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Applied and Environmental Soil Science
Volume 2012 (2012), Article ID 535646, 12 pages
http://dx.doi.org/10.1155/2012/535646
Research Article

Investigations into Soil Composition and Texture Using Infrared Spectroscopy (2–14 m)

1School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
2CSIRO Earth Science and Resource Engineering, P.O. Box 1130, Bentley, WA 6102, Australia
3Geological Survey of Queensland, Level 10, 119 Charlotte Street, Brisbane, QLD 4000, Australia
4Geoscience Australia, GPO Box 378, Canberra, ACT 2601, Australia

Received 17 February 2012; Accepted 27 August 2012

Academic Editor: Sabine Chabrillat

Copyright © 2012 Robert D. Hewson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The ability of thermal and shortwave infrared spectroscopy to characterise composition and texture was evaluated using both particle size separated soil samples and natural soils. Particle size analysis and separation into clay, silt, and sand-sized soil fractions was undertaken to examine possible relationships between quartz and clay mineral spectral signatures and soil texture. Spectral indices, based on thermal infrared specular and volume scattering features, were found to discriminate clay mineral-rich soil from mostly coarser quartz-rich sandy soil and to a lesser extent from the silty quartz-rich soil. Further investigations were undertaken using spectra and information on 51 USDA and other soils within the ASTER spectral library to test the application of shortwave, mid- and thermal infrared spectral indices for the derivation of clay mineral, quartz, and organic carbon content. A nonlinear correlation between quartz content and a TIR spectral index based on the 8.62 μm was observed. Preliminary efforts at deriving a spectral index for the soil organic carbon content, based on 3.4–3.5 μm fundamental H–C stretching vibration bands, were also undertaken with limited results.

1. Introduction

Mapping and analysing soils for their composition and textural characteristics typically involves extensive field work and laboratory techniques that are traditionally time consuming. However the measurement and determination of soil texture and composition is important for the mapping of areas vulnerable to soil erosion, driven by water and wind. Coarser-textured soils are more resistant to detachment and transport via raindrops, thus less affected to water-assisted erosion [1]. Soils with a silt content above 40% are considered highly erodible while clay particles can potentially combine with organic matter to form aggregates or clods which assist in their resistance to erosion [1]. Also, studies of the critical shear wind velocities required for transportation of different-sized soil particles indicate, those with diameters between 0.10 to 0.15 mm are the most vulnerable to wind erosion [1].

Another motivation to determine a soil’s texture and composition, including mineralogy, is with the aim to measure a soil’s ability to retain water or enable drainage. Clay minerals such as montmorillonite can exhibit swelling behaviour, absorbing and storing water, within their layered lattice structure [2]. Such finer textured clay rich soils can offer more water for plant growth than sandy soils. Sandy soils are more vulnerable to drought than clayey soils, storing less water and likely to lose water more rapidly by the growing plants [3]. However under flood conditions, clay rich soils exhibit poor drainage of excess water and may become waterlogged.

The incorporation of spectrally derived textural and compositional information into soil classification schemes will be particularly undertaken for special-purpose classification with specific aims, such as mapping erodibility [3]. White [2] describes the usefulness of texture classification of soils for drought vulnerability and poor aeration. In addition, estimation of surface soil texture and the identified presence or absence of certain minerals is still potentially an important input for general-purpose soil classification based on quantifiable properties that define its morphology, as distinct from its genesis. Soil classification in the USA and Australia use mostly such soil morphology as a basis for classification although this also requires observing properties varying with depth and horizons [2]. Incorporating spectroscopy information into a comprehensive 3D morphology description therefore requires the use of proximal spectral measurements of excavated soil samples.

The overall interest to determine a soil’s texture and composition can be summarized also by the need to monitor and map areas vulnerable to desertification, typically demonstrated by increased soil erosion. A detailed study by [3] examined the key indicators of desertification, including soil properties, with the aim of mapping areas vulnerable to future desertification. The study described the soil parameter, erodibility, as primarily a property of the soil texture, with highest values for fine sand and silty soils with low clay content, but which can be decreased significantly with the presence of organic carbon matter [3]. With the world population expected to reach 9 billion by 2050, food security is an issue that can least afford the effects of erosion and desertification, reducing the existing arable agricultural land to feed a growing world [4].

Both proximal and remote sensing spectroscopy offers the potential of increasing the speed, and reducing the cost of interpreting soil samples for texture and composition. Recently, hyperspectral airborne imaging spectroscopy has been successfully applied to study some soil properties utilising electromagnetic radiation (EMR) within the visible-near infrared (VNIR) wavelengths (0.4–1.0 m) and shortwave infrared (SWIR) wavelength regions (1.0–2.5 m) [5]. Also, developments in airborne hyperspectral mid and thermal infrared remote sensing [6, 7] indicate the potential for mapping and characterising in situ soils. In the laboratory, thermal Infrared (TIR) spectroscopy within the 7–14 m wavelength region has also shown its potential to provide soil mineralogy and textural information [8, 9]. In addition, several key organic carbon rich components (including lignin and cellulose) display diagnostic features related to the fundamental hydrocarbon H–C stretching vibration bands between 3.4 and 3.5 m of the Mid Infrared (MIR) wavelength (3–5 m) region [10, 11]. Spectral libraries, consisting of bidirectional TIR reflectance measurements, reveal diagnostic absorption features of many silicate minerals [9] although directional effects preclude their use for quantifiable comparison with TIR remote sensing signatures. Proximal laboratory spectral measurements within the visible-near infrared (VNIR) wavelengths (0.4–1.0 m) and shortwave infrared (SWIR) wavelengths (1.0–2.5 m) can identify ferric iron oxides, clay (AlOH), sulphates, and carbonate minerals [12] common within soils. However discriminating the quartz or silicate content of soils requires spectroscopy within the MIR region [11], and more commonly, the TIR region [9]. Table 1 summarises these wavelength regions containing diagnostic spectral signatures. Quantifying soil composition and characteristics has also been achievable using diffuse reflectance mid infrared (DRIFTS) spectroscopy techniques with the assistance of partial least squares calibration using a set of control soil samples [13]. However the physics of DRIFTS measurements and sampling operation precludes the conversion of the recorded reflectance signatures to absolute emissivities. The DRIFTS technique requires samples to be ground to a fine-powdered fraction (e.g., <80 m) held in a small cap-like container from which reflectance and transmitted signatures are detected [14].

tab1
Table 1: Accessible windows within infrared spectral regions and their diagnostic compositional elements commonly found in soils.

Proximal and remote sensing bidirectional VNIR-SWIR reflectance measurements can be approximately representative if soils are lambertion (e.g., isotropic for EMR). This assumption will not be suitable for anisotropic surfaces such as clay-dominated scalds. Ideally the comparison of MIR and TIR remote sensing with soil spectroscopy requires directional hemispherical reflectance (DHR) or emission mode proximal measurements of soils. Salisbury [15] demonstrated that for typical terrestrial materials with lambertian surfaces, it is possible to assume Kirchoff’s Law (, where and ), enabling DHR and emissivity spectral signatures to be interchangeable. Later investigations within the 3–14 m wavelength range have indicated that the change in emissivity with observation angle is small for all soils except for sand, where a change of up to 4% occurs within the 8–10 m wavelength region [16].

Studies to interpret multispectral ASTER (Advanced Spaceborne Thermal Emission Reflectance Radiometer) TIR and airborne TIMS (Thermal Infrared Multispectral Scanner) imagery for soil mineralogy and texture however have demonstrated that their limited spatial and spectral resolution restricts this application [17]. Other studies have in particular described the difficulties of comparing heterogeneities within ASTER’s 90 m pixel footprint with temperature and emissivity variability on the ground [18]. ASTER imagery has been available since 2000, with only five bands between 8.3 and 11.3 m acquired for each 90 metre pixel. NASA’s future HyspIRI spaceborne sensor will acquire at a slightly higher resolution of six TIR bands between 8.3 and 12.0 m acquired for each 60 metre pixel [19].

Proximal DHR or high spectral resolution emission/radiance measurements are required to undertake soil spectroscopy to simulate passive hyperspectral MIR/TIR remote sensing techniques. Although extracting emissivity information from MIR/TIR remote sensing radiance imagery, particularly daytime (e.g., sunlit) MIR, is non trivial, several algorithms have been developed and applied [20, 21]. In this study, the derivation of selected compositional and textural soil information is targeted using key spectral absorption features within the 2 to 14 m wavelength region as a pilot investigation for future hyperspectral infrared remote sensing applications.

The routine acquisition of hyperspectral TIR imagery is still at an early stage by airborne sensors such as the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) with 128 bands between the 7.6 and 13.5 m wavelengths [22]. Effectively at present, high-resolution spectral and spatial thermal infrared imagery is limited to targeted airborne acquisitions or spot in situ or sampled-soil measurements. Until more routine and operational airborne (or higher resolution satellite) sensors are available, it is hoped that the use of field spectrometry or laboratory measurements of soil samples will be of use for some faster textural and compositional analysis than traditional laboratory techniques.

The possibility of undertaking three-dimensional soil spectroscopy is feasible using trays that are already currently used to store multiple small samples from regular depth intervals. Spectral sensing of rock chip trays are already used to acquire proximal VNIR-SWIR measurements of samples collected as part of routine mining and exploration drilling programs (http://www.csiro.au/en/Organisation-Structure/Divisions/Earth-Science–Resource-Engineering/HyChips.aspx). Laboratory VNIR-SWIR and potentially TIR measurements of such trays could be undertaken assuming the soil samples are sufficiently and consistently dried, and completely fill each tray compartment. A small field of view would be required to ensure there was no spectral interference with the tray container material or that no potential blackbody cavity effect was detected if utilizing a TIR emission mode spectrometer.

Initially this study was part of a much larger CSIRO-ESRE (Commonwealth Scientific Industrial Research Organisation, Division of Earth Science and Resource Engineering) project to map surface minerals/chemistries using airborne hyperspectral and satellite ASTER imagery, applying visible-near infrared and shortwave infrared sensing (proximal and airborne) techniques [23, 24]. The Tick Hill soils described and used in this paper were part of this CSIRO-ESRE study and collected within north Queensland (21°35′S, 139°55′E) (Figures 1 and 2). The good soil exposure and variability within this regional project area made this a useful study area for soil mapping via remote sensing and spectroscopy. Preliminary results of the TIR spectroscopy investigations undertaken for these Tick Hill soils were presented at the 19th World Congress of Soil Science [25]. This publication describes these results in greater detail, and in combination with more results from DHR measurements of USDA (United States Department of Agriculture) soil samples, available via the ASTER Spectral Library (ASL) [26] (http://speclib.jpl.nasa.gov/search-1/soil). Previous investigations of these USDA ASL spectra had found them useful for direct comparisons with field measurements when convolved to ASTER’s 5 band TIR emissivity spectral resolution [27]. The ASL spectra within this study also included ten additional soil samples from a semiarid environment, Fowlers Gap, in western New South Wales, Australia, collected and analysed as part of a Ph.D. [28]. In addition, this investigation also incorporates further interpretation of short wavelength infrared (SWIR) spectroscopy for clay mineral content of all the examined Tick Hill samples.

fig1
Figure 1: Example of the Tick Hill landscape for sample sites: (a) MI121 and (b) MI124.
535646.fig.002
Figure 2: Location of the Tick Hill study area [24], North Queensland, Australia.

2. Data and Laboratory Methods

2.1. Traditional Analytical Methods

In the past, texture has been assessed qualitatively in field soil surveys by moistening a sample with water and kneading between fingers and thumb until the aggregates are broken down and the soil grains thoroughly wetted [2]. More accurate quantitative but time consuming laboratory analysis of texture are also available by particle-size analysis using sedimentation techniques based on the rate of settling within a soil-water suspension [29]. Likewise traditional compositional analytical techniques involving X-ray diffraction are time consuming and expensive. It should be noted that in this spectroscopy study, “clay” content refers to clay mineral content (e.g., kaolinite, montmorillonite, and illite), that is, less than 2 m, if disaggregated. Care is therefore required when comparing spectrally derived clay mineral content with particle size clay content as the determined 2 m and finer fraction may include fine iron oxides or organic material if not properly pretreated [29].

2.2. Tick Hill Samples

Eight Tick Hill soil samples were chosen and analysed for International System particle size fractions [2], clay (<2 m), silt (2–20 m) and sand (20m–2 mm), by CSIRO land and water (http://www.clw.csiro.au/services/analytical/), using the traditional pipette method [29] (Table 2). According to the Australian Soil Resource Information System (ASRIS) mapping [2, 31], all except one sample used in this study area, were Ferrosols (Table 2), being high in free iron oxide content and low textural contrast between A and B horizons [30]. MI132 was mapped as a Tenosols with weak pedologic structure apart from the A horizon [30]. However at the detailed scale sampled in this Tick Hill area, a much greater diversity of soil properties were observed (Figure 1). These samples were firstly prepared by chemically removing salts, organic matter, and ferric iron [29]. These fractions were separated and dried for later spectral measurements. The resulting sand fraction was also further separated between 20–60 μm and 60 μm–2 mm, and dried for later spectral measurements.

tab2
Table 2: Tick Hill soil sample particle analysis results using Method code 517.08 of [29] where Fe/Al oxides, organic matter, and soluble salts are removed.

The original raw soil and also four particle size fractions (<2 μm, 2–20 μm, 20–60 μm, 60 μm–2 mm) of each of the eight Tick Hill samples, were measured for their TIR spectral emissivity signatures using the Designs and Prototypes microFTIR 102 [32] (http://www.dpinstruments.com/). Soil samples were oven heated overnight at 60°C to obtain a consistent dryness. MicroFTIR emission measurements were acquired from heated soil samples within ceramic crucibles, also at 60°C, from an approximate 20 mm field of view and using 16 scan integrations. Measurements were calibrated to radiance units (W//sr/μm) using hot and cold black body measurements set to 65°C and 30°C, respectively. Background radiance (e.g., “downwelling”) was removed by measuring the emission of a brass plate at room temperature (determined via a Pt thermocouple). Temperature-emissivity separation of the acquired radiance measurements was calculated using in-house software developed by CSIRO (Green, pers. comm.) to provide absolute emissivity spectral signatures. Each soil sample was also analysed for mineralogy using X-ray diffraction (XRD).

Emission measurements by the microFTIR of each particle size fraction was repeated as a check for slight signature variations when the sample surface was disturbed with a spatula. Only minor changes in absolute emissivity values were observed with no effective change in signature shape. The resulting TIR emissivity signatures were imported into CSIRO software for processing proximal spectral data, “The Spectral Geologist” (TSG, http://www.thespectralgeologist.com/). Interpretation of the emissivity signatures for mineralogy was assisted by comparison with the ASL [26]. Additional spectral measurements of the Tick Hill samples were also undertaken using the PIMA II (Portable Infrared Mineral Analyser; http://www.hyvista.com/) and the Fieldspec FR (http://www.asdi.com/) spectrometers to acquire SWIR reflectance signatures of the Tick Hill raw soil and particle-size fractions.

2.3. ASTER Spectral Library

Fifty one of the soil samples accessible at the ASTER Spectral Library [26] (http://speclib.jpl.nasa.gov/search-1/soil) were used in this study and represented all major soil types. Detailed descriptions and classifications are given for most of the soils, including percentages of sand, silt, and clay provided by the USDA National Soil Survey Laboratory, where clay size is less than 2 μm; silt size ranges from 2 μm to 50 μm; sand size ranges from 50 μm to 2 mm. The USDA system definitions of silt and sand by particle size differ from the International System where silt and sand is defined by particle sizes 2 μm to 20 μm and 20 μm to 2 mm, respectively [2]. This prevented the use of the silt and sand breakdown for the eight Tick Hills samples from being combined with these USDA samples. When available, the USDA determined the clay mineralogy semiquantitatively from the XRD analysis, while mineralogy of the silt and sand was determined by petrographic microscope. The supplied USDA quartz content percentage estimate was recalculated into a total soil quartz content estimate for this study using the clay particle size percentage estimate to derive an adjustment (e.g., ). The USDA’s percent organic carbon content was also used in this study and obtained by wet combustion analysis [33]. Estimates of quartz contents for the Fowlers Gap samples were derived from normative analysis of soil’s XRF elemental results [34]. Particle size analysis of the Fowlers Gap soils was determined using sieving and laboratory techniques assuming USDA system texture classes [34].

The USDA samples included in the ASL were measured for DHR spectra using a Nicolet 5DXB FTIR spectrophotometer at constant 4 cm−1 spectral resolution from 2 μm to 14 μm (e.g., SWIR-TIR wavelengths) [35]. A directional (10 degree) hemispherical reflectance attachment was used for measuring a 2.5 cm sample diameter. In this study the ASL TIR signatures were converted from DHR to emissivity, as generated from the microFTIR, assuming Kirchoff’s Law [15]. These spectra were also combined with additional 0.4 μm to 2.0 μm spectral measurements for each sample, although the VNIR wavelength region was not studied here.

3. Spectral Analysis and Results

3.1. Tick Hill

XRD analysis of the Tick Hill soils identified minerals such as quartz, smectite, kaolinite, and minor amounts of illite. MicroFTIR emissivity signatures of raw soil samples confirmed the predominance of quartz and clay minerals. In particular, samples MI122 and MI128 indicated the presence of quartz and kaolinite/smectite minerals (Figure 3). Although the quartz “reststrahlen” feature between 8 and 9.5m is reduced in the raw soil samples compared to the JHU library spectra, the 8.62 m feature remains distinctive. Likewise, the 9.0 m kaolinite feature is less distinctive in the raw soil mixture although the 9.5 m feature remains.

535646.fig.003
Figure 3: Example MicroFTIR emissivity spectra of Tick Hill soil samples and ASL [26] highlighting the presence of mixtures of clay and quartz minerals within the samples.

The corresponding emissivity signatures for the various particle size fractions of samples MI122, and MI128, highlight examples of the trend of an increasing quartz reststrahlen 8.62 m spectral feature and decreasing clay 9.5 m feature, with increasing particle size (Figure 4). Also within the 10.5–12 m wavelength region, “volume” scattering quartz features (QVS) are associated with the 2–20 m and 20–60 m soil particle fractions [36] (Figure 4).

535646.fig.004
Figure 4: Example MicroFTIR emissivity spectra of particle size separated Tick Hill soil samples showing diagnostic mineral and textural related spectral features.

By comparison, the reststrahlen quartz feature is associated with specular scattering from coarser quartz grains [36]. Several samples, including the displayed MI128, also indicate small amounts of kaolinite within the 2–20 m and 20–60 m particle size fractions, as shown by their 9.0 and 9.8 m spectral features (Figure 4).

The TSG software was customised to target those emissivity features associated with kaolinitesmectite, quartz, and its volume scattering fine-grained variation. In particular, spectral indices were devised to estimate the coarser quartz content, the clay mineral content, and the effects of fine quartz volume scattering; , , and , respectively. Generally the individual detector bands or closest wavelength thereof were used here. These spectral indices were devised to target the spectral absorption feature in a similar method as by [37]. where is the mean value within 30 nm (e.g., between 8602 and 8664 nm), and and are the values at wavelengths 8383 and 8897 nm, respectively.

Figure 5 highlights these wavelengths in relation to the quartz reststrahlen spectral feature. The estimation of quartz content using (1), as a spectral index based on the diagnostic reststrahlen absorption feature, follows examples of the previous application of spectral indices with proximal and airborne hyperspectral data [23, 24].

535646.fig.005
Figure 5: Examples of USDA TIR soil signatures of variable quartz content (XRD analysis of silt-sand fraction) in relation to the diagnostic 8.63 μm reststrahlen feature.

Similarly, indices were devised for Clay (kaolinite) and estimates of the quartz volume scattering effect.

The results of these spectral indices for each Tick Hill soil fraction, processed using TSG, are shown in Figures 6, 7, and 8. Figure 6 indicates an approximate increasing trend in the quartz spectral parameter with increasing grain size. A higher quartz volume scattering behaviour for the mid-sized fractions (e.g., 2–60m) is shown by the auxiliary colour coding (green to red, Figure 6) for the sample points. Figure 7 shows a clear inverse trend between the clay and quartz spectral indices. However a high clay value can still appear within silty fractions (e.g., cyan coding, Log ~1.0 or ~10 m) and some coarser fractions (e.g., red) exhibit a high clay spectral index. The versus relationship shown in Figure 7 shows a high correlation of determination () value of 0.85. Although it should be noted that this dataset included repeated microFTIR measurements for each sample fraction. Developing predictive clay% algorithms is complicated by such residual clay mineral content within the separated soil fractions. However Figure 8 suggests predicting clay mineral content within fractions finer than 10 m with low quartz content (e.g., blue) is possible.

535646.fig.006
Figure 6: Quartz content spectral index () versus log of the particle size fraction and colour coded by the fine quartz volume scattering ( (e.g., red = high scattering associated with fine quartz), showing an approximate decrease in quartz content with decreased grain size.
535646.fig.007
Figure 7: Relationship between quartz and clay content spectral parameters colour coded by the log of the particle size (e.g., red >2.8 or ~60 m–2 mm; blue <1 or ~<2m), showing the inverse relationship between TIR interpreted quartz and clay mineral contents.
535646.fig.008
Figure 8: Relationship between the clay content spectral parameter and the log of the particle size colour coded by the quartz content spectral parameter (e.g., low quartz content: blue, high quartz: red).

A spectral index was determined for the SWIR spectral measurements collected using the PIMA II. A TSG-based algorithm calculating the depth of a 4th-order polynomial at the 2.2 m clay absorption feature was applied to continuum-removed spectra (“claykaolinite (SWIR)”) for all Tick Hill soil particle fractions (Figures 9(a) and 9(b)). The value of claykaolinite (SWIR) was set to 0 when no 2.2m SWIR absorption feature was identified. Comparing the SWIR and TIR mineral clay indices for all the Tick Hill fractions showed a broad range of values (Figure 9(a)). However a better correlation between particle size and TIR-derived mineral index was apparent when only the three 2m, 2–20 m and 20–60 m fractions were examined (Figure 9(b)). It appears likely that there is some contamination of clay mineral content within the coarser fractions, possibly as a coating to the grains. This was suggested by examples of clay spectral features (e.g., MI128 fractions, Figure 4).

fig9
Figure 9: (a) Comparison of clay mineral content determined by SWIR defined clay/kaolinite spectral index and TIR defined for the four Tick Hill particle size fractions. (b) Close up of (a) and line of best fit for results for finest three particle size fractions.

Applying these spectral indices to the eight natural Tick Hill soil samples did not reveal clear relationships with texture although the number of samples was small. The mineral index versus lab derived clay content for these natural samples (Figure 10) clearly indicated a much larger population and range of soil samples is required to test the application of these spectral techniques, as further investigated in the following section.

535646.fig.0010
Figure 10: TIR-derived mineral index versus the particle size determined clay% of the eight natural Tick Hill samples.
3.2. ASTER Spectral Library Soils

SWIR and TIR spectral signatures of 51 soils from the ASL [26] were included in this study to evaluate the devised spectral indices for composition and texture of natural soil samples. Examples of these ASL USDA and Fowlers Gap spectral signatures are shown in Figures 11(a) and 11(b), respectively. These spectra highlight the diverse range of soil environments represented, including organic-rich Spodosol loam (874264), aeolian based sandy Alfisol loam (87P2376), micaceous Inceptisol loam (88P2535), and quartz rich alluvial Entisol sand (FGG027) (Figures 11(a) and 11(b)).

fig11
Figure 11: (a) Examples from the USDA soil spectra included within the ASL. (b) Example from the semiarid Fowlers Gap (Australia) included in the ASL [26].

The same spectral indices as described in (1)–(3) were applied to the ASL soil spectra using Excel rather than TSG in this application. Differences between the ASL Nicolet 5DXB FTIR and the CSIRO-ESRE MicroFTIR measurements required a slight adjustment of the spectral indices although the differences in the TIR wavelengths were minor.

A distinct SWIR absorption feature at 2.2 m (e.g., 2200 nm) can be observed for several of AlOH clay and phyllosilicate minerals that could comprise the clay fine particle fraction of soils (e.g., montmorillonite, kaolinite, and muscovite, Figure 12). A relative band depth index, , was devised for this 2200 nm feature to discriminate such clay minerals (Figure 12) by (4) where is the reflectance value at 2100 nm, and so forth.

535646.fig.0012
Figure 12: USGS VNIR-SWIR mineral library reflectance signatures for AlOH or clay phyllosilicate minerals highlighting the main 2.2 μm absorption spectral feature [12].

In particular, (4) uses an average estimate of the absorption spectral feature at 2195 and 2215 nm as the denominator and the shoulders of the absorption at 2100 and 2300 nm for the numerator as a variation of the relative band depth technique.

Both the and indices for the combined Tick Hill and ASL data sets show no coherent correlation between clay mineralogy and clay particle size despite the use of an enlarged sample population of soils with a wider range in clay content (Figure 13).

535646.fig.0013
Figure 13: Comparison between the SWIR derived, , and TIR derived, , mineral spectral indices, and the clay particle size content supplied by USDA.

The poor result for the spectrally derived clay content% (Figure 13) could be due to a number of factors including interference from the 8 to 9.2 m quartz reststrahlen feature on the 9.5 m clay absorption-based spectral index, limitations of the relative band depth spectral algorithm applied for clayAlOH mineral content, nonlinear effects from multiple scattering between clay mineral particles coating coarser grain particles, or the presence of fine nonclay mineral particles less than 2 μm distorting the measured clay fraction (e.g., iron oxides, organics). A variation of the clay () spectral index, calculated using (2), was subsequently generated using the 9.0 μm kaolinite spectral feature, that remains even with the dominant presence of quartz (Figure 3) and described here in (5): However the resulting index also produced a poor result showing no effective correlation with the USDA clay content%.

The results of the compared to the petrographic determination of quartz content associated with the USDA soil spectra indicated a relationship involving a power function () with a moderate coefficient of determination, (Figure 14). Including the Fowlers Gap soils into this sample population did not alter this result significantly. However the Fowlers Gap quartz content was derived from a different method involving a normative calculation based on XRF elemental analysis, and therefore not necessarily consistent with the USDA results.

535646.fig.0014
Figure 14: Comparison between the and USDA determined quartz% content with calculated linear (red) and power relation (green) models.

The quartz volume scattering index (QVS) showed some minor increasing trend with silt content% for the USDA and Fowlers Gap samples, however the was a low 0.27. This index appears to be of more potential interest for the study of separated soil fractions than for natural soil samples.

Attempts were also undertaken to derive nonmineral organic carbon soil composition from the ASL soil spectra, using spectral indices calculated from MIR absorption features and USDA determined organic carbon results. An examination of several USDA soil DHR spectra revealed organic carbon features at 3.41 m (“A”) and 3.5 m (“B”) (Figure 15). Note that the displayed USDA soil, 874264, is an organic-rich Spodosol loam containing the highest organic carbon of 28% within the ASL sample collection. These MIR DHR spectra (also shown in Figure 11(a)) highlight the processing between 3.3 m and 3.6 m where the hull continuum removal acts as normalisation process [38] and simplifies the calculation of spectral indices as absorption depths (Figure 15).

535646.fig.0015
Figure 15: Examples of hull removed 3.3 m to 3.6 m ASL reflectance spectra (see Figure 11(a)) highlighting organic carbon spectral absorption features at 3.41 m (“A”) and 3.5 m (“B”) applied to several USDA soil samples.

Several depth indices were devised, centred at 3.41 and 3.5 μm features as included in (6)–(8): where is the mean ρ value within  nm (e.g., between 3405 to 3425 nm) where is the mean ρ value within  nm (e.g., between 3475 and 3505 nm)

In addition, a spectral index was derived from the area bounded by the hull (=1 in Figure 15) and the spectral signature between 3.3 and 3.6 m. This required a reversal of the hull signatures and calculation of area under the resulting curve. The highest “correlation” of the four indices to the USDA organic carbon estimates was achieved using the MIR organic carbon depth index A, of 0.53 (Figure 16(a)). However this included an extreme outlying sample containing 28% organic carbon and the correlation reduced to a of 0.39 when this was omitted (Figure 16(b)). Likewise, the MIR organic hull average depth and Areal based indices produced a of 0.37 and , respectively, that reduced to values of 0.19 and 0.18, respectively, when this single outlying high organic carbon sample was excluded. Although these preliminary results showed no coherent correlation, this outcome would be confirmed if more soils were included containing a greater range of organic carbon between 10 and 30%. However, as the majority of terrestrial soils contain less than 10% organic carbon, its determination using such spectral indices appears unlikely. Figure 17 also shows this limitation with the MIR organic hull depth average Index result for values between 0 and 10% organic carbon.

fig16
Figure 16: MIR organic carbon depth indices A and B versus the USDA organic carbon content; (a) for all data; (b) omitting outlying organic carbon rich sample at 28%.
535646.fig.0017
Figure 17: MIR organic hull average depth index versus the USDA supplied Organic Carbon content for organic carbon less that 10% content.

4. Conclusions

The study of the particle size fractions derived from Tick Hill soil samples show the intimate connection between mineralogy and texture when examining TIR spectra. In particular, coarse and fine-grained quartz components have distinct TIR spectral features. The spectral results also indicate that residual clay minerals may still be present in the sand fraction, even with thorough particle size separation processes, and potentially bias the determination of “clay” content if based on sedimentation analysis alone (e.g., <2m). The proximal spectral measurements of Tick Hill samples suggested its potential to analyse for clay mineral content. In particular, the Tick Hill results suggested that a TIR spectral index based on the 9.5 μm absorption feature is useful for the determination of clay content within soil particle size fractions.

Further investigations using a larger population of natural soil samples, available from the ASL, showed no strong correlations between laboratory-determined clay particle size and the clay derived from either SWIR or TIR based spectral indices. Possible causes for this may be interference from the 8 to 9.2 μm quartz reststrahlen feature on the 9.5 μm and 9.0 μm clay absorption based spectral indices, limitations of the spectral relative band depth algorithm, nonlinear effects from clay mineral particle coatings over coarser grain particles, or the presence of nonclay mineral particles finer than 2 μm but included within the “clay” particle fraction. A moderate nonlinear correlation was indicated between the TIR quartz derived index, based on the 8.62 μm reststrahlen feature, and the petrographic-derived quartz content. Preliminary efforts were also undertaken to derive an organic carbon spectral index using absorption features between 3.4 and 3.5 μm associated with the fundamental H–C stretching vibration bands. No strong correlations with the USDA organic carbon estimates were apparent using hull continuum removed spectra to provide indices based on depth and area of the absorption features.

Acknowledgments

Adrian Beech, former manager of Analytical Services, CSIRO Land and Water, is sincerely thanked for undertaking the extensive particle size analysis and sample separation of the Tick Hill soils. Assistance from Peter Mason of CSIRO has also been helpful in the application of TSG software. Encouragement and support from Vittala Shettigara of DSTO, Simon Hook of JPL, Simon Jones of RMIT, Alan Marks, and Ian Goicoechea is also gratefully appreciated. Matilda Thomas publishes with the permission of the Chief Executive Officer of Geoscience Australia.

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