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The Cryosphere, 6, 85–100, 2012
www.the-cryosphere.net/6/85/2012/
doi:10.5194/tc-6-85-2012
© Author(s) 2012. CC Attribution 3.0 License.
The Cryosphere
Geochemical characterization of supraglacial debris via in situ and
optical remote sensing methods: a case study in Khumbu Himalaya,
Nepal
K. A. Casey1,*, A. K¨
a¨
ab1, and D. I. Benn2,3
1Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway
2Department of Geography, University of St. Andrews, St. Andrews, UK
3The University Centre in Svalbard (UNIS), P.O. Box 156, 9171 Longyearbyen, Norway
*now at: NASA Goddard Space Flight Center, Cryospheric Sciences Laboratory, Greenbelt, MD 20771, USA
Correspondence to: K. A. Casey (kimberly.a.casey@nasa.gov)
Received: 28 November 2010 – Published in The Cryosphere Discuss.: 7 February 2011
Revised: 22 July 2011 – Accepted: 13 December 2011 – Published: 19 January 2012
Abstract. Surface glacier debris samples and field spec-
tra were collected from the ablation zones of Nepal Hi-
malaya Ngozumpa and Khumbu glaciers in November and
December 2009. Geochemical and mineral compositions of
supraglacial debris were determined by X-ray diffraction and
X-ray fluorescence spectroscopy. This composition data was
used as ground truth in evaluating field spectra and satel-
lite supraglacial debris composition and mapping methods.
Satellite remote sensing methods for characterizing glacial
surface debris include visible to thermal infrared hyper- and
multispectral reflectance and emission signature identifica-
tion, semi-quantitative mineral abundance indicies and spec-
tral image composites. Satellite derived supraglacial debris
mineral maps displayed the predominance of layered sili-
cates, hydroxyl-bearing and calcite minerals on Khumbu Hi-
malayan glaciers. Supraglacial mineral maps compared with
satellite thermal data revealed correlations between glacier
surface composition and glacier surface temperature. Glacier
velocity displacement fields and shortwave, thermal infrared
false color composites indicated the magnitude of mass flux
at glacier confluences. The supraglacial debris mapping
methods presented in this study can be used on a broader
scale to improve, supplement and potentially reduce errors
associated with glacier debris radiative property, composi-
tion, areal extent and mass flux quantifications.
1 Context
Many of the world’s glaciers have moderate to significant
surface debris. Supraglacial debris is derived from either lo-
cal rock and ice fall or from atmospheric deposition of par-
ticulates. Inspection of debris composition can reveal these
distinct geologic source contributions. Dust layers studied at
the surface (e.g. Adhikary et al., 2000) or in ice cores (e.g.
Thompson et al., 2000) can indicate climate conditions such
as temperature and aridity. Local geologic sources, such as
accumulation zone geologic compositions which reemerge
supraglacially in the ablation zone, signal the magnitude and
pathway of englacial transport. Debris mantle patterns and
geologic assemblages indicate slope processes (i.e. rock or
ice falls) (Benn and Evans, 2010), glacier flow regimes (i.e.
continuous, pulsed or of surge type) and the kinematic his-
tory of a debris covered glacier.
While spaceborne remote sensing of ‘clean’ bare glacier
ice extent can be done quite successfully (Hall et al., 1988),
even on a semi-automated basis (Paul et al., 2002), strate-
gies for supraglacial debris satellite mapping remain in de-
velopment (e.g. Bhambri et al., 2011). Spaceborne glacier
debris areal extent mapping methods use thermal, geomor-
phometric or recently synthetic aperture radar approaches
(e.g. see Lougeay, 1974; Bishop et al., 1999; Bolch et al.,
2007; Shukla et al., 2010; Atwood et al., 2010; Strozzi et al.,
2010). Repeat satellite images can be used to estimate de-
bris covered glacier surface velocity and theoretical surface
particulate flow (e.g. K¨
a¨
ab, 2005; Scherler et al., 2008).
Published by Copernicus Publications on behalf of the European Geosciences Union.
86 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
In addition to glacier extent and kinetic studies, Wessels
et al. (2002) used ASTER data to analyze spectral variability
of supraglacial lakes in the Everest region; K¨
a¨
ab (2005) uti-
lized false color ASTER band composites to indicate debris
patterns and indication of flow regimes at Hispar glacier in
the Karakorum, Pakistan and Unteraar glacier in the Swiss
Alps; Suzuki et al. (2007) mapped thermal resistance of de-
bris covered glaciers in the Lunana and Khumbu Himalayas;
and Mihalcea et al. (2008) utilized ASTER data to map
glacier debris spatial distribution and thickness. A summary
of spaceborne glacier mapping challenges and recommenda-
tions for glacier parameter analysis is offered by Racoviteanu
et al. (2010).
Spaceborne remote sensing of glacier debris towards ge-
ologic composition has not been investigated. We demon-
strate that supraglacial composition can be mapped via spec-
tral satellite data and is relevant to many glaciologic vari-
ables, including radiative absorption, ablation, generation of
supraglacial melt as well as englacial and supraglacial mass
flux. Further, these supraglacial debris composition impacts
are applicable to glaciologic understanding at regional and
global scales.
2 Background
2.1 Sensors
Earth observing satellite technology has advanced greatly in
recent decades, offering rich spatial, temporal and spectral
imaging of Earth’s glaciers. Intended particularly for spec-
tral signature studies (Abrams, 2000), the ASTER instrument
measures 14 optical bands at spatial resolutions from 15 to 90
m. Specifically, ASTER measures three bands in the visible
and near infrared (VNIR, 0.4–0.9µm) at 15 m spatial reso-
lution, six shortwave infrared bands (SWIR, 1.0–2.5µm) at
30 m spatial resolution and five thermal infrared bands (TIR,
3.0–12 µm) at 90 m spatial resolution. Unfortunately, SWIR
detectors failed in April 2008, therefore SWIR from 2008
to present is unavailable. However, ASTER VNIR and TIR
bands continue to perform well at the date of publication,
and SWIR data from 2000 to 2008 is usable. Landsat 7’s
Enhanced Thematic Mapper Plus (ETM+) provides 16-day
temporal resolution with eight spectral bands: one panchro-
matic (pan) band at 15 m resolution, six VNIR-SWIR bands
at 30 m resolution, and one TIR band at 60m resolution.
Additionally, Landsat 5 TM, with similar spectral (7 VNIR-
TIR bands, no pan) and spatial (30 m VNIR-SWIR, 120 m
TIR) resolution, continues to acquire data. Further details on
ASTER and Landsat sensors, including ETM+ scan line cor-
rection can be found in: Watanabe et al. (2011), Tucker et al.
(2004) and Storey et al. (2005).
Part of a technological demonstration and validation mis-
sion, NASA’s EO-1 satellite was launched in 2000 and car-
ries two pushbroom sensors: Hyperion and Advanced Land
Imager (ALI). Hyperion uses a VNIR and a SWIR spectrom-
eter to acquire 242 spectral bands from 0.4 to 2.5 µm (in
10 nm nominal increments) at 30 m spatial resolution. Of
the 242 spectral bands, only 220 are calibrated due to low re-
sponse of detectors in non-calibrated bands. Approximately
24 bands measure at the same wavelength between the VNIR
and SWIR spectrometers. Thus, there are 196 distinct spec-
tral bands – VNIR bands 8 through 57, and SWIR bands 77
through 224. Similar in spectral resolution to the Landsat se-
ries, ALI offers one pan band and 9 visible to shortwave in-
frared bands. Compared to Landsat TM and ETM+, ALI of-
fers improved VNIR to SWIR spectral resolution at the same
spatial resolution (30m), and improved pan band spatial res-
olution (10 m vs. ETM + pan 15 m). However, ALI offers
no TIR spectral data. Although EO-1 was only planned to
run for 2-yr, operation has continued successfully and EO-
1 is now managed by the United States Geological Survey
(USGS) (all Hyperion and ALI instrument data after Beck,
2003). Figure 1 provides a visual summary of spectral cov-
erage of the different instruments, including MODIS which
can be used to monitor larger glaciers.
2.2 Lithologic remote sensing
Pioneering laboratory and field based spectroscopy inves-
tigations toward deriving mineral and chemical composi-
tion were conducted by McClure (1957), Lyon (1965), Hunt
and Salisbury (1970) and Clark and Lucey (1984) focusing
strictly on ice/rock mixtures. These and other related studies
determined that dominant anions, cations, trace constituents
and crystal field structures strongly influence reflectance
and emission spectra and can be used to resolve geochem-
ical composition. Satellite spectral derivation of mineral
and chemical constituents has progressed over the past sev-
eral decades (e.g. Vincent and Thomson, 1972; Goetz and
Rowan, 1981; Fu et al., 2007). Rowan et al. (1986); Rowan
and Mars (2003) demonstrated that transition metal enrich-
ment can be detected via use of VNIR. Hydroxide, sulfate
and carbonate minerals can be analyzed using SWIR (Kruse,
1988; Rowan and Mars, 2003; Ninomiya et al., 2005). Sili-
cate, carbonate, oxide, phosphate, and sulfate minerals have
been measured via TIR spectral studies (e.g. Gillespie et al.,
1984; Hook et al., 1992; Ninomiya et al., 2005).
To validate remotely sensed geologic composition, analyt-
ical geochemical techniques are often used. X-ray diffraction
(XRD) identifies mineralogy, while X-ray fluorescence spec-
troscopy (XRF) measures oxide compound weight percent
(e.g. SiO2, Al2O3, Fe2O3, CaO) and trace element concen-
tration (e.g. V, Co, Zn, Pb). The XRD derived mineralogy
and XRF measured geochemical composition provide an in-
dependent assessment of debris composition to compare with
spectrally determined measurements.
In this study, we evaluate full optical spectrum VNIR-TIR
spectral techniques toward measuring mineralogy and geo-
chemical composition of surface glacier debris. Multi- and
The Cryosphere, 6, 85–100, 2012 www.the-cryosphere.net/6/85/2012/
K. A. Casey et al.: Optical remote sensing of debris covered glaciers 87
2.0 5.0 10.01.00.5
100%
0%
Atmospheric transmission
Wavelength (µm)
Terra
ASTER
VNIR
VIS NIR SWIR TIR
15m 30m 90m
Terra, Aqua
MODIS
1 2 3 4 5 - 9 10 - 14
1 - 32
250, 500m, 1km
1 - 220 EO-1
Hyperion
pan
pan
EO-1
ALI
30m
11’ 2 3 55’ 74’
1 2 3 5 674 LANDSAT
ETM+
EO-1
ALI
30m
30m
30m 60m
11’ 2 3 55’ 74’4
+4
30m
15m
10m
Fig. 1. Spectral and spatial resolution of Hyperion, ALI, Landsat ETM+, ASTER and MODIS sensors shown with respect to visible to
thermal infrared atmospheric transmission (revised after (K¨
a¨
ab, 2005)).
hyperspectral data of the Khumbu Himalayas, acquired from
a variety of sensors are used. Ngozumpa and Khumbu glacier
field collected spectra and XRD and XRF debris sample min-
eralogy and geochemical composition serve as ground truth.
To the authors’ knowledge, the is the first study to explore
full optical spectrum satellite derived qualitative and quanti-
tative characterization of supraglacial debris.
3 Study area
The Hindu Kush-Himalayas constitute one of the largest ar-
eas of land-based ice apart from the Greenland and Antarc-
tic ice sheets. Debris covered glacier area in the Hindu
Kush-Himalaya is sizeable (e.g. discussed in Scherler et al.,
2011), constituting roughly 14 % of Hindu Kush-Himalaya
ice (based on an unpublished Hindu Kush-Himalaya satel-
lite data glacier inventory estimate by the 2nd author). The
extreme topography of the Khumbu Himalaya region re-
sults in frequent rock falls and ice avalanches at regional
glaciers. These rock falls and ice avalanches contribute to
the heavy debris cover, high supraglacial activity, sediment
transport, deposition and glacial erosion on both Ngozumpa
and Khumbu glaciers (Benn and Owen, 2002).
Ngozumpa glacier is the longest glacier in Nepal – ap-
proximately 25 km in length (Benn et al., 2001); Khumbu
glacier is 17km in length (Hambrey et al., 2008) (Fig. 2).
Ngozumpa and Khumbu glacier surface debris consists pri-
marily of leucogranite, greenschist and sillimanite-gneiss
sands, gravels, rocks and boulders, with primary mineral
components including quartz, feldspars, micas, and carbon-
ates (Carosi et al., 1999; Searle et al., 2003). Extensive debris
cover on both glacier tongues increases in depth down glacier
and insulates the underlying ice (Kadota et al., 2000). Both
glaciers are typified by considerable supraglacial relief, with
the height difference between peaks and troughs estimated at
20–50 m (observed during 2009 field work and reported in
Benn and Owen, 2002).
Downwasting or thinning of Khumbu Himalayan glaciers
has been observed over the past several decades (Bolch et al.,
2008a, 2011; Nuimura et al., 2011). In addition to downwast-
ing, backwasting – the ablation which occurs on exposed ice
faces in debris covered areas – is another primary melt mech-
anism active on Khumbu Himalayan glaciers. Backwasting
was found to account for up to 20% of the total ablation of
debris covered area by Nakawo et al. (1999). Backwasting
related topographic inversion processes (detailed in Benn and
Evans, 2010) occur on these debris-mantled glaciers, yield-
ing complex debris assemblages and numerous supraglacial
melt ponds (Wessels et al., 2002).
4 Data collection and methods
4.1 In situ data collection
Over 3360 field spectra were collected on Ngozumpa and
Khumbu glaciers in November and December 2009. Specif-
ically, more than 1800 spectra were collected in the mid-
dle ablation area of the Ngozumpa glacier (approx. 4735–
4790 m a.s.l) from 26–29 November 2009, and more than
1560 spectra were collected in the upper ablation area of the
Khumbu glacier (approx. 5100–5285m a.s.l.) from 4–6 De-
cember 2009. The locations of the data collection sites are
detailed in Fig. 2 and Table 1.
Surface radiance was measured with an Analytical Spec-
tral Devices (ASD) FieldSpec Pro (Analytical Spectral De-
vices, 2002), collecting nadir “raw mode” surface radiance
on clear sky days within two hours of local solar noon
(10:00 a.m.–02:00 p.m., LT). An 18 ◦foreoptic was used with
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88 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
Table 1. Summary of in situ sample attributes, including location, elevation, as well as data collection date, sample type and sample ID. The
locations of the field measurement sites are mapped in Fig. 2 and sample ID’s are also referred to in Table 3.
Location Date Latitude / Longitude Elevation (ma.s.l.) Spectral signature class,
(corresponding sample ID)
Upper Ngozumpa (UN) 27 November 2009 27.9568◦N 86.6980◦E 4760 snow, rock, gravel, sand, mud (1N–6N)
Mid-Ngozumpa (MN) 29 November 2009 27.9537◦N 86.6992◦E 4750 ice, snow, rock, gravel (7N,8N)
Lower Ngozumpa (LN) 26 November 2009 27.9511◦N 86.7020◦E 4790 snow, boulders, gravel, soil
Upper Khumbu (UK) 6 December 2009 27.9998◦N 86.8511◦E 5280 ice, snow, gravel (19K–22K)
Mid-Khumbu (MK) 5 December 2009 27.9874◦N 86.8405◦E 5180 ice, snow, rock, sand (14K–18K)
Lower Khumbu (LK) 4 December 2009 27.9763◦N 86.8304◦E 5100 ice, snow, mud, rock (9K–13K)
86.67 E 86.73 E 86.80 E 86.87 E 86.93 E
27.93 N 28.00 N 28.07 N
0 2.5 5 km
N
UK
MK
LK
UN
MN
LN
Fig. 2. Landsat TM (31 October 2009) true color composite of
the Khumbu Himalaya study area. Ngozumpa glacier (left) and
Khumbu glacier (right) in situ measurement locations are labeled
as detailed in Table 1. The approximate area presented in the Hy-
perion Imja and Lhotse Shar glacier analysis is highlighted with a
yellow box in the lower right hand corner.
the spectrometer to target distances ranging from 10cm to
1 m (relating to approximately 3 cm2to 30 cm2surface res-
olution). A Spectralon calibration panel served as the refer-
ence material to glacier surface measurements.
Spectral targets included snow, ice, variations of dust and
debris, and partially vegetated glacier surfaces. For each tar-
geted glacier surface material, 20–30 measurements were ac-
quired. In situ sample sites representative of both pure (e.g.
bare ice, granitic debris) and mixed (e.g. ice with schistic
pebbles) targets were selected to maximize thematic spectral
acquisitions while safely maneuvering on the active glacier
in the short solar noon temporal window. Geographic loca-
tion and glacier surface temperatures were recorded for each
set of spectral class measurements.
In conjunction with spectral measurements, 19 snow and
ice samples (in acid prepared 500 ml low density polypropy-
lene Nalgene bottles), and 22 supraglacial debris samples (in
clean polyethylene bags, obtaining approximately 100 grams
of material per sample) were collected. All in situ samples
were taken in duplicate and double polyethylene bagged.
4.2 Analysis of in situ data
Post collection analysis of field spectra involved converting
instrumental digital numbers to calibrated surface radiance
to spectral reflectance (after Nicodemus et al., 1977). Mea-
sured target reflectance was divided by Spectralon calibrated
reference surface reflectance, and multiplied by both calibra-
tion panel offsets and the user defined reflectance scale for
each wavelength (0.35–2.5µm). Due to FieldSpec Pro instru-
ment variability from the 57 VNIR/SWIR individual optical
fiber responses (MacArthur et al., 2007), signal-to-noise ra-
tios, three detectors (Analytical Spectral Devices, 2002), as
well as for comparison of field spectra with satellite spec-
tral data, repeat field spectra over each target were averaged
to form field spectral reflectance class signatures (here after
referred to as spectral signatures).
We measured the mineralogy, oxide and elemental com-
position of Ngozumpa and Khumbu glacier debris samples
using powder XRD and XRF at the University of Oslo, De-
partment of Geosciences. Glacier debris samples were pre-
pared for XRD and XRF analysis by drying (2 days at 80 ◦C)
and crushing to a fine powder (less than 125µm particle size)
via a vibratory ringmill. Powder XRD was conducted via use
of a Philips XPERT diffractometer (manufactured by PANa-
lytical B.V., Almelo) with samples analyzed in FORCE Bulk
Mode measuring from 2◦to 65 ◦22. Mineralogy was de-
rived via use of PANalytical’s X’pert Highscore software,
with semi-quantitative peak area and weight factor estimates
of percent composition were calculated after Moore (1997).
For XRF, ten grams of oven dried fine powder was prepared
into sample tablets and measured on a Philips PW2400 XRF
spectrometer run via SuperQ Version 3 software in TRACES
7B mode. The following oxide compounds and trace ele-
ments were measured: SiO2, Al2O3, Fe2O3, MnO, MgO,
The Cryosphere, 6, 85–100, 2012 www.the-cryosphere.net/6/85/2012/
K. A. Casey et al.: Optical remote sensing of debris covered glaciers 89
Table 2. A listing of the satellite products and scene dates used for the optical remote sensing methods evaluated in this study.
Comparison method Sensor, data product Date(s) of scene(s), further details
Satellite reflectance
multispectral ASTER, AST 07XT 29 November 2005, surface reflectance
hyperspectral Hyperion, L1GST 13 May 2002, 4 October 2010, top of atmosphere reflectance
True and false color composites ALI, L1T 4 October 2010, 10 m pan enhanced true color
ASTER, L1B 29 November 2005, SWIR/TIR false color
Landsat TM, L1T 31 October 2009, true color
Landsat ETM+, L1G 24 January 2003, SWIR/TIR false color
Hyperion, L1T 13 May 2002, true color, SWIR false color
Mineralogic mapping:
SWIR/TIR indices ASTER, L1B 29 November 2005, at-sensor radiance
SiO2weight percent ASTER, AST 05 29 November 2005, emissivity
Land surface temperature Landsat TM, L1T 31 October 2009, at-sensor radiance converted to LST
Glacier velocity, streamlines Landsat ETM+, L1G 30 October 2000, 4 October 2002, 15 m pan
Landsat TM, L1T 5 November 2005, 31 October 2009, 30 m near infrared
CaO, Na2O, K2O, TiO2, P2O5, and V, Co, Zn, Rb, Pb, Sr, Y,
Zr, Nb, Th, U, Ba, S. Accuracy of XRF results is 98%.
4.3 Optical satellite data acquisition and processing
Satellite remote sensing data were acquired with the closest
temporal and seasonal (post-monsoon dry season) correla-
tion to in situ collected data, avoiding instrument anomalies
(i.e. Landsat ETM+ scan line correction, ASTER SWIR band
failures). The specific satellite data products used include:
ASTER L1B at-sensor radiance, ASTER AST 07XT surface
reflectance, ASTER AST 05 surface emissivity, Landsat TM
Level 1T and ETM + Level 1G at-sensor radiance, and Hy-
perion Level 1GST at-sensor radiance. Satellite data acqui-
sition dates, data products and methods used to investigate
Khumbu Himalayan glacier debris cover are listed in Table 2
and further described in the following sections.
4.3.1 Mineral mapping
The rich SWIR and TIR spectral resolution provided by
ASTER allow for qualitative and quantitative approaches to
mapping surface mineralogy. ASTER is used in this study,
however, comparable spectral bands (e.g. see Fig. 1) can also
be used to map supraglacial debris mineralogy. Three min-
eral mapping methods are presented: SWIR indices, TIR in-
dices and TIR emissivity silica weight percent.
1. SWIR indices
SWIR mineral indices use wavelength dependant spectral
absorption features to estimate mineral abundance. Several
mineral indices are available and were evaluated (e.g. Vin-
cent and Thomson, 1972; Ninomiya, 2003, 2004; Ninomiya
et al., 2005). Based on the dominant minerals in the Khumbu
Himalaya study area, the following SWIR mineral indices
were chosen for evaluation: layered silicate (LS) (Eq. 1), cal-
cite (CA) (Eq. 2), hydroxyl-bearing (OH) (Eq. 3) and alunite
(AL) (Eq. 4).
LS=(AST4×AST8)
(AST5×AST6)(1)
CA=(AST6×AST9)
(AST82)(2)
OH=(AST4×AST7)
(AST62)(3)
AL=(AST72)
(AST5×AST8)(4)
where ASTncorresponds to ASTER spectral band number n.
ASTER L1B at-sensor radiance data is used for mineral
index estimations. The different indices are designed to ex-
tract absorption features key to the targeted mineral. For ex-
ample, the LS and CA SWIR indices target bands that mea-
sure hydroxyl (2.2µm) and carbonate (2.35 µm) absorption
features, respectively. At-sensor radiance band ratios reduce
atmospheric and topographic influences, including illumina-
tion variability (Abrams et al., 1983; Mather, 1987; K¨
a¨
ab,
2005), highlight information not evident in single band or
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90 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
three-band true or false color composite images, and provide
a quantitative estimate of mineral abundances.
2. TIR indices
Thermal at-sensor radiance indices offer the first strictly ther-
mal spectrum based technique presented in this study. Al-
though the thermal portion of the spectrum is described as
the most important region of the spectrum for differentiating
geology of terrestrial materials (Gupta, 2003), TIR satellite
spatial resolution is considerably lower than VNIR or SWIR
(i.e. ASTER VNIR 15 m, SWIR 30 m, TIR 90m spatial res-
olution). Nevertheless, TIR offers unique potential to tar-
get the abundance of carbonate, quartz and silicate mineral
groups. TIR based band ratios to estimate carbonate, quartz
and silica containing lithology are presented in Eqs. (5–7)
(after Ninomiya et al., 2005).
CI=(AST13)
(AST14)(5)
QI=(AST112)
(AST10×AST12)(6)
MI=(AST12×AST143)
(AST134)(7)
where ASTncorresponds to ASTER spectral band number n.
The carbonate index (CI) is utilized to detect the primary
carbonate minerals calcite and dolomite – with high values of
CI indicating presence of these minerals (absorption features
at 11.4µm for calcite, 11.2 µm for dolomite). Pure carbonate
will provide a high CI value in conjunction with and low QI
and MI values. The quartz index (QI) not only is indicative of
quartz, but also low QI values signal presence of potassium
feldspar and gypsum. The mafic index (MI) correlates with
silicate content, and is also sensitive to carbonate content in
rocks. A simple silicate index ratio of band 12 to band 13 can
be used – but for the differentiation from carbonates, Eq. (7)
is used. A MI value greater than 0.90 corresponds to mafic
rocks, while a MI value greater than 0.92 corresponds to ul-
tramafic rocks (Ninomiya et al., 2005). To note, Ninomiya
et al. (2005) concluded the stability of TIR mineral indices ir-
respective of surface temperature, elevation and atmospheric
condition.
3. TIR silica abundance
Another technique to extract the dense geologic informa-
tion available in the thermal bands is emissivity derived sil-
ica abundance mapping (Hook et al., 1992; Miyatake, 2000)
Eq. (8) (after Watanabe and Matsuo, 2003). Using the
ASTER Surface Emissivity AST 05 product, silica weight
percent abundance is calculated by targeting characteristic
silicate emission features in TIR.
SiO2=56.20−271.09×Log(AST10+AST11+AST12)
(3×AST13)(8)
where ASTncorresponds to ASTER AST 05 surface emis-
sivity product band number n.
4.3.2 Shortwave and thermal infrared false color
composites
False color image composites can highlight geologic differ-
ences not evident in true color images. Landsat and ASTER
offer SWIR and TIR bands that can be used to inspect
supraglacial debris (K¨
a¨
ab, 2005). Landsat ETM+ thermal
band 6 (11 µm) allows for spectral emission analysis of car-
bonate and silicate content in supraglacial debris, and Land-
sat ETM + SWIR bands 5 and 7 (1.65 and 2.2 µm, respec-
tively) indicate hydroxyl content, common to clays and hy-
drated silicates.
4.3.3 Hyperspectral reflectance
Although Hyperion coverage of the in situ measurement sites
does not exist at the time of publication, Hyperion data of
nearby Lhotse Shar and Imja glaciers from 13 May 2002
exists and was analyzed for supraglacial debris characteris-
tics. Hyperion Level 1 GST terrain-corrected digital num-
bers were converted to at-sensor radiance using sensor- and
band-specific calibration settings (e.g. gain, offset, solar ir-
radiance). At-sensor, top-of-atmosphere (TOA) planetary re-
flectance was calculated for each band after Markham and
Barker (1986) and atmospheric gas interferences from wa-
ter vapor, oxygen and carbon dioxide were removed. To
note, several commercial software programs are available
to estimate surface reflectance (primarily addressing atmo-
spheric correction) (e.g. see Dadon et al., 2010; Wang et al.,
2010). Commercial software was not available to the authors,
thus, Hyperion TOA reflectance is presented with removal of
known atmospheric absorption interference features.
4.3.4 Complementary supraglacial debrismapping
methods
Optical remote sensing glacier debris spatial analysis tech-
niques complementary to the supraglacial debris composi-
tion mapping methods include calculation of glacier surface
temperature and velocity. Land surface temperature (LST)
over glaciers can be derived from thermal band spectral satel-
lite data. For this study, we calculated LST from Land-
sat ETM+ and TM thermal band spectral data (method after
Barsi et al., 2005; explained in Hall et al., 2008) using a dirty
ice emissivity of 0.96 after (Qunzhu et al., 1985). Glacier sur-
face velocity, or horizontal surface displacements were de-
rived using normalized cross-correlation repeat Landsat TM
near-infrared band and repeat ETM+ pan data (methods de-
tailed in K¨
a¨
ab and Vollmer, 2000; Debella-Gilo and K¨
a¨
ab,
2011). With the assumption that the calculated velocity field
The Cryosphere, 6, 85–100, 2012 www.the-cryosphere.net/6/85/2012/
K. A. Casey et al.: Optical remote sensing of debris covered glaciers 91
is constant over time, relative surface ages were estimated
from horizontal surface displacement velocity field interpo-
lation (Haug et al., 2010). Streamlines then give theoretical
transport time of glacier mass.
5 Results
5.1 In situ spectra and debris geochemical composition
Several snow and ice spectral signatures (clean snow, fine
particulate covered snow, granitic gravel on snow, bare ice,
ice with schistic pebbles, and full schistic debris cover) col-
lected in the upper Khumbu glacier are presented in Fig. 3.
As glacier surface dust and debris cover increases, VNIR re-
flectance decreases, as visualized by the “granitic gravel on
snow” compared with “clean snow” and “schistic pebbles on
ice” compared with “bare ice” spectral signatures in Fig. 3.
Both fine particulates and granitic gravel reduce VNIR re-
flectance of clean snow, with fine particulates displaying an
absorption feature minima at approximately 0.5µm. Larger
scale gravel shows a more marked broad reduction in snow
reflectance in VNIR, with absorption features beginning ear-
lier, at approximately 0.38 µm. Full supraglacial debris
cover results in loss of characteristic snow and ice VNIR
reflectance absorption features, and debris mineralogy domi-
nates the VNIR-SWIR reflectance signature. SWIR in partic-
ular is used to differentiate mineral components, while VNIR
can signal transition metal abundance. To note, minor de-
tector related signal influences can be seen in some Fig. 3
spectra at 1.0 and 1.8 µm (further discussed in Painter, 2011).
Satellite reflectance from the ASTER AST 07XT data prod-
uct is plotted for corresponding bare ice and full schistic de-
bris, and exemplifies the glacier surface composition differ-
entiation capabilities of satellite derived surface reflectance.
A detailed investigation of all gravel on snow spectra sug-
gested that absorption features similar to sillimanite gneiss
ca. 0.35–1.0µm is resolved upon fine-scale spectral inspec-
tion. Some Khumbu and Ngozumpa snow spectra displayed
slight reflectance features near the organic carotinoid and
chlorophyll absorption (typically 0.55 and 0.68µm, respec-
tively) (Painter et al., 2001). However, algal growth was not
expected to dominate at the time of spectral measurements
(Yoshimura et al., 2000), nor was visible in large quantities
on either glacier. Conversely, fine debris was clearly visible
and many of the snow spectra VNIR absorption features are
thus attributed to mineral dust deposition, transition metal-
rich as confirmed by XRF results. Several minerals were
identified in Ngozumpa and Khumbu supraglacial debris
spectra, with dominant mineral classes identified including
biotite, silicates and calcite. Moisture content of supraglacial
debris was also analyzed using field spectra reflectance mois-
ture estimation (after Liang, 2004). Supraglacial debris water
content was estimated at 5 % and 15–20% from two separate
debris spectral signatures collected on the Upper Ngozumpa
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5
Field Spectral Reflectance
Wavelength (µm)
clean snow
fine particulates on snow
granitic gravel on snow
bare ice
AST_07XT bare ice
schistic pebbles on ice
schistic debris
AST_07XT schistic debris
Fig. 3. Khumbu glacier snow, ice and debris field collected surface
reflectance. Note the reduction in VNIR reflectance with increasing
dust and debris compared to clean snow and bare ice signatures,
and SWIR dominant schistic supraglacial debris signature. Satellite
reflectance (ASTER AST 07XT) is compared with field reflectance
for bare ice and schistic debris signatures.
glacier site (denoted UN in Fig. 2). More detailed descrip-
tions of mineral spectra and supraglacial debris moisture re-
sults are presented in the accompanying Cryosphere Discus-
sion paper, (Casey et al., 2011).
XRD derived supraglacial rock, gravel, soil and silt miner-
alogy was measured to consist primarily of quartz, feldspars,
carbonates, and micas (see Table 3). Specifically, Ngozumpa
glacier debris samples were largely quartz, feldspar – in
the form of calcium albite, and mica – in the form of bi-
otite, while Khumbu glacier debris samples were dominated
by mica – in the form of muscovite and feldspar – in the
form of calcium albite and quartz. Minor amounts of cal-
cite were measured on both glaciers, more prevalence in
Ngozumpa glacier debris samples (7 of 8 samples), com-
pared to Khumbu glacier samples (2 of 14 samples). In con-
trast, chlorite was found to be more abundant on Khumbu
glacier, with trace amounts measured in 9 of the 14 Khumbu
glacier samples compared to 2 of the 8 Ngozumpa glacier
samples. XRF determined silica weight percent results are
displayed in Table 3 and full XRF analytical results are given
in Casey et al. (2011) (snow and ice geochemical results are
presented in Casey, 2012). XRF silica measurements are
also compared with ASTER based silicate mapping results
(Sect. 5.2).
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92 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
Table 3. In situ and satellite comparison of mineralogy and silica abundance at Ngozumpa and Khumbu glacier sample locations. XRD-
derived minerals are listed in order of greatest abundance per sample, along with XRF measured SiO2weight percent and ASTER TIR
estimated SiO2weight percent. ASTER TIR SiO2weight percent is averaged from the nearest two 90 m pixels to the in situ sample site.
Sample ID Site Debris type XRD determined mineral classes in order of abundance XRF derived ASTER TIR
SiO2estimated SiO2
1N UN mud quartz, feldspar, mica, K-feldspar, calcite 66.3 72.8
2N UN mud quartz, feldspar, mica, K-feldspar, calcite 66.4
3N UN sand mica, quartz, feldspar, K-feldspar, calcite 62.7
4N UN sand quartz, mica, feldspar, K-feldspar, calcite 65.1
5N UN gravel feldspar, quartz, K-feldspar, mica, calcite 71.9
6N UN gravel mica, feldspar, quartz, microcline, clinochlore 62.5
7N MN rock feldspar, feldspar, calcite, quartz, K-feldspar 37.2 70.7
8N MN rock calcite, wollastonite 6.6
9K LK mud mica, feldspar, quartz, microcline, clinochlore 61.4 77.9
10K LK mud mica, feldspar, quartz, K-feldspar, nimite 62.0
11K LK rock phlogopite, feldspar, anorthite, quartz, nimite 63.8
12K LK rock feldspar, mica, phlogopite, quartz, clinochlore 54.1
13K LK rock calcite, feldspar, quartz 7.1
14K MK sand quartz, feldspar, microcline, mica, mica 71.5 78.7
15K MK sand feldspar, quartz, K-feldspar, mica 71.7
16K MK soil feldspar, feldspar, quartz, K-feldspar, mica, illite 71.1
17K MK soil feldspar, quartz, mica, K-feldspar 68.1
18K MK soil mica, quartz, feldspar, K-feldspar, nimite 69.2
19K UK gravel mica, quartz, feldspar, clinochlore, K-feldspar 63.1 58.6
20K UK gravel mica, quartz, feldspar, ankerite, nimite 61.0
21K UK sand phlogopite, quartz, feldspar, microcline, clinochlore 63.5
22K UK sand mica, feldspar, quartz, microcline, calcite, nimite 62.8
5.2 Optical remote sensing of supraglacial debris
composition
5.2.1 Mineral mapping
ASTER SWIR and TIR mineral indices provide semi-
quantitative estimates of mineral abundance in supraglacial
debris. ASTER SWIR based layered silicate, calcite, alu-
nite and hydroxyl-bearing mineral abundance indices are dis-
played in Fig. 5. For the scope of this study grayscale im-
ages of the 4 SWIR mineral indices are presented showing
relative abundance of dominant surface mineralogy. Lay-
ered silicates are most abundant in Ngozumpa ablation zone
supraglacial debris, calcite and hydroxyl-bearing minerals
are less abundant, however, calcite and hydroxyl-bearing
mineralogy displays significant variability. In contrast, the
alunite index shows little contrast and low abundance over
the entire region. Also displayed in Fig. 5, evidence of kine-
matics and pulse flow processes can be seen by the variations
in layered silicate debris abundance (i.e. cyan arrows). To
note, the SWIR and TIR indices can be used to further de-
rive quantitative estimates of mineral abundances (e.g. see
Ninomiya, 2004), create thematic mineral abundance maps,
or apply threshold mineral abundance estimates toward fur-
ther glaciologic calculations such as radiative absorption or
object oriented kinematics.
Silica weight percent was derived for the Khumbu Hi-
malaya region glaciers (Eq. 8, Fig. 6). To target mapping
of supraglacial debris, a thematic silica abundance thresh-
old of 60 % was selected based on average Si abundance of
regional geology. A digital elevation model was utilized to
remove areas mapped at high silica content due to extreme
terrain and active rock fall (i.e. slopes of 35–90 degrees).
In the Khumbu Himalaya test region, this resulted in a first-
order indication of debris covered ice extent. We speculate
this is due to the high supraglacial activity, mass transport,
particulate deposition and sediment erosion processes on the
glacier surface. These processes result in higher SiO2weight
percent on glacier as compared to surrounding non-active ter-
rain.
Further, ASTER derived silica weight percent estimates
were compared with in situ sample XRF determined SiO2
weight percent. ASTER to XRF results generally agreed (Ta-
ble 3), especially in consideration of the spatial resolution
variation (in situ ca. 20cm2point vs. 90 m2ASTER TIR
resolution), as well as the temporal resolution discrepancy
of approximately 4 yr (in situ collected 2009, ASTER data
from 2005). The difference between the XRF derived SiO2
and TIR emissivity calculated SiO2of the Ngozumpa and
Khumbu glacier field measurement sites ranged from a 4.0
to 15.1 percent. In situ to satellite SiO2results are similar to
The Cryosphere, 6, 85–100, 2012 www.the-cryosphere.net/6/85/2012/
K. A. Casey et al.: Optical remote sensing of debris covered glaciers 93
Fig. 4. Hyperion (13 May 2002) top-of-atmosphere supraglacial debris and ice reflectance plot (left) is displayed with a true color ALI
(4 October 2010) 10 m pan-enhanced image of the Imja and Lhotse Shar glaciers. Atmospheric water vapor, oxygen and carbon dioxide
absorption features were removed from the reflectance signatures. The locations of the Hyperion derived spectra for debris are indicated by
the blue (Imja glacier debris) and red (Lhotse Shar glacier debris) dots and the Lhotse Shar lightly dust covered ice by the black X.
LS = ( AST4 x AST8 )
( AST5 x AST6 ) CA = ( AST6 x AST9 )
( AST82)
OH =( AST4 x AST7 )
( AST62)AL = ( AST72)
( AST5 x AST8 )
Fig. 5. ASTER SWIR radiance based mineral abundance maps of
Ngozumpa supraglacial debris, clockwise from the top left, layered
silicate, calcite, alunite and hydroxyl bearing. Cyan arrows on the
layered silicate index point to evidence of pulse like glacial flow.
The location of the transect used to investigate differing mineral
abundances with respect to supraglacial temperatures in Fig. 7 are
indicated by the white lines across Ngozumpa glacier.
the accuracy reported by Hook et al. (2005), who note that
accuracy can be improved if the SiO2weight percent algo-
rithm is fine tuned to the specific region.
5.2.2 Shortwave and thermal infrared false color
composites
We differentiated silicon dioxide rich vs. carbonate-rich
Khumbu Himalayan supraglacial debris using Landsat and
ASTER SWIR and TIR false color composites. Landsat
ETM + SWIR band 5, 7 and TIR band 6 false color image
composite from 24 January 2003 of regional glaciers is dis-
played in Fig. 9. Supraglacial debris composition variability
is indicated by the coloring – yellow indicates silicon dioxide
rich granites, while blue indicates carbonate-rich gneisses.
The Khumbu glacier longitudinal schistic supraglacial de-
bris band starting from the icefall down the center of the
glacier can be visualized in blue, with granite debris bands
visualized in yellow. Similar carbonate-rich vs. silicate-rich
longitudinal supraglacial debris bands are visible on upper
Ngozumpa glacier.
Although similar total SiO2content was found at all six
Ngozumpa and Khumbu in situ samples sites (Table 3), the
more uniform spatial distribution of silicates on Ngozumpa
glacier can be easily differentiated from the more distinct
silicate-rich vs. carbonate-rich mineral classes found on the
Khumbu glacier using the SWIR/TIR false color composite
technique. False color composites can also provide an indi-
cation of mass flux. In Fig. 9, a suggestion of limited mass
flux transfer at glacier confluences can be seen at the Khangri
Shar, Khangri Nup and Khumbu glacier confluence (red ar-
rows) by the distinct shifts in supraglacial mineralogy.
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94 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
Fig. 6. Debris covered glacier areas are evident by elevated silica content in this ASTER thermal emissivity SiO2weight percent thematic
map.
5.2.3 Hyperspectral reflectance
Hyperion TOA reflectance spectral signatures of Imja and
Lhotse Shar supraglacial debris and ice are shown in Fig. 4
along with an ALI true color 10m pan enhanced true color
image composite. The Hyperion reflectance plot displays the
distinct granitic vs. schistic supraglacial debris types; qual-
itative transition metal as well as hydroxyl and carbonate
absorption area differences are demonstrated at 0.4–0.8 and
2.1–2.3 µm, respectively. Hyperion analysis of ice, snow,
and general mineral class qualitative differentiation was also
achieved on Gyubanare and Khangri Nup glaciers from a 4
October 2010 Hyperion scene (not shown).
Limitations to Hyperion data use include relatively low
signal-to-noise ratios of approximately 50:1, compared with
500:1 for airborne hyperspectral imaging (Pearlman et al.,
2003; Kruse et al., 2003) and atmospheric interference.
Quantitative VNIR-SWIR mineral differentiation via Hype-
rion requires atmospheric correction as seen in the Lhotse
Shar ice planetary reflectance spectral signature with promi-
nent atmospheric effects, despite the removal of broad wa-
ter vapor, oxygen and carbon dioxide absorption features
(Fig. 4). Hyperion data used in conjunction with commer-
cial spectral and atmospheric correction software could be
successful in discriminating small-scale wavelength and ab-
sorption depth-dependent characteristics of supraglacial de-
bris (e.g. using continuum removal techniques by Clark and
Lucey (1984) to determine debris components and concen-
trations).
5.2.4 Glacier surface temperature
Glacier surface temperature variation in conjunction with
supraglacial debris composition changes are demonstrated in
Landsat TM calculated surface temperature and calcite abun-
dance maps in Figs. 7 and 8. Surface temperature transects
display temperature variances of approximately 10 and 15 ◦C
with calcite mineral abundance variation on Ngozumpa and
Khumbu glaciers, respectively.
In situ glacier surface temperatures measured in Novem-
ber and December 2009 field work sites were compared with
the Landsat TM 31 October 2009 derived surface tempera-
tures. In situ temperatures of snow, ice, and debris classes
were averaged at each of the three Ngozumpa and three
Khumbu glacier measurement locations. In situ temperatures
of 0.4 ◦C for upper Khumbu, 2.4 ◦C for mid-Khumbu, and
6.7◦C for lower Khumbu sample sites relate to Landsat TM
120 m pixel resolution LST’s of −3.2, 4.8, 3.4◦C, respec-
tively. In consideration of the spatial and temporal resolution
disparities (satellite acquisition time 10:00 a.m. with some
areas in shadows vs. in situ measurement disparity 2h about
solar noon, also point to 120 m2spatial disparity), general
agreement was found between temperature measurements.
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K. A. Casey et al.: Optical remote sensing of debris covered glaciers 95
Fig. 7. Ngozumpa glacier ASTER L1B calcite abundance (top left)
is compared to Landsat TM derived supraglacial temperatures (top
right). The plot beneath the images displays calcite abundance vs.
LST transect values across the upper Ngozumpa ablation area. Note
that calcite rich areas correspond with higher glacier surface tem-
peratures.
5.2.5 Glacier surface velocity, streamlines
We estimated Ngozumpa and Khumbu glacier velocities us-
ing Landsat ETM + orthorectified 15m pan data from 30
October 2000 and 4 October 2002. In addition to the high
spatial resolution 2000–2002 velocity estimation, we used
coarser spatial resolution, but more recent Landsat TM 30m
near-infrared data from 5 November 2005 and 31 October
2009 to derive glacier velocities. Ngozumpa and Khumbu
glacier surface velocities did not change significantly be-
tween 2000–2002 and 2005–2009 time periods. The re-
sults from the 2000–2002 velocity estimation are presented
and used to derive streamlines as the higher spatial resolu-
tion allows greater accuracy. Velocity of both Ngozumpa
and Khumbu glaciers is estimated at 60m yr−1in the up-
per ablation zones and less than 5 m yr−1in the lower abla-
tion zones, (±0.5-1 pixel; i.e. 4–7 m yr−1error) (see Fig. 9).
Glacier velocity rates estimated in this study are comparable
to Khumbu Himalayan glacier velocity rates derived from
synthetic aperture radar (SAR) feature tracking (Luckman
et al., 2007; Hambrey et al., 2008; Quincey et al., 2009)
as well as optical imagery (SPOT – Seko et al. (1998);
Fig. 8. Khumbu glacier ASTER L1B calcite abundance (top left) is
compared with Landsat TM derived surface temperature (top right).
The plot beneath displays calcite abundance and surface tempera-
ture variation down glacier. In situ surface temperature measure-
ments are indicated on the top right map by the purple “X” anno-
tations. Glacier surface temperature increases approximately 15 ◦C
two kilometers down glacier from the Khumbu icefall and increases
further toward the terminus.
Ikonos and ASTER – Bolch et al. (2008b); and COSI-Corr
(Co-registration of Optically Sensed Images and Correlation,
from Leprince et al., 2007) derived ASTER – Scherler et al.,
2008).
Theoretical supraglacial particulate streamlines on
Ngozumpa and Khumbu glaciers indicate time scales of 380
and 450yr, respectively (Fig. 9). Streamline interpolation
stops where glacier velocities decrease below the error
margin of approximately 4m yr−1. Thus, 380 yr Ngozumpa
and 450 yr Khumbu glacier streamline estimations represent
minimum glacier ablation mass transport ages. The Khumbu
glacier streamline estimate from this study corresponds well
with the Fushimi (1978) in situ, structural-derived estimate
of Khumbu glacier ice at 410±110 yr.
6 Synthesis
Spectral supraglacial debris composition methods used
in combination can describe glacier extent, kinematic
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96 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
Fig. 9. Ngozumpa (upper image pair) and Khumbu (lower image pair) glacier velocity field (left) and SWIR/TIR false color composites
(right) display granitic (yellow) vs. schistic (blue) supraglacial debris flux patterns. Indication of limited mass flux can be seen at the
confluence of the Khangri Shar, Khangri Nup and Khumbu glacier (red arrows) by both the distinct change in supraglacial debris mineralogy
as well as the low velocities mapped. Theoretical surface particulate flow from “a” to “b” on Ngozumpa glaciers and “c” to “d” on Khumbu
glacier, indicate transport times of 380 and 450 years, respectively.
processes, weathering and energy balance parameters. In this
section, we detail these glaciologic applications of the debris
composition methods.
1. Improved satellite debris covered ice extent mapping
may be achieved by using silica abundance thematic
mapping, false color SWIR/TIR composites, glacier
surface temperature, and/or velocity fields. Used in
combination or with other mapping methods (e.g. SAR,
geomorphometric approaches) the debris characteriza-
tion methods may highlight debris covered ice areas at
glacial boundaries that have proved challenging to de-
tect to date.
2. Mass flux may be signaled by combining the
supraglacial debris composition methods presented.
Supraglacial composition can be identified via spectral
reflectance signatures or false color composites. False
color composites can show glacier debris sources and
multidimensional (e.g. englacial) debris transport paths.
The Cryosphere, 6, 85–100, 2012 www.the-cryosphere.net/6/85/2012/
K. A. Casey et al.: Optical remote sensing of debris covered glaciers 97
Glacier velocity and streamlines can suggest flow paths
of debris down glacier or flow processes at glacier con-
fluences. The degree of mass flux of not only present
day, but also years and decades past can be determined
due to the debris patterns archived in the supraglacial
debris.
3. Glacier debris activity rates, erosion and diagenesis may
be analyzed via emissivity percent silica mapping, min-
eral indices, velocity estimations and reflectance signa-
tures. The silica percent thematic map provides indica-
tion high silica supraglacial debris composition in ar-
eas of highest glacial velocity. Reflectance signature
data can be used to confirm higher silica content of
pre-weathered supraglacial debris in the upper ablation
zone vs. lower silica content of more heavily weathered
supraglacial debris in the lower ablation zone. Mapping
of silica content of supraglacial debris may offer in-
sight toward glacial sediment transport, erosion or areas
prone to differential melt based on differing supraglacial
debris composition. Theoretical particle streamline dat-
ing can also give a crude, first-order approximation of
‘debris age’ according to supraglacial debris type. Used
with robust glaciologic sediment dating studies, such as
Benn and Owen (2002); Owen et al. (2009), streamlines
could roughly estimate glacier sediment dates.
4. Glacier energy balance variables in relation to
supraglacial debris composition can be studied via sur-
face temperature, mineral indices, hyperspectral re-
flectance derived moisture content and velocity maps.
Glacier surface temperature variance depends on the
amount of debris cover, surface position and compo-
sition of the debris. Spectrally estimated supraglacial
moisture content could be used for calculation of debris
surface albedo (Liang, 2004). Inclusion of mineral com-
position mapping may reduce errors in satellite glacier
surface radiative absorption estimates. Further synergy
of thermal and velocity data could be used to investi-
gate climate interaction with debris covered ice, and to
assess stagnation of flow toward the glacier terminus,
the formation of supraglacial meltwater and subsequent
glacial disintegration. Such glacier debris pattern anal-
ysis could reveal low magnitude, high repeat frequency
mass events vs. episodic pattern high magnitude event,
low frequency mass events – thus improving predictions
of glacier mass loss.
7 Conclusions
Southern Himalayan glaciers are characterized by extensive
debris cover and are losing significant ice mass in recent
decades (e.g. Bolch et al., 2011). Sustained and widespread
ice loss in the Himalayas (Berthier et al., 2007) and in other
glacierized regions contributes to crustal uplift (Tamisiea
et al., 2001; Larsen et al., 2005) and sea-level rise (Meier
et al., 2007). Improved satellite mapping of debris covered
glaciers can assist with quantification of these land ice mass
changes, and thus is of paramount importance.
This study presented new methods for satellite mapping
of glacier debris cover geochemistry and mineralogy, in-
cluding qualitative (hyperspectral satellite TOA reflectance,
SWIR/TIR false color composites) and semi-quantitative
(mineral indices, emissivity estimated silica weight percent)
methods. Kinematic glacial movements, including surges
and confluence flow, were mapped via use of mineral in-
dices, SWIR/TIR composites and velocity fields. Glacier sur-
face temperature maps analyzed in conjunction with mineral
composition revealed correlation between changes in min-
eral abundances and changes in glacier surface temperature.
Satellite methods presented were tested in combination to
improve description of debris covered ice areal extent, sur-
face glacier debris mineral composition, glacier surface de-
bris weathering and kinematics and to investigate radiative
absorption of different debris types. Additionally, this study
provides quantitative geochemical, mineralogic and in situ
spectral data from Khumbu Himalaya glaciers not previously
reported.
Continued high spectral and spatial resolution optical re-
mote sensing is essential for debris covered glacier monitor-
ing. ESA’s forthcoming Sentinel satellites with optical sen-
sors aimed at collecting 10 m spatial resolution, 5 day tem-
poral resolution global data are promising for glacier stud-
ies. NASA’s ALI, ASTER, Hyperion, Landsat and MODIS
sensors offer over a decade of freely available global spectral
data. This wealth of spectral satellite data has the potential to
be used more broadly in supraglacial debris quantifications.
Improved satellite glacier debris cover characterization will
lead to reduction of uncertainties in glacier extent mapping,
glaciologic thermal parameters and glacial kinematic estima-
tions, ultimately advancing our understanding of debris cov-
ered ice ablation and ice loss.
Acknowledgements. This work was funded by the Department
of Geosciences at the University of Oslo, Norway and European
Space Agency’s GlobGlacier project (21088/07/I-EC). We thank
A. MacArthur and C. MacLellan of the Natural Environment
Research Council Field Spectroscopy Facility for assistance
with the field spectrometer loan (585.1210 to DIB). We also
thank University of Oslo Department of Geosciences personnel
R. Xie, B. L. Berg as well as M. Debella-Gilo for assistance
with XRD/XRF and velocity analysis, respectively. We are very
grateful to T. Bolch and A. Racoviteanu and for detailed manuscript
reviews. Special acknowledgement is due to N. S. Rai for field
assistance and R. Thapa, S. Bajracharya at the International Centre
for Integrated Mountain Development IC I M OD for logistic
support in Kathmandu, Nepal.
Edited by: A. Klein
www.the-cryosphere.net/6/85/2012/ The Cryosphere, 6, 85–100, 2012
98 K. A. Casey et al.: Optical remote sensing of debris covered glaciers
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