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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-25, NO. 6, NOVEMBER 1987
751
Snow Property Measurements Correlative to
Microwave Emission at 35 GHz
ROBERT E. DAVIS, JEFF DOZIER, MEMBER, IEEE, AND ALFRED T. C. CHANG, MEMBER, IEEE
Abstract-Snow microstructure, measured by plane section analy-
sis, and snow wetness, measured by the dilution method, are used to
calculate input parameters for a microwave emission model that uses
the radiative transfer method. The scattering and absorbing properties
are calculated by Mie theory. The effects of different equivalent sphere
conversions, adjustments for near-field interference, and different snow
wetness characterizations are compared for various snow conditions.
serve new snow in February, variable layered snow in
March, and melting snow in April.
The radiometer used to make the 35-GHz brightnesstemperature measurements is a periodically calibrated accoupled total power type. The radiometer was hand-held
about 1 m above the snow. The brightness-temperature
observations consisted of views at zenith angles ranging
from 00 to 700 in 100 increments for both horizontal and
vertical polarizations. The observations compared with the
model for each data are averages of two or more scans.
I. INTRODUCTION
In the radiative transfer equation [3] radiance terms can
T HE GOALS of this study are: 1) to develop and eval- be replaced by brightness temperature at microwave freuate field techniques to obtain the ice and liquid phase quencies. The brightness temperature of a snow pack can
data suitable for input to microwave emission models, and be found by solving the radiative transfer equation.
2) to illustrate their utility to drive a discrete scatterer
dTB
(1)
= -TB(TV, tt) + J(V, ii).
model of microwave emission at 35 GHz for a seasonal
dTv
alpine snow pack with semi-infinite optical depth.
The snow pack is characterized in the radiative transfer TB ( TV, ) is the monochromatic brightness temperature at
problem as one or two layers of uniform spheres that have optical depth T, in direction cos-1 It J,(rv, tu) is the
been obtained by averaging snow property measurements. source function, which accounts for scattering of diffuse
The background medium is considered to be a mixture of radiation from other directions and for emitted radiation.
(
air, ice, and liquid water. The model results are compared
WV
P0(r0; ., p/) dp/
X T
to apparent brightness-temperature measurements at hor2
izontal and vertical polarizations for a variety of view angles. We compare different equivalent sphere conver1
(2)
(T0)] T(TV)
+ [ sions, adjust the refractive index to compensate for close
proximity of the ice grains, and evaluate two geometric wv and Pv are the single-scattering albedo and phase function; these are generally piecewise continuous functions
configurations of liquid water in snow.
of depth for a nonuniform medium. The optical thickness
II. EXPERIMENTAL DESIGN
of a layer is
Snow property measurements were carried out coinciOiZQext
(3)
dent with observations of microwave brightness temperIrv = Nzext = 4
4r
ature during three periods in the 1984-1985 snow season
at a study plot on Mammoth Mountain in the eastern Sierra N is the number density of scatterers of radius r, z is the
Nevada, California. The facilities at the plot include elec- layer thickness, orext is the extinction cross section at fretrical power, shelter, energy balance instrumentation, and quency v, 0i is the volume fraction of ice, and Qext is the
arrays of thermistors in the snow pack [1], [2]. The pe- extinction efficiency at frequency v. The parameters used
riods of radiometric measurements were scheduled to ob- in the Mie calculations are the radius of the equivalent
sphere r for the layer and the relative index of refraction
Index Terms-Snow, microwave, microstructure, wetness, radiative
transfer.
I,)
Manuscript received September 30, 1986; revised July 10, 1986.
R. E. Davis is with the Sierra Nevada Aquatic Research Laboratory,
Mammoth Lakes, CA 93546.
J. Dozier is with the Center for Remote Sensing and Environmental Op-
tics, University of California, Santa Barbara, CA 93106 and the Jet Pro-
pulsion Laboratory, California Institute of Technology, Pasadena, CA
of the spheres, as compared to the background medium.
III. MICROSTRUCTURE MEASUREMENTS
Snow samples were obtained from snow pits at the same
91109.
time conventional snow properties were observed. Wet
Goddard Space Flight Center, Greenbelt, MD 20771.
snow samples were quick frozen using dry ice and a
A. T. C. Chang is with the Laboratory for Terrestrial Physics, NASA
IEEE Log Number 8716805.
cooler. In addition to the field description, micrographs
0196-2892/87/0600-0751$01.00 ©) 1987 IEEE
752
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-25, NO. 6, NOVEMBER 1987
of selected snow samples were used with reference grids
to obtain estimates of the particle average diameter.
Each sample of snow was subdivided into specimens Of
about 3 x 3 x 5 cm, carefully avoiding the edges of the
blocks. Sections were prepared from the specimens according to recently reported procedures [4]-[6]. For each
specimen at least three sections were prepared, two that
were parallel and perpendicular to the structure of the
snow, and one with random orientation. Micrographs then
were taken of the sections with a Nikon HFX photomicroscope using Kodak Ektachrome 400 35-mm slide
transparency film. A fiberoptic ring illuminator was used
to provide a cool light source, almost coaxial to the main
optic tube of the photomicroscope. The lighting arrangement produces bright reflection from the pore filler and
maximum light penetration into the exposed ice grains,
which appear darker.
The micrographs were digitized with 8-bit brightness
levels as 512 x 512 pixels using a frame-grabber video
digitizer, part of a Model 70F image computer from International Imaging Systems. The classification procedure
starts by calculating the snow density corresponding to
most values of the brightness level threshold. By comparing the calculated densities to those measured independently for the snow specimen, this relationship guides the
user in determining a threshold brightness value. Next a
visual threshold is determined, which best replicates the
micrograph appearance with a real-time density slicing
operation while the image is displayed on the monitor.
The classified images are processed with a parallel-line
sampling technique that results in three measurement parameters and their distributions:
1) Point density PP, the number of pixels falling on ice
profiles divided by the total number of pixels.
2) Intercept number density NL, the number of ice-pore
and pore-ice
and pore-ice
transitions.
transitions.
3) Ice intercept lengths L, the distances between poreice and ice-pore transitions.
All of these are ratio estimates of statistical parameters
and are subject to the standard estimates of error, which
become smaller with increasing numbers of sample estimates [7]. Volume density is equal to the point density,
i.e., the ice volume fraction 0i of the frozen specimen
=
0,
VV
=
PP
=
Pi
-
(4)
IV. MEASUREMENT OF LIQUID WATER CONTENT
Measurements of snow wetness were obtained by the
dilution technique because tests show that cold calorimetry is inconsistent and requires too much time, and
because dielectric techniques require sophisticated instrumentation that was unavailable. Refinements and testing
of the dilution method for measuring snow liquid fraction
have been reported [8] in which the tests show an accuracy of 1.5 percent and an acquisition rate of about 3 to
5 mm per sample.
The dilution method relies on the dilution of an aqueous
solution when it is mixed with wet snow. The concentration change forms the basis for measurement. A stock solution of mass S and impurity concentration Cs, is mixed
thoroughly into a wet snow sample of mass M, with unknown water mass W. The solution is at 0°C and mixing
is in an insulated container, so that melt or refreezing is
minimized. The impurity concentration in the stock solution is small enough so that freezing point depression is
negligible, but large enough to be well above the impurity
concentration C,, in the liquid water in the snow sample.
The mixture of stock solution and snow liquid water has
impurity concentration Cm
SCs + WCw
(6)
S+ W
This can be solved for W and divided by the snow mass
M to give the liquid water mass fraction xw. Typically, xw
x 100 is in the range 0 to 30 percent
C
W
W
M
S
M Cm
CS
Cm
CS
CW(
C5
The absolute concentrations Cs, Cm, and C, are not
needed, only their ratios. The volume fraction of liquid
can be obtained
water ony
W= A,,,-.
Pi
(8)
If mixing of the stock solution is complete, errors in the
dilution method result from errors in the measurements of
S, M, Cm / Cs, and Cw/ Cs.
V. APPROXIMATION OF RADIATIVE PARAMETERS
A. Equivalent Spheres
Pi is the number of pixels falling on ice, and PT iS the It has been shown that the optical properties of irregular
total number of pixels. The snow density is pS = Pp, particles of ice can be approximated equivalent
by
spheres
where Pi = 917 kg m-3is the density of ice.''in the microwave part of the spectrum
in
and
[9]
theoretThe surface density or surface area per containing vol-
ume,
IS
~~~~N.
scattening
it generally has been assumed that the equivalent sphere has the same diameter as the snow particles
LT
disaggregated from the pack. The best conversion to
where NL is the intercept density of the grain boundaries, mimic the actual snow grains has not been determined adNi is the number of profile boundary intersections, and LT equately. In addition, the effects of the dominant orienis the length of the line scan.
tation of microstructure features are not well understood.
SV= 2NL = 2 -i
(5)
753
DAVIS et al.: SNOW PROPERTY MEASUREMENTS
The conversion procedures for equivalent spheres tested
are: 1) the sphere of mean chord length equal to the mean
intercept of the ice phase, 2) the sphere of equal volumeto-surface ratio, 3) the sphere of equal mean diameter to
particles in micrographs, and 4) the sphere of equal mean
diameter to snow pit estimates. The section data were limited to conversions (1) and (2) in this study and the particle parameter measurements to conversions (3) and (4).
For the conversion (1) we assume that the mean intercept length of all the ice profiles, convex and concave, is
equal to the mean intercept lengths of circles that would
form the profiles of the equivalent spheres. The relationship between the average radius of random circles cutting
spheres of equal size and the true radius of the spheres is
[7]
RL = - -r
(9)
shaergeraiuo
RL is the sperrdisnd
circular section profile. Similarly, it can be shown that the
average radius and mean intercept length of the circles are
related by
2(10)
L
To estimate the sizes of the ice spheres and water inclusions from rT, Oi, and O, we assume that melt occurs
uniformly around the equivalent spheres. The radius of
the ice cores ri for different water contents is
1/3
(15)
- K( oi
1
ri-
rT\10910w +
The central ice spheres decrease in radius and number
density as the snow liquid water content increases.
We consider two configurations of the ice and liquid
water in approximating the average radiative properties.
The first characterization treats the equivalent spheres as
ice covered by a thin layer of water [11], [12]. While this
treatment is questionable for low water contents because
constrains the liquid to occur as menisci between grains, and it is inconsistent with
mixing formulae comparisons to dielectric measurements,
geit is used to illustrate the effect of different ice-water
ometry. The problem of scattering from concentric spheres
equilibrium thermodynamics
was solved by Aden and Kerker [13] and is not repeated
here.
The second characterization treats the ice and water
where L is the mean intercept length. Thus
separately. The size of the ice spheres as melt progresses
is calculated using (15), and the water is assumed to occur
8
=
=
L.
0.81
-L
(11) as small menisci approximated by spherical shapes are
RL
held between two ice spheres. This is a more realistic
used
treatment since the liquid water in snow nestles between
are
The surface density Sv and the volume density Vv
the
grains, but it assumes complex shapes. The ratio of
to calculate the radius of the sphere of equal volume-tothe number of water spheres to the number of ice spheres
surface ratio
is arbitrarily selected based on Colbeck's [11] suggestion
/Vv\
(12) that wet seasonal snow may be dominated by two-grain
RV = 3 S
bonds. Thus, the radius of the water sphere rw can be calThe diameter-equivalent sphere can be obtained from the culated from the number density of ice spheres N and the
particle' information by using the mean diameters from the liquid water volume fraction 0,,
3OW 1/3
particle observations from the microscope and field.
=
(16)
__
\2NW/
(13)
RDM =
Once the radius of the water spheres is determined, the
relative refractive index of the spheres is calculated and
and
the combined scattering and absorption properties of the
D
F
(14) layer are estimated according to Dozier and Warren [14].
RDF = 2
_ SQ
ext
Sice iceext +~Siwater Qwater
where DM is the mean estimated grain diameter from the
ice + 5water
micrographs of disaggregated particles, and DF is the
water
S ice ± 5
mean estimated grain diameter from the field observa(18)
Qsca - iceQsca + waterQsca
tions. RDM and RDF are the radii of the equivalent spheres.
ice 5water
For the wet snow cases, the samples returned from the
(~ ~ ~ ~ ~ ~ ~3W)
Q-(17)
Q
field were frozen so that the section and disaggregate pa-
rameters represent the combined dimensions of the ice and
water. Further, the field measurements of particle dimensions also incorporate the liquid inclusions. Therefore, the
equivalent-spheres, which are part ice and part water, can
be described by a total radius rT.
6o= Qsca/Qext
(19)
where 5ice and 5water are the geometric cross sections of
ice and water, respectively. Equation (3) is used to calculate the optical depth by adding the contributions of ice
and water.
754
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-25, NO. 6, NOVEMBER 1987
C. Optical Properties of Ice and Water
The refractive index of ice at frequency 35 GHz (X =
8.57 mm) is interpolated from the data compiled by Warren [15]. While the real part of the refractive index of ice
is independent of temperature, and is 1.78 v = 35 GHz,
the imaginary part is temperature dependent, varying from
3.5 x 10-3 at - 1°C to 1.4 x 10-3 at -20°0C. This afa therefore, the single scattering albedo W.
ice and,
fects ext
The refractive index of water is interpolated from the data
of Lane and Saxton: [16] N wal = 3.95 + i 2.44.
D. Effects of Grain Contact
In some of the model runs we attempt to compensate
for the effects of close particle spacing usLng the method
proposed by Gate [17]. The real part of the relative refractive index of the Mie spheres is divided by the effective refractive index of the surrounding medium. For dry
snow
(20)
nmed = Goflice + Ganair
and for wet snow
nmed
- (E' )1/2.
n =(Ews
(21)
(21)
The estimation of the dielectric function of wet snow E'
is discussed in a following section. Here we only adjust
the real part of the index of refraction of the equivalent
spheres and assume that there is no effect on the imaginary part. Since the wavelength is large, the medium immediately surrounding the spheres is assumed to have the
same constituent mix as the bulk.
E. Averaging for Snow Stratigraphy
Some of the snow pit observations show many layers
with quite different properties. Rather than increase the
model complexity to accommodate several layers, we average the parameters into two layers. The averaging
scheme is based on the optical thickness of the layer and
its optical depth
X
Xi (
n
j
'ri
2
) exp
[-(ri+l
+
(i+I- T1i exp [-(Ti+1 +
r0)/2]
r0)/2]
The~~~~~~~~~~~~~~~~~~~~
rerctv ine
iE(23)
ice at
For dry snow we sum the refractive indices of air and ice
weighted by volume fraction, and square the result to obtam the real and imaginary parts of the dielectric function.
Ed5 = (Gini + nafair + io kice)2
(24)
ai
where Oa is the volume fraction of air, and nair
= 1. This
is a convenient formula because it gives the real and
imaginary parts with one calculation and shows good
agreement with empirical formulae. For wet snow we use
the empirical relationships of Tiuri et al. [18] in which
the dielectric effect of liquid water is superposed on the
deeti
rpriso
r
nw
Ac' = Ew - Ed
(25)
= (0.100w + 0.80Gw) ew
(26)
(27)
+
80G2
e= (0. 100, 0. )cE
where E,' and E- are the real and imaginary parts of the
dielectric function of water. These are given by the Debye
relaxation spectra
AW
E(V) = c-, + ES-
E
1 + t)0
Substituting
E' = 4.9 +
82.8
1 + (v/vo)
.
(28)
2
(29)
and
,,
W
82.8 (v/vo)
+ (v/vo)2
1 +
(30)
where 82.8 is the difference between the high-frequency
limit and the static dielectric function of water, v is the
frequency under consideration (35 GHz), and vo = 8.8
GHz is the relaxation frequency. These values agree well
with interpolated experimental data from Lane and Saxton
~ ~~~~~~[16].
Ti
+I
e = e + ie" = (n + ik)2.
((22)
\2
where x is the parameter being averaged. We average the
temperature of the snow pack and the single scattering
albedos for all model calculations.
F. Dielectric Properties of the Medium
We use formulae that fit the empirical data to estimate
the dielectric properties of the medium. Considering the
data available,4 we will avoid considerations requiring detailed information about the shape of the constituents,
though it is recognized that no physical insight can be
obtained from empirical formulae.
The complex dielectric function e of a medium is related to the complex refractive index n
VI. RESULTS
A. Snow Property Measurements
The primary stereologic measurements based on the real
dimensions of the sections are influenced by the microscopic resolution. In general, mean intercept lengths decrease and surface densities increase with increased magnification. This is because the ice-grain profiles appear
more convoluted, partly a result of the preparation technique. We use low magnification to maximize the number
of profiles included, but at very low magnification, uneven illumination is more of a problem.
The dilution method for measuring snow wetness is
convenient as a field technique and measurements show
good correlation to brightness-temperature data in the case
of a thin (0-3 cm) actively melting snow layer at the surface (Fig. 1).
ikq
755
DAVIS et al.: SNOW PROPERTY MEASUREMENTS
280
270
O3
260
3 0 0
0
03
_270
D
0.10
0
1000
240
1N00
11C 0 120014C0
TB
0.06
e
wa04
2rees30elvin as the snow surac lye metswih ncrasngliui0
00
e
-
O
_
\
240
0
6
250
~~~~~~~~~~~~~~~~~~230-
0
220
0
260
00
250
TB I
0
45 HORIZONTAL POLARIZATION
_
0.02
0
210
1200
1300
grw2o30c1000 hcns1100 s wapoce
0.6
1400
0.04
TM
TB
=5l000
210
0
Fig. 1. Increase in brightness temperature TB (left axis), expressed in degrees Kelvin as the snow surface layer melts with increasing liquid water
content 35, (right axis). The snow was initially frozen and the wet layer
grew to 3 cm thickness as O, approached 0.06. s=1 Fe. ll,0198
T21
w=0.45
180
170
B. Modeling and Measurementsurements
Spheres with equal volume-to-surface ratio and equal
0_
mean chord lengths underestimate the volume scattering1ON
I
I I
in snow at 35 GH-z as shown in Figs. 2 and 3 for a new
60
70 60
40 5
10 20 30
snow condition and an old shnow condition, respectively.
35
at
the
overestimate
scattering
Equal-diameter spheres
Fig. 2. Results for Feb. 11, 1985. Symbols represent measurements, and
lines represent results from a single-layer model. Units of TB are degrees
GHz (Figs. 2 and 3), a result shown in other studies, unKelvin and the view angle is expressed in degrees from vertical.
less the calculations are adjusted for near-field intei-fermodel
between
which
the
correspondence
improves
ence,
results and radiometric measurements.
280
The adjustment to the relative refractive index- to ac270
count for the close spacing and contact of the ice grains
T
causes a reduction in the amount of scattering. Therefore, d
only the model parameters using the larger equivalent
\
spheres from the particle measurements have been modi0
\
fled. Figs. 4 and 5 show the results for newthesnow and old
240
0
snow conditions. The coffections improve
correspon0 w
230
dence between the model and the radiometric measureopoor-efaciv
inde
ments considerably, although the model shows
agreement with the horizontally polarized data. The horTB 210
izontal-polarization data show a much larger dynamic
and
exso
between
differences
theory
range, and larger
200
periment could be expected. However, the effects of
10E
strong dielectric contrasts within the pack also may acT269K
in
differences
count for the larger
bfrightness temperature.
e=1.58+0.0032i
180
be
data
could
matched
the
by
w=re.
probably
Altenatively,
170n[3 VERTICAL POLARIZATI
finding equivalent sphere sizes somewhere between those
HORIZONTAL POLARIATION
tested, rather than by adjusting the refractive index of the f
160
constitutes
the
best
what
equivalent
Thus,
sphere
spheres.
150
conversion is unresolved. Also, the possible dependence
I
I I I I
of the appropriate equivalent sphere size on frequency re-I
10 20 30 40 50 60 70 80
mains unaddressed, as well as the effects of orientation of
VWNk
microstructure features. More accurate microwave mea-
bettermoe
yields thigeprtesher
emissivitiesiand
e
thanrmrnsuigmaurmnsfo peiu
results.
Whereas the model calculations for the March wet snow
as
then adding a wet snow layer to the top, the model cal-
756
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. GE-25, NO. 6, NOVEMBER 1987
290
_
280
270
270
260
260
23400
0
013
-
-
240>°
-X
230
220
0
\
-230-
~~~~~~~~~~~~~~~~~0
-O0
0 0
220
2100
TB
\
0
B
210
°
-
0
SINGLE LAYER ADJUSTED
e=1 .51+O.0019i
190
Bw =0.016
UPPER CURVES: CONCENTRIC
DUAL LAYER
190
T=261K~~~~~~~~~~~~~~~~~~~~~~~~~8
180
180~
T=271.9
w=0.0021 (TOP LAYER)
w=0.45
170
170
-
ri
VERTICAL POLARiZATION
160
150
0
HORIZONTAL POLARIZATION
10|
30
20
10
70
60
=47002
10
20
VERTICAL POLARIZATION
0 HORIZONTAL POLARIZATION
e=1.47+0.032i
80
Fig. 4. Results for Feb. 11, 1985. The refractive index of the spheres has
been adjusted, densities near the surface have been used to calculate e.
0
w=0.0027(TOP LAYER)
5
50
40
MBNNANGLE
LOWER CURVES: SEPARATE
T=269.7
160
30
40
50
60
80
70
VEWANGLE
Fig. 6. Results from Mar. 20, 1985, showing difference in the geometry
of liquid water specified in the model. Upper curves are obtained by
using a concentric-sphere configuration and the lower curves by using
separate spheres of ice and water. Measurements and calculations are for
low water content.
270
260_
250 [t
0:
\Z0
n.
230|
0
N ]-
090270
240
[
0
0
0 2
220
TB
0
260
0
230
_ DL L_
0
21024
230-
200~~~~~~~~~~~~~~~~~~~~~~~~~2
o DUALLAYER
T-269KT
180
170
TBT
e=1.58+0.0032i
24~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~-=7.10
210
w=0.092
2150)
VERTICAL
w=0.59 1
POLARiZATION
e
0 HORIZONTAL POLARV-ATION
DU0ALLAYER
180
150
fr
=
0.058
CURVES: CCNCENTRr~~~~~~~~~~~~~~~~~~~~~~~~~~UPPER
160
T-272.7
w=0.0015(TOPLAYER)
170
10
20
30
40
50
60
70
T=270.9
60
VIBNANG-kE
culations for the wet spring snow in April use a single
layer. Fig. 6 illustrates the results for a low liquid water
content from the March data. It shows that the separatesphere geometry of water and ice gives better model re-
sults, but at O,w 2 0.05 the concentric-shell treatment of
~~~~
~~~~~~160w=0.002iITOPLYER)
B
Fig.in 7.the Results
from Mar. water
showingThe
difference
in liqiud geometry
1985,content.
model, at greater20,
upper curves result from
concentric-shell geometry and the lower curves result from separate-
sphere geometry.
DAVIS et al.: SNOW PROPERTY MEASUREMENTS
757
liquid water gives better results, as shown in Fig. 7. The [18] M. Tiuri, A. H. Sihvola, E. G. Nyfors, and M. T. Hallikainen, "The
complex dielectric constant of snow at microwave frequencies," IEEE
temperatures shown on the figures are the result of the
J. Ocean. Eng., vol. OE-9, pp. 377-382, 1984.
property averaging scheme described by (22). Neither [19] A. Denoth, "The pendular-funicular liquid transition in snow," J.
characterization predicts the increase in brightness temGlaciology, vol. 25, no. 91, pp. 93-97, 1980.
perature depicted in Fig. 1. This result may reflect the [20]-, "The pendular-funicular liquid transition and snow metamorphism," J. Glaciology, vol. 28, no. 99, pp. 357-364, 1982.
change in dielectric behavior observed at lower frequencies [19], [20] when the snow undergoes a transition be*
tween the pendular and funicular saturation regimes or it
may be an artifact of the characterization of wet snow.
|l
Robert E. Davis received the BA. degree in geREFERENCES
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ography in 1976 from the University of California, Santa Barbara. He received the M.A. degree
in 1980, and the Ph.D. degree in 1986 from the
same institution.
He is currently working as an assistant researcher in the Center for Remote Sensing and Environmental Optics at The University of California, Santa Barbara. His research interests are snow
physics, snow property measurements, remote
sensing of snow, and heat and mass transfer in
porous media. Currently he operates an experimental field station at the
Sierra Nevada Aquatic Research Laboratory, Mammoth Lakes, CA.
*
Jeff Dozier (M'86) received the B.A. degree in
geography in 1968 from California State University, Hayward, and the M.Sc. and Ph.D. degrees
in 1969 and 1973, respectively, from the University of Michigan, Ann Arbor.
He has taught since 1974 at the University of
California, Santa Barbara, where he is now Professor of Geography and a researcher in the Center for Remote Sensing and Environmental Optics. Recently, he joined the Jet Propulsion
Laboratory, California Institute of Technology,
Pasadena, part-time, as a member of the technical staff in the Earth and
Space Sciences Division and as Project Scientist for the high-resolution
imaging spectrometer (HIRIS). His research interests are in remote sensing
of snow properties, energy balance modeling of snow processes in alpine
terrain, and snow chemistry and runoff.
Dr. Dozier serves on the Committee on Glaciology of the National
Academy of Sciences.
*
Alfred T. C. Chang (M'86) was born in Shanghai, China, in 1942. He received the Ph.D. degree
in physics from the University of Maryland, College Park, in 1971.
Since 1974, he has been at the NASA Goddard
Space Flight Center, Greenbelt, MD, where he is
a Research Physicist in the Laboratory for Terrestrial Physics. His areas of research include microwave radiometry, radiative transfer calculations in
relation to remote-sensing applications, and development of techniques for determining the properties of snow, soil, ice, and the atmosphere by remote sensing.