1. Reflectance
Spectra
of
the
Juneau
Icefield
Adrian
Peter,
University
of
Zurich,
ZH,
Switzerland
Elizabeth
Perera,
DePaul
University,
IL,
USA
4.2
Analysis
of
Snow
Impurities
in
Suncups
4.3
Organic
Matter
Analysis
• Percent
organic
matter
within
each
sample
correlated
with
reflectance
readings
taken
on
the
Icefield.
• Dirty
snow
largely
showed
the
most
organic
matter,
followed
by
red
algae
and
finally,
clean
snow.
• Both
organic
and
inorganic
matter
contribute
to
darkening.
• Outliers
may
be
due
to
changing
cloud
conditions.
4.
Results
4.1
Clean
Snow
Grain
Size
Analysis
• High
JIF
reflectances match
published
spectra
(Nolin
&
Dozier
2000).
• Grain
size
estimated
using
size
of
certain
absorption
features.
• Snow
on
the
JIF
is
both
very
wet,
with
large
grains.
• No
discernable
difference
in
reflectance
of
small
grain
sizes
versus
large
grain
sizes,
but
more
variability
seen
in
spectral
grain
size
measurements
than
in
field
observations.
3.
Data
Collection
• Hemispherical-­‐directional
reflectance
(HDR)
spectra
were
collected
during
the
summer
of
2015.
• An
18
degree
foreoptic was
used,
yielding
a
viewing
footprint
approximately
40
cm
in
diameter.
• Sample
locations
were
distributed
throughout
the
accumulation
and
ablation
zone
of
the
Juneau
Icefield
(JIF).
Figure
2.
Schematic
of
HDR
data
collection
method.
• Biomass
was
analyzed
using
a
chemical
digestion.
Material
taken
from
water
samples
was
dried,
hydrogen
peroxide
added,
and
the
sample
heated.
After
24
hours
in
the
oven,
percent
organic
matter
was
determined
from
the
difference
in
weight.
1.
Introduction
The
reflectance
of
snow
and
its
associated
albedo
effect
the
snowpack’s
energy
balance
(Dozier
et
al.
2009).
Several
different
properties
affect
the
reflectance
of
snow
including:
water
content,
grain
size,
and
the
presence
of
dust
and
organic
matter
on
the
snow
surface.
Variations
in
these
conditions
effects
the
amount
solar
radiation
that
is
reflected
off
the
surface
and
back
into
the
atmosphere
(Bøggild et
al.
2010),
and
each
effect
has
a
different
spectra
response.
Figure
1.
How
variations
in
grain
size
and
the
presence
of
impurities
affects
reflectance.
Changes
in
the
amount
of
solar
radiation
that
is
reflected
off
the
snow
in
turn
effects
the
amount
and
rate
of
surface
melting
that
occurs
across
the
glacier
during
the
ablation
season
(Painter
et
al.
2013).
References
Cited:
Bøggild et
al.
2010.
J.
of
Glac.,
56
(195),
101-­‐113.
Dozier
et
al.
2009.
Remote
Sens.
Environ.,
113,
S25-­‐S37.
Nolin
and
Dozier
2000.
Remote
Sens.
Environ.,
74,
207-­‐216.
Painter
et
al.
2013.
J.
of
Geophys.
Res,
118,
9511-­‐9523.
2.
Research
Questions
Impurities,
algae,
and
large
snow
grains
all
reduce
the
reflectance
of
shortwave
radiation
but
with
unique
spectral
signatures
(e.g., Dozier
et
al.
2009).
1. Spectral
reflectance
was
measured
for
a
range
of
targets,
creating
a
spectral
catalogue
of
the
Taku
glacier
system.
2. Using
this
spectral
library:
• Red
algae
biomass
was
linked
to
spectral
reflectance
• Effect
of
impurities
in
suncups was
quantified
• Snow
grain
size
retrieval
algorithm
was
tested.
Small Grain Size Large Grain Size
Impuri1es
(dust and organic ma8er)
5.79
8
0.54
2.7
1.07
3.83
0.23 0.32 0.080
1
2
3
4
5
6
7
8
9
0.5 1 1.5 2 2.5 3 3.5
Percentages
(%)
Sample
number
Percent
Organic
Matter
Dirty
Snow
Red
Algae
Clean
Snow
Figure
3. Grain
size
algorithm
versus
ground-­‐truth
data.
a)
b)
Katherine
Popyack,
Hartwick College,
NY,
USA
Lara
Hughes-­‐Allen,
University
of
Southern
California,
USA
Allen
Pope,
National
Snow
and
Ice
Data
Center,
CIRES,
University
of
Colorado
Boulder,
CO,
USA
– allen.pope@nsidc.org
a)
Qualitative
snow
categories
mean
with
standard
deviation
show
as
dashed
lines.
b)
Impurity
reflectance
ratio
(RR)
at
588
nm
absorption
shoulder
calculated
by
using
the
concept
of
continuum
removal.