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USGS Digital Spectral Library splib06a

By Roger N. Clark,1 Gregg A. Swayze,1 Richard A. Wise,1 K. Eric Livo,2 Todd M. Hoefen,1 Raymond F. Kokaly,1 and Stephen J. Sutley2

1U.S. Geological Survey Crustal Imaging and Characterization Team
2U.S. Geological Survey Central Region Minerals Team

U.S. Geological Survey
Box 25046 Denver Federal Center
Denver, CO 80225

U.S. Geological Survey Data Series 231

Director's Approval September 13, 2007

First Published: September 20, 2007
Revised: September 20, 2007

How to reference:
Clark, R.N., Swayze, G.A., Wise, R., Livo, E., Hoefen, T., Kokaly, R., Sutley, S.J., 2007, USGS digital spectral library splib06a: U.S. Geological Survey, Digital Data Series 231, http://speclab.cr.usgs.gov/spectral.lib06.

(Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.)

Contents

Introduction

We have assembled a digital reflectance spectral library that covers the wavelength range from the ultraviolet to far infrared along with sample documentation. The library includes samples of minerals, rocks, soils, physically constructed as well as mathematically computed mixtures, plants, vegetation communities, microorganisms, and man-made materials. The samples and spectra collected were assembled for the purpose of using spectral features for the remote detection of these and similar materials.

Analysis of spectroscopic data from laboratory, aircraft, and spacecraft instrumentation requires a knowledge base. The spectral library discussed here forms a knowledge base for the spectroscopy of minerals and related materials of importance to a variety of research programs being conducted at the U.S. Geological Survey. Much of this library grew out of the need for spectra to support imaging spectroscopy studies of the Earth and planets. Imaging spectrometers, such as the National Aeronautics and Space Administration (NASA) Airborne Visible/Infra Red Imaging Spectrometer (AVIRIS) or the NASA Cassini Visual and Infrared Mapping Spectrometer (VIMS) which is currently orbiting Saturn, have narrow bandwidths in many contiguous spectral channels that permit accurate definition of absorption features in spectra from a variety of materials. Identification of materials from such data requires a comprehensive spectral library of minerals, vegetation, man-made materials, and other subjects in the scene.

Our research involves the use of the spectral library to identify the components in a spectrum of an unknown. Therefore, the quality of the library must be very good. However, the quality required in a spectral library to successfully perform an investigation depends on the scientific questions to be answered and the type of algorithms to be used. For example, to map a mineral using imaging spectroscopy and the mapping algorithm of Clark and others (1990a, 2003b), one simply needs a diagnostic absorption band. The mapping system uses continuum-removed reference spectral features fitted to features in observed spectra. Spectral features for such algorithms can be obtained from a spectrum of a sample containing large amounts of contaminants, including those that add other spectral features, as long as the shape of the diagnostic feature of interest is not modified. If, however, the data are needed for radiative transfer models to derive mineral abundances from reflectance spectra, then completely uncontaminated spectra are required. This library contains spectra that span a range of quality, with purity indicators to flag spectra for (or against) particular uses.

Acquiring spectral measurements and performing sample characterizations for this library has taken about 15 person-years of effort. Software to manage the library and provide scientific analysis capability is provided (Clark, 1980, 1993). A personal computer (PC) reader for the library is also available (Livo and others, 1993). The program reads specpr binary files (Clark, 1980, 1993) and plots spectra. Another program that reads the specpr format is written in IDL (Kokaly, 2005).

In our view, an ideal spectral library consists of samples covering a very wide range of materials, has large wavelength range with very high precision, and has enough sample analyses and documentation to establish the quality of the spectra. Time and available resources limit what can be achieved.

Ideally, for each mineral, the sample analysis would include X-ray diffraction (XRD), electron microprobe (EM) or X-ray fluorescence (XRF), and petrographic microscopic analyses. For some minerals, such as iron oxides, additional analyses such as Mossbauer would be helpful. We have found that to make the basic spectral measurements, provide XRD, EM or XRF analyses, and microscopic analyses, document the results, and complete an entry of one spectral library sample, all takes about 1 person-week. Collecting additional spectra of the same sample (such as, a grain size series) increases the time by approximately 0.5 day per spectrum (mostly for sample preparation). We had hoped that as our experience increased, this time would decrease, but it has not in our experience of more than 20 years of developing this and previous spectral databases.

Vegetation samples and vegetated areas can also have exacting demands for data collection and documentation. For leaf samples measured in the laboratory or field spectra of single plants, basic documentation requirements include the date of sample collection and measurement and correct species identification. Additional documentation is desired on leaf water and pigment concentrations, requiring the associated laboratory analysis of those samples. For field-level measurements of vegetated areas (vegetation communities), the species in the measured area and the associated percent cover of each species are desired. A single plot can take up to 4 hours to measure, with data reduction, reviewing, and compiling taking nearly as long.

This release of this library does not have all samples completely characterized. The characterization of samples will continue as our resources allow, and results will be added in future releases of the database. This library, however, contains more extensive sample documentation and fills in gaps present in the documentation for the splib05a library (Clark and others, 2003c). A new component of the splib06a library is that many samples now include digital photos, as well as spectra with greater wavelength range. Some mineral samples were found to be more contaminated than previous support data indicated, so some samples have been renamed from earlier versions of the library. If you cannot find a sample by its name from a previous library, search by using its sample number.

This report describes spectral library 06: splib06a. Libraries 01, 02, and 03 were unpublished experimental exercises that investigated what to include in a spectral library. Library 04 (splib04a, Clark and others, 1993), was our first release. Clark and others (splib05a, 2003c), has been our main working library until this release. The splib06a library includes all spectra from the splib04a and splib05a libraries with additional documentation derived since publication of those libraries.

Types of Materials

Minerals from arsenate, borate, carbonate, element, halide, hydroxide, nitrate, oxide, phosphate, silicate (cyclosilicate, inosilicate, phyllosilicate, nesosilicate, sorosilicate, and tectosilicate), sulfate, and sulfide classes are represented. X-ray and chemical analyses are tabulated for many of the entries, and all samples have been evaluated for spectral purity. The library also contains end and intermediate members for the olivine, garnet, scapolite, montmorillonite, muscovite, jarosite, and alunite solid-solution series. We have included representative spectra of H2O ice, kerogen, ammonium-bearing minerals, rare earth oxides, desert varnish coatings, kaolinite crystallinity series, kaolinite-smectite series, zeolite series, and an extensive evaporite series.

Because of the importance of vegetation to terrestrial studies, we have included spectra of trees, shrubs, grasses, flowers, leaves, and lichens and other microorganisms. Also included are vegetation community spectra. Laboratory spectra of leaves from a single plant and field spectra of a single species, or of a plant in its entirety, are listed by the common name of the species. Scientific names of vegetation are given in the supporting documentation. Field and AVIRIS spectra of vegetation communities are listed by the dominant species (for example, lodgepole pine), or by biome (such as grassland or shrubland), or by a land-use term (such as rangeland). The vegetation types are representative of the types that we have encountered and studied in our published works.

Terrestrial remote sensing also encounters man-made materials, so we have included spectra of plastics, roofing materials, processed wood, paint, and other man-made materials. Spectra of liquids from water to organic liquids mixed with minerals are also included. Together, the spectral library encompasses a broad range of materials that may exist in a remotely sensed image.

In some cases, several spectra were measured to provided a series that spans a compositional solid-solution series or a grain-size series or both. We tried to include spectra of all mineral classes, particularly those important to hyperspectral remote sensing. In other cases, we have studied particular solid-solution series because we are mapping them in the field with imaging spectroscopy or studying that mineral in detail. This explains, for example, why there are so many alunite, olivine, and topaz samples in the database. Future releases of the database will probably include additional spectra of other solid-solution series.

Spectral Library Chapter Organization

The digital spectral library splib06a is organized in chapters:

The placing of some samples into a particular chapter was not always clear cut. For example, pure minerals are often difficult to find, or it may be difficult to process the sample to purify the mineral. Such a sample could be considered a mixture. We placed some samples of this type in the minerals chapter (Chapter 1: M) because we are using the sample and its spectra to match to pure minerals. We tended to place in the mixture chapter (Chapter 2: S) spectra of samples that show specific spectral properties of mixtures that we use for identification and mapping of those mixtures. Usually these include overlapping absorption features.

Spectrometers

The spectra in our library were measured on at least one of four different spectrometers: (1) a Beckman 5270 covering the spectral range 0.2 to 3 µm (micrometers), (2) an Analytical Spectral Devices (ASD) portable field spectrometer covering the range from 0.35 to 2.5 µm, (3) a Nicolet Fourier Transform Infra-Red (FTIR) Interferometer Spectrometer covering the range from about 1.3 to 150 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.4 to 2.5 µm. The spectral resolution of these instruments is shown in figure 1.


image FIGURES/plot.bandpasses.tgif.gif
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Figure 1A. The spectral bandpass for the spectrometers used to obtain spectra for digital spectral library splib06a. The bandpass is expressed as the full width at half maximum (FWHM) in micrometers.




image FIGURES/plot.fig1a.nic_fwhm_bp_um.gif
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Figure 1B. The spectral bandpass for the Nicolet spectrometer in the near-infrared. The bandpass is expressed as the full width at half maximum (FWHM) in micrometers. The bandpass is a constant 4 wavenumbers (cm-1) over the entire range of the instrument, for the data discussed in this document.


Spectra run on our modified Beckman 5270 spectrometer from 0.2 to 3.0 µm were corrected to absolute reflectance. This instrument measures directional-conical light as described in Clark and others (1990b). Samples were run with a signal-to-noise ratio of at least 500 at a reference reflectance level of 1.0. A few minerals were also run over a slightly smaller wavelength range because of sample size limitations. For example, small sample quantities, necessary for purity, were measured by using apertures in the beam to restrict the spot size of the spectrometer. This reduced light made integration times longer, and the achievable range was sometimes reduced, typically to 0.3 to 2.7 µm. In some cases, this also limited the signal-to-noise ratio that was achievable. It is not possible with the current Beckman instrumentation to substantially improve the spectral data on small volume samples. The ice sample, measured at 77 Kelvin, only includes the infrared range, 0.8 to 3.0 µm.

Spectra measured on the ASD spectrometer used a directional light source and fiber-optic probe to collect light. The incidence angle was variable but typically ranged from 20 to 40 degrees, as did the emission angle. Spectra are corrected to absolute reflectance by using a Spectralon standard with correction methods similar to that described in Clark and others (1990b). Field spectra measured using the ASD spectrometer have been collected under various sky conditions. Most are collected under optimum conditions of clear skies and within an hour of noon. Because of limited time for field work, some spectra have been collected under partly cloudy skies and up to 3 hours before or after solar noon.

Spectra were also run on a commercial Nicolet Fourier Transform Infra-Red (FTIR) interferometer spectrometer. Most spectra were run on a Nicolet model 740 until 1998 when spectra were run on a Nicolet Magna-IR 760 spectrometer. Spectra were measured over a range from about 1.3 to 150 µm. To cover the factor of 100 in wavelength range from 1.5 to 150 µm, two detectors (some samples used three detectors) and two beam splitters had to be used. For all samples, the near IR (1.3 to 5 µm, referred to here as the NIR) and the mid-IR (~2.5 to 25 µm, MIR) were measured with the same beam splitter and with no change in the sample. On the Nicolet 740, only a change in light source was required: a simple change of a lever. On the Nicolet Magna, an internal mirror automatically switched the light source and detector. With some setup changes in an instrument or between instruments, a wavelength region was measured at a different time; thus, each sample was put back in its bottle between measurements. There was sufficient overlap in the data to confirm consistency across the spectral boundary.

The AVIRIS spectra in this library are of samples where it would be difficult to make measurements with normal field portable spectrometers, such as tall trees (requiring cranes or other means to lift the spectrometer and operator above the sample). Calibration of AVIRIS data is discussed in Clark and others (2003a).

Sample Naming

The name for each sample occurs in three places: (1) in the specpr (Clark, 1993) title field for the spectrum, (2) in the specpr title field for the description entry, and (3) after the keyword in the description. We have tried to use only proper mineral names as given in Fleischer (1980), Fleischer and Mandarino (1995), and Klein and Hurlbut (1999). Some users of the library may be unfamiliar with all the mineral names. For example, if you want to find all scapolites, you would have to know that dipyre is a scapolite if you only looked at the specpr title fields. Because of the 40-character limit in a specpr title field, we could not include all common names there; however, we used the keywords (described below) in the description for each sample. Here you could search for scapolite and find all entries in the "scapolite group" (dipyre, marialite, meionite, and mizzonite).

We have used specific mineral names except in a few cases where we still do not have sufficient data. For example, in the database, we avoid the general term "hornblende" where possible, instead using names like ferro-hornblende and magnesio-hornblende, which due to space limitation we call Hornblende_Fe and Hornblende_Mg respectively. We do have two samples where we cannot make the distinction, so they are labeled hornblende (samples HS16.3B and HS177.3B).

The specpr title field is limited to 40 characters. Some sample names, especially mixtures, are too long to fit in this space; thus, names are abbreviated. The abbreviations used in this library are listed here: click here.

Sample Documentation

Each spectrum has a sample description page describing the origin and sample purity from available data. The sample description pages follow those from Clark and others (1993, 2003c) with keyword=value or keyword followed by text and end_keyword. The description pages are in html format. The html format still contains the keyword organization. Different sample types have different documentation formats. For example, minerals require some different documentation from plants.

Templates for the documentation types are:

Every entry in the description pages is not complete for every sample. Thus, the description pages reflect the growing and evolving state of a dynamic spectral library.

This spectral library includes an updated set of sample description pages that also apply to the Clark and others (1993, 2003c) libraries. Many new X-ray diffraction (XRD) analyses have been completed, so most mineral samples now have XRD data.

Digital Photographs

Many samples now include digital photographs. For minerals, all digital photos are at a scale of 8 µm per pixel. For the splib06a library, these photos were obtained in the laboratory with a 180 mm macro lens on a 6-megapixel digital camera (a Canon D60). Photos were obtained in raw mode and included in splib06a as jpeg conversions from the raw data with no manipulation. The photos were obtained with standard meter settings using a spectralon reflectance target, the same reference surface used for the ASD field spectrometer reflectance calibration. By using one reference target, intensities can be compared between samples. In a future release, the raw camera data may be reduced to produce 16-bit tif reflectance calibrated images.

The digital photos of vegetation are field photos and are at various scales.

Spectral Purity and Spectral Range Flags

Each spectrum has coded into the Specpr title the spectral wavelength range, the spectral resolution and spectrometer used, and spectral purity information. In the Clark and others (1993) spectral library, the code was: W1R1Bx. The "W" stands for "wavelength range" followed by the wavelength range measured, which is given as W1-W4 (table 1). Range W1, for example, is a nominal range of 0.2 to 3.0 µm. The "R" stands for "resolution" followed by the resolution index. For the first version of the spectral library (splib04a), only Resolution 1 of the Beckman spectrometer (R1B) was used. The "x" is a non-numerical character (a, b, c, d, ?, or _) describing the spectral purity in the indicated spectral range and is further defined below.

Table 1
Spectral Ranges
Spectral Range Number Range
W1 0.2 - 3 µm (50,000 - 3,333 cm-1)
W2 1.5 - 6 µm (6,666 - 1,666 cm-1)
W3 6 - 25 µm (1,666 - 400 cm-1)
W4 20 - 150 µm (500 - 66.7 cm-1)

The wavelength, resolution, and spectrometer codes in this new library are given in tables 1 and 2 and are more complex to reflect the additional spectrometers, their resolutions, and wavelength ranges. In the new library, B=Beckman spectrometer, F=ASD field spectrometer, N=Nicolet spectrometer, and A=AVIRIS (Table 2). Two additional spectral range numbers are used: W5, consisting of W2 + W3, and W9, consisting of ranges W2 + W3 + W4. Three additional spectrometers are listed, where F=ASD field spectrometer, N=Nicolet spectrometer, and A=AVIRIS (table 2). Additional purity codes may also be used depending on the number of spectral ranges measured. For example, W9 has three spectral ranges, hence three spectral purity codes listed in increasing wavelength order.

Table 2. Spectral range, spectral resolution, spectrometer used, and spectral purity coding.

For this release, splib06a, some spectra have all spectral regions measured and evaluated.

Spectral purity codes are defined below:

a: The spectrum and sample are pure based on significant supporting data available to the authors. The sample purity from other methods (for example, XRD, microscopic examination) indicates essentially no other contaminants. Spectrally, no contaminants are seen in the spectrum.

b: The spectrum appears spectrally pure. However, other sample analyses indicate the presence of other minerals that probably affect the absolute reflectance level to a small degree, but do not add any significant spectral features in that region. The spectral features of the primary minerals may be slightly less intense, but the feature positions and shapes should be representative. For example, in the visible-NIR wavelength regions (W1 or W2), quartz would tend to increase the reflectance level and decrease absorption band strength but would not add any measurable features to the spectrum. Such a sample would rate a "b." In a few cases, where we have little support data but the spectra for that mineral are well known, we assigned the spectral purity based on the spectral data, possibly along with information derived from a microscopic examination of the sample. There are a few "b" ratings assigned this way. AVIRIS data of vegetated areas have been assigned a "b" rating due to the imperfect atmospheric correction that can be performed on remotely sensed spectra.

c: The spectrum is spectrally pure except for some weak features with depths of a few percent or less caused by other contaminants. For example, some minerals may have some slight alteration that is apparent. Spectroscopic detection of alteration minerals is easier for more transparent minerals. For example, some of the albite spectra show weak 2.2-µm features due to alteration. From the knowledge of the mineral formula, one can often tell which spectral features do not belong to the mineral. Albite, for instance, does not have OH in the formula, so water or hydroxyl features (1.4, 1.9, and 2.2 µm in the spectrum) are not due to albite. However, one could argue that incipient alteration due to weathering is common in minerals at the Earth's surface. Thus, spectral bands due to weathering are somewhat characteristic of many samples (such as feldspars), even if they are not a property of the pure mineral. Spectral features of alteration minerals might be useful in some cases, but such alteration and their spectral effects still reduce spectral purity as far as this library is concerned. Note, also, that almost every sample has some absorption near 3 µm due to water in the sample. Water adsorbs onto mineral grains and is common. Water absorptions at 3 µm are not considered contaminants and do not affect spectral purity.

d: Significant spectral contamination. The spectrum is included in the library only because it is the best sample of its type currently available, and because the primary spectral features can still be recognized (it may also have a better spectral purity in another wavelength range). However, the spectrum should be used with care. The sample description should be consulted as a guide to which spectral features are due to the actual mineral. This sample may be purged from the database in future releases as better samples become available.

?: There are insufficient analyses or knowledge (or both) of the spectral properties of this material to evaluate its spectral purity. In general we have included such samples because we believe their spectra to be representative. These are samples for which we are concentrating future analyses in order to resolve the purity issue. Note that there may be sufficient knowledge to assign spectral purity in one spectral region but not in another.

_: The spectral range was not measured, but a place holder is included for the future.

Mixtures, soils, rocks, and coatings can be spectrally complex. The spectral purity of complex mixtures can still be high (assigned "a" status; see the section on Spectral Purity, if the goal is to document the spectral features in such a mixture. Mixtures that have unintentional contamination have a lower spectral purity than if contaminants were not present, following the guidelines jus given.

The spectral purity should be consulted before use of spectra to see if the sample is appropriate for the intended purpose. For example, examination of the spectra for hypersthene PYX02 will show that the spectral purity is a "c," meaning contaminating spectral features overlap diagnostic hypersthene spectral features. However, as documented in the sample description, the pyroxene sample contains a small amount of tremolite alteration. Whereas the tremolite absorptions are narrow absorptions at 1.4 and 2.3 µm, the pyroxene absorptions are very broad. If your spectral analysis can ignore the contaminant's narrow absorptions, then this sample's spectrum can be used as an excellent reference spectrum for hypersthene.

For another example of spectral purity, consider the spectrum of orthoclase sample NMNH142137. The spectral purity is a "b." There are weak alteration mineral absorptions in the 2 to 2.5 µm spectral region that are not indicative of feldspars. Most feldspars for which we have seen or measured spectra show evidence of alteration to muscovite at 1.4 µm and at 2 to 2.5 µm. These absorptions are not due to feldspar and should not be used to identify feldspars.

The spectrum for each mineral should be evaluated for contaminant features and how those features might interfere with a particular analysis. Knowledge of the spectral properties of minerals and other materials is important in using any spectral database's reference spectra for identifying other minerals and materials. See Clark (1999) and references therein for discussions of spectral properties of materials and principles of spectroscopy.

Spectral Measurement Side Effects

1) Residual Water Absorptions in the Samples. Even anhydrous minerals show an absorption near 3 µm due to water adsorbed onto the surfaces of the mineral grains. Our experience has shown that these water absorptions are still present, although slightly weaker in dry nitrogen purged environments. Consequently, the spectra measured on the Nicolet in a dry atmosphere will have different absorption than will those same samples measured by other spectrometers such as the Beckman in a normal room atmosphere. Spectra of similar samples obtained at other facilities - those in Hawaii or on the east coast of the United States - have shown that the water absorptions in the spectra from relatively dry Colorado are really quite small in comparison. Placing the sample in a dry nitrogen atmosphere or in a vacuum oven has little effect on the water absorption because water from the atmosphere will readsorb onto the sample by the time it reaches the spectrometer. Experiments at the University of Hawaii have also shown that most of the adsorbed water remains, even under a strong vacuum at room temperatures (Clark, 1981). In general we decided not to heat our samples in order to avoid any temperature-induced alteration.

2) Low Signal Artifacts. The Nicolet FTIR near-infrared measurements have an artifact toward shorter wavelengths. From wavelengths of about 2 µm down to 1.3 µm, the reflectance often decreases giving a convex shape to the spectrum. This rollover is not correct. Attempts to derive a correction have not been successful because the amount of rollover is variable from sample to sample. We include this spectral range because the very high spectral resolution can be quite useful for studying narrow spectral features. The rollover becomes more intense at wavelengths shorter than about 1.5 µm. We sometimes include data to 1.3 µm if narrow spectral features are present, but the broader upward convex curvature should not be interpreted as spectral structure.

3) Purged Samples and Residual Atmospheric Features. All Nicolet samples in this library were measured in a dry nitrogen atmosphere, scrubbed of H2O and CO2 by a Balston air purification system. The sample and the reflectance standard were each placed in the sample chamber, and a period of time was allowed to pass before a measurement was made. The standard and reference were each measured at approximately the same time after installation into the chamber, so the purge and any residual atmospheric gases which could cause absorptions should be roughly comparable and therefore should cancel. This method did not always work perfectly, and residual atmospheric absorptions (H2O and CO2) are sometimes present at a low level (for example, see Salisbury and others, 1991). Such residual absorptions (fig. 2A, B) are difficult to remove completely because of the intense nature of the absorptions. Similar features are reported in other databases covering this spectral region (for example, Salisbury and others , 1991). For field and AVIRIS spectra of vegetation, the spectrometer channels that are strongly influenced by atmospheric absorptions have been set to the deleted point value of -1.23x1034.


Figure 2a. Residual atmospheric H<sub>2</sub>O absorptions.
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Figure 2A. Residual atmospheric H2O absorptions are seen in the andesine spectrum (arrows) but are almost absent in the annite spectrum. Residual H2O can also be seen near 6 µm (not shown).


Figure 2b. Residual atmospheric CO<sub>2</sub> absorptions.
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Figure 2B. Residual atmospheric CO2 absorptions are seen in the analcime spectrum (arrows) but are absent in the muscovite spectrum.

4) Spectral Measurement Geometry. The measurement of reflectance with our Nicolet spectrometer results in an unusual side effect due to a small beam size. The basic geometry of the Nicolet spectrometer biconical reflectance attachment is shown in figures 3A and 3B. The reflectance attachment focuses the spectrometer beam onto a spot about 2 mm in diameter (fig. 3B) via an elliptical mirror. A second mirror collects the reflected light and sends it to a detector. The small spot size produces side effects at wavelengths where the sample is weakly absorbing and multiple scattering of reflected photons is a significant portion of the signal. Some photons will scatter from grain to grain, leaving the surface outside the focus of the collection mirror, and therefore are not seen by the detector (fig. 3B). This results in a relative loss in signal and a lower measured reflectance at wavelengths where multiple scattering is significant. This phenomenon is illustrated in figure 4, where a Nicolet measurement is compared to a spectrum from the Clark and others (1993) library. In general, the Nicolet spectra are scaled to match the reflectance measured with the Beckman and (or) ASD spectrometers at 2.25 µm. In figure 4 the Nicolet measurements show weaker 2.16, 1.75, and 2.3 µm absorptions due to this effect. The Nicolet measurement is also lower in reflectance at wavelengths smaller than 2 µm due to this effect. The increased spectral structure in the Nicolet spectrum near 2.5 µm is due to its higher spectral resolution compared to that of the Beckman spectrometer.


Figure 3a.  Sample viewing geometry.

Figure 3A. The sample viewing geometry for the Nicolet spectrometer is biconical. The measurement has maximum 60-degree entrance and exit beams (30-degree average), with about a 60 phase angle.


Figure 2b. The small spot measured by the Nicolet spectrometer.

Figure 3B. The small spot measured by the Nicolet spectrometer results in some loss of photons (labeled B) when the photons are multiply scattered. Only photons scattered within the exit field of view (labeled A) are measured. This edge effect is insignificant in the mid-infrared where absorption coefficients are higher.


Figure 4
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Figure 4. The sample viewing geometry and small spot size of the Nicolet spectrometer reflectance attachment (fig. 3) results in some loss of photons. Here, a Nicolet measurement is compared to a spectrum from the Clark and others (1993) library. The spectra are scaled to match at 2.25 µm. The Nicolet measurements show weaker 2.16, 1.75, and 2.3 µm absorptions due to the light loss effect. The Nicolet measurement is also lower in reflectance at wavelengths smaller than 2 µm due to this effect. The increased spectral structure in the Nicolet spectrum near 2.5 µm is due to the much better spectral resolution compared to that of the Beckman spectrometer.

The narrow focus range of the Nicolet measurement geometry results in large changes in reflectance level with small changes in the position of the surface of the sample. Because of this uncertainty, data from the Clark and others (1993, 2003c) visible-near-infrared libraries and newer spectra were used as a guide to scale the Nicolet to the correct reflectance levels. Because of the light loss effect described above, the reflectance level of samples high in reflectance (greater than about 0.4) in the 1.5-2.5 µm spectral range is accurate only to about 10 percent. Darker samples should have more accurate reflectance levels.

Organic materials at low concentrations can leave residual spectral features (fig. 5). This usually affects only spectral features in the region near 3.4 µm. However, organic compounds can be very absorbing at other wavelengths, especially in the ultraviolet. Organic compounds can darken the spectrum from the ultraviolet to infrared, lowering reflectance levels. In reflectance, these effects are nonlinear, with a small abundance of dark material having a large influence on reflectance (Clark, 1983, 1999). Users of the library wanting to derive optical constants or to conduct other quantitative work should consult the sample documentation and evaluate the data for what effect contaminants would have on an analysis.


Figure 5. Illustration of an organic contaminant.
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Figure 5. Illustration of an organic contaminant in Nicolet spectrometer data.

Wavelength Precision

The wavelength precision of our custom-modified, computer-controlled Beckman spectrometer was checked using Holmium Oxide filters in the visible and using the positions of known mineral absorptions in the near infrared. In particular, we developed pyrophyllite as a wavelength standard because of its many narrow absorptions (Clark and others, 1990b). The positions of the absorptions have been checked, using the same pyrophyllite standard, on three FTIR spectrometers. In general, the wavelength accuracy is on the order of 0.0005 µm (0.5 nm) in the near-IR and 0.0002 µm (0.2 nm) in the visible, always a small fraction of the spectral resolution. Wavelength precision for this instrument is further discussed in Clark and others (1990a).

The wavelength precision of the ASD portable field spectrometer was monitored using REE impregnated glass and a mylar plastic sheet with strong absorption features. The instrument was also calibrated annually by the manufacturer. The position of the mylar spectral features was precisely measured with our Nicolet FTIR instrument, described next.

The wavelength precision of a Fourier Transform Spectrometer is accurate to better than the bandwidth of the system. The Nicolet FTIR systems use a red laser wavelength as a fringe counter in the interferometer, a fraction of the shortest wavelength measured in this library. We also confirmed wavelength positions with our pyrophyllite standard (Clark and others 1990b, 1993) and its many narrow absorption bands, which were measured using three different FTIR spectrometers and our Beckman spectrometer and found to be very consistent.

The wavelength calibration of the NASA AVIRIS system was performed by NASA/JPL (Green and others, 1990, 1996).

Spectral Plots and Data Precision

Plots of the spectra presented here are limited to one of several vertical scales (including 0.0 to 0.1, 0.15, 0.25, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0, or 1.1) and use the same horizontal range for easy comparison. The overlapping vertical ranges ensure the spectrum is framed well yet still shows spectral detail, and the range that best shows the spectrum was selected for each plot. The error bars are plotted only when they are above a threshold that allows them to be distinguished on the plot. Most error bars are too small to be distinguished. Each plot shows the specpr title, date and time of acquisition, file name, record number, and wavelength set. Not all spectra include error estimates.

Each spectrum was run with a desired signal-to-noise ratio of at least 500 relative to unity reflectance. In practice, it would take too long to obtain such a signal-to-noise ratio in regions where the signal is low, so an upper limit to the integration time per channel was also specified with our Beckman spectrometer. Thus, typically at the ends of the spectra (both short and long wavelength ends), the precision drops slightly. Refer to the error bars for each spectrum or examine the channel-to-channel noise to determine the precision at a given wavelength for any individual spectral channel. The ASD and Nicolet spectrometers typically have higher signal-to-noise ratios but do not report error data. Thus, signal-to-noise ratios must be estimated by the spectral channel-to-channel variability.

Mineral Mixtures and Optical Constants

This spectral library contains pure materials as well as mixtures. For computing intimate mineral mixtures (such as rocks or soils), radiative transfer algorithms using the Hapke reflectance model (Hapke, 1981) are part of the specpr package. To compute mixture or pure end-member spectra, a set of optical constants are required as a function of wavelength. The algorithms use optical constants so that spectra can be calculated as a function of grain size, abundance in a mixture, and viewing geometry. Reflectance spectra of grain-size distributions can also be simulated by computing a mixture of the same mineral (or even several minerals) at several grain sizes.

Pure samples are extremely rare. Spectroscopy, especially over the 0.2 to 150 µm spectral range (a factor of 750 in wavelength range), is very sensitive to low-level contaminants at some wavelengths. Spectroscopy can detect a trace contaminant even in samples ground from single crystals that appear pure. Thus, most samples in this library could be considered mixtures at some level. In other cases, deliberate mixtures are included because of the common associations in which they occur. If you intend to use a spectrum for a particular purpose, confirm the spectral purity to be sure it is appropriate for your needs.

The Digital Data File

The digital spectral library data are all included in one file in "specpr" format (see Clark, 1993). This file, splib06a, is 51 megabytes in size and has been assembled and managed using the spectral processing software package, specpr. The data are in IEEE binary floating point format. The entire library is assembled, plotted, and printed by command files consisting of Unix shell commands, which in turn generate specpr commands to build the library.

Specpr runs on Unix workstations. If the binary file is read on an IBM-PC compatible machine, the floating point numbers need their bytes swapped (this is automatically done with the program by Livo and others, 1993). ASCII versions of all spectra along with plots are presented in the data table at the end of this Web document.

The specpr data file can also be read using the "View_SPECPR" ENVI/IDL software (Kokaly, 2005; see link to URL in reference section). This software is a set of IDL programs that can be downloaded and installed on computers running the commercial ENVI/IDL software. The View_SPECPR program allows the user to 1) list the contents of SPECPR spectral library files, 2) plot and overplot spectra in the library, and 3) view and print the supporting documentation and photos.

The organization of the binary data file, in the form of a specpr listing, is shown in table 3. The listing shows the record number, title, length of the data set (number of channels for spectra; number of bytes for text), and the time and date of data acquisition. Records containing the wavelength sets for the spectra and records containing the spectral resolution data sets occur one time near the beginning of the file. The resolution for each spectrometer is shown in figure 1. Entries with the keyword "DESCRIPT" are sample description records and contain all the sample documentation. After the DESCRIPT are (usually) two empty records (title ..) for future expansion of the description. Next comes the reflectance data, with the keyword AREF, RREF, or RTGC. The identifier such as "W1R1Bx" signifies the wavelength range, resolution, spectrometer, and spectral purity, which was described earlier (the "x" is a lower case spectral purity letter code). After the reflectance record is the "errors to previous data" record. These are the standard deviations of the mean for each reflectance value if those values exist, or otherwise a placeholder of zeros. The next record in the listing contains the feature analysis for the spectrum. This feature analysis was done using the specpr f45 special function and is described in Clark (1987) and Clark and others (1993).

The reflectance type, AREF, RREF, or RTGC, is defined as follows. AREF is absolute reflectance using standards as described in Clark and others (1990b). RREF is relative reflectance. All Nicolet spectra used a stainless steel mirror as the standard and are therefore relative to the reflectance of stainless steel. RTGC stands for "Radiative Transfer Ground Calibrated," the method used for all AVIRIS spectra and defined in Clark and others (2003a).

Table 3. Organization of the binary Specpr data file

     1  USGS Digital Spectral Library: splib06a    397 Characters of TEXT
     2  ****************************************    41 Characters of TEXT
     3  ****************************************    41 Characters of TEXT
     4  ****************************************    41 Characters of TEXT
     5  ..                                          41 Characters of TEXT
     6  Wavelengths USGS Denver Beckman STD 1x     480  02:57:26.00  10/15/1985
     8  Bandpass (FWHM) USGS Denver Beckman STD    480  02:57:26.00  10/15/1985
    10  Wavelengths Standard ASD FR 0.35-2.5um    2151  21:54:44.00  02/05/2002
    16  Bandpass (FWHM) ASD FR 0.35-2.5um         2151  00:00:00.00  01/28/2003
    22  Wavelengths to Nicolet 1.3 - 5.2 microns  3325  00:00:00.00  02/07/1994
    32  Bandpass (FWHM) Nicolet FTIR 1.3-5.3 um   3325  12:30:00.00  03/24/1988
    42  Wavelengths to Nicolet 1.3-150 microns    4280  00:00:00.00  12/29/1992
    54  Bandpass (FWHM) Nicolet 1.3-150 microns   4280  19:39:49.00  06/16/1998
    66  Wavelengths AVIRIS 1996 0.4-2.5 microns    224  00:00:00.00  01/01/1996
    67  Bandpass (FWHM) AVIRIS 1996 0.4-2.5 um     224  00:00:00.00  01/01/1996
    68  ****************************************    41 Characters of TEXT
    69  ****************************************    41 Characters of TEXT
    70  ----------------------------------------    41 Characters of TEXT
    71  Acmite NMNH133746 Pyroxene      DESCRIPT  5732 Characters of TEXT
    75  ..                                          41 Characters of TEXT
    76  ..                                          41 Characters of TEXT
    77  Acmite NMNH133746 Pyroxene   W1R1Ba AREF   480  15:18:47.00  03/23/1988
    79  errors to previous data                    480  15:18:47.00  03/23/1988
    81  Acmite NMNH133746 Pyroxene       FEATANL   315  15:18:47.00  03/23/1988
    83  ----------------------------------------    41 Characters of TEXT
    84  Actinolite HS116.3B             DESCRIPT  6080 Characters of TEXT
    89  ..                                          41 Characters of TEXT
    90  ..                                          41 Characters of TEXT
    91  Actinolite HS116.3B          W1R1Bb AREF   480  08:41:01.00  07/11/1991
    93  errors to previous data                    480  08:41:01.00  07/11/1991
    95  Actinolite HS116.3B              FEATANL   396  08:41:01.00  07/11/1991

The full listing of the splib06a data file can be found here: splib06a.list.asc

In the spectral library, any value of -1.23x1034 is considered a deleted point. Because of the inherent floating point inaccuracies of single precision numbers on various computers, values in the range -1.23001x1034 to -1.22999x1034 should be considered deleted points. In the ASCII listings, deleted points are expressed as a string of asterisks (*).

The spectral library containing the spectra, standard deviation error values, sample description pages, and a spectral feature analysis of each spectrum is compiled in a specpr-format binary file. Binary files of the original and instrument-convolved spectral libraries will be maintained for ftp file transfer from http://speclab.cr.usgs.gov/spectral-lib.html.

Information describing the samples is stored in the specpr file as "DESCRIPT" records. These records contain the sample documentation in html format and are located in records preceding the spectral data. The information in these records is described in the sample documentation section.

Instrument Spectral Libraries

The intent of the spectral library is to serve as a knowledge base for spectral analysis. Comparison of spectral data is best done when the spectral resolutions of the knowledge base and the spectra undergoing analysis are identical. The specpr software (Clark, 1993) has tools for convolving the spectral library to the resolution and sampling interval of any instrument. The native (laboratory) spectral library will also be convolved to AVIRIS spectral resolution and sampling for the terrestrial instruments, and to Galileo NIMS, MGS-TES, and Cassini VIMS for the planetary imaging spectrometer instruments. Other instrument-specific spectral libraries will be convolved as the instruments become operational and when wavelength and bandpass information becomes available. These convolved data files, as well as specpr command files for convolving the database to your own instruments, will be made available on the spectroscopy lab web site (http://speclab.cr.usgs.gov/spectral-lib.html) and in the anonymous ftp directory. Each convolved instrument library will be provided in both binary specpr and ascii formats.

ASCII Spectral Data

ASCII data for each spectrum are accessible via the web-based Data Table (below). The ASCII data consist of three columns: wavelength, reflectance, and standard deviation of the mean.

Other resources on spectroscopy can be found at http://speclab.cr.usgs.gov.


Table of Spectral data

The table of links to sample descriptions, plots of spectra and ASCII data can be found by following the link below.

Click Here for the Digital Spectral Library Data Table.


Acknowledgments

A successful spectral library has extensive sample documentation. We are indebted to J. S. Huebner and Judy Konnert of the USGS for their support in analyzing the X-ray diffraction data on minerals for the last couple of years in the splib04 library (Clark and others, 1993), from which part of this library was derived. We thank the late Norma Vergo for many of the earlier X-ray analyses. Norma's attention to detail has certainly made this spectral library a quality product, and we miss her. Without these dedicated people providing superb analysis and feedback, this library would not have been possible.

Of course, a spectral library needs quality samples. We are indebted to Charlie Alpers, Jim Crowley, Skip Cunningham, Jim Post, Fred Kruse, Jim Post, Jack Salisbury, Richard V. Morris, and Roger Stoffregen for donating excellent samples. Thanks to the British Museum and the National Museum of Natural History for mineral samples. We thank Trude King, Brad Dalton, and Barnaby Rockwell for contributing some samples and spectra. We thank Robert McDougal for help in reducing spectral data on the artificial samples and early examinations of lists of spectra to be included.

This project has been funded by the USGS Minerals Program, the NASA Mars Global Surveyor Thermal Emission Spectrometer Team (R. Clark, Co-I), and the NASA Cassini VIMS Team (R. Clark, Team Member).


References

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Clark, R.N., Swayze, G.A., Gallagher, A., King, T.V.V., and Calvin, W.M., 1993, The U.S. Geological Survey, digital spectral library: version 1: 0.2 to 3.0 microns: U.S. Geological Survey Open-File Report 93-592, 1,340 p. http://speclab.cr.usgs.gov

Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., King, T.V.V., Dalton, J.B., Vance, J.S., Rockwell, B. W., Hoefen, T., and McDougal, R.R., 2003a, Surface reflectance calibration of terrestrial imaging spectroscopy data: a tutorial using AVIRIS: AVIRIS Workshop Proceedings, p. 43-63. Online at: http://speclab.cr.usgs.gov/PAPERS.calibration.tutorial

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