Journal of Paleolimnology

, Volume 54, Issue 2–3, pp 253–261 | Cite as

Characterizing clay mineralogy in Lake Towuti, Indonesia, with reflectance spectroscopy

  • Andrea K. Weber
  • James M. Russell
  • Timothy A. Goudge
  • Mark R. Salvatore
  • John F. Mustard
  • Satria Bijaksana


We tested the use of visible to near-infrared (VNIR) reflectance spectroscopy to characterize the relative abundances of clay minerals in sediments from Lake Towuti, a large tectonic lake in Sulawesi, Indonesia. We measured VNIR spectra of lake and river sediments from Lake Towuti and its catchment to identify clay minerals, fit major VNIR absorption features with a modified Gaussian model to estimate relative abundances of these minerals, and compared these absorptions to the samples’ chemistry to test the utility of VNIR spectroscopy to characterize sediment compositional variations. We found that major absorptions are caused by vibrations of Al–OH in kaolinite (2.21 μm), Fe–OH in nontronite (2.29 μm), Mg–OH in saponite and serpentine (2.31 μm), and Mg–OH in serpentine (2.34 μm). This was confirmed with X-ray diffraction data. The correlations between absorption band areas for Fe–OH, Al–OH, and Mg–OH vibrations and Fe, Al and Mg concentrations, respectively, are statistically significant, varying between r = 0.51 and r = 0.90, and spatial variations in inferred clay mineralogy within the lake are consistent with variations in the geology of the catchment. We conclude that VNIR spectroscopy is an effective way to characterize the clay mineralogy of lake sediments, and can be used to investigate changes in mineral inputs to lake deposits.


Clay mineralogy Lake sedimentology Paleolimnology Spectroscopy Modified Gaussian modeling 


The clay mineralogy of lake sediments can provide valuable insight into the history of sediment provenance (Mitchell 1955; Johnson 1970), weathering processes (Asikainen et al. 2006), and depositional environments. Clay mineralogy is commonly measured using X-ray diffraction (XRD), although this can be a relatively difficult process involving numerous and often destructive pretreatments of large sediment samples (Yuretich et al. 1999). In contrast, visible to near-infrared (VNIR) reflectance spectroscopy is a rapid, non-destructive method that can provide information on the mineral composition of sediments through the position and shape of the absorption features, which are controlled by crystal structure and mineral chemistry. Infrared spectroscopy has been used to measure organic carbon, biogenic opal, and carbonate mineral concentrations in lake sediment samples to understand long-term paleoenvironmental variations (Rosén and Persson 2006; Vogel et al. 2008; Rosén et al. 2010), to identify and map clay minerals in paleolake deposits on Mars (Ehlmann et al. 2008; Milliken and Bish 2010), and to determine the composition of terrestrial soils (Viscarra Rossel et al. 2006, 2009). Despite the sensitivity of VNIR reflectance data to variations in clay mineralogy, this technique has not been widely employed to measure clay mineralogical variations in lake sediment samples.

This paper evaluates VNIR reflectance spectra of sediment samples from Lake Towuti, Indonesia, which is located in the East Sulawesi Ophiolite. This ophiolite consists largely of serpentinized and unserpentinized peridotites, lehrzolites, and mafic–ultramafic rocks including gabbros, dolerites and basalts, as well as recent sediments derived from those rocks (Fig. 1; Kadarusman et al. 2004). Nickeliferrous oxisol soils formed on these ultramafic rocks dominate the region and contain up to 60 % iron oxide (Golightly 1981). VNIR reflectance data of surface sediment samples from Lake Towuti were compared to their bulk elemental composition to test whether VNIR reflectance spectra can be correlated to spatial variations in sediment composition and clay mineralogy.
Fig. 1

Location and geology surrounding Lake Towuti in Indonesia. Map modified from Costa et al. (2015). (Color figure online)

Materials and methods

Sample preparation and analysis

River sediment samples (9) and lake surface sediment samples (33) were collected in 2011 and 2012. Offshore lake sediment samples were characterized visually and microscopically and consist almost entirely of very fine silt and clay, but river samples had widely variable grain sizes, ranging up to coarse gravel. River sediment samples were therefore sieved to <125 µm before being powdered to prevent spectroscopic and geochemical measurements from being biased by large grains (coarse sands and gravels). Samples were freeze-dried, powdered, and homogenized, and aliquots were prepared via flux fusion to analyze their bulk elemental chemistry following the procedure outlined by Murray et al. (2000). Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) was used to measure Al, Ca, K, Fe, Si, Ti, Mg, Mn, and Cr concentrations. The VNIR spectra of separate aliquots were measured using an Analytical Spectral Devices (ASD Inc., CO, USA) FieldSpec 3 portable spectrometer (subsequently referred to as an ASD Spectrometer), a VNIR reflectance spectrometer that covers the wavelength range from 350 to 2500 nm [see Electronic Supplementary Material (ESM) for details on measurements]. High-resolution spectral data were also acquired for three lake sediment samples at the Brown University Keck/NASA Reflectance Experiment Lab (RELAB; Pieters 1983) to evaluate the lower-resolution VNIR data from the ASD Spectrometer (see ESM for discussion). Powder XRD data on select samples were also collected using a Bruker D2 PHASER X-ray diffractometer to provide complementary mineralogy to VNIR reflectance measurements (see ESM for details on measurements).

VNIR data analysis and modified Gaussian modeling

A modified Gaussian model (MGM) was used to quantify the strengths of clay mineral absorption features in the VNIR spectra, which are, to first order, related to the relative abundances of clay minerals in samples (Clark 1999). The MGM, first presented by Sunshine et al. (1990), was initially developed and validated for crystal field absorptions in pyroxene (see ESM for further details). Subsequently, it has been shown that the MGM can also be used to model vibrational absorption bands caused by OH within actinolite (Mustard 1992). Modified Gaussian fits to each absorption band of interest in our samples were used to determine the band area, which is controlled by the absorption strength and can be used as a proxy for the relative abundances of different minerals, in contrast to absolute abundances. Whereas band depth is also a useful proxy for mineral abundance, the overlapping absorption features of many clay minerals necessitates the use of absorption band area to deconvolve complex spectral data into a series of individual absorption features.



The ICP-AES data indicate large chemical variations in sediment within the lake (Fig. 2a–c). Iron concentrations in the lake sediments vary between 8.2 and 18.9 wt%, and generally increase from west to east and north to south across the lake. Lake surface sediment samples show the opposite trend in aluminum, with concentrations increasing from 2.6 wt% in the east to 6.8 wt% in the west. This E–W gradient is also apparent in the rivers, with a maximum Al concentration of 7.09 wt% in the Loeha River that flows into the northwest corner of the lake, and maximum Fe concentrations of ~26 wt% measured in the Lemo-lemo River that flows in from the east. Mg concentrations are high in rivers on Towuti’s eastern shoreline, but Mg concentrations are highest in the central part of the lake, south of the Mahalona River. The Mahalona River has Mg concentrations that exceed 10 wt% compared to an average concentration of 5.3 wt% in all rivers.
Fig. 2

Map of sample locations in Lake Towuti. Squares are lake sediment samples and triangles are river sediment samples. The highest values are in red and the lowest are in green. The color bins vary between maps. a Al wt%, b Fe wt% and c Mg wt%. Figures d, e, and f show the MGM areas calculated for each absorption band of interest. These areas were normalized to the sum of absorption band areas for that spectrum. d Al–OH (kaolinite) absorption band areas, e Fe–OH (nontronite) absorption band areas, f Mg–OH (saponite + serpentine) absorption band areas. (Color figure online)

VNIR spectroscopy and XRD mineralogy

VNIR spectroscopy is based upon the absorption of electromagnetic radiation at specific wavelengths through excitation of electronic transitions or molecular vibrations within mineral structures. The precise position and shape of VNIR absorption features, or absorption bands, are related to the optical constants of the material being investigated. In minerals, the position and shape of the absorption features are controlled by the crystal structure and mineral chemistry. Whereas each individual absorption feature is related to a specific cation’s coordination state and electrons (electronic transition absorptions) or bond (vibrational absorption), taken together, multiple absorption features can be diagnostic of specific minerals. Different minerals have unique VNIR spectral properties, and thus sample mineralogy can be uniquely identified on the basis of infrared reflectance data (Burns 1993; Farmer 1974; Hunt 1977; Hunt and Salisbury 1970).

VNIR spectra of both the lake and river sediment samples have distinct absorption features at ~1.4, ~1.9, 2.21 µm and a complex set of features from 2.29 to 2.4 µm (Fig. 3a, b). Spectra from the high-resolution BDR spectrometer and the lower-resolution ASD Spectrometer have the same spectral features in these areas, indicating that the detection of these features in the ASD Spectrometer data is robust (see the ESM for discussion).
Fig. 3

a, b Are averages of the grab, river, and core top sediments. Spectra in part (a) are continuum removed. These averages clearly define the area of interest to be from ~2.20 to 2.40 µm. c, d Are library spectra of kaolinite, nontronite, saponite, and an example of the Mg-rich serpentine chrysotile. Spectra in part (c) are continuum removed. Spectra are from the USGS spectral library (Clark et al. 2007). (Color figure online)

Many of the absorption bands present in the Lake Towuti samples are common to multiple minerals. The sharp absorption at ~1.9 µm indicates the presence of structural water common to many phyllosilicates and opal (Clark et al. 1990; Bishop et al. 1994), whereas the absorptions at ~1.4 µm are caused by the first overtone of structural OH, as well as combination tones of structural H2O (Clark et al. 1990; Gaffey et al. 1993). In contrast, absorption bands in the ~2.2 to 2.4 µm region are caused by combinations of the stretching and bending modes of OH and metal-OH, respectively, and are diagnostic for many clay minerals in weathered oxisol soils (Fig. 3c, d; Clark et al. 1990). River and lake sediment samples from the Lake Towuti basin show several absorption features in this wavelength region. Based on the analysis of the locations and shapes of the identified absorption features in the measured spectra, in comparison to those in pure minerals (Clark et al. 1990), we attribute absorptions at ~2.21 µm to Al–OH vibrations in kaolinite, absorptions at ~2.29 µm to Fe–OH vibrations in an Fe-bearing smectite (nontronite), and absorptions at ~2.31 and 2.34 µm to Mg–OH vibrations in a combination of an Mg-bearing smectite (saponite) and serpentine.

The absorption band at ~2.21 μm is interpreted to be caused by Al–OH vibrations in kaolinite, as opposed to some other Al-bearing phyllosilicate such as montmorillonite or illite, due to the precise position of the 2.21 μm absorption coupled with an asymmetric shoulder in the band near ~2.16 µm (Clark et al. 1990; Bishop et al. 2008). The complex spectral signal near ~2.3 μm is a region often characteristic of Fe/Mg-bearing phyllosilicates. Nontronite is a dioctahedral ferric iron-bearing smectite and has an absorption centered at ~2.29 µm, caused by the Fe3+–OH bond (Clark et al. 1990; Bishop et al. 2002, 2008). Saponite, a trioctahedral magnesium-bearing smectite, has an absorption centered at ~2.31 to 2.32 µm caused by Mg–OH (Clark et al. 1990). Although some samples show strong absorptions at ~2.29 or ~2.32 µm, indicating nontronite or saponite, respectively (Clark et al. 1990; Bishop et al. 2002, 2008), many samples have absorption features between 2.29 and 2.32 µm, likely from either Fe/Mg substitution between nontronite and saponite or physical mixtures of the two mineral species. Absorption bands that fall between the endmember wavelengths are interpreted as a mixture of clays with varying Mg/Fe ratios (Grauby et al. 1994).

The absorption band at ~2.33 µm and a more subtle absorption feature at ~2.1 µm in several samples indicates the presence of serpentine. Serpentine has a prominent absorption feature caused by Mg–OH that overlaps with the absorption features of the Fe/Mg-bearing smectites (Fig. 3c, d), but with a band center at slightly longer wavelengths, near ~2.33 to 2.34 µm (King and Clark 1989; Bishop et al. 2008). Serpentines also have a shallow, but broad diagnostic absorption feature centered near ~2.1 μm (Fig. 3c, d; King and Clark 1989; Bishop et al. 2008) that may relate to the presence of Mg–OH (Clark et al. 1990).

Our XRD results indicate the presence of several mineral phases in these samples, including serpentine, kaolinite, smectite and quartz (ESM Fig. 3). Serpentine is identified based on prominent 001 and 002 reflections, as well as a 100 reflection peak. Kaolinite is identified based on 001 and 002 reflections, and a prominent 020 reflection. Smectite is identified based on a small 001 basal reflection peak. These XRD results confirm the major clay mineral phases inferred from our VNIR reflectance spectroscopy results.

Modified Gaussian modeling

The modified Gaussian model fits the clay mineral absorption features well (ESM Fig. 2). A single modified Gaussian absorption band fits the kaolinite Al–OH absorption, with root-mean-square error (RMSE) values ranging from 0.10 to 0.28 %. The complex absorption band from ~2.3 to 2.35 µm shows several absorption features that are optimally fit by three modified Gaussians, resulting in fits with RMSE values ranging from 0.08 to 0.31 %. Based upon these results, we modeled sample spectra with modified Gaussians centered at 2.205 μm (σ = 0.0127 μm), 2.295 μm (σ = 0.0115 μm), 2.315 μm (σ = 0.0106 μm), and 2.345 μm (σ = 0.0106 μm), which represent contributions from Al–OH (kaolinite), Fe–OH (nontronite), Mg–OH (saponite + serpentine), and Mg–OH (serpentine), respectively (ESM Table 1).

The absorption band areas calculated from the MGM indicate substantial variations in the relative absorption areas (or relative abundances) of Al–OH (kaolinite), Fe–OH (nontronite), and Mg–OH (saponite + serpentine) among samples (Fig. 2d–f). Samples with the strongest Al–OH (kaolinite) absorptions are located in the western part of the lake (Fig. 2d), whereas samples with the strongest Fe-smectite absorptions are located in the east (Fig. 2e). Samples with the strongest Mg-smectite/serpentine absorptions are located in the central part of the lake (Fig. 2f). Although we did not perform quantitative XRD analysis, our XRD results qualitatively confirm the variation in mineralogy inferred from our MGM modelling results. The sample with a strong Mg–OH (serpentine) absorption has an XRD pattern that is more dominated by serpentine than the sample with a strong Al–OH absorption, which appears to have a higher proportion of kaolinite (ESM Fig. 3).

Chemistry and absorption area correlations

The modeled Al–OH (kaolinite) absorption area is strongly correlated to Al concentration (Fig. 4a; r = 0.81, p < 0.01), and the sum of the modeled absorption areas for Mg–OH (saponite + serpentine) is even more strongly correlated to Mg concentration (Fig. 4b) with r = 0.90 (p < 0.01) (ESM Table 2). The Fe–OH (nontronite) absorption band is less well correlated to sedimentary iron concentrations, with r = 0.51 (Fig. 4c). Iron is present in the surrounding soils in many different minerals, including highly abundant iron oxides and oxyhydroxides (Golightly 1981). We attribute the poor correlation between Fe concentration and Fe–OH (nontronite) absorption area to the presence of iron oxyhydroxides in the sediments, in addition to nontronite. Despite this complication, the correlation between Fe and OH (nontronite) absorption area and Fe concentration is statistically significant (p < 0.01), and the strongest Fe–OH (nontronite) absorption features are observed on the eastern shoreline where the river inputs are most Fe-rich. The spatial variations in the relative abundances of these clay minerals within the lake (Fig. 2d–f) correspond strongly to gradients in lake and river sediment chemistry (Fig. 2a–c). Aluminum phyllosilicates increase from east to west, Fe-smectites increase from west to east, and the Mg-rich phyllosilicates saponite and serpentine increase towards the middle of the lake (Fig. 2d–f).
Fig. 4

Plots of area versus concentration for a Al–OH (kaolinite), b Mg–OH (saponite + serpentine) and c Fe–OH (nontronite) with a linear regression shown in red. The minerals assigned to the Al–OH, Fe–OH and Mg–OH bands are based on analysis of the full VNIR spectrum. (Color figure online)


We found a strong correlation between Fe, Mg, and Al elemental concentration and VNIR spectral reflectance features that distinguish Fe, Mg, and Al-rich clay minerals, in lake and river sediments from the Towuti basin. Elevated concentrations of aluminum and inferred high relative kaolinite abundance on the western shore, elevated concentrations of iron with the inferred high relative nontronite abundance on the eastern shore, and elevated concentrations of magnesium with the inferred high relative saponite/serpentine abundances in the central part of the lake correspond to variations in the geology of Lake Towuti’s catchment, namely, the presence of felsic mélanges to the west, highly serpentenized peridotites to the north, and ultramafic lithologies to the east. These results show that VNIR reflectance spectroscopy is an effective, statistically significant way to characterize the clay mineralogy of Lake Towuti sediment. Despite the fact that samples must be freeze-dried to remove the effects of interstitial water on the VNIR spectra, our work suggests that VNIR spectroscopy is a very practical, time- and cost-effective tool for clay mineral characterization relative to other procedures such as XRD. Although this study demonstrates the utility of VNIR spectroscopy using select absorption features, many other wavelengths could be investigated using VNIR spectra to characterize mineralogy. For example, absorption bands under 1 μm can be diagnostic of iron oxides and absorption bands up to 4 μm are diagnostic of carbonate features (Clark et al. 1990; Bishop et al. 2008). Moreover, as VNIR spectrometers may now be employed on automated core logging devices, this work suggests there is potential to characterize the relative abundances of clays in sediment cores at relatively high resolution, which can be used for paleoenvironmental studies.

The ability to correlate the chemical composition of lake sediment samples to their mineralogy deduced from VNIR reflectance spectra has important implications for characterizing clay mineralogy and sediment sources not only on Earth, but also on Mars. Lake Towuti and many martian paleolake deposits have strong spectral signals of weathered clay minerals and contain many of the same mineralogic constituents (Ehlmann et al. 2008; Milliken and Bish 2010). Previous authors have explored the use of clay mineralogy to determine sediment source regions for martian paleolakes (Ehlmann et al. 2008), and to infer martian hydrologic history (Milliken and Bish 2010), yet these mineral identifications have rarely been validated using natural lake sediment samples. Our findings thus provide important ‘groundtruthing’ for clay mineralogies inferred from spectroscopic data. This study also suggests that clays in this dilute, ultramafic basin primarily represent allochthonous material that records catchment weathering and transport processes, with important implications for the interpretation of long-term climate records. Overall, this work shows that VNIR reflectance spectroscopy is a powerful tool when combined with chemical analysis. With proper caution, it can be used to characterize the primary clay mineral constituents of natural sediment samples.



The authors would like to thank Dave Murray, Joe Orchardo and Dr. Takahiro Hiroi for technical support and assistance and Satrio Wicaksono, Sinyo Rio, and PT Vale for field assistance in Indonesia. The authors would also like to thank Dr. Kevin Robertson for assistance with XRD data interpretation and two anonymous reviewers who provided excellent feedback to strengthen this paper. Research permits for this work were granted by the Indonesian Ministry of Research and Technology (RISTEK). This material is based upon work supported by the National Science Foundation under Grant Number EAR-1144623 to J. Russell.

Supplementary material

10933_2015_9844_MOESM1_ESM.doc (631 kb)
Supplementary material 1 (DOC 629 kb)


  1. Asikainen CA, Francus P, Brigham-Grette J (2006) Sedimentology, clay mineralogy and grain-size as indicators of 65 ka of climate change from El’gygytgyn Crater Lake, Northeastern Siberia. J Paleolimnol 37:105–122CrossRefGoogle Scholar
  2. Bishop JL, Pieters CM, Edwards JO (1994) Infrared spectroscopic analyses on the nature of water in montmorillonite. Clays Clay Miner 42:702–716CrossRefGoogle Scholar
  3. Bishop J, Madejová J, Komadel P, Fröschl H (2002) The influence of structural Fe, Al and Mg on the infrared OH bands in spectra of dioctahedral smectites. Clay Miner 37:607–616CrossRefGoogle Scholar
  4. Bishop JL, Lane MD, Dyar MD, Brown AJ (2008) Reflectance and emission spectroscopy study of four groups of phyllosilicates: smectites, kaolinite-serpentines, chlorites and micas. Clay Miner 43:35–54CrossRefGoogle Scholar
  5. Burns RG (1993) Mineralogical applications of crystal field theory, 2nd edn. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. Clark RN (1999) Spectroscopy of rocks and minerals, and principles of spectroscopy. Wiley, New YorkGoogle Scholar
  7. Clark RN, King TVV, Klejwa M, Swayze GA, Vergo N (1990) High spectral resolution reflectance spectroscopy of minerals. J Geophys Res 95:12653–12680CrossRefGoogle Scholar
  8. Clark RN, Swayze GA, Wise R, Livo E, Hoefen T, Kokaly R, Sutley SJ (2007) USGS digital spectral library splib06a. US Geol Surv Digit Data Ser, p 231Google Scholar
  9. Costa K, Russell JM, Bijaksana S, Vogel H (2015) Hydrological connectivity and mixing of Lake Towuti, Indonesia, in response to paleoclimatic changes of the past 60,000 years. Palaeogeogr Palaeoclimatol Palaeoecol 417:467–475CrossRefGoogle Scholar
  10. Ehlmann BL, Mustard JF, Fassett CI, Schon SC, Head JW III, Des Marais DJ, Grant JA, Murchie SL (2008) Clay minerals in delta deposits and organic preservation potential on Mars. Nat Geosci 1:355–358CrossRefGoogle Scholar
  11. Farmer VC (1974) The infrared spectra of minerals. Mineralogical Society, LondonCrossRefGoogle Scholar
  12. Gaffey SJ, McFadden LA, Nash D, Pieters CM (1993) Ultraviolet, visible, and near-infrared reflectance spectroscopy: laboratory spectra of geologic materials. In: Pieters CM, Englert PAJ (ed) Remote geochemical analysis: elemental and mineralogical composition. Cambridge University Press, Cambridge, pp 43–77Google Scholar
  13. Golightly JP (1981) Nickeliferous laterite deposits. Econ Geol 75:710–735Google Scholar
  14. Grauby O, Petit S, Decarreau A, Baronnet A (1994) The nontronite-saponite series: an experimental approach. Eur J Mineral 6:99–112CrossRefGoogle Scholar
  15. Hunt GR (1977) Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 42:501–513CrossRefGoogle Scholar
  16. Hunt GR, Salisbury JW (1970) Visible and near-infrared spectra of minerals and rocks: I silicate minerals. Mod Geol 1:283–300Google Scholar
  17. Johnson LJ (1970) Clay minerals in Pennsylvania soils* relation to lithology of the parent rock and other factors-I. Clays Clay Miner 18:247–260CrossRefGoogle Scholar
  18. Kadarusman A, Miyashita S, Maruyama S, Parkinson CD, Ishikawa A (2004) Petrology, geochemistry and paleogeographic reconstruction of the East Sulawesi Ophiolite, Indonesia. Tectonophysics 392:55–83CrossRefGoogle Scholar
  19. King TVV, Clark RN (1989) Spectral characteristics of chlorites and Mg-serpentines using high-resolution reflectance spectroscopy. J Geophys Res 94:13997–14008CrossRefGoogle Scholar
  20. Milliken RE, Bish DL (2010) Sources and sinks of clay minerals on Mars. Philos Mag 90:2293–2308CrossRefGoogle Scholar
  21. Mitchell WA (1955) A review of the mineralogy of Scottish soil clays. J Soil Sci 6:94–98CrossRefGoogle Scholar
  22. Murray RW, Miller DJ, Kryc KA (2000) Analysis of major and trace elements in rocks, sediments, and interstitial waters by inductively coupled plasma-atomic emission spectrometry (ICP-AES). ODP Technical NoteGoogle Scholar
  23. Mustard JF (1992) Chemical analysis of actinolite from reflectance spectra. Am Mineral 77:345–358Google Scholar
  24. Pieters CM (1983) Strength of mineral absorption features in the transmitted component of near-infrared reflected light: first results from RELAB. J Geophys Res 88:9534–9544CrossRefGoogle Scholar
  25. Rosén P, Persson P (2006) Fourier-transform infrared spectroscopy (FTIRS), a new method to infer past changes in tree-line position and TOC using lake sediment. J Paleolimnol 35:913–923CrossRefGoogle Scholar
  26. Rosén P, Vogel H, Cunningham L, Reuss N, Conley DJ, Persson P (2010) Fourier transform infrared spectroscopy, a new method for rapid determination of total organic and inorganic carbon and biogenic silica concentration in lake sediments. J Paleolimnol 43:247–259CrossRefGoogle Scholar
  27. Sunshine JM, Pieters CM, Pratt SF (1990) Deconvolution of mineral absorption bands: an improved approach. J Geophys Res 95:6955–6966CrossRefGoogle Scholar
  28. Viscarra Rossel RA, McGlynn RN, McBratney AB (2006) Determining the composition of mineral-organic mixes using UV–Vis–NIR diffuse reflectance spectroscopy. Geoderma 137:70–82CrossRefGoogle Scholar
  29. Viscarra Rossel RA, Cattle SR, Ortega A, Fouad Y (2009) In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma 150:253–266CrossRefGoogle Scholar
  30. Vogel H, Rosén P, Wagner B, Melles M, Persson P (2008) Fourier transform infrared spectroscopy, a new cost-effective tool for quantitative analysis of biogeochemical properties in long sediment records. J Paleolimnol 40:689–702CrossRefGoogle Scholar
  31. Yuretich R, Melles M, Sarata B, Grobe H (1999) Clay minerals in the sediments of Lake Baikal; a useful climate proxy. J Sediment Res 69:588–596CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Andrea K. Weber
    • 1
    • 3
  • James M. Russell
    • 1
  • Timothy A. Goudge
    • 1
  • Mark R. Salvatore
    • 1
    • 4
  • John F. Mustard
    • 1
  • Satria Bijaksana
    • 2
  1. 1.Department of Earth, Environmental, and Planetary SciencesBrown UniversityProvidenceUSA
  2. 2.Global Geophysics Research Group, Faculty of Mining and Petroleum EngineeringInstitut Teknologi BandungBandungIndonesia
  3. 3.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  4. 4.School of Earth and Space ExplorationArizona State UniversityTempeUSA

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