other plants in Figure 25c. Shape matching algorithms, like that presented in Clark et al. Figure 25a. Reflectance spectra of four types of vegetation. Each curve is offset by 0. Figure 25b. Continuum removal example for a chlorophyll absorption in vegetation. Figure 25c. Continuum-removed chlorophyll absorptions for 8 vegetation types including the 4 from Figure 25a showing that the continuum removed features can show subtle spectral differences.

We have seen tremendous variation in the spectral properties of minerals and materials in general, due to composition, grain size, and mixture types. So far viewing geometry has not been discussed. Viewing geometry, including the angle of incidence, angle of reflection, and the phase angle: the angle between the incident light and observer the angle of reflection , all affect the intensity of light received.

Varying the viewing geometry results in changes in shadowing and the proportions of first surface to multiple scattering e. Hapke, ; Nelson, ; Mustard and Pieters, , which can affect band depths a small amount except in rare cases like extreme specular reflection off a mirror or lake surface. While measuring precise light levels are important for things like radiation balance studies, they are of lesser importance in spectral analysis.

The following illustrates why. First, your eye is a crude spectrometer, able to distinguish the spectral properties of materials in a limited wavelength range by the way we interpret color. Pick up any colored object around you. Change the orientation of the local normal on the surface of the object with respect to the angle of incident light, the angle at which you observe it called the emission or scattering angle , and the angle between the incident and scattered light the phase angle. As you do this, note any color changes. Unless you chose an unusual material like a diffraction grating or very shiny object , you will see no significant color change.

Theory of reflectance and emittance spectroscopy

Plant leaves appear green from any angle, a pile of hematite appears red from any angle. This tells you that the spectral features do not change much with viewing geometry. The continuum removal does a similar but more sophisticated normalization. The band depth, shape, and position are basically constant with viewing geometry. Band depth will only change with the proportion of specular reflection added to the reflected light.

For surfaces and at wavelengths where multiple scattering dominates, that change in band depth is minimized. Ratioing two spectra with spectral features can cause spurious features in the ratio e. Clark and King, However, this ratio can be used to advantage. Consider two spectra, with an absorption edge, such as conduction bands in cinnabar Figure 9 , sulfur, or the chlorophyll-absorption edge in plants at 0. If one spectrum is shifted relative to the other and then the two ratioed, the resulting ratio has a residual feature that looks like either an absorption or emission feature depending on the direction of the shift and which spectrum is the numerator and which is the denominator.

An example residual caused by such shifts is shown in Figure This effect has recently been used to determine subtle shifts in the chlorophyll absorption edge in plants Clark et al. The intensity of the feature in the reflectance ratio is proportional to the amount of shift between the two spectra, and the shape of the ratio does not change if the shifts are small. Such sensitivity indicates the wavelength stability of a spectrometer must be very good for such analyses.

The ratio of two spectra, one slightly shifted from the other results in a spectral feature. The two shapes in this example are from different chlorophyll bands. Shifts as small as 0. The shape, type 1 profile is from the shifted spectra shown at the bottom of the figure, while the shape, type 2 is from shallower chlorophyll absorptions. Iron oxides, hydroxides, and sulfates are a special case for remote sensing because they are so ubiquitous. Several hematite reflectance spectra at different grain sizes are shown in Figure 27a.

Nanocrystalline hematite Morris et al. The iron absorption at 0. Absorptions in transmittance, as in the thin film case, are 2 times narrower in width see Clark and Roush, Larger grain sizes show increased saturation of the 0. Continuum removal and scaling the hematite absorption to similar depth shows the wide variety of band shapes and positions that can be found in nature Figure 27b. Figure 28a. Spectra of iron oxide, iron hydroxide, and iron sulfate spectra.

There are a whole suite of iron oxides, iron hydroxides, iron sulfates, etc. A few examples are shown in Figure 28a.


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Note that hematite has a narrower absorption at a slightly shorter wavelength than goethite. However, a coarse-grained hematite has a broader absorption, approaching the position and width of a fine-grained goethite or a thin-film goethite. Jarosite has a narrow absorption near 0. Jarosite, an iron sulfate, has a diagnostic absorption at 2. However, this feature is weaker than the electronic absorptions in the visible and is often masked by clay or alunite jarosite often occurs in hydrothermal deposits with alunite.

The features near 1. Ferrihydrite is an amorphous iron oxide, and its spectrum appears very similar to the orange precipitate, an amorphous iron hydroxide, obtained downstream from the Summitville, Colorado mine King et al. However, if we remove the continuum and compare the positions and shapes of the bands Figure 28b , we can see they are indeed different. Figure 28b. The spectra are offset for clarity. Note the shift in the band center between the two spectra despite the similarities in Figure 28a. As discussed above, there are many iron-bearing minerals and amorphous materials with similar but distinct absorption bands.

How many can be distinguished with reflectance spectroscopy is still a matter of research. An even harder question to answer is how many can be distinguished and not be confused with mixtures of other iron-bearing materials. Detailed spectral analysis, including continuum removal to isolate absorption features can certainly improve the success of distinguishing them. Iron oxides, hydroxides, and sulfates are additional cases where spectroscopy detects at very low levels because of the strong absorption bands in the visible and ultraviolet. In nature, there appear to be many amorphous iron oxides, hydroxides, etc.

Thus, spectroscopy can not only detect them at levels below other methods e. X-ray diffraction , but in the case of amorphous materials detect them when other methods are not sensitive to their presence when they are major fractions of the sample! Back to Contents. There have been many attempts over the years to quantify the scattering process.

Kubelka-Munk theory was one of the first and still finds uses today e. Wendlandt and Hecht, ; Clark and Roush, Either method has its uses in controlled situations, but there is a modern, more effective alternative. The limitations of these older methods are due to a poor representation of the scattering process and are discussed in Clark and Roush and Hapke Fortunately, in the early 's three independent investigations Hapke, , Gougen, ; and Lumme and Bowell, provided reasonable solutions to the complex radiative transfer problem as applied to particulate surfaces.

These theories provide for non-isotropic scattering of light from particles, shadowing between particles, and first surface reflection from grain surfaces, important processes not considered in earlier theories. One theory, that of Hapke , also provides for mixtures, and because of its relative simplicity compared to the other two, has become the dominant theory used in the planetary and to some degree the terrestrial remote sensing communities.

Because of this deviation, a table interpolation subroutine using "exact" values from Chandrasekhar can be used. The table interpolation is computationally faster than the Hapke approximation and more accurate. The single scattering albedo is the probability that a photon survives an interaction with a single particle, which includes Fresnel reflection, absorption, scattering, and diffraction due to the presence of an individual grain.

Hapke developed the theory further by deriving a relation between the single scattering albedo, the complex index of refraction, the grain size, and a scattering parameter to describe scattering centers within non-perfect grains. The single scattering albedo of a grain can be found from his equation For a multimineralic surface, w' can be computed from equation 17 of Hapke :. With the Hapke , reflectance theory, and the optical constants of minerals, reflectance spectra of pure minerals at a single grain size, spectra of a pure mineral with a grain size distribution, and mineral mixtures with varying grain size components can all be computed.

Clark and Roush also showed that a reflectance spectrum can be inverted to determine quantitative information on the abundances and grain sizes of each component. The inversion of reflectance to quantitative abundance has been tested in laboratory mixtures e. Johnson et al. Some quantitative inversion attempts have been undertaken with imaging spectroscopy data e.

Mustard and Pieters, b, Li et al , , Adams et al. Other spectral libraries include the mid-infrared work of Salisbury et al. This site includes the Salisbury library and additions since the original publication. As spectral libraries are currently a focus of activity, it is probably best to search the internet and check with the authors referenced in this chapter for the latest information on what is available.

A word of caution with spectral libraries, and spectra obtained from other sources in general: wavelength errors are common except from data obtained on interferometers. This author and colleagues at the USGS have evaluated many spectrometers and other spectral libraries and have found many to have significant wavelength shifts. Other specific libraries and spectrometers are not mentioned here because some may have wavelength shifts and must each be validated. One mineral with a stable absorption feature is a well-crystallized kaolinite, which has a sharp absorption at 2.

When obtaining spectral library data, confirm that wavelength positions of known features are measured at the correct positions. Absorptions due to rare-earth oxides are often used as wavelength standards in the visible. Mid-IR systems can be checked by interferometer measurements, which is now probably the most common spectrometer in use for this wavelength region. Also be cautious of spurious spectral features from incomplete reduction to true reflectance.

All measurements are made relative to a "white" standard. However, these standards also have spectral features. For example, the common visible and near infrared standards, Halon and Spectralon and derivatives, have significant spectral features in the 2. Mid-infrared standards are more difficult, mainly due to the large wavelength range usually covered. Nash reviewed some common mid-IR reflectance standards. Reflectance spectroscopy is a rapidly growing science that can be used to derive significant information about mineralogy with little or no sample preparation. It may be used in applications when other methods would be too time consuming or require destruction of precious samples.

For example, imaging spectrometers are already acquiring millions of spatially gridded spectra over an area from which mineralogical maps are being made. It is possible to set up real-time monitoring of processes using spectroscopy, such as monitoring the mineralogy of drill cores at the drilling site. Research is still needed to better understand the subtle changes in absorption features before reflectance spectroscopy will reach its full potential. Good spectral databases documenting all the absorption features are also needed before reflectance spectroscopy can be as widely used a tool as XRD.

Spectral databases are now becoming available e. For certain classes of minerals, however, spectroscopy is already an excellent tool. Among these classes are clay mineralogy, OH-bearing minerals, iron oxides and hydroxides, carbonates, sulfates, olivines and pyroxenes. Space limits the contents of any review article covering such a broad topic. Other review articles are Adams , Hunt , , Gaffey et al. The Hunt CRC book chapter in particular presents more spectra, both visible-near-IR and mid-IR, than most other works and seems to be an overlooked but important work.

Thanks goes to reviewers John Mustard, Gregg Swayze, and Eric Livo, whose comments substantially improved the manuscript. Adams, J. Geophys Res. Smith, and A. Pieters, and P. Englert, eds. Berk, A. Bernstein, and D. Burns, R. Chandrasekhar, S. Clark, R. Fanale, and M. Burns and M. Matthews, eds. King, M. Klejwa, G. Swayze, and N. Gallagher, and G. JPL Publication , Swayze, and A. Swayze, A. Gallagher, T. King, and W. Calvin, b, The U. Geological Survey, Open File Report , pages. King, C. Ager, and G. Proceedings: Summitville Forum '95, H. Posey, J. Pendelton, and D.

Van Zyl Eds. Ager, G. Cloutis, E. Gaffey, , Pyroxene Spectroscopy revisited: Spectral-Compositional Correlations and relationships to goetherometry, J. Gaffey, T. Jackowski, and K. Crowley, J. Cruikshank, D. Brown, and R. Klinger et al. Reidel Publishing Company, Duke, E. Farmer, V.

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Farmer, ed. Mineralogical Society, London, pp. Gaffey, S.

Three Methods of Measuring Reflectance

McFadden, D. Nash, and C. Goetz, A. Vane, J. Solomon, and B. Rock, , Imaging spectrometry for earth remote sensing, Science , , Goguen, J. Thesis, Cornell Univ. Green, Robert O. Conel, Veronique Carrere, Carol J. Bruegge, Jack S.


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Hapke, B. Theory, J. Henderson, B. Lucey, and B. Jakosky, , New laboratory measurements of mid-IR emission spectra of simulated planetary surfaces, J. Herzberg, G. Hunt, G. Carmichael, ed. Silicate minerals, Mod. Geology 1, Carbonates, Mod. Geology 2, Oxides and hydroxides, Mod. Sulphides and sulphates, Mod. Geology 3, Halides, arsenates, vanadates, and borates, Mod. Additional silicates, Mod. Geology 4, Johnson, P. Smith, and S. Taylor-George, , A semi-empirical method for analysis of the reflectance spectra of binary mineral mixtures. Johnson P. Smith, and J. Adams, , Simple Algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra, J.

King, T. Clark, C.

Theory of Reflectance and Emittance Spectroscopy (Topics in Remote Sensing)

Li, L. Mustard, and G. Lucey, P. Lumme, K. Morris, R. Lauer, C. Lawson, E. Gibson, G. Nace, and C. Mustard, J. Pieters, , Photometeric phase functions of common geologic minerals and applications to quantitative analysis of mineral mixture reflectance spectra, J. Pieters, , Quantitative abundance estimates from bidirectional reflectance measurements, Proc. Pieters, , Abundance and distribution of serpentinized ultramafic microbreccia in Moses Rock dike: Quantitative application of mapping spectrometer data, J.

Nash, D. Nelson, M. Nicodemus, F. Applied Optics , 4 , Post, J. Noble, The near-infrared combination band frequencies of dioctahedral smectites, micas, and illites, Clays and Clay Minerals.

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Salisbury, J. Walter, N. Vergo, and D. D'Aria, , Infrared 2. Sherman, D. Coyne, S. McKeever, and D. Drake, eds.

OSA | Spectral Reflectance and Emittance of Particulate Materials. 1: Theory

Shipman, H. Adams, , Detectability of minerals on desert alluvial fans using reflectance spectra, J. Spitzer, W. Sunshine, J. Pieters, , Extraction of compositional information from olivine reflectance spectra: new capability for lunar exploration abstract , in Lunar and Planetary Science XXI , , Lunar and Planetary Institute, Houston. Pieters, , Identification of modal abundances in spectra of natural and laboratory pyroxene mixtures: a key component for remote analysis of lunar basalts abstract , in Lunar and Planetary Science XXII , , Lunar and Planetary Instute, Houston.

Pieters, and S. Pratt, , Deconvolution of mineral absorption bands: an improved approach, J. Swayze, G. Clark, A. H Goetz, N. Gorelick, and T. Chrien, , Spectral Identification of surface materials using imaging spectrometer data: evaluating the effects of detector sampling, bandpass, and signal to noise ratio using the U.

Tricorder Algorithm, to be submitted to J Geophys. Wendlandt, W. White, W. Vane, G. Duval, and J. Wellman, , Imaging spectroscopy of the earth and other solar system bodies, in Remote Geochemical Analysis: Elemental and Mineralogical Composition C. Back to Contents Phone email and regular mail addresses of spectroscopy lab personnel for further information. Geological Survey , a bureau of the U. Roger N. Polarization Reflectance spectroscopy Thermal emission and emittance spectroscopy Simultaneous transport of energy by radiation and conduction Appendix A.

A brief review of vector calculus Appendix B. Functions of a complex variable Appendix C. The wave equation in spherical coordinates Appendix D. Fraunhoffer diffraction by a circular hole Appendix E. By: Bruce Hapke. Current promotions. Insect Microscopy. More Info. Pollen Microscopy. Botanical Life. The Plant Stem. Practical Microscopy for Beekeepers. Transmission Electron Microscopy. Adventures with a Microscope. The Microscope and How to Use it. Other titles from CUP. Ecology and Conservation of Forest Birds. The Nature of Plant Communities.

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