Surface mineral mapping at Steamboat Springs, Nevada, USA, with ...

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Remote Sensing of Environm

Surface mineral mapping at Steamboat Springs, Nevada, USA,

with multi-wavelength thermal infrared images

R. Greg Vaughan a,*, Simon J. Hook b, Wendy M. Calvin a, James V. Taranik a

aDepartment of Geological Sciences, University of Nevada Reno, United StatesbJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States

Received 14 September 2004; received in revised form 13 April 2005; accepted 28 April 2005

Abstract

The purpose of this study was to evaluate the use of airborne multispectral and hyperspectral thermal infrared (TIR) image data for

mapping surface minerals characteristic of active geothermal systems. TIR image data from the MASTER multispectral spectrometer and the

SEBASS hyperspectral spectrometer were acquired over Steamboat Springs, Nevada, USA, in September 1999. Spectral emissivity

information was extracted from the data and used to generate mineral maps. Using the MASTER data it was possible to map the extent of the

active geothermal area as well as silica-rich vs. clay-rich areas. Using the SEBASS data the same areas could be mapped and additionally the

presence of several minerals, including opal, quartz, alunite, anorthite, albite, and kaolinite could be identified. The SEBASS data also

detected previously unidentified hydrous sulfate minerals (tamarugite and alunogen) forming around active fumaroles. Laboratory spectral

data and XRD analyses of field samples confirmed the dominant mineral phases identified remotely. The airborne, field and laboratory data

together with temperature anomaly and fumarole location data from other studies were synthesized and linked to the surface geology to

determine the diagnostic surface mineral expression that could be mapped with TIR remotely sensed data for future exploration for similar

geothermal systems. Opaline sinter deposits are the most diagnostic surface expression of hot spring activity at Steamboat Springs because

they represent areas of the recent geyser activity. The differentiation between opal and chalcedony deposits and the identification of hydrous

sulfate minerals forming around active fumaroles are also important because chalcedony represents ancient sinter deposits and hydrous

sulfate minerals indicate locations of fumaroles that may be venting diffusely with no geyser activity. The surface expression of the

geothermal system at Steamboat Springs is small, which emphasizes the need for high spatial resolution airborne imagers. The 90-m pixels of

ASTER TIR data over the same area were only capable of resolving the area of past and recent sinter deposits together. MASTER

multispectral TIR data covered the entire area (¨12 km2) at 5-m spatial resolution and were capable of defining the extent of the sinter

deposits and zones of hydrothermally altered rocks. The SEBASS image swath was too narrow (256 m) to add significant spatial information

to the overall map, but the high spectral content identified several important minerals related to the local geology and the mineralogic surface

expression of the geothermal system.

D 2005 Elsevier Inc. All rights reserved.

Keywords: Thermal infrared; Multispectral; Hyperspectral; Emissivity; Mineral mapping; Geothermal exploration

1. Introduction

Airborne imaging spectrometers fill an important niche

for small- to intermediate-scale geologic remote sensing

research that compliments the global-scale data sets of

0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2005.04.030

* Corresponding author.

E-mail address: [email protected] (R.G. Vaughan).

satellite sensors. This study evaluates the use of thermal

infrared (TIR) remote sensing instruments for geothermal

resource exploration in Nevada, USA. Nevada is considered

to have the largest untapped geothermal resources in the

United States (http://www.eere.energy.gov/geothermal) and

Steamboat Springs is one of only nine active geothermal

systems in Nevada that is currently producing geothermal

power. Recent US Department of Energy initiatives com-

mitted to developing geothermal resources in the western

ent 99 (2005) 140 – 158

http://masterweb.jpl.nasa.gov

http://www.aero.org

http://asterweb.jpl.nasa.gov

http://www.eere.energy.gov/geothermal

R.G. Vaughan et al. / Remote Sensing of Environment 99 (2005) 140–158 141

US emphasize the importance of characterizing and

identifying both known and yet-to-be-discovered geother-

mal resource targets. Specifically, this study seeks to

identify the mineralogic surface expression that is diagnostic

of an active geothermal system like Steamboat Springs and

determine the important indicator minerals that can be

mapped by TIR remote sensing tools in the exploration for

similar geothermal systems.

The TIR spectral range (8–12 Am) is widely used in

terrestrial and planetary geologic studies to map surface

minerals. Instruments that are sensitive to TIR wave-

lengths can measure known spectral features that are

related to the fundamental vibrational frequencies of

interatomic bonds within common rock-forming minerals

(Abrams, 2000; Kahle & Goetz, 1983; Kahle & Rowan,

1980; Hook et al., 1999; Rowan & Mars, 2003). Surface

mineral groups that have been mapped using TIR spectral

measurements include carbonate, sulfate, phosphate, and

felsic vs. mafic silicate minerals (Crowley & Hook, 1996;

Gillespie et al., 1984; Kahle et al., 1988; Kahle & Goetz,

1983; Ramsey et al., 1999; Sabine et al., 1994). These

0.8

0.9

0.9

Em

issi

vity

(of

fset

for

clar

ity)

Wavelength (µm)8 9 10 11 12

Albite

Quartz

Hedenbergite

Muscovite

Actinolite

Fayalite

1.0

0

1.0

0.9

0.5

0.6

0.95

0.85

0.9

1.0

A

Fig. 1. A. TIR spectra of silicate minerals. Spectral features are related to Si–O

wavelengths with increasing isolation of SiO4 tetrahedra. B. TIR spectra of other m

are reproduced from the ASTER spectral library and the ASU thermal emission sp

spectroscopy laboratory for this study. Emissivity values are scaled and offset for

studies utilized airborne TIR measurements, which were

typically only available at a few locations. Recently,

satellite instruments were launched with similar spectral

capabilities but lower spatial resolution, including the 90-

m spatial resolution Advanced Spaceborne Thermal

Emission and Reflection Radiometer (ASTER) and the

1-km spatial resolution Moderate Resolution Imaging

Spectroradiometer (MODIS) instruments (Abrams, 2000;

Salomonson et al., 1989). In addition to the new satellite

instruments, new airborne instruments have been devel-

oped, including the MODIS and ASTER airborne simu-

lator (MASTER) and the Spatially Enhanced Broadband

Array Spectrograph System (SEBASS), which are able to

make higher spatial and spectral resolution measurements

than their airborne predecessors such as the Thermal

Infrared Multispectral Scanner (TIMS). These instruments

provide new tools for remotely mapping minerals that are

important to geothermal resource exploration, which are

typically exposed on small scales that require higher

spatial resolution data than can currently be provided by

space borne sensors.

8 9 10 11 12Wavelength (µm)

Gypsum

Alunite

Kaolinite

Vegetation

Calcite

Montmorillonite

Opal

1.0

1.0

0.9

.95

0.9

0.8

0.7

0.8

1.0

0.9

0.8

0.8

0.9

B

stretching vibrations within the silicate crystal structure and shift to longer

inerals and vegetation common to the Steamboat Springs area. All spectra

ectral library except for the opal spectrum, which was measured at the JPL

clarity.

R.G. Vaughan et al. / Remote Sensing of Environment 99 (2005) 140–158142

2. Physical basis for thermal infrared spectroscopy

Primary rock-forming minerals as well as many secon-

dary weathering and alteration minerals exhibit wavelength-

dependent (spectral) absorption features throughout the

visible and infrared wavelength ranges of the electro-

magnetic spectrum. These features result from the selective

absorption of photons with discrete energy levels and are

dependent on the elemental composition, crystal structure,

and chemical bonding characteristics of a mineral, and are

therefore diagnostic of mineralogy (Burns, 1993; Clark,

1999; Hunt, 1980). In silicate, carbonate and sulfate

minerals fundamental molecular vibrations cause strong

spectral features that appear as emissivity minima (reflec-

tance maxima) in the 8–12 Am region (Hook et al., 1999;

Salisbury, 1993). This region coincides with a window

where Earth’s atmosphere is relatively transparent to TIR

radiation and is also the region of maximum emission from

a 300 K body (e.g., Earth), making it ideal for terrestrial

geologic remote sensing applications (Kahle et al., 1993).

For silicate minerals the wavelength position of the

emissivity minima (‘‘reststrahlen bands’’) shifts to longer

wavelengths with the increasing isolation (decreasing

polymerization) of tetrahedral SiO4 molecules in the crystal

structure (Farmer, 1974; Hook et al., 1999). The spectral

Nevada

Steamboat Springs, NV

MASTER

SEUS

Fig. 2. Map showing the location of Steamboat Springs, Nevada. Also shown are t

analyzed in this study.

features illustrated in Fig. 1 are for minerals that occur in

felsic to mafic igneous rocks, hydrothermally altered rocks,

and the mineral deposits present in the study area. For

example, a strong double emissivity minimum occurs in

quartz between 8.2 and 9.2 Am (Fig. 1A) with a character-

istic peak at 8.62 Am; a narrower emissivity feature for opal

(Fig. 1B), centered on 9.0 Am distinguishes opal from quartz

(Michalski et al., 2003).

Fundamental vibration modes for the CO32� ion occur

throughout the TIR region, and in carbonate minerals the

most distinguishing feature occurs around 11.3 Am (Fig.

1B). Measurable variations in the wavelength position of this

feature result from cation substitution in carbonate minerals

and are diagnostic of mineralogy (Lane & Christensen,

1997; White, 1974). Fundamental modes for the SO42�

ion also occur in the TIR region and cation substitution in

sulfate minerals can cause the location of resultant spectral

features to occur anywhere between 8.1 and 10.4 Am (Ross,

1974). For example, the strongest spectral emissivity feature

for alunite occurs around 9.0 Am and secondary features

occur at 8.58 and 9.75 Am (Fig. 1B). The TIR spectra of

some types of vegetation display weak emissivity features

due to organic compounds like cellulose and lignin, but have

very high emissivity values and low spectral contrast

(Salisbury & D’Aria, 1992). Combined with canopy

N1 km

BASS

39.317o N

119.

715o

W

US

395431

Geologic Map in Fig. 3

he outlines of the MASTER and SEBASS airborne remote sensing data sets


scattering, which further reduces spectral contrast, vegeta-

tion in general behaves like a blackbody emitter (Fig. 1B)

(Salisbury & D’Aria, 1992).

3. Location and geology of study area

Steamboat Springs is an active geothermal system about

16 km south of Reno, Nevada, USA (Fig. 2). It is

characterized by exposures of siliceous sinter deposits,

hydrothermally altered country rock, and structurally

controlled open fissures venting H2S-rich steam. The

geologic map (Fig. 3) is simplified from Bonham and

Rogers (1983) and Bonham and Bell (1993). The active

thermal area is about 5 km2 and lies near the northeastern

end of Steamboat Hills, coincident with a northeast

Map Color Legend

Hot Springs Sinter: Siliceous sinter depositscomposition, and ranging in age from late P

Steamboat Hills Basaltic Andesite: Basaltic plagioclase, olivine, and sparse pyroxene, ~

Granodiorite: Medium-grained granodiorite hornblende, biotite. Argillized and acid-leachLate Cretaceous in age.

Gardnerville Formation: Metavolcanic and Mage.

Undifferentiated Alluvial Deposits

Steamboat Roads

Qal

sr

Tsb

Mzvs

Kgd

Qal

Qal

Tsb

Kgd

Mzvs

Kgd

Tsb

Tsb

To Lake Tahoe

Kgd

Faults Fissures

N 1 km

Fig. 3. Simplified geologic map of Steamboat Springs (from

trending line of four rhyolite domes, which may be related

to the uplift of Steamboat Hills and to the hydrothermal

alteration of subsurface rocks (Hudson, 1987; White et al.,

1964). The basement is composed of early Mesozoic

(Triassic?) metamorphic rocks (labeled Mzvs in Fig. 3) and

Cretaceous granodiorite (Kgd). The oldest hot-spring sinter

(sr) was deposited about 3 Ma, prior to the extrusion of the

Steamboat Hills basaltic andesite (Tsb) 2.5 Ma, and the

emplacement of the Steamboat Hills rhyolite domes (not

shown on geologic map in Fig. 3) from 1.14 to 3.0 Ma

(Silberman et al., 1979). Sinter deposits are interlayered

with Pleistocene alluvium (Qal), and sinter deposition has

continued to the present. Thermal activity has been

intermittent since the late Pliocene, but nearly constant

for the past 0.1 my, varying only in magnitude (White,

1981).

ranging from chalcedony to opal in liocene to present.

andesite flows with phenocrysts of 2.5 Ma.

with Na-plagioclase, microcline, quartz, ed in the Steamboat Springs area.

etasedimentary Rocks. Triassic? in

Ditch

sr

Mzvs

Kgd

Kgd

Tsb

sr

fissures

To Reno

Qal

US

395

Steambo

at Hills

US 431

Bonham & Bell, 1993; Bonham & Rogers, 1983).


The Steamboat Springs system is a modern analog to

ancient hydrothermal systems associated with epithermal

precious metal deposits throughout the Great Basin of the

western United States (Hudson, 1987; White, 1981; White

et al., 1964). This hydrothermal system is notable for the

transport and deposition of anomalous concentrations of Hg,

Au, Ag, Sb, As, Tl, B, and S. Recent sinter deposits are

composed of opaline silica, which transforms to h-cristo-balite and chalcedony with increasing depth and age

(Hudson, 1987). Also, as a result of the remote mineral

mapping from this study, hydrous Na–Al sulfate crusts

(tamarugite and alunogen) have been discovered forming

around some active fumaroles. In the subsurface, sulfuric

acid (H2SO4) solutions produced by H2S reaction with

atmospheric O2 above the water table leaches the surround-

ing rock leaving opal, quartz, or quartz+alunite, adularia or

kaolinite/montmorillonite alteration assemblages. These

minerals are characteristic of a steam-heated acid sulfate

type alteration system (Rye et al., 1992) and are exposed at

the surface locally as outcrops of argillized and acid-leached

granodiorite and basaltic andesite.

4. Instrumentation and data collection

Table 1 is a list of the remote sensing instruments used to

collect data over the study area. The Advanced Spaceborne

Thermal Emission and Reflection Radiometer (ASTER)

was built by the Japanese Ministry of International Trade

and Industry and included on the Terra satellite, launched

by NASA in December 1999 (Abrams, 2000; Yamaguchi et

al., 1998). The ASTER level 1b at-sensor radiance data

used in this study were acquired over Steamboat Springs

in June 2001. The MODIS–ASTER airborne simulator

(MASTER) was designed by the NASA Ames Research

Center and JPL to simulate the MODIS and ASTER instru-

ments on board the Terra satellite (Hook et al., 2001). The

Spatially Enhanced Broadband Array Spectrograph System

(SEBASS) is an airborne hyperspectral imaging spectrom-

eter designed by Aerospace Corporation (Hackwell et al.,

Table 1

Instrumentation used to collect data over the study area

Instrument Spectral range (Am)* # of channels S

MASTER1 0.4–2.4 25

3.1–5.2 15

7.8–12.9 10 4

SEBASS2 3.0–5.0 128

7.5–13.5 128

ASTER3 0.55–0.81 3

1.65–2.4 6

8.3–11.3 5 3

* – Only data within the 8–12 Am TIR spectral range were used.

** – Calculated from image data.

1 – MODIS/ASTER Airborne Simulator (http://masterweb.jpl.nasa.gov).

2 – Spatially Enhanced Broadband Array Spectrograph System, Aerospace Corp

3 – Advanced Spaceborne Thermal Emission and Reflection Radiometer (http://

1996). Both MASTER and SEBASS data were acquired

over Steamboat Springs in September 1999.

The remote identification of a material based on its TIR

emissivity spectrum requires access to the spectra of well-

characterized specimens. Laboratory spectral measurements

from reference spectral libraries, as well as new measure-

ments of field samples were used to compare to the remote

spectral measurements for mineral identification. TIR

spectral data are available from the ASTER spectral library

and the ASU spectral library (http://tes.asu.edu/speclib). All

ASTER library measurements in this study were measured

as directional hemispherical reflectance and converted to

emissivity via Kirchhoff’s law (e =1� r) (Salisbury et al.,

1994). All ASU measurements were measured in emission

using heated samples (Christensen et al., 2000; Ruff et al.,

1997). New directional hemispherical laboratory spectral

measurements of field samples were made for some

minerals and mineral mixtures that were not already

available in reference spectral libraries using the same

procedure used for the ASTER library spectra (http://

speclib.jpl.nasa.gov).

Field sites for the validation of the remotely derived

mineral maps were chosen by locating areas in the images

that were representative of the mineralogic diversity of the

area and also relatively free of vegetation, and thus clearly

measured by the remote sensing instruments. Finally, X-ray

diffraction (XRD) analyses of bulk rock and mineral

separate samples were performed at two different laborato-

ries for mineralogical verification.

5. Data processing and mineral mapping methods

The methods summarized by the flow chart in Fig. 4

were used to extract the desired spectral emissivity

information from the airborne multi-channel TIR images.

The first step is the atmospheric correction of at-sensor

radiance to calculate at-surface radiance (described in

Section 5.1). At-surface radiance was then used in two

ways to extract information that could be related to the

pectral bandpass NEDT Spatial resolution/swath

40 nm 5 m/3.7 km

50–60 nm 5 m/3.7 km

00–700 nm �0.3 K** 5 m/3.7 km

2 m/256 m

35–65 nm �0.3 K** 2 m/256 m

60–100 nm 15 m/60 km

30–100 nm 30 m/60 km

50–700 nm �0.3 K 90 m/60 km

oration (http://www.aero.org).

asterweb.jpl.nasa.gov).

http://tes.asu.edu/speclib

http://speclib.jpl.nasa.gov

http://masterweb.jpl.nasa.gov

http://www.aero.org

http://asterweb.jpl.nasa.gov

At-Sensor Radiance

At-Surface Radiance

TemperatureSpectral Emissivity

Atmospheric Correction

Temperature-Emissivity Separation

Decorrelation Stretch

MNF

PPI

nDv

SAM/MF

Mineral MapSynthesized Map

Spatial Information from different sources related to the geology

Field Observations

Spe

ctra

l Dat

a P

roce

ssin

g

Fig. 4. Flow chart illustrating data processing methods.


composition of surface materials: 1) a decorrelation stretch

(DCS) (Gillespie et al., 1986) and 2) temperature-emissivity

separation (TqS) (Gillespie et al., 1998). Spectral emissivity

data, particularly for hyperspectral data sets, often contain a

surplus of information about the surface, as well as remnant

noise. Therefore a minimum noise fraction (MNF) trans-

formation was used to minimize noise and reduce the

overall data dimensionality (Boardman & Kruse, 1994;

Green et al., 1988; Kruse & Huntington, 1996). Spectral end

member pixels were then chosen from within the image and

used to generate pixel classification maps (Boardman, 1993;

Boardman et al., 1995). Next, minerals and mineral

assemblages within the mapped end member regions were

identified by comparison to reference spectra of pure

minerals and newly measured laboratory spectra. Finally,

the spatial distribution of spectrally mapped minerals was

combined with other spatial information from the DCS

images, field observations, and other mapping studies to

generate a mineral map that could be related to the local

geology and mineralogic surface expression of the geo-

thermal system at Steamboat Springs.

5.1. Atmospheric correction

In the TIR region, the measured at-sensor radiance

(Lm(T,k)), for a given pixel, is the sum of atmospheric path

radiance (Lp(T,k)) and upwelling at-surface radiance (Ls(T,k)),

which is attenuated by atmospheric absorption (s(k)):

Lm T ;kð Þ ¼ Ls T ;kð Þ T s kð Þ þ Lp T ;kð Þ ð1Þ

where T is temperature, k is wavelength, s(k) is atmospheric

transmittance as a function of wavelength, Lp(T,k) is

atmospheric path radiance as a function of temperature

and wavelength, and Ls(T,k) is upwelling at-surface radiance

as a function of temperature and wavelength (Gu et al.,

2000). The upwelling at-surface radiance (Ls(T,k)) is a

combination of surface emitted radiance and surface

reflected radiance and can be expanded into Eq. (1) to yield:

Lm T ;kð Þ ¼ e kð Þ T Wbb T ;kð Þ þ 1� e kð Þ� �

T Lsky T ;kð Þ� �

T s kð Þ

þ Lp T ;kð Þ ð2Þ

where e(k) is emissivity, as a function of wavelength;

Lsky(T,k) is reflected down-welling sky radiance, as a

function of temperature and wavelength, and Wbb(T,k) is

the Plank blackbody function, as a function of temperature

and wavelength (Gu et al., 2000; Hook et al., 1992).

5.1.1. MASTER

For MASTER, the MODTRAN radiative transfer model

(Berk et al., 1989) was used to atmospherically correct the

TIR at-sensor radiance to at-surface radiance. The MOD-

TRAN model uses atmospheric profile data to calculate the

atmospheric parameters (s(k), Lp(T,k)) so that Eq. (1) can be

solved for at-surface radiance (Ls(T,k)). It also calculates

reflected down-welling sky radiance values (Lsky(T,k)) that

are used in the TqS method described in Section 5.2.

Atmospheric profile data were obtained from the National

Center for Environmental Prediction (NCEP). NCEP pro-

duces global atmospheric profile models on a 1- by 1- gridat 6-h intervals. Since the study area did not reside at a grid

node, the surrounding data were interpolated to the

appropriate time and location of the data acquisition. The

interpolated profile together with the acquisition parameters

were used to run MODTRAN twice; once with a nadir

looking geometry, and once for the case of viewing at the

maximum deflection angle from nadir. Values of trans-

mittance and path radiance were calculated for each pixel in

the scene by interpolation, where it was assumed that each

quantity varies linearly with path length. The upwelling at-

surface radiance was then computed using Eq. (1).

To assess the accuracy of the atmospheric correction, the

temperature values from a homogenous area with a known

emissivity and assumed constant temperature were

extracted. Similar studies have used a body of water for

this purpose; however there was no water in this scene, so

an area of thick vegetation, which has a known and

constant spectral emissivity was used instead (Hook et al.,

1992; Hook et al., 2001; Sabine et al., 1994). The area used

consisted of a dense grove of conifers on a north-facing

slope; the emissivity for conifers was taken from the

ASTER spectral library and convolved to the MASTER

TIR channel widths. Ideally, the atmospheric correction

would result in constant kinetic temperatures calculated for

each channel; however a temperature variation for the


homogenous area after atmospheric correction was

observed to be <4 -C across all 10 channels. Channel 41

(7.8 Am) and channel 50 (12.8 Am) are both strongly

influenced by atmospheric water absorption and difficult to

correct accurately. For channels 42–49 the temperature

variation for the homogenous area was observed to be <1.5

-C. Spread in these temperature values may still be

attributed to incomplete atmospheric modeling or possibly

to true temperature variations in the grove of conifers

measured.

5.1.2. SEBASS

For the SEBASS data the Aerospace Corporation

developed an ‘‘In-Scene Atmospheric Compensation’’

(ISAC) algorithm to correct at-sensor radiance (Lm(T,k)) to

at-surface radiance (Ls(T,k)). The ISAC algorithm is

described in detail by Johnson (1998), Young (1998) and

Young et al. (2002), and is briefly reiterated by Cudahy et

al. (2000), Kirkland et al. (2001), and Vaughan et al.

(2003). In the ISAC method all atmospheric parameters

(s(k), Lsky(T,k), and Lp(T,k)) were assumed to be homoge-

neous across the image (i.e. independent of pixel location).

Furthermore, some of the pixels in the scene were assumed

to be blackbodies (e =1) (Johnson, 1998). To determine the

blackbody pixels, the at-sensor radiance data were con-

verted to brightness temperature for each wavelength

channel. Each spectrum was then searched for the wave-

length of its maximum brightness temperature and the

wavelength with the ‘‘most hits’’ was selected as a

reference wavelength. Pixels whose maximum brightness

temperature occurred at this reference wavelength were

selected as the blackbody pixels. For these pixels e =1 and

there is no reflected down-welling sky radiance (Lsky(T,k));

thus Eq. (2) reduces to Eq. (1), which shows a linear

relationship between at-sensor radiance and at-surface

radiance (Johnson, 1998). A plot of at-sensor radiance vs.

the Planck radiance function for the blackbody pixels

produces a linear scatter plot where a straight line fit to the

upper edge of the scatter distribution has a slope equal to

the atmospheric transmittance (s(k)), and an offset that is

equal to the upwelling atmospheric path radiance (Lp(T,k)).

Spread in the scatter plot is attributed to non-unity

emissivity and fitting a line to the upper edge of the data

distribution minimizes these effects (Cudahy et al., 2000;

Young et al., 2002). The at-surface radiance was then

calculated from Eq. (1).

5.2. Decorrelation stretch and temperature-emissivity

separation

Thermal infrared at-surface radiance is a function of both

the temperature and emissivity of the surface, which

commonly exhibit small variations among geologic materi-

als on the Earth’s surface, resulting in a high degree of

correlation between TIR channels. The decorrelation stretch

(DCS) in an image enhancement method that displays

emissivity variations (and corresponding mineralogic varia-

tions) as differences in color, and temperature variations

(mainly due to topography) as differences in brightness

(Gillespie et al., 1986). The DCS has been widely used with

data from other TIR instruments, e.g., TIMS (Hook et al.,

1998; Kahle, 1987; Kahle et al., 1988; Ramsey et al., 1999;

Sabine et al., 1994).

Rather than merely enhancing emissivity information, the

temperature–emissivity separation (TeS) method provides a

way to extract the emissivity component of the data. The

emissivity information was extracted from the MASTER

TIR data using the TqS technique described by Gillespie et

al. (1998). This technique uses an emissivity normalization

algorithm to estimate a temperature, from which emissivities

are calculated and ratioed to their mean (Gillespie et al.,

1998). The residual spectrum has the proper shape, but not

the correct amplitude, of the actual emissivity spectrum. For

each pixel, the minimum–maximum difference (MMD) of

the residual spectrum is used to determine the actual

emissivity spectrum by comparison to a ‘‘emin vs. MMD’’

calibration curve developed from laboratory spectra. The

calibration curve developed by Gillespie et al. (1998) was

for the ASTER instrument, and since the MASTER

channels are different, a different calibration curve was

developed for MASTER (Hook et al., 2005). The TqSalgorithm then iteratively removes reflected down-welling

sky radiance (Lsky(T,k)), which was calculated by the

MODTRAN atmospheric correction, to obtain the surface

emissivity (Eq. (2)).

Temperature and emissivity separation is a challenge for

multi-channel TIR sensors because radiance measured by an

instrument with n channels will have n +1 unknowns (n

emissivities, and 1 temperature), making the solution

equations underdetermined (Gillespie et al., 1998; Kealy

& Hook, 1993). Although in theory this is also under-

determined for hyperspectral data, the large number of

continuous narrow channels mimics the Planck radiance

function for a single temperature and provides a good

baseline for regions where e is close to 1. The SEBASS at-

surface radiance data were converted to apparent emissivity

using an emissivity normalization routine (Kealy & Hook,

1993; Vaughan et al., 2003). The spectrum for each pixel

was converted to brightness temperature, and at the wave-

length of maximum brightness temperature the emissivity

was set to 0.96 (Kealy & Hook, 1993). The resultant

emissivity spectra have a generally low spectral contrast

(overall feature depth); this is largely due to the lack of

correction for down-welling sky radiance (Lsky(T,k)) by the

ISAC method (Cudahy et al., 2000). Because the rest-

strahlen bands reflect most at the wavelengths where they

emit least, emissivity minima tend to be ‘‘filled in’’ by

reflected down-welling sky radiance, which reduces spectral

contrast (Salisbury & D’Aria, 1992).

In addition to low spectral contrast, many ISAC-

corrected at-surface radiance spectra have remnant atmos-

pheric features such as the overall spectral down slope


below 8.2 Am due to strong atmospheric absorption, and the

channel-to-channel saw-tooth pattern (particularly below 9.0

Am and above 11 Am) due to narrow atmospheric absorption

bands in this region (Cudahy et al., 2000; Hewson et al.,

2000; Vaughan et al., 2003; Zhengming & Zhao-Liang,

1997). To account for the observed spectral down slope

below 8.2 Am, the SEBASS emissivity data were fit with a

convex hull (Cudahy et al., 2000), or continuum function,

and divided by that function to remove the continuum. The

upper hull fit does not, however, remove the narrow,

remnant atmospheric features, which can overlap with

secondary mineralogic features. For example, the saw-tooth

pattern evident below 9 Am sometimes makes it difficult to

resolve the 8.62 Am peak within the characteristic quartz

doublet, and the narrow features around 12.50 and 12.77 Amcan be confused with quartz secondary features near these

wavelengths. To account for the narrow atmospheric

features, a spectral smoothing function was applied to the

continuum-removed emissivity data. Essentially a three-

point running mean, this process reduces channel-to-channel

noise without significantly affecting spectral emissivity

information that is diagnostic of mineralogy (Vaughan et

al., 2003). These processing steps can result in shifts in the

position of some emissivity features; however these slight

shifts are insignificant relative to the broad emissivity

features of minerals and thus should not affect the mineral

mapping results.

5.3. Pixel classification and spectral mineral mapping

There is a series of processing steps that has become

standard in remote sensing data analysis that yields

reproducible results (Kruse et al., 1999; Kruse et al.,

2003; Kruse & Huntington, 1996). These methods, shown

in Fig. 4, can be summarized by: 1) Minimum Noise

Fraction (MNF) transformation to minimize noise and

reduce data dimensionality, 2) Pixel Purity Index (PPI)

calculation to locate spectrally unique pixels, 3) n-dimen-

sional visualization to define spectral end members, and 4)

Spectral Angle Mapper (SAM) and Matched Filtering (MF)

methods for pixel classification and mapping of spectral end

members. The purpose of this methodology is to focus only

on the information that is relevant to characteristic

mineralogic features within the image.

The MNF transformation is a statistical operation based

on two principle components transformations that estimate

noise in the data and output a new set of channels arranged

in the order of decreasing noise components (Boardman &

Kruse, 1994; Green et al., 1988; Kruse & Huntington,

1996). With the SEBASS data, the MNF transformation was

applied to a total of 72 channels of the continuum-removed

and smoothed emissivity data that correspond to wave-

lengths 8.3–12.0 Am. This eliminates channels associated

with remnant atmospheric H2O absorption below 8.2 Amand above 12 Am. The MNF transformation produced eight

channels (MNF channels 4–11) with significant surface

compositional information. Similar to the findings of

Cudahy et al. (2000), MNF channels 1–3 show the effects

of broad instrument noise, channels 12 to ¨25 show sharp,

detector-specific noise, and channels ¨26 and higher show

random instrument/electronic noise. For the MASTER data,

with only eight emissivity channels, the MNF transforma-

tion was not necessary.

Emissivity data were used in three ways to find pixels

that represent spectral end members. First, the PPI

computation and spectral dimensionality analysis

described by Boardman (1993), Boardman et al. (1995),

and Kruse and Huntington (1996) were used to find

spectrally ‘‘pure’’ pixels (target spectra), which represent

compositionally distinct areas (Kruse & Huntington,

1996). This significantly reduces the number of pixels

that need to be searched for mineralogic spectral

information. The distribution of these points in n-dimen-

sional space was determined to define end members with

distinct spectral signatures and these end members were

used to classify other pixels in the image. The second

method was similar, except the pure pixels were chosen

manually based on analysis of the image data. Regions of

interest were defined around obvious areas of geologic

interest (e.g., non-vegetated areas) and these pixel spectra

were viewed as points in n-dimensional space to select the

outliers (apical points). This helps reduce the number of

pixels that are chosen by the automated PPI process that

are not related to geologic diversity, e.g. vegetation, roads,

and roofs. Thirdly, in some cases, spectral end member

pixels were chosen based on a priori knowledge and

sampling of the field area. In this case, just a few pixels

were selected as end member spectra to eliminate the need

for n-dimensional analysis. Each spectral end member was

used as a target spectrum, to ‘‘train’’ the pixel classi-

fication method described next.

Pixels were classified using two different supervised

classification methods: 1) Spectral Angle Mapper (SAM)

(Kruse et al., 1993) and Matched Filtering (MF) (Boardman

et al., 1995). SAM classifies pixels together based on their

spectral similarity by treating spectra as vectors in n-

dimensional space and calculating the angle between them.

The threshold angle used was 0.05 radians. MF generates

proportional spectral end member abundance maps based on

partial unmixing of image target spectra (Boardman et al.,

1995). It maps the abundances of pixels that most closely

match the target spectrum of known end members while

filtering out unmatched background pixels. For each end

member mapped, a region of interest (ROI) was defined by

selecting a range of threshold values from the abundance

maps that corresponded to the highest abundance. Each ROI

was assigned to a unique color and displayed over a gray-

scale image to produce a mineral classification map. Finally,

based on matching the spectra of classified pixels to the

spectra of pure minerals from reference libraries, mineralogy

was assigned to classified pixel regions to produce mineral

maps that will be discussed in Section 6.

Quartz

Albite

Kaolinite

Linear Mixture 1

Linear Mixture 2

0.7

0.6

0.8

0.9

1.0

Em

issi

vity

(of

fset

for

clar

ity)

8 9 10 11 12

Wavelength (µm)

0.5

Fig. 5. Example of linear mixing of pure mineral reference spectra in the

TIR wavelength region. Linear mixture 1 is 10% quartz, 45% albite, and

45% kaolinite; linear mixture 2 is 40% quartz, 30% albite, and 30%

kaolinite.


5.4. Mineral mapping limitations and linear spectral mixing

Natural geologic surfaces are often partially covered with

non-geologic materials (e.g., vegetation) or composed of

mixtures of minerals with varying grain sizes and differing

degrees of compaction/solidification. These factors influ-

ence remote spectral measurements and limit the number of

pixels that can be classified and mapped. In the TIR spectral

range, decrease in particle size can increase volume

scattering and decrease the spectral contrast (overall depth)

of the reststrahlen features–unless the particles are com-

pacted sufficiently to scatter coherently, in which case the

spectral contrast remains relatively high (Salisbury & Wald,

1992). Reference spectra have been acquired for some

minerals with different grain sizes to study the effects of

grain size on spectral characteristics (e.g. Salisbury et al.,

1991). Not all minerals, however, have been subjected to

such systematic spectral characterization. Therefore, the

reference spectra used in this study are from measurements

of coarse grained (>250 Am) and solid samples, and no

attempt was made to model grain size variations based on

image spectra.

Mixing can exist at various scales and also affects the

measured infrared spectral properties of an area (Clark,

1999). Even high-spatial-resolution (2-m pixel) images can

have contributions from multiple sub-pixel-scale compo-

nents. Using TIR data, Ramsey and Christensen (1998)

showed that a linear unmixing technique that models the

percentage of each end member composition can be used to

identify individual surface minerals within a single pixel. A

similar approach was used in this study by calculating linear

mixtures of reference spectra from the ASTER and ASU

spectral libraries and plotting them with the remote and field

spectra for comparison (Vaughan et al., 2003). To begin

with, reference spectra for pure minerals were chosen based

on initial spectral analyses and knowledge of the geology

and alteration minerals expected in each study area. Then

linear combinations of these pure mineral spectra were

plotted and compared to SEBASS image spectra. Since the

broad spectral band pass of MASTER does not permit

unique mineral identification, only SEBASS emissivity

spectra were modeled by linear mixtures of reference

spectra. Fig. 5 shows examples of spectral plots for pure

minerals and the results of linear combinations. Through

visual inspection and trial and error, spectral mixtures were

matched to image spectra to identify the dominant mineral

phases present. It is important to note that the percentages of

mineral components used in the spectral mixtures do not

reflect the actual percentages of the minerals in the pixel. In

some cases, reference spectra from pure minerals and

mineral mixtures had to be scaled down to directly overlay

with the image spectra. This is a function of the generally

lower spectral contrast that appears in remotely acquired

spectra due to either grain size effects, mixing with either

non-geologic components or other minerals that have low

spectral contrast, or in the case of SEBASS, the lack of

correction for reflected down-welling radiance in the

atmospheric correction process. As a result of this semi-

quantitative treatment of linear mixtures, spectral feature

depth and absolute emissivity values were not perfectly

preserved, and no attempt was made to quantitatively model

mixtures of pure minerals to match the SEBASS spectra. As

will be shown in the next section, only spectral shapes and

feature locations were used for mineral identification and

these methods were sufficient to detect the presence of the

dominant mineralogy of mixed pixels and produce a mineral

map.

6. Results and interpretation

6.1. Decorrelation stretch

Fig. 6A shows a decorrelation stretch (DCS) image of the

90-m ASTER TIR channels 14, 13, and 12 (wavelengths

11.32, 10.66, and 9.08 Am) displayed as red, green, and blue

(RGB), respectively. With these channels, the silica-rich

sinter area around Steamboat Springs stands out as yellow in

the center of the image because silica has a low emissivity

around 9.0 Am (channel 12–blue) and higher emissivity at

10.66 and 11.32 Am (channel 13–green and channel 14–

red, respectively). For a comparison the accompanying Fig.

6B shows a false color composite of ASTERVNIR channels

golf course

sinter area

acid-sulfate area

irrigated fields

Steamboat Hills

MASTER

SEBASS

sinter area

irrigated fieldsgolf

course

Steamboat Hills

1 kmN

A

B

1 kmNFig. 6. A. ASTER TIR DCS image over Steamboat Springs. ASTER TIR

DCS channels 14, 13 and 12 are displayed as RGB, respectively. This set of

channels (different than those typically used for DCS images) more clearly

highlights differences between silica and clay. The sub-horizontal noisy

scan lines are due to the ASTER TIR scanner. The areas imaged by

MASTER and SEBASS are outlined by the rectangles. The spatial

resolution for the ASTER TIR image is 90 m. B. ASTER VNIR image

over Steamboat Springs. ASTERVNIR channels 2, 3N and 1 are displayed

as RGB, respectively. The areas imaged by MASTER and SEBASS are

outlined by the rectangles. The spatial resolution for the ASTER VNIR

image is 15 m.


2, 3N, and 1 (wavelengths 0.66, 0.81, and 0.55 Am,

respectively) displayed as RGB. In this image, healthy

vegetation (e.g. irrigated fields) appears bright green, and

dry desert vegetation appears various shades of purple. The

bright, white areas correspond to the siliceous sinter area

around Steamboat Springs and an area of acid-sulfate

alteration to the west. With the 15-m pixels of the VNIR

image more detail about the region can be seen, including

roads, fields, urban areas, the sinter area and acid-sulfate

alteration area, areas with recent construction activity, a golf

course, small lakes, and healthy vegetation along stream

drainages. At the 90-m spatial resolution of the TIR

channels (Fig. 6A), the siliceous sinter area (yellow) and

large vegetated areas (dark) are the only regional spatial

features that are clearly mappable. Outlines of the high-

spatial resolution airborne images acquired by the MASTER

and SEBASS instruments are indicated with black rectan-

gles in each image.

Fig. 7A shows the DCS of MASTER channels 48, 45,

and 44 (wavelengths 11.20, 9.68, and 9.05 Am, respectively)

displayed as RGB. This channel combination was chosen to

mimic the color scheme of the SEBASS DCS (described

next). Silica-dominated regions appear yellow because silica

has a low emissivity associated with the reststrahlen band

around 9.0 Am (channel 44–blue) and high emissivity at 9.6

and 11.2 Am (channel 45–green and channel 48–red,

respectively). For example, the large yellow region on the

right side of the image represents the siliceous sinter

deposits around Steamboat Springs. The Ormat (formerly

SB Geo, inc.) geothermal power plant is located just north

of most of the recent sinter deposition (brightest yellow).

Clay-dominated areas appear magenta to purple because

clay has a low emissivity value around 9.6 Am (channel 45–

green) and higher values at 9.0 and 11.2 Am (channel 44–

blue, and channel 48–red). The low emissivity values of

clays (phyllosilicate minerals) are also associated with a

reststrahlen band but the minimum is shifted to slightly

longer wavelengths (see Fig. 1A). For example, on the left

side of the image, west of Steamboat Springs, there are a

number of areas with active urban development and the

exposed soils and dirt roads are clay-rich. In contrast,

ASTER is not capable of separating clay-rich from silica-

rich areas due to the lack of a spectral channel between 9.1

and 10.6 Am, where clay minerals have their strongest

spectral features. Healthy vegetation (trees, lawns and

fields) is dark green; for example, the sports fields around

Galena High School left center of image. Areas that appear

cyan to dark purple are generally covered by dry desert

vegetation (sagebrush and cheat grass) and are thus

spectrally featureless and indistinguishable in the TIR.

However, because differences in albedo also affect colors

in the DCS image, some geologic information can be

interpreted from the differences between cyan areas (under-

lain by variably altered granodiorite) and the darker purple

areas (underlain by basaltic andesite), e.g., the area just

southwest of the main sinter terrace. Roads appear in

various shades of purple and orange due to differences in

composition: asphalt, cement or dirt/gravel, and roofs in

urban areas appear green, blue, or yellow.

The channel combination used for all the DCS images

here is not the same combination that has commonly been

used with other multispectral TIR data, such as TIMS,

which results in silica-rich areas displayed as red and clay-

rich areas as purple (Kahle, 1987; Ramsey et al., 1999;

Sabine et al., 1994). The channel combination used here

produces a more obvious visual distinction between the

silica- and clay-rich areas that dominate the geologic

variance in this scene. Furthermore, with the hyperspectral

SEBASS data, the wavelength channels used in the DCS

Fig. 7. A. MASTER decorrelation stretch (DCS) image over Steamboat Springs displaying channels 48, 45, and 44 as RGB. This set of channels (different than

those typically used for DCS images) more clearly highlights differences between silica and clay. Color variations are related to emissivity differences, and thus

surface compositional differences; temperature variations are displayed as intensity differences. The dashed outline indicates the extent of the SEBASS data

coverage. The spatial resolution for the MASTER image is 5 m. B. SEBASS DCS image over Steamboat Springs (modified from Vaughan et al., 2003)

displaying channels 66, 45, and 44 as RGB. The color scheme is the same as for Fig. 9, additionally. The spatial resolution for the SEBASS image is 2 m.


here produce a color separation of other mineral groups. The

SEBASS DCS uses channels 66, 35, and 24 (wavelengths

11.0, 9.5, and 9.1 Am, respectively) as RGB (Fig. 9). The

resultant colors are similar to those of the MASTER DCS

with silica-rich areas in yellow and clay-rich areas in

magenta, e.g., the bright yellow siliceous sinter area on the

east side of the Steamboat Springs image, and some clay-

rich (magenta) areas in the acid-sulfate alteration region to

the west. Because of the higher spectral resolution of

SEBASS, the DCS separates more compositional end

members, for example pale green areas that contain sulfates

like alunite (also in the acid sulfate alteration region). In all

the DCS images temperature variations appear as intensity

differences that look like shadows on the north-facing

slopes.

6.2. Mineral maps and emissivity spectra

Using the spectral information measured by each

airborne instrument, mineral maps were created using the

methods described in Section 5.3. In general, surface cover

types included vegetation, rock outcrop, soil, or man-made

materials such as roofs or roads. Roofs and other man-made

materials sometimes have characteristic spectral features in

the TIR spectral region, but to focus on mapping minerals

related to local geology, pixels over roads and urban areas

were not mapped. In some cases pixels were mineralogi-

cally homogenous, containing only one mineral phase

across the entire pixel (e.g., on the sinter terrace). In other

cases pixels were mineralogically heterogeneous. A pixel

that contains a mixture of different materials produces a

spectrum that represents a linear combination of each

component. As described in Section 5.4, reference spectra

for pure minerals from TIR spectral libraries were mixed

together in a linear fashion to approximate the remotely

measured SEBASS spectra and identify the presence of the

dominant mixed components. In many pixels however,

even small (2-m) pixels, contribution from non-geologic

components (primarily vegetation) adversely affected the

mineralogic spectral signature. In general, pixels dominated

by (containing >50%) non-geologic materials were not

classified and mapped. In the TIR region vegetation

approximates a blackbody with a very high emissivity that

is invariant with wavelength (Salisbury & D’Aria, 1992).

As a result, mixture with vegetation reduces the overall

spectral contrast of mineralogic spectral features within a

mixed pixel, so the presence of vegetation was not modeled

using TIR data.


Fig. 8 (top) is the MASTER mineral map over Steamboat

Springs. There are two different mineralogical compositions

mapped: yellow areas are generally silica- or sulfate-rich

and represent either quartz, alunite, or opal; magenta areas

are generally clay-rich and represent either kaolinite or

montmorillonite. The silica-rich areas were classified based

on a common emissivity minimum centered around 9.1 Am,

Fig. 8. MASTER mineral map over Steamboat Springs (top). The two mapped reg

either silica-rich or clay-rich. MASTER spectra (lower plots) for selected sites (8, 1

to the map legend and compared to mineral reference spectra resampled to MASTE

The dashed outline represents the extent of the SEBASS data coverage shown in

and the clay-rich areas were classified based on an

emissivity minimum around 9.7 Am. In the spectral plots,

MASTER spectra in yellow (left) match the ASU library

reference spectra for either quartz, alunite, or opal; the

spectra in magenta (right) match the ASU library reference

spectrum for clay minerals such as kaolinite and montmor-

illonite. The spectral resolution of MASTER was not

ions are overlain onto a gray-scale temperature image, and distinguished as

1b, 12, 16, and 18–marked with triangles on the map), are color coordinated

R’s spectral resolution. Other field sites are marked with circles on the map.

Fig. 9. The spatial resolution for the MASTER image is ¨5 m.


sufficient to resolve the subtle spectral differences between

the minerals within each class.

Fig. 9 shows the SEBASS mineral map of Steamboat

Springs (dashed outline in Fig. 8), which is improved over

the SEBASS mineral mapping shown in Vaughan et al.

(2003). Although it covers a very narrow swath, the spectral

resolution of SEBASS was capable of separating more

spectral end members and mapped more unique minerals

than the multispectral MASTER data. The SEBASS

spectrum in orange from the siliceous sinter area (site 8–

left spectral plot) matches the reference spectrum of opal. In

this case, due to limited samples in existing spectral libraries,

a new reference spectrum for opal was determined using

Map Color Legend

Alunite

Opal

Albite + Kaolinite + Alunite Kaolinite

Na-Al sulfate (Tamarugite/Alunogen)

Quartz

11b12

Albite + Andesine

9

21

Em

issi

vity

(of

fset

for

clar

ity)

8 10 11 12

0.7

0.8

0.9

1.0

9

Wavelength (µm)

Site 12

Site 11b

Site 8

opal

quartz

alunite

0.25 kmN

Fig. 9. SEBASS mineral map over Steamboat Springs (top) (modified from Vaug

emissivity image, and distinguished by their dominant mineralogy as noted in the

11b, 12, 19, and 21–marked with triangles on map) are color coordinated to the m

mixtures of mineral reference spectra. The spatial resolution for the SEBASS im

measurements at the JPL spectroscopy laboratory of samples

from the siliceous sinter terrace at Steamboat Springs. The

narrow emissivity minimum around 9.0 Am is characteristic

of opal and XRD analyses confirm the composition of

siliceous sinter as pure opal. The SEBASS spectrum in

yellow from the acid-sulfate alteration area (site 9–left

spectral plot) matches the ASU library reference spectrum of

quartz. The large doublet feature between 8.2 and 9.2 Amwith an emissivity peak at 8.62 Am is characteristic of

crystalline quartz. XRD analyses confirm that quartz is the

dominant mineral at this site. The SEBASS spectrum in blue

from the acid-sulfate alteration area (site 12–left spectral

plot) matches the JPL library reference spectrum of alunite.

19

8

7

Site 9

Site 7

Site 19

8 10 11 129Wavelength (µm)

Site 21

SEBASS spectra

Plot Color Legend

Reference spectra of pure minerals or linear mixtures

kaolinite

kaolinite + albite + alunite

anorthite + kaolinite

gypsum + mirabilite

han et al., 2003). The seven regions mapped are overlain onto a gray-scale

legend. SEBASS spectra (bottom, left and right) from the field sites (7, 8, 9,

ap legend and compared to reference spectra from pure minerals, or linear

age is ¨2 m.

Table 2

Minerals identified at Steamboat Springs with each instrument

Field site MASTER

(Fig. 8)

SEBASS

(Fig. 9)

Lab

spectroscopy

XRD

SS 7 ND Anorthite

Kaolinite

ND Anorthite

Albite

Augite

SS 8 Silica Opal Opal Opal

SS 9 Clay Kaolinite Feldspar Albite

Anorthite

Clay Montmorillonite

Augite

SS 11b Silica Quartz Quartz Quartz

SS 12 Silica Alunite Alunite Alunite

Quartz

Kaolinite

Opal

SS 16 Clay ND Kaolinite Kaolinite

SS 17 Silica ND Quartz

(Chalcedony)

Quartz

SS 18 Clay ND ND Kaolinite

Alunite

Quartz

Orthoclase

SS 19 Silica Sulfate Sulfate Alunogen

Tamarugite

Kieserite

SS 21 ND Kaolinite

Albite

Alunite

ND Kaolinite

Quartz

Opal

Na-alunite

Minamiite

ND=Not determined.


The TIR emissivity spectrum for alunite is characterized by a

broad minimum around 9.0 Am, with secondary features at

8.57 and 9.7 Am. XRD analyses indicate the presence of

alunite, quartz, kaolinite and minor opal. The SEBASS

spectrum in magenta from an exposure of soil on top of the

Steamboat Hills basaltic andesite (site 9–right spectral plot)

matches the ASU library reference spectrum of kaolinite.

An emissivity minimum around 9.5 Am is characteristic of

many clay minerals, but the secondary features at 8.88 and

11.0 Am, and the shoulder at 9.9 Am are diagnostic of

kaolinite. XRD analyses, however, indicate the presence of

montmorillonite with minor albite, anorthite, and augite.

The SEBASS spectrum in brown is from an exposure of the

Steamboat Hills basaltic andesite (site 7–right spectral

plot). The extremely low spectral contrast makes mineral

identification more difficult; however, the emissivity

minima at 8.6 and 9.6 Am, with a shoulder at 9.9 Ammatch a linear mixture of 50% anorthite and 50% kaolinite

from the ASU spectral library. XRD analyses of unweath-

ered samples indicate the presence of albite, anorthite, and

augite; the kaolinite is present on the surface as a weath-

ering product. The SEBASS spectrum in green from the

acid-sulfate alteration area (site 21–right spectral plot) has

spectral features at 11.0, 9.9, 9.6, 9.3, and 8.8 Am, and

matches a linear mixture of ASU library reference spectra

for albite (40%), kaolinite (40%), and JPL library alunite

(20%). XRD data indicate the presence of kaolinite, quartz,

Na-alunite, minamiite (a Ca-rich alunite mineral), and

minor opal. Lastly, the SEBASS spectrum in red, from an

exposure of hydrous sulfate crusts forming around an active

fumarole (site 19–right spectral plot) has a broad emissivity

minimum around 8.8 Am, similar to many sulfate minerals.

XRD analyses indicate the presence of the hydrous Na- and

Al-sulfate minerals, tamarugite and alunogen with minor

kieserite (Mg-sulfate). These sulfate minerals have not been

characterized in any reference spectral libraries, therefore

the USGS library reference spectrum of gypsum (50%)

mixed with mirabilite (50%), another hydrous Na-sulfate

mineral, is shown for comparison.

Table 2 is a summary of the major minerals identified and

mapped at Steamboat Springs with MASTER and SEBASS

data, as well as corroboratory measurements made by

laboratory spectroscopy and XRD analyses. With the

exception of MASTER, which cannot uniquely identify

minerals, the minerals mapped remotely agree with the

dominant mineralogy determined by XRD analyses. In

some cases (sites 7, 9, 11b, 12, 18, and 19) XRD analyses of

field samples identified minor mineral constituents that were

not identified by remote spectroscopy. In other cases (sites

7, 9 and 21) there were minerals identified remotely that

were not found in the samples subjected to XRD analyses.

In all cases these discrepancies can be attributed to

differences in scale between the area covered by the remote

sensing data and the samples collects from each site. While

every effort was made to collect samples that were

representative of the larger areas viewed by the remote

images, this was not always possible, and XRD analyses of

some samples were not necessarily representative of entire

pixel area. Site 17 is notable because although it is only

mapped by the MASTER data, it is important in the

interpretation of the mineralogic surface expression of the

Steamboat Springs geothermal system. The presence of

micro-crystalline quartz (chalcedony) was only positively

identified by field sampling and laboratory analyses. Had

the SEBASS swath covered this area, it would have

differentiated between this crystalline quartz and the opal

on the sinter terrace.

6.3. Spatial data synthesis and interpretation

The active geothermal system at Steamboat Springs is

characterized by 1) opaline sinter deposits that are indicative

of recent geyser activity, 2) chalcedonic sinter deposits that

are indicative of older, but geologically recent geyser

activity, 3) hydrous Na–Al sulfate deposits that are

indicative of active fumaroles, and 4) the presence of

hydrothermally altered rocks with minerals like alunite and

kaolinite. While these hydrothermal alteration minerals may

also be indicative of ancient acid-sulfate hydrothermal

systems, their presence among the other three diagnostic

characteristics provides clues about the location and

duration of past hydrothermal activity in the area.


Fig. 10 is a mineral map of the Steamboat Springs area

that combines all the mineralogic information derived from

both remote sensing data sets and field mapping showing

the surface minerals related to the local geology, and to past

and recent geothermal activity. The Steamboat Hills basaltic

andesite (Tsb–pale brown) from the geologic map in Fig. 3

is more widespread than the limited area covered by the

SEBASS image. SEBASS spectra (Fig. 9) from the areas

mapped in dark brown indicate the presence of anorthite,

which is common in basaltic andesite. The high-spatial-

resolution, hyperspectral SEBASS data were capable of

detecting the subtle spectral characteristics of the basaltic

andesite unit in between trees and bushes. Much of the area

underlain by this unit is heavily vegetated, and significant

(>50%) vegetation cover can hinder the spectral identifica-

tion of the underlying soil and rock. However, information

Fig. 10. Mineral map of the Steamboat Springs area showing the distribution of m

from the geologic map in Fig. 3 are shown in pale, partly transparent colors. The

solid, opaque colors. The extent of the overlapping SEBASS image is outlined in

derived from both MASTER and SEBASS DCS images was

also used to determine the spatial distribution of the basaltic

andesite. Although, the remotely mapped distribution of the

basaltic andesite unit is not comprehensive, there is good

agreement with respect to the spatial distribution recorded

on the original geologic map.

The Cretaceous granodiorite (Kgd–pale green) from the

geologic map is also widespread and largely hydrothermally

altered where exposed. Within the SEBASS swath, this unit

is mapped by the identification of quartz, albite, alunite and

kaolinite spectral signatures; these are the primary minerals

that compose the altered granodiorite. Outside of the

SEBASS swath the Kgd unit is mapped in areas where

MASTER detects silica-rich material and field mapping and

sampling determined the presence of altered granodiorite.

Also, in the MASTER DCS image, the cyan colored areas

inerals related to both active and past hydrothermal activity. The rock units

mineral units mapped with the remote sensing and field data are shown in

black. The spatial resolution of the DOQ in the background is ¨1 m.


generally correspond to vegetated areas underlain by the

granodiorite unit.

Of primary importance in locating active geothermal

systems like Steamboat Springs is the location of hot spring

sinter. The spatial distribution of sinter deposits from the

geologic map (sr–pale yellow) and from the remote mineral

map (opaque yellow) is very similar. However, on the

geologic map there is no differentiation between recent

sinter, which is opal, and older sinter, which is a form of

micro-crystalline quartz called chalcedony. MASTER spec-

tral data classify these materials together, but mapping based

on field observations and sampling differentiated between

these areas. The SEBASS data were also capable of

differentiating between opal and more crystalline silica;

however the SEBASS swath did not cover the area with

older (chalcedonic) sinter.

Another characteristic of the Steamboat Springs geo-

thermal system that can be identified by TIR remote

sensing methods is the distribution of surface temperature

anomalies. Zones of anomalously high temperatures

mapped by Coolbaugh (2003) using overlapping day-time

and night-time TIMS data in conjunction with Airborne

Visible Infrared Imaging Spectrometer (AVIRIS) data, are

shown in magenta outlines in Fig. 10. Also, the locations

of known fumaroles are shown as blue squares. There are

three very small areas (annotated on the map) where the

hydrous Na–Al sulfate minerals, tamarugite and alunogen,

were mapped by the SEBASS data. These deposits

correspond to the locations of both active fumaroles and

some of the high temperature areas. Although there is a

good correlation between the occurrence of hydrous

sulfates, active fumaroles, and hot spots, it is important

to note that not all active fumaroles have sulfate forming

around their surface interface. There may be sulfates

forming at depth, or not at all. Also, since these sulfates

are water soluble, they would presumably wash away after

a rain storm and reform some time later. Questions about

the formation time, duration and ephemeral nature of these

hydrous sulfate deposits, as well as the associated micro-

bial communities will be important to address in future

studies.

With respect to surface mineral mapping for geothermal

resource exploration, the identification of sinter as an

indicator mineral is considered to be of primary importance.

The differentiation between opaline sinter and more

crystalline silica phases such as chalcedony or quartz,

which can be present in many geologic environments other

than geothermal systems, is also critical. Hydrous sulfates

and hydrothermal alteration minerals are of secondary

importance in the location of active systems. At Steamboat

Springs the current orientation of recent sinter deposits in a

generally north–south alignment (along highway 395) is the

surface expression of the structurally controlled fissures that

provide conduits for geothermal waters. Also, the presence

of older sinter to the west may indicate an eastward shift in

geyser activity with time. The characterization of structures

at depth and the mineral deposits at the surface ultimately

plays an important role in understanding the spatial/

temporal evolution of the geothermal system, and in

geothermal energy exploration.

7. Summary and conclusions

In addition to the comparison of multispectral and

hyperspectral data for spectral mineral mapping, this study

combines remote mineral mapping with field mapping data

and relates the spatial information to the local geology and

geochemical environment. For Steamboat Springs, this

study resolves questions about what geologic and minera-

logic surface characteristics are important to geothermal

energy exploration and what multi-channel infrared remote

sensing instrument parameters are necessary to map

minerals associated with active geothermal systems. This

study also represents the first direct comparison of over-

lapping multispectral and hyperspectral TIR data as well as

a unique synthesis of multiple remote and field-derived data

sets directly linked to local geology and alteration units and

focused on the identification of important indicator minerals

for geothermal exploration. It also resulted in the first

remote detection of hydrous sulfate minerals forming

around active fumaroles at Steamboat Springs.

The mineralogic surface expression of the Steamboat

Springs geothermal system consists of (1) siliceous sinter

deposits composed of opaline silica, which are indicative

of recent geyser activity, (2) massive chalcedony deposits,

which are indicative of older, but geologically recent

geyser activity, (3) hydrous Na–Al sulfate crusts forming

around active fumaroles, and (4) rocks that have under-

gone steam heated acid-sulfate alteration, which offer clues

about the location and duration of past geothermal activity.

In the exploration for similar geothermal systems opaline

sinter is the primary indicator mineral that is characteristic

of active, or recently active hot springs and can be

identified by TIR hyperspectral data. Chalcedonic sinter

deposits are also important, so it is critical to be able to

separate amorphous silica from crystalline silica phases

that could be present in a variety of geologic environ-

ments. This differentiation can be achieved remotely only

by hyperspectral TIR data or by using laboratory spectral

measurements. Hydrous sulfates are of secondary impor-

tance because they are not present around all fumaroles

and their presence is ephemeral, as they would dissolve

and wash away during a storm. These sulfate minerals can

be detected by hyperspectral TIR data, but unique

identification requires more library reference spectra for

these uncommon sulfate minerals.

It is sometimes merely presupposed that high-spatial- and

high-spectral-resolution measurements are necessary for

detailed spectro-lithologic mapping. This study has pre-

sented specific examples of the advantages of such

measurements and should be helpful in placing some limits


on, and setting some goals for, the specifications of future

airborne and spaceborne remote sensing systems for geo-

logical applications. For example, the SEBASS mineral

mapping at Steamboat Springs illustrates that spectral

feature position and shape are more important than absolute

value and spectral feature depth for mineral identification.

These spectral parameters are best preserved at high spectral

resolution and this is the reason that hyperspectral measure-

ments can be modeled by linear mixtures of reference

spectra, and that hyperspectral instruments like SEBASS are

capable of uniquely identifying more minerals than multi-

spectral instruments like MASTER. High spatial resolution

offers the ability to image areas in between trees and bushes

reducing the negative effects of sub-pixel mixing and also

makes the data products easier to register to other maps for

spatial analysis and display. One of the most common non-

geologic components interspersed with geologic materials is

vegetation, even in the desert. In this study, pixels where

soil and rock were mixed with >50% vegetation were

generally not mappable based on mineralogic spectral

features. High spatial resolution, however, is often associ-

ated with a narrow swath (e.g. the 256 m swath of the

SEBASS image). The spatial resolution vs. swath trade-off

is an important consideration that depends on the scale of

the mapping application. The fact that wide-swath, large

spatial footprint images acquired by spaceborne systems are

best suited for global and regional scale mapping empha-

sizes the necessity of airborne instruments for more detailed

intermediate- to small-scale geologic remote sensing

research.

Acknowledgements

The research described in this paper was carried out in

part at the Jet Propulsion Laboratory, California Institute

of Technology, under contract with the National Aero-

nautics and Space Administration (NASA) as part of the

Earth Observing System, Mission to Planet Earth Pro-

gram. This work has been supported by the Nevada Space

Grant Consortium, the Arthur Brant Laboratory for

Exploration Geophysics, and NASA grant NGT5-50362

(the NASA Graduate Student Research Program Fellow-

ship). The authors would like to thank Alan Gillespie and

Mike Abrams for their constructive reviews of this

manuscript; John Hackwell and Patricia Lew at Aerospace

Corporation for the opportunity to participate in the 1999

SEBASS group shoot and for helping with raw data

reduction and implementation of the ISAC routine; Cindy

Grove and Ron Alley at the Jet Propulsion Laboratory for

providing TIR laboratory measurements of field samples

and atmospheric profile data for the calibration of

MASTER data; and Mario Desilets at the Nevada Bureau

of Mines and Geology, and Paul Schroeder at the

University of Georgia for assistance with XRD analyses

of field samples.

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