JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, E03007, doi: /2003je002197, 2004

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2003je002197, 2004 Thermal infrared analysis of weathered granitic rock compositions in the Sacaton Mountains, Arizona: Implications for petrologic classifications from thermal infrared remote-sensing data Joseph R. Michalski, Stephen J. Reynolds, Thomas G. Sharp, and Philip R. Christensen Department of Geological Sciences, Arizona State University, Tempe, Arizona, USA Received 9 October 2003; revised 28 January 2004; accepted 11 February 2004; published 18 March [1] Critical to spectral interpretations of geologic surfaces on the Earth and Mars is an understanding of the relationship between the composition of weathered rock surfaces and whole-rock mineralogy. In this study, thermal infrared spectroscopic and remote-sensing analyses were used to determine the composition of weathered granitoid rock surfaces in the Sacaton Mountains, Arizona. A linear spectral deconvolution approach was employed to determine the mineralogies of naturally exposed, weathered surfaces and artificially exposed, fresh rock surfaces. Deconvolution results from fresh rock surfaces yield similar bulk mineralogy to results from point counting of rock slabs and thin sections. Deconvolution of weathered rock spectra indicate that compared to fresh samples, weathered surfaces are deficient in plagioclase feldspar and are enriched in clay minerals. The differential weathering of feldspar minerals impacts the interpretation of whole-rock mineralogy and the spectral classification of bedrock geology, which, for plutonic rocks, is dependent on the relative proportions of different feldspars. Deconvolutions of multispectral remote-sensing or laboratory data show a deficiency of total feldspar in the weathered rock units but lack sufficient spectral resolution for the discrimination between different feldspars. These results demonstrate that the differential breakdown and removal of primary minerals in rock surfaces due to weathering can affect the interpreted rock petrology from remote-sensing studies. The relationships observed in our granitic samples could be applied to any rock type and may be important to consider for remote sensing of Mars. INDEX TERMS: 5464 Planetology: Solid Surface Planets: Remote sensing; 5410 Planetology: Solid Surface Planets: Composition; 5415 Planetology: Solid Surface Planets: Erosion and weathering; 5494 Planetology: Solid Surface Planets: Instruments and techniques; 8035 Structural Geology: Pluton emplacement; KEYWORDS: granite, thermal infrared, weathering Citation: Michalski, J. R., S. J. Reynolds, T. G. Sharp, and P. R. Christensen (2004), Thermal infrared analysis of weathered granitic rock compositions in the Sacaton Mountains, Arizona: Implications for petrologic classifications from thermal infrared remote-sensing data, J. Geophys. Res., 109,, doi: /2003je Introduction [2] Thermal infrared spectroscopy is a powerful tool for compositional analysis of geological materials. The depth, shape, and position of vibrational absorption features in spectra are diagnostic of mineralogy [Lyon, 1965; Vincent and Thompson, 1972] and the spectral signatures of mixtures of minerals add linearly [Ramsey and Christensen, 1998]. This relationship has been exploited to determine the mineralogy of rocks in the laboratory [e.g., Hamilton et al., 1997; Ruff, 1998; Feely and Christensen, 1999; Hamilton and Christensen, 2000; Wyatt et al., 2001] and remotely [e.g., Gillespie, 1992; Ramsey et al., 1999; Bandfield et al., 2000; Christensen et al., 2000a; Hamilton et al., 2001; Bandfield, 2002; Rogers and Christensen, 2003; Hamilton et al., 2003] by linear spectral deconvolution. The ultimate Copyright 2004 by the American Geophysical Union /04/2003JE goal of many deconvolution studies is to understand the primary mineralogies of rocks within a geologic-petrologic context. The spectral signature from a rock originates in its uppermost surface (<100 mm). Therefore interpretations of whole-rock mineralogy can be severely impacted by the presence of weathered materials or the absence of primary minerals (due to weathering) within the outer surfaces of rocks. Rock classifications are dependent on the relative proportions of minerals present. It is therefore critical to investigate how the weathering of primary minerals and formation of secondary minerals within rock surfaces impacts spectral interpretations and classifications of rocks. [3] Spectral deconvolutions of Thermal Emission Spectrometer (TES) data have been used to classify igneous rock compositions on the Martian surface [Bandfield et al., 2000; Hamilton et al., 2001], but there is debate about whether some surface compositions observed by TES are indicative of primary mineralogy or weathered mineralogy [Wyatt and McSween, 2002; Kraft et al., 2003; Morris et al., 2003; 1of15

2 Ruff, 2003; McSween et al., 2003]. The results indicate that Martian dark region materials are dominated by andesitic basalt and andesite rocks [Bandfield et al., 2000; Hamilton et al., 2001]. These compositions have tremendous implications for the petrologic evolution of the Martian crust [Rogers and Christensen, 2003; McSween et al., 2003], if they represent primary compositions. It is possible that observed compositions on Mars are partially affected by Martian weathering. If primary minerals have been removed from rock surfaces at different rates due to weathering, as may be expected from terrestrial studies [Colman, 1982], it would have an impact on the perceived relative proportions of minerals present, and confound rock classifications. The debate remains open as to whether surface compositions observed on Mars represent primary igneous compositions or altered compositions. [4] Further progress toward a resolution in this debate will be made when spectrally derived compositions can be tied to surface map units, to understand their geologic context. This will be possible with spectral mapping using recently acquired data from the Thermal Emission Imaging System (THEMIS). The TES instrument has high spectral resolution (143 bands, 5 cm 1 resolution), but low spatial resolution (3 km) [Christensen et al., 2001]. The THEMIS instrument has 100 m spatial resolution with 8-band thermal infrared spectral resolution (of surface information) [Christensen et al., 2004]. Future mapping with the THEMIS instrument will help connect TES spectral compositions to bedrock outcrops and surface geologic units [Christensen et al., 2003]. However, critical to this type of research, are analogue studies on Earth that relate compositional information from hyperspectral to multispectral analyses, as well as compositional information from whole-rock mineralogy to rock surface mineralogy. [5] In this study, we used remote-sensing and spectroscopic analyses to study a suite of well-exposed granitoid rocks in the Sacaton Mountains, Arizona. One of the goals of this study was to understand the relationship between weathered rock mineralogy and whole-rock mineralogy, and granitic rocks provide a mineralogically simple case to investigate this relationship. To this end, we performed spectral deconvolution analyses of both fresh and weathered rocks, as well as traditional petrographic analyses. Rock classification from fresh rock mineralogy is compared to classification from weathered natural rock surface mineralogy. The other goal of this study was to understand the relationship between mineralogical information obtained remotely using 6-band Thermal Infrared Multispectral Scanner (TIMS) data and hyperspectral data obtained in the laboratory. To accomplish this, we performed linear deconvolution on: 1) TIMS emissivity spectra to produce mineral maps, 2) hyperspectral laboratory data, and 3) laboratory data that have been resampled to TIMS and THEMIS spectral resolutions. 2. Background 2.1. Previous Thermal Infrared Studies of Granitic Rocks [6] Thermal infrared spectroscopy has been used in the past to analyze granitoids. Ruff [1998] performed an exhaustive deconvolution analysis of granitoid fresh rock compositions. In that study, relatively unweathered samples were used because the purpose was to classify granitic samples according to their whole-rock mineralogy. In other studies, TIMS emissivity data have been used to estimate the silica content of plutonic rocks from the position of the maximum silicate absorption [Sabine et al., 1994] and to qualitatively map compositional differences in the Piute Mountains, California [Hook et al., 1994]. Rivard et al. [1993] studied the spectra of granitoid rocks in the TIMS wavelength region and found that spectra of felsic rocks are dominated by rock and weathered rock debris. TIMS data have also been used to map granite-derived sand dune materials using a linear spectral deconvolution technique [Ramsey et al., 1999; Bandfield et al., 2002]. [7] Christensen and Harrison [1993] applied a linear deconvolution algorithm to the analysis of granitic rocks with desert varnish coatings. The purpose of their study was to determine the degree to which a natural varnish coating obscures spectral information from the rock substrate. They concluded that relatively thick varnish coatings (40 50 mm) can be penetrated using thermal infrared spectroscopy to determine the substrate composition. They point out that desert varnish, being clay-rich by nature [Potter and Rossman, 1977], is recognized as a clay-rich component in rock surface spectra. While that study provided important insight into the effects of exogenic rock coatings on rock spectra, it did not examine the effects of in situ weathering on interpretations of rock mineralogy. [8] A systematic analysis of the spectrally derived compositions of fresh and weathered rocks using spectral deconvolution has never been performed. In order to understand the impact of weathering on the interpretation of rock mineralogy, it is important to understand how the abundance of each mineral group is affected by weathering, and how the relative proportions of minerals used for rock classification differ between the whole-rock and rock surface assemblages Geology of the Sacaton Mountains Field Site [9] During the Late Cretaceous and early Tertiary Periods (the Laramide), Arizona was a site of lithospheric subduction and intense magmatism [Coney, 1976]. Associated with the magmatism was the formation of many of North America s classic porphyry copper deposits [Titley, 1982]. After ore emplacement, mid-tertiary extension resulted in tectonic deformation of the Laramide ore bodies and associated rocks. The Sacaton Mountains in Arizona are a classic example of Laramide plutonic rocks with an associated porphyry copper deposit, both of which were exposed and deformed in the mid-tertiary. [10] The Sacaton Mountains are an arc-shaped mountain range located approximately 15 km north of Casa Grande, Arizona (Figure 1). The range consists of northeast-trending, northwest-trending, and irregular mountains, separated by broad, shallow pediments and basin fill. The geology of the Sacaton Mountains includes Precambrian metamorphic and igneous rocks, Laramide plutonic igneous rocks, metasedimentary rocks of an unknown age, broad Pliocene to Pleistocene pediment surfaces, and Quaternary alluvium and pavement surfaces. Rocks are generally well exposed, with the greatest abundance of outcrop being Precambrian and Laramide plutons (Figure 1). The Laramide plutons are 2of15

3 Figure 1. A generalized geologic map of the Sacaton Mountains [after Balla, 1972] showing the distribution of Laramide granodiorite (Lgd) and Precambrian granite (pcg). I-10 is US Interstate 10. X-X is the cross section line shown in Figure 2. The black outline is the context for Figure 13. associated with porphyry copper mineralization, an example of which exists in the Sacaton open pit mine located approximately 3 km south of the mountains. [11] Wilson and Moore [1959] performed early reconnaissance geologic mapping of the Sacaton Mountains and mapped the entire range as Precambrian granite. Wilson [1969] distinguished both Precambrian and Laramide plutons. Balla [1972] completed the first detailed geological work in the Sacaton Mountains and recognized two different Laramide plutons: the Three Peaks Monzonite in the west side of the range and the Sacaton Peak Granite in the east side. Recent, detailed geologic mapping of the Sacaton Mountains by Skotnicki and Ferguson [1996] identified only one Laramide pluton but described several phases on the basis of abundances of accessory minerals and textural differences. [12] Mineralized rocks exposed in the Sacaton mine to the south include hydrothermally altered, porphyritic intermediate to felsic intrusive rocks. Wilkins and Heidrick [1995] showed that these rocks were probably transported along a Tertiary detachment fault to their present location from above the Sacaton Mountains (Figure 2). Although there is hydrothermal alteration associated with the mineralization in the mine, no widespread hydrothermal alteration was recognized in the Sacaton Mountains [Cummings, 1982]. Despite this fact, there is evidence for abundant clay minerals in parts of the Sacaton Mountains. Using a traditional band ratio of Landsat TM bands 5 ( mm) and 7 ( mm), which takes advantage of the 2.2 mm hydroxyl vibrational absorption in clays [Sabins, 1999], occurrences of clay minerals can be imaged. Figure 3 is a 5/7 ratio image of the western Sacaton Mountains and mine area, showing abundant clay associated with the Laramide granodiorite, but not with the Precambrian granite. Because the Sacaton Mountains display no evidence of widespread hydrothermal alteration [Cummings, 1982], the observed clay pattern may be related to differential weathering of bedrock surfaces among the granitoid plutons. 3. Laboratory Spectroscopy of Granitoid Samples 3.1. Sample Collection and Preparation [13] Granitoid samples were collected from 10 localities in the Sacaton Mountains along a generally west-east traverse. Skotnicki and Ferguson s [1996] geologic map was used to aid in sample collection from a representative suite of map units. At each sample locality, a large cobblesized sample (representative of the local bedrock) was collected. Each cobble produced several samples for laboratory analysis. [14] Samples were broken and/or sawed to separate fresh and weathered pieces. Slabs corresponding to surfaces of different depths within the sample were cut for use in determining the weathering gradient within these rocks in the following manner: 1) 3 cm 3 cm columns were cut through the cobble-sized pieces, perpendicular to the exposed natural surfaces; 2) columns were subsequently sawed into slabs to expose three different surfaces - the Figure 2. A generalized geologic cross section through part of the western Sacaton Mountains based on data of Wilkins and Heidrick [1995] and Cummings [1982]. Rock units shown are: Laramide granodiorite (Lgd) exposed in the Sacaton Mountains; Precambrian Pinal Schist (pcps) with near vertical foliation; Tertiary Whitetail conglomerate (Twt), which is northeast-dipping and unexposed except in drill holes; and Quaternary alluvium (Qal). Arrows show offset directions in faults. The number of fault blocks and precise style of faulting is not entirely known between the mine area and the mountains, but this diagram illustrates important structural relationships between rocks exposed in the mine and those exposed in the Sacaton Mountains. 3of15

4 Figure 3. A Landsat 5/7 ratio image of the western Sacaton Mountains and the Sacaton mine area showing Laramide granodiorite (Lgd) and Precambrian granite (pcg). The 5/7 ratio takes advantage of the 2.2 mm OH absorption in clays and shows clay-rich regions as bright areas. Clays are known to exist in the pit area [Cummings, 1982], but hydrothermal alteration is limited in the Sacaton Mountains. The image suggests that a correlation exists between clay abundance and the Laramide intrusion in the Sacaton Mountains. North is up; the image is 11 km across. outer weathered surface, a surface at 1 cm depth below the weathered surface, and a surface at 3 cm depth below the weathered surface. A total of 46 slabs were derived from the 10 sample localities Methods [15] Emission spectra of samples were recorded at the Arizona State University Mars Space Flight Facility. The spectrometer used is a Nicolet Nexus 670, adjusted to 2 cm 1 spectral resolution. This instrument and setup allows the direct measurement of emitted energy, rather than reflected energy. Ruff et al. [1997] provided a detailed description of the spectrometer setup and calibration. The sample chamber of the instrument was purged with N 2 gas to minimize spectral contributions from CO 2 and water vapor during sample collection. The instrument field of view was an ellipse with a 3 cm major axis. [16] Emission spectra were recorded during several sessions over a 2-month period. Prior to each laboratory session, samples were heated overnight to 80 C to increase the signal-to-noise ratio. Samples were placed in the instrument sample chamber and 150 scans were recorded over approximately 3 minutes. Midway through each session, spectra of two blackbody targets were recorded. Warm and hot blackbodies at temperatures of 70 C and 100 C, respectively, were used to calibrate the raw data to radiance. Radiance data were processed to emissivity according to Ruff et al. [1997]. [17] Emissivity spectra of rocks were deconvolved to end-member abundances using a linear deconvolution algorithm. The algorithm employs a Chi-squares minimization technique, in which an input library of known emissivity spectra is used to model the measured mixed spectrum of a sample by weighting the appropriate input end-members so as to minimize the error difference between the measured and modeled spectrum [Ramsey and Christensen, 1998]. [18] A library of 44 spectral end-members and one blackbody end-member was used to deconvolve granitoid spectra (Table 1) over the spectral range cm 1. These end-members were chosen from the ASU TES library [Christensen et al., 2000b] for appropriateness to probable granitoid primary and secondary mineralogy. It should be noted that the end-members are not true mineralogical endmembers with regard to crystal chemistry or structure, but are end-members only in the sense that they represent endmembers of our spectral library. The incorporation of a blackbody component into the deconvolution routine allows for differences between the spectral contrast of the library end-members and the sample spectrum. The abundance of blackbody is normalized out in the end result because it contains no mineralogical information [Hamilton et al., 1997]. [19] Spectrally derived mineral modes were compared to modes determined from more traditional means, such as point counting (of both thin sections and rock slabs). Rock slabs (15 cm 15 cm) were stained for feldspar according to Laniz et al. [1964] and one slab from each sample locality was analyzed. Four classes of minerals were counted: plagioclase feldspar, alkali feldspar, quartz, and other. Few pure albite crystals were found, but those that were observed were counted as alkali feldspar (according to Le Maitre [1989]). The group other included unstained portions that appeared to be clays, as well as mafic and opaque minerals. [20] In addition to slab point counts, thin section analyses were performed. One thin section was cut for each sample locality, with the long thin section dimension (46 mm) oriented from the inside of the sample outward, through the weathering rind. The outer 1 cm of the thin section, representing possible weathering products, was ignored in point counting. The same four classes of minerals were counted for each thin section. The abundances of individual other minerals, such as hornblende, biotite, sphene, epidote, sericite, clay minerals, and opaques, were qualitatively estimated, rather than counted. Anorthite number for the plagioclase samples was also determined using albite twins. [21] The error associated with deconvolution has been determined empirically [Ramsey and Christensen, 1998; Feely and Christensen, 1999; Wyatt et al., 2001]. Feely and Christensen [1999] reported errors of 0 15% for major (>5%) igneous phases and 0 17% for major metamorphic phases. These results are comparable to typical errors associated with point counting, and we assume those error estimates in this study. One type of error that can commonly occur is the inclusion of inaccurate accessory minerals at the 1 5% level, which arises from mathematical modeling of noise or other non-compositional information in emissivity spectra [Ramsey and Christensen, 1998]. For this reason, we do not base any conclusions on minor mineralogical information from deconvolution. [22] One source of error in both point counting and spectral analysis is the relationship between texture of the 4of15

5 Table 1. Library Minerals Used for Deconvolution, Selected From the Arizona State University Spectral Library a Mineral Class Group TES Lab ID Spectral Library Number Albite Alkali feldspar WAR Albite (Cleavelandite) Alkali feldspar WAR Albite Alkali feldspar WAR Albite Alkali feldspar WAR Andesine Plagioclase feldspar BUR Andesine Plagioclase feldspar WAR Anorthite Plagioclase feldspar BUR Anorthite Plagioclase feldspar WAR Bytownite Plagioclase feldspar WAR Labradorite Plagioclase feldspar BUR-3080A 176 Labradorite Plagioclase feldspar WAR-RGAND Labradorite Plagioclase feldspar WAR Oligoclase Plagioclase feldspar BUR Oligoclase Plagioclase feldspar BUR Oligoclase Plagioclase feldspar BUR-060D 69 Oligoclase Plagioclase feldspar WAR Oligoclase Plagioclase feldspar WAR Microcline Alkali feldspar BUR Microcline Alkali feldspar BUR-3460A 57 Orthoclase Alkali feldspar WAR-RGSAN Quartz Quartz BUR Hornblende Other NMNH-R Magnesiohornblende Other WAR Magnetite Other WAR Hematite Other BUR Ilmenite Other WAR Biotite Other BUR Muscovite Other WAR Wollastonite Other WAR Diopside Other WAR Talc Other BUR-4640C 53 Calcite Other ML-C Dolomite Other C Illite Other IMt Kaolinite Other KGa-1b 184 Kaolinite Other KGa-1b 185 Kaolinite Other KGa-1b 186 Na-montmorillonite Other SWy Na-montmorillonite Other SWy Na-montmorillonite Other SWy Dickite Other WAR Ca-montmorillonite Other STx Chlorite Other WAR Quartz Quartz S. Ruff; personal collection - a Christensen et al. [2000b]. Class Group is the mineral group that each belongs to for classification purposes discussed in the manuscript. rock and size of the sample. The coarser the texture of a rock, the larger the sample size must be in order to accurately estimate its bulk composition. Slab point counts were done in addition to point counts of thin sections to help offset adverse textural effects. In the case of coarser grained rocks, multiple fresh and weathered surfaces for each sample were analyzed spectrally as well. The results were then averaged to include a single measurement from each surface depth, for each sample Results From Laboratory Spectroscopy Fresh Surfaces [23] Figure 4 shows a sample spectrum and a model spectrum of one granodiorite sample from the Sacaton Mountains. Spectral analysis of fresh granitoid samples reveals modal mineralogy that is dominated by 3 components: plagioclase feldspar, potassium feldspar, and quartz. The average of deconvolution results for all fresh Laramide samples is 15% alkali feldspar, 48% plagioclase feldspar, 22% quartz, and 15% other minerals. Average results for all fresh Precambrian samples is 40% alkali feldspar, 17% plagioclase feldspar, 34% quartz, and 9% other minerals. Minor constituents include hornblende, biotite, and clays. Deconvolution results suggest that the plagioclase is mostly oligoclase-andesine (An ), which is comparable to the average anorthite number for plagioclase determined petrographically (An 20 -An 40 ). Potassium feldspar is modeled as either microcline or orthoclase. [24] Average point-counted modes are compared with average spectrally derived modes for each locality in Table 2. Plagioclase was most accurately determined by each method (R 2 = 0.94). Abundances of alkali feldspar and quartz were determined with good accuracy (R 2 = 0.69 and 0.56, respectively). Overall, the total spectrally derived modes correlate well with the point-counted modes (Figure 5), with an R 2 value of of15

6 Figure 4. A measured emissivity spectrum of the fresh surface of sample 11 (granodiorite) and the model spectrum created by linear deconvolution [Ramsey and Christensen, 1998]. The pie graph shows modeled proportions of minerals: 20% alkali feldspar, 49% plagioclase feldspar, 18% quartz, and 13% other hornblende, biotite, clay, and minor hematite. The RMS error was 0.245% for this sample. Table 2. Comparison of Modal Mineralogy (in Percent) of Granitoid Rocks Determined by Deconvolution and Point Counting and R 2 Values for Each Group a Sample Rock Unit Alkali Feldspar b Plagioclase Feldspar c Quartz Other Spectra Pt Ct Spectra Pt Ct Spectra Pt Ct Spectra Pt Ct 1 pcg Lgd pcg Lgd Lgd Lgd Lgd Lgd pcg Lgd R a Sample numbers represent localities. Spectral data for each locality are average deconvolved modes from all fresh surfaces at that locality. Point count results are the average of modes determined by point counting of slabs and petrographic thin sections from each locality. Errors are approximately 0 15%. b Alkali feldspar includes albite, orthoclase, and microcline. c Plagioclase feldspar includes all in the plagioclase series, except albite. Figure 5. A scatterplot of mineral modes determined by point counting versus those determined spectrally for fresh surfaces. Mineral modes are well correlated between the techniques (R 2 = 0.83), showing that deconvolution is a viable means of recovering quantitative mineralogical information for granitoid rocks. A one-to-one correlation line is shown for reference. [25] Deconvolution results were used to classify rock samples by the International Union of Geological Sciences (I.U.G.S.) classification scheme for felsic plutonic rocks [Le Maitre, 1989]. The I.U.G.S. classification scheme is dependent upon abundances of 3 types of minerals: quartz (Q), alkali feldspar (A), and plagioclase feldspar (P). Alkali feldspar includes orthoclase, microcline, and albite. Plagioclase includes all non-albite plagioclase (An # >An 05 ). Q, A, and P abundances are normalized to Q + A + P = 100, and all other minerals are either ignored or used as modifiers. Alkali and plagioclase feldspar abundances are used to determine a rock s plagioclase ratio, where plagioclase ratio = P/(A + P). Together, the plagioclase ratio and normalized quartz abundance are used to assign a rock name. Compositionally, our samples comprise two populations, granodiorite and granite, and the compositional groups coincide with the age differences; Precambrian rocks are granite (syenogranite) and Laramide rocks are granodiorite Results From Weathered Samples [26] In general, emissivity spectra of weathered surfaces analyzed in this study are shallower and have broader spectral shapes than spectra of fresh surfaces. Surfaces of our weathered granitoid samples are dominated by four mineral end-members: alkali feldspar, plagioclase feldspar, quartz, and clay. However, mineralogies deconvolved from spectra of weathered surfaces are not well correlated with mineralogies determined by point counting of associated fresh surfaces (Figure 6) because the weathered surfaces are depleted in plagioclase relative to the fresh surfaces. If the spectrally derived modes from natural surfaces are used to classify the rocks (as may be the case in a remote-sensing situation), all 10 sample localities classify as granite-alkali granite. Weathered rocks have a plagioclase ratio that is approximately 40% lower on average than their fresh counterparts, causing a misclassification of the Laramide rock samples (Figure 7). [27] Spectra of the rock slabs from different depths were deconvolved to determine the depth of weathering. All of the mineral abundances for the surface spectra were averaged, as were all of the abundances from the 1 cm depth surfaces and abundances from the 3 cm depth surfaces. Surfaces at 1 cm depth and 3 cm depth are generally the same and represent the bulk rock mineralogy (Figure 8). Plagioclase and clay vary inversely with depth through the sample. Alkali feldspar and quartz are generally much less affected by surface exposure. [28] Thin-section analyses show that patchy replacements of feldspar, interpreted to be clays, occur in situ near or at the rock surfaces. To confirm the presence of clays, X-ray diffraction (XRD) of weathered surfaces was performed according to the technique of Moore and Reynolds [1997]. 6of15

7 Figure 6. A scatterplot of fresh rock mineral modes versus those determined spectrally for weathered surfaces. The weathered mineralogy is related to bulk rock mineralogy, but not correlated with it (R 2 0). A one-to-one correlation line is shown for reference. Weathered rock was removed using a rotary tool and the clay-size fraction was segregated, rinsed of salts, and decanted down to a slurry. An oriented powder mount was created from the slurry, and was analyzed by X-ray diffraction from 2q 5 85 over a 30 minute scan time. XRD results indicate the presence of smectite, with minor amounts of illite and kaolinite or chlorite. No attempt was made to correlate the individual clay minerals identified with XRD to those identified with deconvolution, but these Figure 8. The variations of quartz (Q), alkali feldspar (A), plagioclase feldspar (P), and clay (C) content versus depth based on deconvolution of laboratory spectra. An example of the surfaces measured is shown at the right (see text for explanation). Arrows point to the surfaces measured spectrally. Natural rock surfaces are depleted in plagioclase and enriched in clay, relative to 1 cm and 3 cm deep surfaces. results verify the presence of clays in the weathered surface of the granodiorite. 4. Laboratory Spectroscopy at Degraded Spectral Resolution 4.1. Methods [29] Laboratory spectra were degraded to the spectral resolutions of the THEMIS and TIMS instruments Figure 7. The I.U.G.S. classification of granitoid rocks from the Sacaton Mountains from spectrally derived mineralogy from natural bedrock surfaces and artificially exposed fresh surfaces. Classification is based on: quartz (Q), plagioclase (P), and alkali feldspar (A) [Le Maitre, 1989]. Precambrian samples are labeled x and Laramide samples are labeled +. Solid symbols mark the weathered compositions and empty symbols mark the fresh rock compositions. The gray vectors originate at the fresh rock compositions and terminate at the weathered rock compositions. The fresh Laramide rocks, which are nearly all granodiorite, show a consistent decrease in plagioclase abundance due to weathering. The Precambrian granitic rocks show no consistent weathering trend in the few data points collected. If the rocks were classified on the basis of remotely sensed mineralogy, they would appear more alkaline than they are due to the differential weathering of feldspars. 7of15

8 Figure 9. Thermal infrared emissivity spectra of natural weathered surfaces of Precambrian granite and Laramide granodiorite from the Sacaton Mountains. Samples are shown at laboratory resolution, at (resampled) THEMIS resolution, and at TIMS resolution. Resampled spectra are offset for clarity. (Figure 9) to determine if multispectral data can be deconvolved to produce mineralogical results for our rocks. The spectral library for linear deconvolution of n-band data is limited to n possible end-members, with better results for the less end-members used [Ramsey et al., 1999]. A subset of average mineral end-members was selected on the basis of anticipated granitoid mineralogy. The major mineralogy of weathered surfaces was anticipated to be alkali feldspar, plagioclase feldspar, quartz, and clay on the basis of previous laboratory spectral deconvolutions. The spectral end-members listed in Table 1 were convolved to THEMIS and TIMS resolutions and the members of each mineral group were then averaged to produce average alkali feldspar, average plagioclase, average quartz, and average clay spectra (Figure 10). Average alkali is the equally weighted average of orthoclase, microcline, and albite. Albite is more spectrally similar to microcline than it is to other plagioclase minerals and it was included because it counts as alkali feldspar in the I.U.G.S. classification. Average plagioclase is the average of oligoclase, andesine, labradorite, and bytownite. Quartz is the average of the two available quartz spectra. Average clay is the average of illite, kaolinite, and smectite Deconvolution Results [30] Figure 11a compares the deconvolved abundances of alkali feldspar, plagioclase feldspar, quartz, clay, and total feldspar, for deconvolutions at full laboratory spectral resolution, THEMIS resolution, and TIMS resolution. Error bars of ±10% are shown for the highest resolution data. Figure 11b summarizes the number of times that the abundance of each mineral group was modeled, using multispectral data, to within the 10% error of the same mineral abundances from deconvolution of full-resolution data. At TIMS-spectral resolution, mineral abundances were accurate to within 10% of the high-resolution results % of the time, with quartz being modeled with the greatest accuracy. At THEMIS-spectral resolution, mineral abundances were accurate to within 10% of the highresolution results 20 60% of the time. Quartz was modeled with the greatest accuracy, but was also severely overestimated in several cases. [31] Although the THEMIS instrument has 8 surfacesensing spectral bands and the TIMS instrument has only 6, the deconvolution results using THEMIS resolution data were not as accurate as the results at TIMS resolution. This is attributable to the fact that the THEMIS filters are favorably placed for identification of carbonates and sulfates, as well as silicates on Mars. The TIMS filter functions were selected to detect silicates in the terrestrial atmospheric window. Also, the THEMIS filters are broader than the TIMS filters (Figure 12) to increase the signal-to-noise ratio of measurements of the cold Martian surface. The narrower TIMS filters allow for better discrimination of silicates in the cm 1 region. Deconvolution results at both TIMS- and THEMIS resolutions indicate that discrimination between various feldspars will be difficult in a remotesensing situation using either TIMS or THEMIS data. However, reasonable estimates of the relative abundances of major mineral groups, such as total feldspar, quartz, and clay can be performed with these instruments, though the absolute abundances should be understood to be estimates. 5. Mineral Mapping With TIMS Data 5.1. Image Processing [32] The TIMS data were calibrated to radiance using two onboard reference blackbodies [Palluconi and Meeks, 1985]. A MODTRAN midlatitude-summer model was used to correct for atmospheric effects. The TIMS data were then georeferenced to Landsat data. Data were converted to emissivity using an emissivity normalization method [Kealy and Hook, 1993], which assumes a maximum emissivity value, 0.97 in this case, without specifying the band with Figure 10. Emissivity spectra of the mineral end-members used to deconvolve the degraded laboratory spectra. Mineral spectra are shown at both THEMIS and TIMS resolutions. The spectra are offset for clarity. 8of15

9 Figure 11. A comparison of mineral abundances determined by linear deconvolution, shown by mineral (a). Sample localities are listed along the x axis; abundances on the y axis are percent abundances. The 10% error bars associated with deconvolution results [Feely and Christensen, 1999] are given for the fullresolution laboratory data. Histograms showing the number of times that deconvolution of multispectral data, at each of TIMS and THEMIS resolutions, produced results within the 10% error of the full resolution deconvolutions are shown in b. Tot is total feldspar. the highest emissivity. The algorithm computes the temperature for each pixel using an assumed emissivity for each band. The band that produces the highest temperature is chosen to have the assumed maximum emissivity. The calculated temperature is then used along with the radiance values to calculate emissivity for the remaining bands. Further details about our emissivity calibration are given by Michalski [2002]. [33] Figure 13 is a TIMS emissivity image of the Sacaton Mountains area. The context for the TIMS data is shown in Figure 1. Bands 5, 3, and 1 are red, green, and blue, respectively. In this band combination, felsic or quartz-rich rocks appear pink-red and mafic rocks appear blue. In general, the Precambrian granite is dark pink to red in this image and the Laramide granodiorite is light pink. Blue areas within the granodiorite correspond to the mafic border phase, which is slightly enriched in hornblende and biotite [Skotnicki and Ferguson, 1996]. Sample locations are shown by number. Slightly higher emissivity values observed on north-facing slopes indicates that some residual temperature information remained in the data after the emissivity calibration method. [34] The TIMS emissivity data were linearly deconvolved to end-member abundances [Boardman, 1989; Ramsey et 9of15

10 Figure 12. The filter functions of the TIMS [Palluconi and Meeks, 1985] and THEMIS [Christensen et al., 2004] instruments. Filters are labeled by number. al., 1999]. Four end-members were used: average feldspar, quartz, average clay, and blackbody. The output of the algorithm consists of image layers for each end-member where the pixel values are equal to the fractional abundance of that end-member, and an RMS error image. The abundance images of quartz, feldspar, and clay were normalized to zero blackbody abundance TIMS Results [35] Abundance maps for quartz, clay, and feldspar for the western Sacaton Mountains (draped over digital topography) are shown in Figure 14. Bedrock outlines on the images were determined from a temperature image, where bedrock surfaces are relatively colder during the day, and from Skotnicki and Ferguson s [1996] geologic map. Bedrock surfaces in Figure 14 are labeled either pcg or Lgd for Precambrian or Larmide bedrock [Balla, 1972]. The quartz, feldspar, and clay abundance maps were used to interpret bedrock geology. [36] The mountain in the center of the image is clay-rich and quartz- and feldspar-poor. The mountain in the west side of the image is quartz- and feldspar-rich and clay-poor. 10 of 15

11 Figure 13. A TIMS emissivity image of the Sacaton Mountains area. See Figure 1 for image context. Bands 5, 3, 1 are red, green, blue, respectively. Qualitative differences in surface composition are evident in color differences in the image. Light pink mountains and blue hills are Laramide granodiorite and darker pink-red mountains are Precambrian granite [Balla, 1972]. The blue granodiorite areas correspond to a mafic border phase [Skotnicki and Ferguson, 1996]. Sample localities are located by number on the image. The inset shows the location for which deconvolution results are displayed in Figure 14. Figure 14. Results from deconvolution of TIMS data. Views are looking north; the image area is approximately 7 km 3 km. Deconvolved abundances of quartz, feldspar, and clay were draped over digital topography. Abundance scales are: quartz, 0 60%; feldspar, 0 60%; and clay, 15 90%. Map lines show the approximate outcrops of Laramide granodiorite (Lgd) and Precambrian granite (pcg). The pcg is clearly quartz and feldspar-rich compared to the Lgd. Lgd is characterized by feldspar deficiency and abundant clay. The lenticular-shaped quartz-rich area in the east-central portion of the image is an outcrop of quartzite (qtz). 11 of 15

12 Figure 15. A scatterplot of the mineral modes determined from deconvolution of 6-band laboratory data versus mineralogy determined by deconvolution of TIMS data. The TIMS data represent the 4-pixel averages of blackbodynormalized abundances of minerals at each sample location. Deconvolution with TIMS results in consistent overestimation of clay abundance and underestimation of feldspar and quartz abundances. One-to-one correlation line is shown for reference. The differences in clay and quartz abundance allow for easy distinction between the granite and granodiorite rocks in the western Sacaton Mountains. The Laramide rocks within the mountain in the center of the image appear to be fairly homogenous throughout. There is a small area of very quartz-rich material in the east side of the image, which represents the only exposure of metasedimentary rocks in the area. [37] Quartz, feldspar, and clay abundances were extracted from a 4-pixel average at each of the sample localities in the TIMS scene. These data were plotted against deconvolution results for 6-band laboratory data of weathered surfaces (Figure 15). Deconvolution of bedrock surfaces with the TIMS instrument results in systematic error. Clay was overestimated in nearly every case, feldspar was underestimated in nearly every case, and quartz was underestimated in every case. Although the absolute abundances of each mineral group were not determined accurately, the relative abundances of these materials determined with deconvolution are accurate. In other words, deconvolution correctly identified clay-rich surfaces as clay-rich, quartzrich surfaces as quartz-rich, feldspar-poor surfaces as feldspar-poor, etc., but the absolute abundances derived from deconvolution of TIMS data were not well constrained. 6. Discussion 6.1. Discussion of Laboratory Results [38] Linear deconvolution of laboratory spectra is a reliable, nondestructive means of determining the modal mineralogy of rocks. However, the disadvantages of deconvolution are twofold. Deconvolution is a forward model, and therefore the possible results are user-defined. In other words, deconvolution analysis can only return abundances of minerals that are in the user s spectral library. Secondly, deconvolution is not very useful for identification of minor or accessory minerals because it can commonly result in false positives of up to several percent. [39] Results from deconvolution of spectra of fresh rocks from the Sacaton Mountains indicate that the Laramide rocks are granodiorite and the Precambrian rocks are granite. Previous researchers classified the Laramide rocks as granite to quartz monzonite. The spectral results suggest higher quartz and plagioclase content in the Laramide rocks than is reported by Skotnicki and Ferguson [1996]. Previous classifications were based largely on field inspection, and it is difficult to accurately estimate the proportions of feldspars or absolute mineral abundances in the field. [40] Mineralogical information can be recovered from degraded laboratory data, but the number of end-members that can be modeled is significantly reduced. Multispectral laboratory data may be most useful for deconvolving the abundances of mineral groups in a scene, but not for distinguishing minerals within a group. Deconvolution of multispectral data is useful for recovering approximate mineralogy of weathered samples because most natural surfaces are composed of only a few mineral types or groups. In this study, deconvolution of multispectral laboratory data correctly estimated the mineralogy of weathered granitoids as average feldspar, quartz, and clay, but was unable to distinguish different feldspars from each other. Feely and Christensen [1999] showed slightly better results for deconvolution of 10-band THEMIS-like data, but made no attempt to distinguish different feldspars at degraded spectral resolution Discussion of TIMS Results and Error Analysis [41] The TIMS instrument is capable of 0.3 K NET [Palluconi and Meeks, 1985], which propagates to roughly emissivity units. However, many other factors contribute error to TIMS data, including atmospheric effects, unmodeled compositional end-members, the presence of vegetation, and the incomplete separation of temperature and emissivity. [42] RMS error for the scene ranged from near zero to a maximum of 2%, and averaged approximately 1%. RMS error appears to have no correlation with any of the endmembers or with elevation. North-facing slopes have slightly higher RMS values, partially due to an increase in the development of soil and vegetation, and partially due to the incomplete separation of temperature and emissivity. [43] Not only were the abundances of water vapor, ozone, and other gases estimated in the MODTRAN atmospheric model, but their concentrations were assumed to be uniform throughout the scene. The abundance of atmospheric gas that is observed is a function of the column height of atmosphere, and the actual concentration of gas. The atmospheric column was estimated from the average elevation of the scene (600 m) and the flight altitude of the instrument. This results in undercorrection for atmosphere at the bases of the mountains and overcorrection at the tops. Further calibration of the emissivity data was attempted by comparing the TIMS spectrum of a limestone outcrop to the laboratory spectrum of the limestone and scaling some 12 of 15

13 atmospheric parameters to match the spectra. However, the residual difference at this locality was still approximately 1% [Michalski, 2002]. [44] The MODTRAN radiative transfer model does not correct for upwelling or downwelling radiance. Downwelling radiance is known to have the effect of shallowing thermal absorption features and it does so non-uniformly [Salisbury and D Aria, 1992]. The amount and structure of downwelling radiance can be measured with a field spectrometer, using a highly reflective surface (gold plate). However, the TIMS data used in this study were collected years in advance of the conception of this project, so no concurrent field measurements could be made during data acquisition. No attempt was made to estimate the amount of downwelling radiance and remove it. This error could potentially have caused the TIMS spectra to have been shallowed by up to 10s of percent, and may be linked to the overestimation of clay and underestimation of quartz and feldspar. But, the cloud-free observing conditions during data acquisition were probably not favorable for significant downwelling radiance to occur. [45] Error occurred due to unmodeled compositional endmembers. The surface composition of the entire scene was assumed to be some combination of feldspar, quartz, and clay. Accessory minerals, including hornblende, biotite, sphene, carbonates, and others were not accounted for. It is possible that hornblende may have been incorrectly modeled as clay, because hornblende has a broad, shallow absorption feature. All phyllosilicates have broadly similar absorption spectra at TIMS resolution, so biotite would have been mapped as clay. The maximum amount of hornblende observed was 0 10%. Biotite occurs at the 0 20% level, but the higher abundances are limited to the mafic border phase. The total error associated with unmodeled end-members may have contributed 0 30% clay abundance to the TIMS results. [46] The presence of vegetation may be responsible in part for the overestimation of clay. Vegetation is not abundant in the Sacaton Mountains and consists of scattered creosote, palo verde, cacti, and various desert shrubs and grasses. It is assumed that most green vegetation does not have spectral features in the thermal infrared due to blackbody cavity effects, and that the spectral effects of vegetation will be removed in the blackbody normalization. However, desert vegetation has spectral features that can affect TIMS data [Nowicki, 1998]. The presence of vegetation may nonuniformly shallow spectra and may result in overestimated clay abundance in partially vegetated pixels. This effect is likely part of the explanation for the overestimation of clay on the north side of slopes in the TIMS scene. Vegetation is more abundant on northfacing slopes in the desert in general and at the Sacaton Mountains site, due to longer surface water residence time and less direct sun. It is difficult to estimate the abundance of vegetation present because the abundance and vigor of desert grasses and small plants can vary greatly from year to year. However, it is unlikely that, with the exception of a few areas in the scene, the vegetation could have covered more than a few 10s of percent of the ground at the scale of TIMS pixels. This could potentially translate to 10s of percent overestimation or underestimation of surface mineralogy. [47] The same factors responsible for the overestimation of clay are responsible for the underestimation of quartz and feldspar. However, quartz abundance was more severely underestimated than feldspar abundance. This is likely because quartz has a distinct absorption spectrum with very deep absorption bands and it is more adversely affected by shallowing of the data. In the Laramide plutons, quartz was only modeled in abundances of a few percent. In the Precambrian plutons, it was estimated in abundances of 8 12%. This is compared with actual abundances of approximately 22% for the Laramide granodiorite and 40% for the Precambrian granite. [48] Error in the TIMS data attributable to downwelling radiance and unmodeled atmospheric components, vegetation, and unmodeled end-members could equate to several 10s of percent of deconvolved abundances. Despite the error present in the TIMS data, the results are useful for compositional mapping from thermal infrared data. 7. Considerations for Remote Sensing of Mars [49] In this study, we investigated the mineralogy of weathered granitic rock surfaces using laboratory spectra and multispectral remote-sensing data. The results demonstrate how weathered rock surface mineralogy is different from, but related to whole-rock mineralogy, and how the weathered rock compositional components can be mapped using multispectral data. Although we studied granitic rocks, and granitic rocks are not widely abundant on Mars [Bandfield et al., 2000], the fundamental relationships discussed here could apply to any rock type and have implications for remote sensing of Mars with thermal infrared data sets. [50] Perhaps the most important implication of this work is that the spectral signature of weathered rocks is controlled by the abundance of unstable phases present in the rock. In the Sacaton Mountains, adjacent granite and granodiorite plutons exist. The granite, which is dominated by alkali feldspar and quartz, weathers much more slowly than the granodiorite, which is dominated by plagioclase feldspar. If this relationship is extrapolated to remote sensing of Mars, it may imply that adjacent rock units could have a very different weathering signature based on their primary mineralogies. For example, a volcanic flow that is enriched in basaltic glass or olivine relative to an adjacent flow could have a more weathered surface appearance. This will be an important consideration for compositional mapping of surface units with THEMIS data. [51] A second implication of this work for remote sensing of Mars pertains to the classification of rock types based on their natural surface mineralogies. In the Sacaton Mountains, the most striking characteristic of the weathered granodiorite in the TIMS mineral maps is the lack of feldspar, not the occurrence of secondary clay. This may suggest that clay minerals, and alteration products in general, are more difficult to spectrally characterize than primary silicate phases, which are well crystalline and commonly relatively coarse grained. On Mars, alteration products may be poorly crystalline and difficult to characterize [Gooding et al., 1992]. However, the existence of weathered rocks may be more strongly indicated by the observed lack of certain primary phases. Studies of 13 of 15