Spectroscopy of sediments in the Ganges–Brahmaputra delta: Spectral effects of moisture, grain...

Remote Sensing of Environment 113 (2009) 342–361

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Remote Sensing of Environment

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Spectroscopy of sediments in the Ganges–Brahmaputra delta: Spectral effects ofmoisture, grain size and lithology

Christopher Small a,⁎, Michael Steckler a, Leonardo Seeber a, Syed Humayun Akhter b, Steven Goodbred Jr. c,Bodruddoza Mia d, Badrul Imam b

a Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964 USAb Department of Geology, University of Dhaka, Dhaka 1000, Bangladeshc Earth and Environmental Sciences, Vanderbilt University, Nashville, TN 37235 USAd Department of Petroleum and Georesources Engineering, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh

⁎ Corresponding author.E-mail address: [email protected] (C. Small

0034-4257/$ – see front matter © 2008 Published by Edoi:10.1016/j.rse.2008.10.009

a b s t r a c t
a r t i c l e i n f o
Article history:
The lithologic composition Received 13 June 2008Received in revised form 29 September 2008Accepted 4 October 2008
Keywords:SpectroscopySedimentMoistureSandSiltGangesBrahmaputraRiver

and grain size distribution of sediments are primary determinants of theirinherent reflectance properties. However, moisture content is also known to have a strong influence onreflectances of soils and sediments. If the effects of sediment composition, grain size and moisture contentcould be distinguished spectrally, it might be possible to map these properties at synoptic scales usinghyperspectral, or perhaps even broadband, remote sensing. Mapping the spatiotemporal distribution ofsediment composition and moisture content could provide unique constraints on both the processes bywhich the sediments are deposited as well as the constraints they may impose on subsequent water flow andsediment transport. The Ganges–Brahmaputra delta (GBD) is formed by the convergence of these two greatrivers and is superlative in both size and geologic activity. Sediment redistribution and channel migrationassociated with the annual floods disrupt the lives of hundreds of thousands of people living on the GBD butis also critical for maintaining the delta area fertile and above sea level. The 30+ year archive of Landsatimagery could provide a basis for spatiotemporal analysis of these fluvial dynamics if sediment propertiescould be inferred or measured from reflectance spectra. However, before confronting the challenge ofbroadband detection we must understand the spectral properties of the sediments under more controlledlaboratory conditions. Bidirectional reflectance spectroscopy of 109 sediment samples from the GBD yields aspectral mixing space that appears to be structured by variations in moisture content, grain size and possiblylithology. Although the individual Empirical Orthogonal Functions of the Principal Components do notcorrespond to unique absorption features, clustering within the mixing space is clearly influenced bymoisture content and grain size. Laboratory spectra of sediment reflectance measured under varyingmoisture content yield distinct trajectories through the spectral mixing space for different grain sizedistributions of sieved sediments. These variations in moisture content account for N98% of spectral varianceobserved in these samples. Drying trajectories of coarse, fine and mixed sediments are distinct and suggestthat moisture and grain size might be spectrally distinguishable. These results are consistent with Angstrom'shypothesis of moisture-driven spectral absorption but more controlled experiments are necessary to test thehypothesis rigorously.

© 2008 Published by Elsevier Inc.

1. Introduction

Despite their remarkable flatness, deltas of large rivers sustain veryactive geologic processes. Huge amounts of water and sediment movefrom the land to the sea, yet maintain a narrow and delicateequilibrium. The same processes that replenish deltas with fertileingredients also make these environments particularly vulnerable andlead to catastrophes. The Ganges–Brahmaputra delta (GBD) is formedby the convergence of these two great rivers (Fig. 1) and is superlative

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in both size and geologic activity. The delta covers the southern halvesof Bangladesh and of the Indian state of West Bengal: a 300×300 kmarea with more than 200 million inhabitants. The GB river systemdrains most of the runoff from the Himalayas and has one of thelargest sediment fluxes of any river. The seasonal variation in flux ishuge because snowmelt and precipitation compound their effectsduring the summer monsoon. The river flow during the monsoonaccounts for 80–90% of the Ganges flow and ∼95% of the Brahmaputraflow (Subramanian & Ramanathan, 1996). The large seasonal varia-tions in river flow, and high sedimentation and subsidence rates, leadto annual floods in which 1/3 of the country of Bangladesh issubmerged.. River channels can move up to a kilometer in a typical

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Fig. 1. A) Drainage basins (black) of the Ganges, Brahmaputra and Meghna (M) Rivers.(yellow) B) SRTM topography and major rivers of the Ganges–Brahmaputra Delta. TheBarind and Madhupur Terraces are Pleistocene uplands that are the inter-fluves of theincised lowstand glacial valleys. The Sylhet basin is downwarped by the upliftedShillong Plateau. (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

343C. Small et al. / Remote Sensing of Environment 113 (2009) 342–361

year, resulting in the displacement of many thousands of people whofarm the banks and islands of both rivers. The interplay of factorscontrolling the fluvial dynamics is not well understood. Spatio-temporal analysis of interannual changes in channel geometry andsediment erosion and deposition could provide the constraintsnecessary to understand these fluvial dynamics.

Moderate resolution optical imagery provides a synoptic view of thespatial and temporal dynamics of the Ganges and Brahmaputra riversystems. In addition to channel geometry, spatial variations in reflectedradiance at 10 to 100 m scales are sensitive to sediment composition(grain size distribution and lithology) and moisture content – as well asfractional vegetation cover. In addition to water levels and channelgeometry, seasonal to interannual variations in reflected radiance aresensitive to changes in moisture content and aeolian resurfacing. Bothspatial and temporal changes reflect the physical processes thatdetermine which areas are subject to erosion and deposition duringthe annual floods. For this reason, detailed maps of sedimentcomposition and moisture content could provide a primary source ofinformation on the fluvial dynamics of the delta and could serve asvaluable resources for anticipating and mitigating the impact of theannual floods.

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper(ETM+) imageryprovides a 25+ year record of thefluvial dynamics of theGanges Brahmaputra river system. The 30 m spatial resolution issufficient tomap the distribution of channels and islands (known locallyas chars) as well as spatial variations in sediment properties and landcover. The visible and infrared spectral resolution is sufficient todiscriminate spatial and temporal variations in reflectance resultingfrom changes in land cover and surface properties that are notnecessarily obvious at ground level. Landsat's 16 day revisit timeresolves both seasonal and interannual changes in water levels andchannel geometry and vegetation cover aswell as changes in reflectancethat may result from either moisture variations or sediment composi-tion.. Quantifying these changes at regional scales on seasonal tointerannual time scales could provide a wealth of constraints on thedynamical processes that determine the behavior of the rivers and theirinfluence on the millions of people who are directly affected by them.Figs. 2 and 3 illustrate the profound changes that occur on both seasonaland interannual time scales on the rivers.

Visual comparison of multitemporal Landsat imagery from the GBdelta reveals a variety of features at different spatial scales. The mostobvious features are the river channels, banks and islands. While thedensely populated banks are the locus of intensive agriculture, the charswithin the river course are generally sparsely vegetated sand and siltdeposits. The spatial distribution of the chars influences river flow, andhence sediment erosion and deposition, during the annual floods. At thescales of the photographs in Fig. 4 the chars appear to be relativelyhomogeneous but the synoptic view provided by Landsat revealsconsiderable spatial variations in surface reflectance at scales of tens tothousands of meters. In the absence of vegetation, the surfacereflectance provides information on both sediment composition(lithology and grain size distribution) and moisture content. Wherethese variations result from grain size, mapping them at basin scalecould provide snapshots of depositional environments in the fallingwater levels at the end of the annual floods.Where the variations resultfrom lithology, mapping them at basin scale could provide constraintson sediment provenance. Where the variations result from moisturevariations mapping them at basin scale could provide constraints onboth elevation and permeability. The primary challenge is to determinethe extent to which these three factors can be distinguished.

The objective of this study is to determinewhether composition andmoisture content can be inferred orquantified fromoptical reflectance–either with the 25 year archive of Landsat or with future hyperspectralsensors. In general, we would like to know howmoisture, lithology andgrain size distribution influence sediment reflectance. However, beforeconfronting the challenge of broadband detection we must understand

the spectral properties of the sediments under more controlledlaboratory conditions. Therefore, we attempt to quantify these effectswith laboratory measurements of full range, high resolution spectra. Inthis study, we attempt to quantify the influence of each factor through aprincipal component analysis (PCA) of laboratory spectra of a widevariety of sediment samples collected from charswithin theGanges andBrahmaputra river channels. The effect of grain size distribution isdetermined by comparing spectra from different size fractions ofdifferent sediment types. The effect of moisture is determined bymonitoring the temporal change in reflectance of drying sedimentsamples. The effect of lithology is inferred from mineralogy ofrepresentative samples. The spectral separability of moisture content,grain size and lithology are then assessed from a principal componentanalysis of the spectra.

2. Background

2.1. Geographic setting

The Brahmaputra and Ganges Rivers drain the back and front of theHimalayan Mountains (Fig. 1), a drainage area of 1.6×106 km2 (Ludwig

Fig. 2. A. Time sequential Landsat ETM+ quicklooks of the Jamuna branch of the Brahmaputra River. Seasonal and interannual changes in water level and channel geometry areapparent throughout. Water level record is from Bahadurabad Ghat. Red points mark dates of images. Red line marks danger level where river crests its banks. B. Time sequentialLandsat ETM+ quicklooks of the Ganges River. The confluence with the Jamuna shown in panel a is at the bottom of each panel. Seasonal and interannual changes in water level areapparent but channel geometry changes less than on the Jamuna. Water level record is from Harding Bridge with 1999 and 2001 estimated using Gorai Railway Bridge station. Redpoints mark dates of images. Red line marks danger level where river crests its banks. (For interpretation of the references to colour in this figure legend, the reader is referred to theweb version of this article.)

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& Probst, 1998). They are the 4th and 5th largest rivers in the world interms of water discharge. Together with the smaller Meghna Riverthey discharge 1.35×1012 m3/year into the Indian Ocean through theGanges–Brahmaputra Delta (GBD). They also carry N1 GT/year ofsediments, 6–8% of the total world sediment flux (Syvitski et al., 2005).This huge sediment discharge has built the Ganges–BrahmaputraDelta and Fan, the world's largest depositional system. This sedimenthas prograded the continental margin over 300 km since the Eocene(40 Ma). The two rivers enter Bangladesh at elevations of b40 m toflow through the low-lying Ganges–Brahmaputra Delta distributingtheir sediment load. The sediment is distributed by the frequentlyavulsing rivers into a thick blanket of overbank muds and silts andchannel sands in which the Holocene deposits can reach 100 m thickwhere the delta was incised during the last sea level lowstand. Thesebroad glacial valleys are bounded by interfluvial uplands where olderPleistocene strata crop out. The lower delta plain, a ∼100 km widecoastal swath with a slope of only about 1:30,000, is cut intonumerous islands by anastomosing channels that are under tidalinfluence. This area has an elevation of less than 3 m and comprisesapproximately half of Bangladesh.

2.2. Geodynamic setting

Tectonically, Bangladesh and the GBD lie across the margin of theIndian craton near the junction of two plate-convergence boundaries: theHimalaya and the Burma arcs. These arcs are convex toward the Indianplate and override it from the north and from the east, respectively. Aheadof theHimalayan front, a sliver of Indian basement, the Shillong Plateau, isoverthrust southward onto the GBD and may be decoupled from theIndianplate. This causes the rapid subsidence of the Sylhet basin in theNEof Bangladesh (Johnson & Alam,1991). On the eastern side of Bangladesh,where the Burma Arc is actively subducting, the GBD sediments form ahuge accretionary prism and foldbelt (Alam et al., 2003; Steckler et al.,submitted for publication). The active foldbelt extends into theGBDwhereit is blind, buried by the thick sediments of thedelta. Subsidence andupliftfrom these tectonic features influence the position of the rivers. The LowerMeghna River, which connects the Ganges and Brahmaputra rivers to theBay of Bengal, follows the buried deformation front. Following anearthquake in the late 18th century, the Brahmaputra river avulsed orshifted 60 km westward. The previous course, the Old Brahamaputra,flanked the Sylhet basin. The newcourse, the Jamuna, is carving a N10-kmwide channel system. There is evidence formultiple such avulsions in theHolocene (Goodbred&Kuehl, 2000), but little is knownof their causes andfrequency.

2.3. Hydrography

The rivers in the delta are strongly seasonal with peak flow during theSW monsoon in the summer. The spring snowmelt in the mountainscompounds the effect, especially for theBrahmaputra. Theflowduring thesummer accounts for 80–90% of the Ganges flow and ∼95% of theBrahmaputra flow (Subramanian & Ramanathan, 1996). Peak waterdischarge averages 10 times theminimum flow and can be 20–25× largerunder extreme flow conditions (Fig. 2). Variation in sediment discharge iseven more extreme. Wet season sediment discharge can reach 100xgreater than in the dry season. The high water flow into the delta duringthe monsoon exceed the capacity of the rivers and cause surface andgroundwater levels to rise by 5–10 m. The rivers fill and overflow theirbanks resulting inwidespread flooding. In a typical year, about 20–60% ofBangladesh is flooded (Mirza, 2003). In extreme events, such as the 1988floods, up to 2/3 of the country can be submerged. In the countryside,villages are built on the elevated active and abandoned levees, commonlymodifiedby inhabitants. Roadsare similarlyelevated leaving the low-lyingrice fields flooded each summer. Despite the adaptation of the populationthat maintains the villages above average flood level, each year waterlevels exceed capacity in some part of the river system. Large floods that

cause widespread disruption occur about every 10 years. In contrast,during the winter months, drought can be a problem in parts of thecountry.

TheGanges and Brahmaputra are alsohighly dynamic rivers. Rates ofdeposition and channel migration are high – it is geologywrit large. Therivers re-arrange their channels every year while overbank floodingdistributes about 30% of the river's total sediment load over the delta(Allison et al.,1998;Goodbred&Kuehl,1998). These sedimentsmaintainthe fertility of the delta, but the changing channel pattern is problematicto many people. Erosion has destroyed 180,000 ha of land along theGanges, Brahmaputra and Padma riverbanks over the last 30 years. Thisloss of land along the Brahmaputra alone has displaced N700,000people. Understanding and predicting river evolution is of majorimportance in this and other low-lying countries (e.g., CEGIS, 2003;EGIS, 1997, 2000; Thorne et al., 1993).

2.4. Fluvial geomophology

The Ganges is a meandering river, while the Brahmaputra is abraided river. The Padma, formed by the confluence of these two riverscontains aspects of both types of planform. The braided Brahmaputracommonly forms two anabranches with an alternation of nodal andisland reaches (Thorne et al., 1993). The anabranches are furthersubdivided into smaller channels and bars, and all of these migrateand reorganize each year. The rapid changes have made theBrahmaputra a target for studies of the yearly to decadal evolutionof fluvial bedforms and sediment deposition (e.g., Ashworth et al.,2000; Best et al., 2003; Best & Ashworth, 1997; Sarker et al., 2003).These investigations have identified a number of facies and grain sizedistributions with distinct stratification characteristics that can beassociated with specific bedforms generated at different stages in theyearly flood cycle and at specific environments in the braided channelsystem. Such detailed investigations have been able to define distinctgrainsize distributions for fluvial sub-environments including bar,channel, splay, proximal and distal floodplain, and abandoned channelsettings (e.g., Best et al., 2003; Bristow,1987; Goodbred & Kuehl, 2003;Weinman et al., in press).

2.5. Spatio-temporal dynamics

The evolution the Brahmaputra River tends to be examined eitherin situ by detailed investigations described above or by synopticstudies of an entire reach using remote sensing. Remote sensing isinvaluable for monitoring changes of the river as a whole. Islam et al.(2001) studied reflectance differences between the Ganges andBrahmaputra Rivers and found that suspended sediment concentra-tion is higher in the Ganges during the high discharge period andhigher in the Brahmaputra during the low discharge period Geometricstudies of the Brahmaputra River have found to be both widening itsbed and to be shifting westward at approximately 50 m/year (e.g.,Khan & Islam, 2003; Sarker et al., 2003; Thorne et al., 1993). Theselarge-scale changes in the river could be related to external factors,such as tectonic deformation or a sediment pulse derived from the1950 Assam earthquake (Goodbred et al., 2003). On a finer scale,Takagi et al. (2007) used remote sensing to investigate spatio-temporal channel variability on the Brahmaputra based on a hardclassification of 3 classes (water, sand, vegetation). Mapping of riverevolution in both the Brahmaputra and Ganges (CEGIS, 2003; EGIS,1997, 2000, 2002) has helped to develop predictive models of bar andisland migration within the channel belt. These models are based ongeometry of features imaged by Landsat and does not incorporatesediment type or grain size, although evidence suggests that theerodability of the river banks is strongly influenced by the bedmaterial (M. Sarker, pers. Comm., 2005).

Broadband imagery like Landsat provide a record of seasonal tointerannual changes in river channel geometry on 30 year time scales.

Fig. 4. Field photos of transitional substrate environments on the Ganges–Brahmaputra delta. In some cases (e.g. D, F) slight elevation changes relative to water table produce largevariations in albedo of a single type of sediment. In other cases (C) finer scale compositional changes produce albedo variation. In many cases (e.g. A, B) both moisture andcomposition conspire. Vegetation is also important (E).


This imagery shows primarily albedo differences but also more subtlevariations in slope and curvature of reflectance spectra. At the scale ofriver channels Landsat imagery highlights the contrast between stableand migrating channels – particularly in comparisons of pre and postflood geometry (Fig. 2). At the scale of decameters to kilometers (10–1000 m) the persistence and variability of islands and channelsprovides constraints on successive changes in geometry from year toyear. If the reflectance variations imaged by Landsat could beinterpreted in terms of moisture content and grain size then imagerymight be used to derive spatio-temporal maps of intrachannel-scalestructure (Fig. 3).

Fig. 3. A. Full resolution Landsat 7 comparisons of temporal change at the Ganges–Jamuna cgreater Ganges turbidity in November images contrasting lower water levels and greater Jamreflectance. B. Full resolution Landsat 7 comparisons of the upper reach of the Jamuna River ithe channels have migrated and almost all of the chars are reconfigured. Diagonal discontinudate imagery.

While both field and remote sensing approaches provide valuableinsights, they remain distinct anddonot generally informeachother tothe full extent possible. This study attempts to address this gap byinvestigating the effects of sediment grain size, lithology, andmoisturecontent on optical reflectance at visible through Shortwave Infrared(SWIR) wavelengths. The ultimate objective is to use remotely sensedimagery to understand geometric changes and lithologic changes atsynoptic scales. This may allow us to address several questions aboutfluvial dynamics and flood hazard. How does the grain size or sortingchange as islands and bars migrate and rearrange? What are thetemporal and spatial scales of variability in deposition and erosion?

onfluence and on the Padma River below the confluence. Note higher water levels anduna turbidity in February and March images. Note also spatial variability in sediment

n 2000 and 2006. In addition to the difference in water level post and pre flood, most ofities in 2006 image result from infilling of Landsat scanline corrector gaps with previous


What characteristics influence temporal stability of chars and banks?Can optical imagery be used to improve prediction of riverine changesand bank erosion? The ability to obtain synoptic space–timeconstraints on bothmorphology andmaterial properties could providethe opportunity for new insight in the dynamics of the Ganges–Brahmaputra River system, and of major rivers in general. Our strategyis to first understand the effects of grain size, moisture content andpossibly lithology on full resolution reflectance spectra underlaboratory conditions and then to consider which of these effectsmight be resolvable with broadband imagery in the field.

3. Data

3.1. Sample environments – chars

Sampling sites were situated within the sandy channel belts of theGanges and Brahmaputra river systems. The high bedload componentof each river's sediment load and the 5–10 m of seasonal water-levelchange give rise to the development of large, complex bar systems(Fig. 3). These bars, locally known as ‘chars’, are prominent featureswithin the channel belt systems, with quasi-stable vegetated charsaccounting for 35–43% of the land-surface between the main riverbanks. Individual chars can be quite large with many examplesexceeding 1.5 kmwide by 3 km long. Somemay also persist for severaldecades before being reworked by channel migration (Sarker et al.,2003). The bars themselves are comprised primarily of fine sands upto 15 m thick; however, the surface sediments exhibit great localheterogeneity because of differing elevation (i.e., wetness), age (i.e.,vegetation type), and sediment transport processes (i.e., high flow,low flow, or aeolian). As the higher elevation environments areprimarily influenced during high river discharge, they typicallycomprise sandy substrates, although silty sediments are trappedwithin the vegetated bar tops. Where vegetation is absent, though, thesands are reworked by aeolian processes during the 8-month dryseason, leading to local sorting of light and heavy minerals (e.g.samples S03 and S48, respectively). Within lower elevation areas ofthe chars, such as chute cutoffs and small bar channels, sediments aredeposited throughout the falling stages of river flow and thus oftenleave a veneer of fine silt or mud that may be 1–30 cm thick overlyingthe bar sands (e.g. samples S26, S39).

3.2. Sample collection

Sediment samples were collected from compositionally and mor-phologically distinct locations on chars. Fig. 4 shows field photos oftypical sample collection sites. Samples were collected from the uppersurface (b1 cm) of representative sediment deposits – particularly inareas where sharp transitions were observed. All samples were sealedairtight in plastic bags to preserve moisture content.

3.3. Sample mineralogy and petrology

The composition of sand samples based on petrographic micro-scopy analyses of 10 representative samples is as follows : quartz – 20to 68% (average 52%), feldspar 1 to 8% (average 5%), lithic grains 4 to10% (average 6%), mica 4 to 39% (average 17%), calcite 3 to 12% (average6%) and non opaque heavy minerals 2 to 30% and black opaque heavyminerals 1 to 35%. Some samples deviate far from a normal sandcomposition either by including excessive mica (as much as 39%) orexcessive heavyminerals (as much as 65% heavymineral of which 35%is metaliferous black opaque heavies i.e. magnetite, ilmenite etc).Almost all samples have some calcite grain (as much as 12%).

From previous studies (Flood Action Plan or FAP 24 river surveyproject 1996) of sand composition from Ganges and Brahmaputrarivers it is shown that a major difference between Ganges andBrahmaputra rivers is the calcite content. The Ganges has significant

calcite grains (2 to 7%) whereas the Brahmaputra has no or rare andtrace calcite grain in its sand. The samples are fine to very fine grainedwith some medium grained ones. The fine to very fine grained sandhave high percentage of clay matrix. The clay matrix varies from about45 to 70% in fine to very fine sands, whereas in medium grained sandclay constitutes 2 to 5%.

3.4. Lab and experimental setup

Spectra were measured using an Analytical Spectral Devices (ASD)Fieldspec Pro JR spectroradiometer. The ASD instrument uses threeseparate spectrometers to measure radiance between wavelengths of350 nm and 2500 nm. The Visible/Near Infrared (VNIR=350 nm to1050 nm) spectrometer uses a 512 channel silicon photodiode arrayoverlaid with an order separation filter to provide a nominal spectralresolution (Full Width Half Max or FWHM) of 3 nm at 700 nm. TheShortwave Infrared (SWIR=900 nm to 2500 nm) portion of thespectrum is acquired with two scanning spectrometers using indiumgallium arsenide (InGaAs) detectors with a scan rate of 100 ms andnominal spectral resolution ranging between 10 nm and 12 nm. Spectraare oversampled to 1 nm throughout the spectral range. The radiancesignal is collected through a bundle of polished and sealed optical fiberswith a nominal conicalfield of viewof 25°. For eachoutput spectrum tenindividual scan spectra were averaged over an interval of 1 s. Radiancespectra were normalized against a 99% Spectralon® white reference toproduce relative reflectance spectra for each measurement.

A common laboratory set up was used for all measurements. Allmeasurements were made with an aperture-target distance of17.5 cm corresponding to a nominal area of 7 cm on the target. Abubble level attached to the fiber optic mount was used for verticalalignment and a co-axial laser directed through the aperture of thefiber optic mount was used to align the target center with the GIFOVcenter. Illumination was provided by a 50 W 14.5 V Ushio halogenbulb powered by a Lambda LM350 DC power source. The bulb wasmounted in a Lowell Pro lamp at a distance of 85 cm directed at thetarget from an elevation angle of 45°. Temporal stability of the lampand the spectrometer were verified by monitoring the variability ofsequential measurements of the relative reflectance of the whitestandard. Over the course of the experiment no temporal fluctua-tions of the light source could be detected in the wavelength rangeof ∼420 nm and ∼2460 nm. Some spectral and temporal noise wasdetected at wavelengths outside this range – presumably a result oflow output energy of the lamp at UV and SWIR wavelengths. Onsome of the longer duration dehydration experiments (explainedbelow) some slight instrument drift was observed. This drift wasmanifest as increasing offset between the VNIR and SWIR1 spectro-meters at 1000 nm. In the most extreme case it results in a ∼2%offset in reflectance at the 1000 nm tiepoint.

3.5. Spectral measurement procedure

Each sample was placed in a 10 cm diameter watch glass andsmoothed either by vibration (dry sand) or with a spatula (wet mud). Allsampledepthsweregreater than4mm–well in excess of the theoreticallysensible optical depth for the largest grain sizes (Liang,1997). Insensitivitywas confirmed by varying background brightness (Spectralon® versusblack paper) of the underlying surface to verify that no difference wasdetected. Each sample was measured four times – each successivelyrotated 90° – to determine the effect of varying illumination geometry.Because surface texture influences bidirectional reflectance, it is necessaryto quantify the effect of varying illumination geometry on the measuredreflectance to confirm insensitivity to sample preparation. As expected,the effects tend to cancel within the sensor's field of view and very littledifference (b∼3%) inmost cases. High resolutionmacrophotographsweremade of each sample under illumination conditions with a Lumix DMC-FX9 digital camera. The camera has an 8 MP Panasonic RGB CCD coupled


with a Leica DC Vario-Elmarit aspherical lens at 8.3 mm focal length.Exposure was set using spot metering on the center of the sample tomaximize dynamic range while avoiding saturation of the imagehistogram. The white balance of the camera was set manually using theSpectralon® white reference under direct lamp illumination. Each targetwas imaged within a larger field of view centered on the region of thetarget imaged by the spectrometer to minimize peripheral lens effects onthe region selected for analysis. All sample photos (both field andlaboratory) and spectra are available online at: www.LDEO.columbia.edu/~small/ Bang/Bang2005.

4. Analysis

4.1. Spectral mixing spaces

Analysis of the sediment sample spectra is done in the context ofthe spectral mixing space defined by the low order principalcomponents of the spectra. The phenomenon of spectral mixing has

Fig. 5. A. Ganges–Brahmaputra spectral mixing space with laboratory spectra and field samcomponents 1 and 2 (center) of 109 field samples shows clustering of different spectral pcoordinate system in which the locations correspond to variations in spectral shape. Thespectrally distinguishable from grain size and moisture. All sample spectra with field andGanges–Brahmaputra spectral mixing space with laboratory spectra and field sample photosfield samples shows clustering of different spectral populations related to Fe minerals, grainwhich the dimensions correspond to variations in spectral shape. Sample spectra, field photo

been recognized at least since the dawn of the Landsat mission(Horowitz et al., 1971). Much of the early work in spectral mixingwas done in the planetary science community in an effort to modelcompositional variations observed in telescopic spectra of the Moonand Mars (e.g. Nash & Conel, 1974). Simple binary and ternary linearmixture models were developed by Singer and McCord (1979) toestimate abundance of high albedo aeolian dust and lower albedocrystalline rocks on Mars. Singer and McCord (1979) were able tomodel these spectra with surprising accuracy but were obviouslyunable to validate their estimates. Subsequent work in the planetaryscience community focused primarily on nonlinear mixing arisingfrom intimate mixtures (e.g. Clark and Lucey, 1984; Johnson et al.,1983; Singer, 1981) in which spectral heterogeneity exists at scalescomparable to the mean optical path length. The concept of themixture space was introduced by Smith et al. (1985) and Johnsonet al. (1985) to test the validity and uniqueness of spectral mixturemodels within a low dimensional parameter space defined by thePrincipal Components (PCs) of the spectra. Our analysis combines

ple photos of peripheral spectra and potential endmembers. 2D projection of principalopulations related to grain size, moisture and lithology. The mixing space provides aseparation of the Ganges and Jamuna samples along P.C. 2 suggests lithology may besample macro photos online at: www.LDEO.columbia.edu/~small/Bang/Bang2005. B.of potential endmembers. 2D projection of principal components 1 and 3 (center) of 109size, organics and moisture content, The mixing space provides a coordinate system ins and sample macro photos online at: www.LDEO.columbia.edu/~small/Bang/Bang2005.

http://www.LDEO.columbia.edu/~small/%20Bang/Bang2005

http://www.LDEO.columbia.edu/~small/%20Bang/Bang2005

http://www.LDEO.columbia.edu/~small/Bang/Bang2005

http://www.LDEO.columbia.edu/~small/Bang/Bang2005


the mixture space concepts developed in the seminal work ofJohnson et al. (1985) and Smith et al. (1985) on planetary spectraand the analyses of spectra of soil vegetation mixtures by Huete(1986) with the principal component analyses of soil spectralproperties by Huete and Escadafal (1991) and Palacios-Orueta andUstin (1998). The objective of our analysis is to investigate theeffects of moisture content, grain size and lithology on sedimentreflectance to determine which, if any, of these properties might bemapped remotely – with either broadband or hyperspectralimagery. The concept of the mixing space is used to represent therelationships among spectra – although the mixture analysis is notdiscussed in this paper.

4.2. Mixing space structure

Both centered (mean subtracted) and uncentered (raw) principalcomponent transformations were applied to the 436 spectra of 109samples. Allmixing spaces are renderedusing the centeredPCsalthoughthe eigenstructure of both transforms is discussed below. Fig. 5 showsorthogonal projections of the first three principal components alongwith example spectra from the periphery of the mixing spacedistribution. The PC1 vs PC2 projection (Fig. 5A), which accounts for

Fig. 5 (cont

almost 99% of the spectral variance, shows a bimodal distribution on PC1with thewetter samples on the lowend and dryer samples on the highend. Samples from theGanges reside at higher values of PC2while thosefromthe Jamunahave lower values. Aswould be expected, samples fromthe confluence and downstream reach of the Padma overlap with bothJamuna and Ganges samples. As these rivers drain different parts of theHimalayan Mountain front and Tibetan Plateau, this suggests thatlithologic differences may be spectrally distinguishable. The bimodaldistribution on PC1 suggests that moisture variations may account forthegreatestdifferences amongspectra. The orthogonal projectionof PC1vs PC3 (Fig. 5B) shows an arcuate distribution in the third dimension ofthemixing space. Samples containingdark, ferromagnesian sands resideoutside the main cluster at higher values of PC3.

While clustering of the PCs reveals spectral similarity, it does notexplain the physical basis for the clustering. If we expect the clusteringto result from diagnostic absorption features some insight might begained from the relative contributions of absorptionwavelengths to thePCs. The eigenstructure of the spectral covariance matrix is illustratedby comparing eigenvalues and eigenvectors with the EmpiricalOrthogonal Functions (EOFs) showing the contribution of each spectralband to each PC (Preisendorfer, 1988). These are depicted in Fig. 6 withthe distribution of spectra for comparison. The differences in the

inued).


eigenvalues and EOFs of the centered and uncentered transformsillustrates the effect of mean subtraction on variance partition. Bothtransforms allocate N99% of spectral variance in the first four PCsalthough the uncentered transform clearly concentrates more variancein fewer PCs. The difference in the EOFs is striking – showing thesensitivity of the results to the rotation chosen. It is important to notethat both rotations produce EOFs containing both positive and negativevalues indicating that representation of spectra in either basis occurs byboth constructive and destructive interference – particularly in the

Fig. 6. Eigenstructure of the spectral covariance matrix. Distribution of 436 lab spectra fromspectral shape in the mean (black), +/−1 s (blue) and max/min range (green). Eigenvalues (Bwith 4 dimensions accounting for N99.9% of variance. Eigenvector matrix of centered covarabsorptions. Empirical Orthogonal Functions (EOFs) show band contributions (loadings) tocontribute to the 1400 and 1900 nm water absorption features. (For interpretation of the rearticle.)

liquid water absorptions at ∼1400 nm and ∼1900 nm. The implicationsof this are discussed below.

4.3. Drying experiments

The qualitative observation that the bimodal distribution ofsamples on PC1 corresponds to the distinction of wet and drysamples suggests that moisture content may be distinguishable fromgrain size and/or lithology. To test this we selected a subset of ten

109 samples of Ganges, Jamuna and Padma sediments (A) shows strong similarity of) show similar distribution of variance for both centered (black) and raw (gray) spectraiance (inset) indicates most high order variability is associated with visible and SWIR4 primary Principal Components for centered and raw (gray) spectra (C–E). All EOFs

ferences to colour in this figure legend, the reader is referred to the web version of this


samples for further analysis. Since lithology is the most difficultfactor to isolate, the ten samples were chosen on the basis ofpresumed lithologic diversity to span the range of PC2 values. Asubset of each sample was sieved to separate sands (N63 μm) fromsilts and clays (collectively referred to here as mud). Of the 10samples, four contained only sand and six contained significantquantities of silt and clay. Of these six, three contained N20% sand

Fig. 7. Fine fraction grain size distributions. Weight fraction for each phi interval corresponmixed (20% to 80%) and coarse (N80%) grained samples. Sand bar shown are grains that pa

and three contained negligible sand. Hence of the ten samples, fourcontained only sand, three contained only mud and three containedsand–mud mixtures. Size fraction distributions of the six muds(b63 μm)weremeasured on aMicromeritics Sedigraph 5100 in 0.05%sodium metaphosphate solution yielding weight percent distribu-tions across seven phi intervals of grain size (4 silt, 3 clay). Theresulting distributions are shown in Fig. 7.

ds to fine fraction weight. Total sample weight percent sand distinguishes fine (b20%),ssed the 63 mm sieve into fine sample.


For the drying experiments saturated wet samples were preparedas described above and spectrawere recorded until the samplewas airdry and successive spectra showed no further change. Spectra weremeasured once per minute in most cases, although we found that atwo minute measurement interval was sufficient on later samples.Experiments were run under a range of relative humidity conditions(39 to 96%) but no correlation was found between humidity anddrying time for any of the three classes of sediment. Presumably thecombination of the close proximity of the lamp and the constantairflow across the surface was sufficient to overcome temporalvariations in water vapor saturation in laboratory air. Spectraldehydration plots are shown for all 16 sand, mud and mixed fractionsof the 10 samples are shown in Fig. 8.

The primary observations derived from the dehydration plots arerelated to the consistency and variation in the spectral evolution as thesample dries. Sands take longer to dry than muds and are moreconsistent in duration and evolution. All of the sands dried in 250 to300 min with average reflectance increasing from ∼0.1 to ∼0.4 whilethe muds ranged from less than 100 to almost 300 min spanningabout the same range of average reflectance as the sands. The darksand took ∼270 min to dry but only attained a maximum averagereflectance of 0.15. Dehydration brightening occurred at all wave-lengths but was most pronounced in the liquid water absorptionbands at ∼1400 nm and ∼1900 nm. All sands showed a similar patternof linearly increasing average brightness to ∼80% of dry reflectancefollowed by an abrupt dehydration in the final stage of drying. Incontrast, the muds showed varying degrees of nonlinearity in theirtemporal drying trajectories – ranging from almost linear to multiplephases of brightening at different rates. In the mixed sediments, themud fraction seemed to control the overall rate and curvature of thedrying trend. In contrast to earlier studies of soil moisture (discussedbelow), the increase in reflectance with decreasing moisture contentis neither linear nor exponential – although for some of the sands itmight be described as initially linear followed by an exponential phaseas the end.

4.4. Mixing space trajectories

The multitemporal dehydration spectra were combined with thesample spectra described above and subjected to a principalcomponent transformation using the same rotation parametersderived from the original sample spectra for consistency. Results areshown in Fig. 9. The continuous spectral evolution seen in Fig. 8 resultsin smooth trajectories through the mixing space for each sample. Alltrajectories showed monotonic increases in PC1 as the sample dried –

confirming our hypothesis that the spectral variability represented byPC1 is controlled bymoisture content. Interestingly, all samples exceptthe dark sand (n102) and two clay dominated muds (n37 and n39)showed parallel sigmoid trajectories that rarely crossed one another inthe primary PC1–PC2 plane.

The mixing space trajectories were even more consistent in thePC1–PC3 plane of the mixing space. All followed a boomerang shapedtrajectory and rarely crossed. Most importantly, the sands and claysoccupied trajectories bounding the silts and mixed sedimentssuggesting that the grain size effects on the spectra may allowsands, silts and clays to be distinguished spectroscopically across therange of moisture contents. Fig. 9 shows orthogonal projections of the3D mixing space for two different grain size classification. Althoughthe crude coarse/mixed/fine classification allows for the most robustdiscrimination, there is some indication that sand, silt and clay may bedistinguishable – particularly in dry samples.

5. Discussion

The correspondence of moisture, grain size and lithology to thedistribution of principal components within the spectral mixing space

suggests that these factors may be spectrally separable – at least underlaboratory conditions. We discuss mechanisms and implications foreach factor separately below.

5.1. Moisture effects

Because of its relevance to agriculture and land surface energybalance, the effects of soil moisture on the absorption of light havebeen studied extensively (see Ben-Dor et al., 1999 for a review). Thewell known effect of moisture is to darken soils. Field measurementsof soil albedo suggest a linear relationship to moisture content (Idsoet al., 1975). Early laboratory studies showed that this darkeningoccurs at all wavelengths but is a nonlinear function of soil moisture(Bowers and Hanks, 1965). More recent laboratory studies havefocused onmoisture effects at visiblewavelengths and suggested non-linear variation of soil reflectance with moisture content (Bedidi et al.,1992). Lobell and Asner (2002) modeled the moisture variation asexponential and demonstrated greater sensitivity at SWIR wave-lengths. Greater SWIR sensitivity would be expected from liquidwater's numerous strong absorptions at wavelengths greater than1000 nm. We observe the same darkening effect with sediments butthe rapid increase in reflectance in the final stages of drying suggestthat the process is neither purely linear nor purely exponential. Whilemost of the samples showed a linear increase in reflectance throughmost of the drying process, the abrupt increase in the final stage isstrongly non-linear and diminishes asymptotically in most samples.We expect that this is a result of multiple phases of evaporation at thesurface (b3 mm) of the sample but more controlled experiments withmore homogeneous samples are necessary. Comparison of theseresults with prior studies of soils is difficult because of the greaterstructural and compositional heterogeneity of most soils. However,insight gained from the study of moisture darkening of sedimentsmayinform our understanding of the analogous processes in soils.

The question of moisture darkening of soils was initially investi-gated by Angstrom (1925) who proposed a simple geometric model inwhich total internal reflectionwithin liquid films coating the particlesincreases the frequency of reflection events thereby leading to greaterabsorption. Angstrom's model has been refined by Lekner and Dorf(1988) to include reduction of refractive contrast at the particlesurface. An alternative mechanism, based on an extension of singleparticle scattering theory, has been proposed for homogeneousparticulate media by Twomey et al. (1986). In the scattering model,the reduction of refractive contrast that results from replacing air withwater in the pore spaces reduces the scattering angle. Greater forwardscattering results in deeper penetration of light into the medium andtherefore greater likelihood of absorption. However, both of thesemodels were proposed before laboratory measurements on homo-geneous particulate media were available. Subsequent laboratoryanalyses by Zhang and Voss (2006) using goniometric measurementsof granular media have revealed that the phenomenon of moisturedarkening is more complex than previously assumed and that thesesimple models do not match the degree of moisture darkening withmost of the samples tested. We note that both the geometric andscattering models are qualitatively consistent with observation butpredict very similar degrees of moisture darkening across the fullrange of albedo. Bothmodels are physically plausible and notmutuallyexclusive – however neither accounts for variations in grain size orshape. The effect of grain size is discussed below.

Given the complexities revealed by laboratory spectra of homo-geneous particulates, we do not expect to determine the mechanism(s)controlling the moisture darkening phenomenon with heterogeneoussediment samples. However, some discussion of the consistenciesobserved in our analyses may provide some insight into the problem.In addition to the liquid water absorptions at ∼1400 and ∼1900 nm, theprimary effect of moisture is to deepen the continuum absorption –

although not linearly. Dry absorption features (e.g. N2000 nm) are not

Fig. 8 (continued).


affected. The darkening with moisture content is consistent withAngstrom's hypothesis of rough surface absorption (Angstrom, 1925) inwhich a thin film of water results in diffusely scattered light beingtrapped by total internal reflection thereby increasing likelihood ofabsorption – both by the absorbing particle and by thewater itself. If thisis the case, absorption should vary with moisture content as modulatedby porosity and permeability and with absorption of the granularmedium. This is suggested by the continuous increase in reflectance asevaporation proceeds but could be tested with a controlled experimentvarying both grain size and granular albedo as described below. The

Fig. 8. A. Spectral dehydration plots for 4 sands. Dark Jamuna sand (n102, bottom) is plottedfollowed by abrupt dehydration at ∼80% dry albedo. B. Spectral dehydration plots for 3 mudssediments. Note highly variable dehydration slopes. D. Dehydration spectra of fine (left) andbetween concave and convex continua on coarse and fine fractions (respectively).

results are also consistent with numerous experiments following theworkof Bowers andHanks (1965) on theeffectofmoistureonsoil albedo.While the sediments in our analysis are compositionally and texturallysimpler than most soils, some of the physical processes responsible forthe moisture darkening are presumably similar. Hence, these resultsshould have implications for spectral estimation of soil moisture.

All sand samples showed a linear increase in average reflectancewith time from ∼30% to ∼75% of dry reflectance before experiencingan abrupt increase corresponding to the final dehydration of thesample. Fine fractions were considerably more variable in their drying

at double reflectance scale for clarity. Note strongly linear albedo increase while drying. Note highly variable drying times. C. Spectral dehydration plots for 3 mixed sand/mudcoarse (right) fractions of 3 mixed sand/mud sediments shown in panel c. Note contrast

Fig. 8 (continued).


rates with some showing the linear-abrupt progression seen for sandand other showing a more continuous –but faster– dehydration.Mixed samples had significantly shorter drying times – although thismay result from their necessarily smaller sample sizes after segrega-tion for coarse/fine fraction subsetting. Interestingly, the mixedsamples also showed the least pronounced abrupt dehydration effect.This may also result from the smaller sample size. If not a result of thesample size, wewould hypothesize that the greater permeability of thesands results in more efficient capillary wicking of water from thematrix by evaporation through the surface. Given the limited samplesize and number of uncontrolled experimental parameters, wehesitate to draw firm conclusions from these drying rate-relatedobservations. However, as discussed below they will certainly informthe design of more controlled experiments.

We postulate that different grain size distributions may influenceevaporation rate and efficiency since they should influence the bulkporosity and permeability and therefore water retention properties ofthe sediment. We will test this hypothesis by conducting dryingexperiments on specific size fraction mixtures of pure quartz sand atdifferent levels of saturation under continuous weight measurementand spectral collection throughout the drying process. By varying thefraction of reflective (white) and absorptive (black) sand fractions theeffect of granular media absorption can also be evaluated.

5.2. Grain size effects

Because of its relevance to planetary spectroscopy, the effects ofgrain size on particulate reflectance has been studied extensively.

Fig.

8(con

tinu

ed).



The seminal work of Adams and Filice (1967) and Hunt and Vincent(1968) established the effects of grain size on monomineralicpowders. The general effect is for reflectance to increase withdiminishing grain size although the effect on specific absorptionbands is more complex. A theoretical basis for this phenomenon isgiven by Hapke (1963, 1981) Simulation of theory with Miescattering models and field verification suggests that the effectsare observable in the field with hyperspectral data (Okin & Painter,2004). Studies of desert sands (e.g. Blount et al., 1990; Paisley et al.,1991) have found some consistencies in broadband reflectance andgrain size in dune sands but have been limited by the compositionalheterogeneity of the sands studied. Analyses of monomineralicgypsum sand with laboratory spectra and hyperspectral imagery byGhrefat et al. (2007) found a consistent linear deepening of twoabsorption bands (1750 nm and 2150 nm) with decreasing grainsize. However, the reflectance properties of particulate mixtures ofvarying grain size are more complex as multiple scattering causesnonlinear spectral mixing. The fundamental observation that soilalbedo generally increases with sand content was quantified withlaboratory measurements by Gerbermann (1979). More recent workby Bachmann et al. (2008) finds evidence for a linear relationshipbetween silt fraction (b45 μm) and absorption (at 397 nm) at lowfractions (b1%). A bidirectional reflectance theory for multiple

Fig. 9. Moisture-varying laboratory spectra and mixing space drying trajectories. Upper plocoarse and fine trajectories in PC1–PC3 space. Lower plots show a more specific categorizatioP.C. 1 with decreasing moisture. P.C. 3 clearly distinguishes spectral variation related to grai

scattering within intimate mixtures developed by Hapke (1981)has been tested and extended by Mustard and Pieters (1987) andsubsequently simplified for operational estimation of mixturefractions by Johnson et al. (1992). In the present study we limitour attention to the observed grain size effects and present thespectral mixture analysis in a separate study.

The visual appearance of most of the coarse, fine and mixed drysediments is similar. Aside from a few outliers containing organics orferromagnesian minerals, most dry samples have rather level spectrawith the most prominent absorptions in the visible (b500 nm) andSWIR (N2000 nm) and varying degrees of absorption in the liquidwater bands (∼1400 nm and ∼1900 nm). We attribute this thepervasive abundance of quartz in the sediments. Some smallabsorption features are also visible in the NIR-SWIR1 range. Themost pronounced difference in continuum curvature seems to occurwith coarser fractions having slightly concave continua between∼500 nm and ∼1900 nm where the finer fractions tend to be morelevel or even convex (Fig. 5B, spectra 1, 7, 8). In some samples (Fig. 5) abroad absorption is seen at ∼1300 nm – presumably related to thepresence of plagioclaise feldspar (Hunt & Salisbury, 1970). Withoutmore detailed compositional analysis of the fine fractions we cannotrelate the spectral variations with grain size to chemical composition.Given the paucity of clay fraction in the mixed sediments, it seems

ts show a simple coarse, fine, and mixed categorization with clear distinction betweennwith comparable distinction. All mixing space trajectories showmonotonic increase inn size – and free water in the fine fraction samples.


reasonable to assume some degree of compositional similarity in thecoarse and fine fractions – although some differences would beexpected on the basis of compositional differences in resistance tochemical and mechanical weathering (e.g. quartz vs. feldspar vs.pyroxene).

Dry reflectance of the coarse and fine fractions of the mixed sand-silt samples do not conform to the brightening effect observed withpure particulates. Finer fractions are somewhat brighter than coarsefractions for two of the three samples in the 400 nm to 1400 nm rangebut there is no consistent difference at longer or shorter wavelengths.We interpret this as indicative of compositional differences betweenthe coarse and fine fractions. In spite of the subtlety of these features,the samples very clearly segregate according to grain size distributionin the third (PC3) dimension of the mixing space (Fig. 9). Interestingly,this segregation is maintained across a wide range of moisturecontents. While we hesitate to speculate as to mechanisms beforeconducting more controlled experiments, we find this to be a veryencouraging result with strong implications for spectral separability ofmoisture and grain size effects.

5.3. Lithology effects

Both the Ganges and Brahmaputra rivers drain lithologicallycomplex basins comprising igneous, metamorphic, and sedimentaryrocks of continental and shallow-marine origin. As such, no singlelithology dominates either system. Mineralogical composition of thebedload and char sediments is immature with high contents of micas,feldspars, and opaque and non-opaque heavy minerals. Optically,there are only a few differences in mineralogy between the two riverbasins that may yield consistent differences in the reflectance spectra.Among the most abundant mineral groups found in the riversediments, feldspars comprise 20–30% wt in both systems. However,the Brahmaputra sediment load has nearly double the concentrationof alkali feldspars (Huizing, 1971), which due to high K content arecommonly pink to red toned, compared with a white to gray color formost other feldspars. On average this could tend to skew theBrahmaputra sediments toward longer visible wavelength. Micas arealso abundant, but highly variable (b5 to N50%) due to hydraulicsorting of these sheet-like minerals. Within the mica fraction, thedominant fractions are light-colored muscovite (white to gray) anddark-colored biotite (green, brown, or black). However, Brahmaputrasediments have a consistently higher fraction of biotites (0.65–0.75)compared with the Ganges (0.45–0.50) (Huizing, 1971). Within theminor constituents of the sand-sized bedload, the Ganges riversediment contain an appreciable amount of carbonate minerals (upto 10% wt.), whereas the Brahmaputra has a near zero carbonatecontent (Huizing, 1971). This is due, in part, to the Ganges erosion ofcarbonate-bearing parts of the Rajmahal traps (Mukherjee, 1971).Within the dark-colored heavy minerals, the primary differencebetween Ganges and Brahmaputra sediments is the ratio of green-colored epidote to reddish-colored garnets, which is 0.4±0.2 and 1.9±0.6, respectively (Heroy et al., 2003). Although the ratio of theseuniquely colored minerals is highly distinct for each river system, theycomprise only a few percent of the total sediment load and may notcontribute significantly to the reflectance spectra. For claymineralogy,the Brahmaputra samples (Heroy et al., 2003) contains dominantlyillite (60%) and kaolinite (30%), while the Ganges contains significantsmectite (30%) and proportionately less of the other minerals.

Most of the sample spectra lack diagnostic mineral absorptions.This is not surprising as the dominant mineral in the sands is quartz,which is known for its high transmissivity and featureless spectrum(Clark, 1999; Hunt & Salisbury, 1970). Some of the dry pure sandscontain minor water absorptions consistent with mature quartz sandswith grains containing microscopic fluid inclusions. Despite theexpected presence of carbonates in the Ganges samples, we find littlecompelling evidence for the characteristic OH absorptions at 2200 to

2400 nm usually associated with carbonate minerals (Hunt, 1977).Some spectra do contain very small amplitude features in the 2300 to2500 nm range (e.g. Fig. 5A, spectra 7, 8) but we do not feel thatspeculation is warranted by the ambiguity of these features.Particularly given the number of minerals with absorptions at thesewavelengths. The lack of OH absorption features is presumably aconsequence of the low carbonate fractions. In the samples containingclay size fractions (s37 and s39) we also find no compelling evidenceof the characteristic OH absorptions. Visually, the spectra aredominated by the water absorptions and distinct differences betweenconcave and convex continua with some variation in the slope of thevisible absorption band and its associated shoulder. This is especiallyapparent in the broad absorption centered at ∼1300 nm that can beseen in the peripheral spectra in the PC1–PC3 plot (Fig. 5B) and in theEOF corresponding to PC3 (Fig. 6). We speculate that this may be due,in part, to ferrous iron associated with plagioclaise feldspars asdiscussed by Hunt and Salisbury (1970) but cannot confirm thiswithout further analysis of a larger sample.

5.4. Implications for remote sensing

If the spectral separability of grain size and moisture contentobserved in laboratory spectra can be duplicated using hyperspectralimagery it should be possible to map sediment composition andmoisture content at synoptic scales. Given the subtlety of the spectralvariation with grain size, this will require high signal/noise (S/N) aswell as accurate atmospheric correction. Airborne sensors like AVIRISand HyMap should be able to resolve this variation in the field. Futurehyperspectral missions, such as ENMAP, may provide sufficient spatialresolution and S/N to achieve this level of discrimination. Broadbandsensors like Landsat can certainly resolve the albedo variationsassociated with moisture content but seem unlikely to resolve thevariations in continuum curvature associated with the grain sizedifferences. However, it may be possible to calibrate simultaneousbroadband and hyperspectral observations (e.g. Hyperion and Land-sat) to determine whether any aspect of the grain size aredistinguishable from VNIR slope resolved by Landsat. If so, this mayenable some degree of discrimination sufficient to map spatiotem-poral variations in moisture content as well as residual variability thatmay result from large grain size differences (e.g. sand vs. clay). Thenarrowband VNIR spectra that will hopefully be provided by theWorldview 2 sensor (due to launch in mid-2009) may also be able toresolve some of these differences in the visible bands.

The consistency of spectral variation with moisture content hasencouraging implications for soil moisture mapping. If the effects ofmoisture content correspond purely to volumetric absorption from liquidwater then they may be separable from textural and compositionalabsorptions in some soils also. Controlled experiments on compositionallypure sands and silts should allow for distinction between volumetricwater absorption and absorption by the granular medium. This suggestsan approach inwhich a sequence of controlled laboratoryexperiments areconducted on porous granular media of increasing compositionalcomplexity to incrementally approach that of common soils.

6. Conclusions

Variations in moisture content and grain size distribution areprimarily responsible for the spectral variations that determine thestructure of the mixing space of the laboratory spectra. Dryingexperiments on the sieved sediments support this qualitative observa-tion. Monotonic drying of water saturated sediment samples result inmonotonic increases in reflectance and monotonic increases in themagnitude of the first principal component accounting for the majorityof the observed spectral variance. The ten samples for which grain sizedistribution was analyzed show distinct drying trajectories suggestingthat grain size distribution may be spectrally distinguishable from


moisture content for both wet and dry samples – although thedistinction is considerably greater for dry samples. Separation of Gangesand Brahmaputra samples within the mixing space suggests thatlithologic differencesmay also be spectrally distinguishable on the basisof continuum curvature. However, we are not able to identify specificabsorption features that correspond to known differences in sedimentcomposition so we hesitate to speculate without further analysis of alarger sample of sediments of known lithology. While moisture contentand grain size distribution appear to be primary determinants of themixing space structure, the Empirical Orthogonal Functions and thePrincipal Components contain both positive and negative valuesindicating that the individual EOFs do not correspond to specificabsorption features.More controlled laboratoryexperiments are neededto confirm these observations and provide a basis for testing hypothesesfor the combined effect of moisture and grain size on aggregatereflectance of porous media.

Acknowledgements

The authors are grateful to four anonymous reviewers for helpfulcomments and suggestions. This work was funded by the NASA SolidEarth & Natural Hazards program grant NNG04GA86G. All analyses weredone with ENVI and Matlab software on Apple and Sun computers.

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Spectroscopy of sediments in the Ganges–Brahmaputra delta: Spectral effects of moisture, grain...

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