JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
Vol. 44, No. 6
AMERICAN WATER RESOURCES ASSOCIATION
December 2008
APPLICATION OF SATELLITE MICROWAVE IMAGES
IN ESTIMATING SNOW WATER EQUIVALENT1
Amir E. Azar, Hosni Ghedira, Peter Romanov, Shayesteh Mahani, Marco Tedesco, and Reza Khanbilvardi2
ABSTRACT: Flood forecast and water resource management requires reliable estimates of snow pack properties
[snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images
to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we
have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM ⁄ I) radiometer along with information on the Normalized Difference Vegetation
Index (NDVI).The algorithm was developed and tested over 19 test sites characterized by different seasonal
average snow depth and land cover type. Three spectral signatures derived from SSM ⁄ I data, namely T19VT37V (GTV), T19H-T37H (GTH), and T22V-T85V (SSI), were examined for correlation with the snow depth and
SWE. To avoid melting snow conditions, we have used observations taken only during the period from December
1-February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth ⁄ SWE and are
most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over
a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established
the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the
new technique was evaluated through its comparison with ground-based measurements and with results of
SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the
National Weather Service. The proposed algorithm was found to be superior to previously developed global
microwave SWE retrieval techniques.
(KEY TERMS: snow; snow depth; SWE; remote sensing; microwave.)
Azar, Amir E., Hosni Ghedira, Peter Romanov, Shayesteh Mahani, Marco Tedesco, and Reza Khanbilvardi,
2008. Application of Satellite Microwave Images in Estimating Snow Water Equivalent. Journal of the American
Water Resources Association (JAWRA) 44(6):1347-1362. DOI: 10.1111 ⁄ j.1752-1688.2008.00227.x
INTRODUCTION
Understanding seasonal variation of snowcover
and snowpack properties is of critical importance for
effective management of water resources. According
to the Federal Emergency Management Agency,
floods are one of the most common hazards in the
United States. A re-analysis of the National Weather
Service showed that flood damage has been increasing despite local and federal efforts to mitigate floods.
Snowmelt is one of the primary reasons for floods.
Accurate information on seasonal variation of snowcover and snowpack properties is critical for flood
1
Paper No. JAWRA-07-0022-P of the Journal of the American Water Resources Association (JAWRA). Received February 5, 2007; accepted
January 30, 2008. ª 2008 American Water Resources Association. Discussions are open until June 1, 2009.
2
Respectively (Azar, Ghedira, Mahani, and Khanbilvardi), Post Doctoral Research Associate, Research Associate Professor, Associate Professor, and Professor, NOAA-CREST, City University of New York, 137th St and Convent Avenue, New York, New York; (Romanov) Research Scientist, NOAA-NESDIS, Camp Springs, Maryland, and (Tedesco) NASA-Goddard Space Flight Center (E-Mail ⁄ Azar: [email protected]).
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prediction and for the effective management of water
resources. Current hydrological models predicting
snowmelt runoff rely on snowpack measurements
made at ground-based meteorological stations. Quite
often the density of station network is not sufficient
to adequately reproduce the snow cover distribution.
Some areas are not covered with surface observations
at all. This fact limits the ability to accurately characterize the river runoff and to predict floods. Satellite observations present an important source of
information on snow cover properties which can be
effectively used to complement traditional groundbased measurements or even substitute them.
The launch of Earth Observatory Satellites (EOS)
in the mid-20th Century and their capability to
observe the earth on large scales encouraged the
meteorologists and hydrologists all around the world
to find alternatives for traditional methods of estimating snowpack properties. The history of using
satellite data for climatological purposes started in
1966 by the launch of National Oceanic and Atmospheric Administration (NOAA) first polar orbiting
satellite capable of obtaining visible images of the
earth designed to estimate snowcover from space.
Satellites operating in the optical wavelength have
monitored snowcover over the Northern Hemisphere
for more than 40 years (Grody and Basist, 1996).
Optical sensors can detect snowcover only during
daytime and under cloud-free conditions. In contrast
to the visible spectral bands, satellite observations in
the microwave do not require daylight and can be
used to detect snowcover through clouds. Beside
information on the snow cover distribution, satellite
microwave instruments offer potential for monitoring
physical properties of the snow pack, particularly its
water equivalent (SWE) and the snow depth.
Snow emission in the microwave domain is highly
sensitive to variation of physical prosperities of the
snowpack. At frequencies higher than 15 GHz, snow
microwave emission tends to decrease as the snowpack thickness increases (Hallikainen, 1984). The
radiance measured by microwave sensors is typically
converted to corresponding brightness temperature
and is expressed in degrees K. Brightness temperature relates surface emissivity (e) to the physical temperature of the object (Ts) (De Seve et al., 1997).
In the last three decades a large number of
algorithms and techniques have been developed to
estimate snow pack properties from satellite observations in the microwave. Chang et al. (1987) proposed a linear relationship between the snow depth
(SD) and the brightness temperature difference at
37 and 18 GHz at horizontal polarization SD =
1.59(T18H-T37H). This relationship was established
assuming that the density of snow pack and the
snow grain size were correspondingly 0.3 g ⁄ cm3 and
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0.3 mm. The algorithm was applied to global
observations of Scanning Multi-channel Microwave
Radiometer (SMMR) and Special Sensor Microwave
Imager (SSM ⁄ I) (Foster et al., 1997). Hallikainen
(1984) have also used the difference between brightness temperatures at 18 and 37 GHz at horizontal
polarization, but related this difference to SWE.
Another algorithm utilizing the difference of brightness temperatures at 37 and 19 GHz at vertical
polarization was employed by Walker and Goodison
(1995) to estimate the snow water equivalent over
Canadian Prairies. A modified version of this algorithm was employed by De Seve et al. (1997) to
assess snowpack properties over James Bay area in
La Grande River watershed, in Quebec, Canada
with SSM ⁄ I data. Tedesco et al. (2004) proposed an
Artificial Neural Network technique for the retrieval of SWE from SSM ⁄ I. They used a multilayer
perceptron with various inputs to estimate SWE.
The accuracy of snow depth and SWE retrievals
with microwave data is generally low. Retrieval
errors vary from 70 to 200% depending on a particular area, land cover type, physical conditions of
the snow pack, etc (Kelly et al., 2003). Underestimations of SWE often occur due to limited dynamic
range of all linear algorithms.
Since 2002 global observations of snow cover are
also performed with a new generation satellite instrument, Advanced Microwave Scanning Radiometer—EOS (AMSR-E). As a prototype AMSR-E global
snow depth estimation algorithm, Kelly et al. (2003)
introduced an algorithm that combines an empirical
snow grain growth model with a densification model
that are used to parameterize a constrained Dense
Medium Radiative Transfer model suite of snow
depth estimates from brightness temperature differences. When compared with snow depth data from
station measurements, their algorithm, had an average error of 21 cm, equivalent to 94%.
Vegetation is another factor that tends to increase
the error of snow depth or SWE retrieval. In order to
account for the vegetation effect, Derksen et al.
(2004) developed a technique incorporating different
linear algorithms for open environments, deciduous,
coniferous, and sparse forest cover. The SWE was
then calculated as a weighted average of all four estimates,
SWE ¼ FD SWED þ FC SWECþ FS SWES þ FO SWEO ;
where (F) is the fraction of each land cover type
within a pixel, D, C, S, and O correspondingly represent deciduous forest, coniferous forest, S sparse
forest, and O open prairie environments.
The effect of forest cover on the emission of snow
covered terrain and, thus on the retrieval algorithm
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performance, depends on its type: deciduous forests
in winter do not have leaves and thus attenuate
microwave radiation to a much lesser extent than
coniferous forests. [Therefore, algorithms simply
incorporating forest fraction without any account for
the forest type (i.e., Foster et al., are not quite
correct).] We propose to use Normalized Difference
Vegetation Index (NDVI) as an indicator of the forest
cover type. In contrast to the vegetation season,
NDVI variation in winter is small. NDVI values are
largest over evergreen needle-leaf forests, and
decreases over mixed forests and deciduous broad-leaf
forests. Over snow-covered non-forested areas NDVI
even reaches negative values because of higher reflection of snow in the visible spectral band than in the
near-infrared.
In this study, we developed and tested a new
algorithm for estimating snow depth and SWE from
satellite observations in the microwave. The primary focus was on the Great Lakes area. The technique incorporates a two-stage algorithm and uses
NDVI to account for vegetation effects. The algorithm was tuned using surface observations. The
accuracy of the algorithm was evaluated through
the comparison of satellite retrievals with surface
observations and with the output of a physicalbased snowpack model developed and run operationally at National Operational Hydrologic Remote
Sensing Center (NOHRSC).
In the first section of this paper, we present the
study area location and land cover characteristics,
satellite data, as well as snow observations and modeled data. The second section describes the methodology to evaluate capability of microwave data in
retrieving snowpack properties in the study area. The
third section discusses the results that were used to
develop a new model. The last section describes the
development of the new algorithm and evaluates its
performance over the Great Lakes area.
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ESTIMATING SNOW WATER EQUIVALENT
the behavior of satellite measured microwave radiations with respect to snow over various land cover
types.
SSM ⁄ I Data
The SSM ⁄ I passive microwave radiometer has
seven channels operating at five frequencies (19, 35,
22, 37.0, and 85.5 GHz) and dual- polarization (except
at 22 GHz which is vertical polarization only)
(Table 1). The sensor spatial resolution varies for
different channels frequencies. In this study, the
Scalable Equal Area Earth Grid EASE-Grid SSM ⁄ I
products distributed by National Snow and Ice Data
Center (NSIDC) were used (Brodzik and Knowles,
2002). In SSM ⁄ I EASE-Grid, all channels below
85 GHz are re-sampled to footprint size of 19 GHz
beam but the sample spacing is slightly more
than 25 km (25.06 km) for all the channels (NSIDC)
(Armstrong et al., 1994). The EASE-Grid SSM ⁄ I data
are available in global cylindrical, and azimutal equal
area. Since our study area is between 45N and 49N,
we have used the Northern Hemisphere Azimuthal
Equal-Area EASE-Grid. EASE-Grids aspect ratio is
about 1.17:1 at 45N as compared with cylindrical
aspect ratio which is 1.50 for 45N, making Azimuthal projections more desirable (Brodzik and
Knowles, 2002). The study area, located between 41
and 49N and 87 and 98W, is covered by 28 X 35
(980) EASE-Grid pixels.
Normalized Difference Vegetation Index
Proposed by Rouse et al. (1973), NDVI is widely
used to characterize vegetation cover. NDVI is
defined as a difference between reflectance in visible
red and near-infrared spectral bands divided by their
sum
ðNDVI ¼ ðNIR VISÞ=ðNIR þ VISÞÞ:
STUDY AREA AND DATA USED
The selected study area is located west of Great
Lakes between 41N and 49N and 87W and 98W
covering parts of Minnesota, Wisconsin, and
Michigan. The area covers hundreds of water sheds
in three major basins of Great Lakes basin,
Souris-Red River basin, and upper Mississippi River
basin. The study area has different land cover types
ranging from bare land and grass land to deciduous
and needle-leaf forests. Diversity of land cover type
was among the major reasons for selection of Great
Lakes area for this study in order to analyze
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TABLE 1. SSM ⁄ I Channels, Polarizations, and Resolutions.
Frequency
(GHz)
Polarization
19.35
19.35
22.235
37
37
85.5
85.5
Vertical
Horizontal
Vertical
Vertical
Horizontal
Vertical
Horizontal
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Footprint
Along
Track (km)
Footprint
Across
Track (km)
69
69
50
37
37
15
15
43
43
40
28
29
13
13
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Live green plants appear relatively dark in the visible and relatively bright in the near-infrared and
thus exhibit high NDVI values (Gates, 1980). Soil
and bare land have lower NDVI which even becomes
negative if the land is covered by snow.
The NDVI data for this study were obtained from
the NOAA ⁄ NASA Pathfinder Advanced Very High
Resolution Radiometer dataset which is distributed
by Goddard Space Flight Center (DAAC). The NDVI
data are extracted from a global 10-day composite
image for January 21-31 in 1994. The composite
images are derived by from images in a 10-day period
with minimum cloud coverage. To facilitate the comparison and matching of the two datasets (NDVI and
SSM ⁄ I) NDVI data were re-sampled and projected to
the EASE-Grid projection at 25 km spatial resolution.
Normalized Difference Vegetation Index has a
seasonal pattern meaning that it increases during
spring and summer and decreases during winter.
The winter NDVI tend to be much lower than summer for all types of land cover. Also, NDVI variation during the winter season is very limited.
Maximum winter NDVI is generally observed over
evergreen needle-leaf forests, which decreases
over mixed forests and deciduous broad-leaf forests.
Over grass land and bare land which is covered by
snow NDVI becomes negative. On the other hand,
microwave scattering is related to land cover. One
of the sources of error in estimating SWE from
microwave data is attenuation of microwave scattering over the forested areas (evergreen and mixed
forests). By using winter NDVI data, the attenuation effect can be estimated well.
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Model Data
Snow products generated by the Snow Data
Assimilation System (SNODAS) of NOAA National
Weather Service’s National Operational Hydrologic
Remote Sensing Center (NOHRSC) are available
beginning October 2003. SNODAS presents a physically based, spatially distributed energy and mass
balance model which incorporates ground-based
observations of snow depth, air-borne measured
gamma radiations, and downscaled output from
regional Numerical Weather Prediction as input
(NOHRSC, 2004).The output of the system includes
fields of snow depth, snow water equivalent, snow
melt, and a number of snowpack characteristics
generated at 1 km spatial and hourly temporal resolution. In order to match the SSM ⁄ I and SWE
datasets, we converted the resolution of SNODASSWE data to 25 km.
Although, NORHSC snow data are produced from
incorporating data from intense network of snow
reporting stations and have very high resolution, but
there are some limitations associated such as dependency on air-borne gamma measurements which are
limited and costly. In addition, these data are available
only over United States and their accuracy is not well
evaluated.
In order to evaluate the consistency of the SNODAS-SWE data with NCDC snow depth observations,
we calculated the correlation coefficient between
TABLE 2. Coordinates of Selected Pixels Along With NDVI
Values, Each EASE-Grid Pixel Contains 3 · 3 NDVI Pixels.
SSM ⁄ I EASE-Grid
Pixels
Ground-Based Snow Measurements
Surface observations performed at first-order and
US Cooperative Network Stations were obtained
from National Climatic Data Center (NCDC). There
are 681 stations within the study area but they are
not uniformly distributed. Most stations are located
in the vicinity of densely populated areas close to
the lake. To develop the algorithm and to evaluate
its performance we have used measurements made
at 19 specifically selected test sites. Each test site
is size of an EASE-Grid pixel (25 km · 25 km). For
the test sites with more than one station, the
observations were averaged. Table 2 lists geographical location of the selected test sites and their
NDVI characteristics including the mean and standard deviation within corresponding EASE-Grid,
25 km resolution cells. The mean and standard
deviation are derived based on difference between
spatial resolution of EASE-Grid (25 km · 25 km)
and NDVI (8 km · 8 km).
AND
Test
Site
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1350
NDVI
Latitude
Longitude
Center
Mean
Standard
Deviation
42.33
42.89
43.63
44.14
44.39
45.12
46.07
45.59
46.09
46.80
46.80
46.83
45.36
45.56
47.26
48.01
47.92
48.40
47.47
)93.62
)91.97
)91.43
)90.57
)89.12
)89.11
)88.19
)88.21
)88.79
)88.46
)88.16
)89.69
)91.18
)92.68
)92.78
)91.88
)94.08
)95.99
)97.47
)0.040
)0.040
)0.032
0.136
0.000
0.016
0.272
0.192
0.256
0.304
0.176
0.248
0.032
)0.024
0.168
0.160
0.056
0.056
)0.024
)0.033
)0.038
)0.025
0.121
0.032
0.034
0.219
0.237
0.268
0.196
0.234
0.247
0.038
0.023
0.123
0.212
0.079
0.035
)0.026
0.008
0.005
0.012
0.020
0.016
0.018
0.029
0.024
0.024
0.026
0.023
0.026
0.022
0.015
0.018
0.025
0.020
0.017
0.007
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NCDC snow depth observations vs. SNODAS-SWE
products over the 19 selected test sites. The results,
shown in Table 1, indicate satisfactory correlations
between NCDC snow depth observations and SNODAS-SWE. It is observed that in some test sites such
as test Sites 4, 5, 11, and 12 the correlation coefficient has decreased significantly as compared with
other test sites. This decrease can be associated to
average seasonal snow fall, and existence of water
bodies within the test site (Azar, 2006).
IN
ESTIMATING SNOW WATER EQUIVALENT
the study area. 3—Sufficient distance between the
test site and the border of the lake where SSM/I measurements could be affected by water. Considering
the mentioned criteria, there was only limited number of locations available to be selected as test sites
(Figure 1). To avoid wet snow conditions only the
data from December 1 to February 28 were considered. Wet snow has a negligible scattering signal and
needs to be excluded from the retrieval (GoodisonWalker 1993). Three datasets (containing 90-day
information) were derived for each winter seasons
2001-2004.
METHODOLOGY
Evaluation of the Microwave SSM ⁄ I Channels
In this study, we are proposing a new algorithm
for SWE ⁄ snow depth estimations which is tuned for
the Great Lakes area and its land cover characteristics (Figure 1). In order to develop the algorithm, first
we examine microwave observations and their potentials for estimating snow pack properties in the Great
Lakes area. Then, we investigate the effect of incorporating NDVI data in microwave-based snow estimates. Finally, using the results of the analysis, we
propose a new algorithm that incorporates both
SSM ⁄ I and NDVI data and evaluate the performance
of the new algorithm over the Great Lakes area.
As it was mentioned earlier the study area
includes various types of land cover (Figure 1). The
area is covered by 28 by 35 SSM ⁄ I EASE-Grid pixels.
Nineteen test sites were selected; each of the sites
had a size of an EASE-Grid pixel (Table 1). In selecting particular test sites we considered three criteria:
1—Availablility of snow depth measurements.
2—Covering different types of land cover throughout
In order to evaluate the potentials of SSM ⁄ I data
in snow depth ⁄ SWE estimations in Great Lakes
area, we investigated the behavior of three SSM ⁄ I
scattering signatures with respect to snow depth and
water equivalent. The first scattering signature, GTH
(19H-37H) is the gradient brightness temperatures
(Tb) between SSM ⁄ I channels in 19 and 37 GHz in
horizontal polarization. This signature was used by
Chang in his global snow depth retrieval algorithm
(Chang et al., 1987). The second scattering signature
was used by Goodison-Walker to estimate SWE in
Canadian prairies. Similar to Chang’s algorithm, the
signature is defined as gradient of brightness temperatures (Tb) between 19 and 37 GHz but in vertical
polarization, GTV (19V-37V) (Goodison-Walker 1995).
The third scattering signature was defined for
shallow snow identification and estimations as the
difference between channels 22 and 85 GHz in vertical polarization, SSI (22V-85V). The fact that 85 GHz
is the most sensitive channel to snow can make SSI
FIGURE 1. Land Cover Image Extracted From USGS National Atlas of
Land Cover Characteristics (USGS, seamless data, last modified July 2003).
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an excellent signature for snow identification but for
SWE and snow depth estimations this channel
(85 GHz) is bounded to saturation problem.
The box-whiskers plot of GTH and GTV values
during the winter seasons are illustrated (Figure
2). The negative outliers in the box plots are due to
either sensor or data processing errors which need
to be eliminated. The radiation in 19 GHz must be
higher than the scattering radiation in 37 GHz
because of higher absorption of emitted radiation in
37 GHz over a snow covered surface. In addition,
GTV mean ranges from 5 to 15 for all the pixels
except for the test Site 12 which is very close to the
lake. In test Site 12, the mean of GTV is around )5.
The relatively large negative value for GTV is due
to contamination of the scattering signals from land
by that of water. The SSM ⁄ I sensors have different
spatial resolutions for different channels. In EASEGrid data, the signal is averaged to the footprint
of l9 GHz (69 km · 43 km), using Backus-Gilbert
technique and then re-sampled to 25 km · 25 km
(Armstrong et al., 1994). Thus, although, the grid
(test Site 12) is not located in the water but its scattering values and consequently its brightness temperatures are contaminated by scattering signals of
water.
Evaluation of the Microwave SSM ⁄ I Channels for
Snow Estimations
FIGURE 2. Box-Whiskers Plot of GTV,
GTH, and SSI for Winter 2003-2004.
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After eliminating the negative outliers, a 3 year
time series of GTV and snow depth for each of the
test sites was produced. Figure 3 illustrates SSM ⁄ I
signature of GTV and snow depth at Site 9. The
plot shows that as snow depth increases during the
winter the GTV increases. This is because of the
high sensitivity of channel 37 GHz to snow. Contradictory to the northern test sites, those test sites
located in the more southerly area, do not show a
consistent seasonal pattern for snow depth and
GTV (Figure 4).
Figures 3 and 4 illustrate high correlations between
snow depth and GTV for northern test sites and low
correlation for southern test sites. In order to quantitatively evaluate the capability of SSM ⁄ I data in estimation of snow depth, the correlation coefficients
between the three SSM ⁄ I scattering signatures, GTV
(19V-37V), GTH (19H-37H), SSI (22V-85V) vs. snow
depth was derived (Table 3). To visualize the results
presented in Table 3, the variation of the correlation
coefficients is presented in Figure 5.
SSM ⁄ I signatures at Sites 11, 12, and 13 does
not show correlations with snow depth since the
land scattering is affected by the lake scattering
since those pixels are close to the lake. In fact,
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IN
FIGURE 3. Three Year Time Series of GTV (19-37V) vs. Snow Depth for Point 9.
FIGURE 4. Three Year Time Series of GTV (19-37V) vs. Snow Depth for Point 2.
TABLE 3. Correlations of Snow Depth vs. SSM ⁄ I Signatures
GTH (19H-37H), GTV (19V-37V), and SSI (22V-85V).
Winter 01-02
Winter 03-02
Winter 03-04
Test
Sites
GTH
GTV
SSI
GTH
GTV
SSI
GTH
GTV
SSI
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
0.00
0.10
0.13
0.20
0.00
0.50
0.70
0.55
0.71
0.30
0.00
0.00
0.35
0.40
0.50
0.60
0.81
0.92
NA
0.00
0.30
0.20
0.20
0.00
0.55
0.68
0.53
0.70
0.50
0.00
0.00
0.35
0.30
0.50
0.60
0.80
0.90
NA
0.40
0.80
0.50
0.20
0.10
0.10
0.00
0.00
0.20
0.00
0.00
0.20
0.00
0.40
0.10
0.30
0.10
0.30
NA
0.20
0.10
0.05
0.05
0.00
0.00
0.00
0.00
0.20
0.00
0.00
0.20
0.00
0.33
0.35
0.40
0.80
0.50
NA
0.10
0.30
0.10
0.12
0.10
0.11
0.00
0.00
0.30
0.10
0.00
0.35
0.00
0.30
0.45
0.50
0.80
0.50
NA
0.30
0.80
0.50
0.25
0.11
0.12
0.00
0.00
0.20
0.10
0.00
0.21
0.00
0.40
0.10
0.30
0.00
0.35
NA
0.52
0.45
0.20
0.60
0.10
0.22
0.25
0.00
0.15
0.05
0.00
0.15
0.00
0.35
0.35
0.45
0.80
0.50
NA
0.60
0.40
0.15
0.55
0.10
0.20
0.30
0.00
0.30
0.10
0.00
0.35
0.15
0.25
0.50
0.53
0.80
0.50
NA
0.70
0.63
0.20
0.70
0.33
0.10
0.05
0.00
0.20
0.00
0.00
0.20
0.00
0.45
0.1
0.30
0.05
0.40
NA
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FIGURE 5. Correlations of Snow Depth vs. SSM ⁄ I Signatures
GTV (19V-37V), GTH (19H-37H), and SSI (22V-85V) for Various
Test Sites (TS) for Winter Seasons 01-02, 02-03, 03-04.
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FIGURE 6. Variation of Correlations of SWE vs SSM ⁄ I
Scattering Signatures for Various Points for Winter 03-04.
those pixels are affected by the lake scattering in
19 GHz channel (69 km) but are not affected in
37 GHz (50 km) and 85 GHz (15 km). It is also
observed that SSI shows higher correlations with
snow depth in test Sites 1-4 where the NDVI values are below zero. On the other hand, GTV and
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GTH show higher correlations with snow depth
where the NDVI values are high.
Similar approach was taken for analyzing the
behavior of SWE products from NOHRSC with
respect to SSM ⁄ I scattering signatures (Figure 6).
The correlation coefficients between SWE and
SSM ⁄ I signatures follow the same pattern except
those are slightly higher that correlations with
snow depth. The scatter plots of SWE vs. the three
SSM ⁄ I signatures (GTV, GTH, SSI) have been produced for the all the test sites. Figure 7 illustrates
the variation of SWE vs. scattering signatures for
selected test sites (2, 9, and 18). The scatter plot of
SSM ⁄ I signatures vs. SWE indicate that the slope
of regression line is different for various test sites
considering their land cover type which is represented by NDVI values. These variations in the
regression slopes originate from scattering attenuation over the forested areas. Variation of regression
FIGURE 7. Scatter Plots of SSM ⁄ I Scattering Signatures Signature [(GTH (19H-37H),
GTV (19V-37V), and SSI (22H-85H)] vs. SWE (SNODAS) for Winter 2003-2004.
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slope over is a source of error for those microwavebased SWE estimating algorithms which do not
take vegetation in to account. Then, in order to
reduce the effect of forest scattering attenuation we
investigated the possibility of using NDVI value for
estimating the regression slope and ultimately SWE
and snow depth.
Table 4 shows how the correlation coefficient, the
slope and intercept of the regression line from scattering signatures of SSM ⁄ I channels varies with
respect to maximum SWE received by the test sites.
Similar to snow depth behavior with respect to
SSM ⁄ I, in test sites that receive <60 mm (1-4) of
SWE, SSI shows higher correlation with SWE. On
the other hand, for test sites which receive more
than 80 mm (7-9, 15-17, 19) of SWE, GTV has the
dominant correlation with SWE. In test sites where
maximum SWE is between 60 and 80 mm, the
behavior of both SSI and GTV is similar, non
shows very high correlation with SWE. On these
test sites, either of the signature can be used but
to find the optimum answer, other criteria need to
be used which will be described in the algorithm
development section.
IN
ESTIMATING SNOW WATER EQUIVALENT
variation, represented by NDVI, and microwave scattering, represented by regression slopes for different
test sites. Azar et al. (2006) conducted a research on
NDVI variation with respect to microwave scattering
in Great Lakes area revealing that the microwave
scattering attenuation by vegetation over a snowpack
is highly correlated with the NDVI computed over
the same area.
Figure 8 illustrates the variation of the slope
regression slopes between GTV and SWE, and NDVI
for all the sites during winter 2003-2004. It is
observed that the regression slope is higher for test
sites with high NDVI and it decreases for the test
sites with low NDVI indicating a high correlation
between the two parameters.
The scatter plots of the regression slopes (derived
from GTV) vs. NDVI are illustrated in Figure 9.
The plot is drawn only for those points (test sites)
which the NDVI is higher than zero. It was shown
that for the NDVI less than zero the SSI has
higher correlation with snow depth (Azar et al.,
2006).
Evaluation of NDVI Variations Over Different Test
Sites With Microwave SSM ⁄ I Channels for Snow
Estimations
Scatter plots of SWE vs. SSM ⁄ I scatterings showed
that there might be a connection between land cover
TABLE 4. Variation of Correlations and Regression Slopes
With Maximum Seasonal SWE for the Selected Test Sites.
Max Correlation
Correlation
Test SWE Coefficient Slope Intercept Coefficient Slope
Site (mm)
SSI
SSI
SSI
GTV
GTV
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
60
35
40
55
70
80
130
120
175
NA
NA
NA
75
50
95
170
90
50
85
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0.7
0.84
0.77
0.7
0.67
0.47
0.50
0.16
0.47
NA
NA
NA
0.52
0.67
0.28
0.38
0.1
0.65
0.6
1.9
)3
)2.4
)2.5
)4.4
NA
NA
NA
NA
NA
NA
NA
NA
)7.7
NA
NA
NA
)4.8
NA
0.7
0.84
0.77
0.70
0.67
NA
NA
NA
NA
NA
NA
NA
NA
0.67
NA
NA
NA
0.6
NA
0.3
0.29
0.35
0.31
0.64
0.67
0.8
0.55
0.77
0.62
NA
NA
0.5
0.44
0.6
0.68
0.84
0.83
0.71
AMERICAN WATER RESOURCES ASSOCIATION
2.2
1.9
2.1
2.2
4.4
6
9.4
7.9
15
5.5
NA
NA
5.3
2.4
3.8
9.3
4.7
1.8
3.9
FIGURE 8. Variations of the Slope of the Regression Lines
in the Scatter Plots With NDVI for the Test Sites for
Winter 03-04, SWE vs. GTV (up), SD vs. GTV (down).
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FIGURE 9. Scatter Plots of the Slope
of the Regression Lines vs. NDVI.
RESULTS AND DISCUSSION
For all the sites GTV and GTH show similar
behavior with respect to snow depth. In other words,
the difference between vertically and horizontally
polarized signatures is negligible in terms of correlations with snow depth. Contrary to GTV and GTH,
SSI has a different pattern. It has the dominant correlation for test sites 1-4 but for sites located in high
latitudes GTV becomes the dominant. This is because
of the saturation of the 85 GHz channel used in SSI
over a deep snow pack. However, SSI can be used to
identify and to estimate SWE over shallow snow.
In case of SWE and SSM ⁄ I signatures, Figure 6
illustrates the correlations between SWE and different SSM ⁄ I spectral signatures. For test Sites 1-4 SSI
has the higher correlation but for the sites (9-7,
15-17, and 19) GTV and GTH show better correlations with SWE. The rest of the test sites are acting
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in between. Figure 6 also shows that the correlations
between SWE and scattering signatures are higher
than those for snow depth. The higher correlation
between SSM ⁄ I and SWE obtained from NOHRSC
can be explained by that fact that NOHRSC SWE
data are produced from combining station observation
with output of weather prediction models that have
low spatial resolution similar to SSM ⁄ I data.
The correlation coefficients between snow depth
and scattering signatures follow a consistent pattern
for all the winter seasons. The only inconsistency is
for sites 6-10 for winter 01-02 which can be explained
by the snow received by those test sites during the
winter season. According to NCDC snow depth data,
test Sites 6-10 received more snow in winter 01-02
compare with winters 02-03 and winter 03-04 when
those test sites received less snow.
No correlations between scattering signatures and
snow depth and water equivalent were observed at
close to Great Lakes (11, 12, 13). This is due to the
contamination of land scattering signal by scattering
signal from water bodies resulting from the re-sampling of SSM ⁄ I to EASE-Grid by using Backus-Gilbert
interpolation technique.
Figure 7 shows the scatter plots of SSM ⁄ I signatures vs. SWE (SNODAS) and SSM ⁄ I signatures vs.
snow depth (stations) for winter 2003-2004 in different test sites. Regression lines for each graph have
various slopes and intercepts (Table 3). The slopes
increase over highly vegetated areas. This demonstrates that having a single linear algorithm (e.g.,
Chang or Goodison-Walker) may not be appropriate
for snow depth or SWE in a variety of environmental
and geographical conditions.
Figures 8 show that the regression slopes are highly
correlated with NDVI values. Over test sites with high
NDVI, highly vegetated, the regression slope tends to
increase. Then, having the NDVI value for a particular
area, we can derive the regression slope (Figure 9).
In short, GTH and GTV are similar and both show
high correlations with snowpack properties. Both of
them are not reliable indicators in vicinity of open
water (69 km). In addition, SSI is more sensitive to
snow variation in particular over shallow snow.
Finally, NDVI can be used to derive the regression
slope over different areas.
NEW ALGORITHM DEVELOPMENT
AND EVALUATION
In this section, propose a new algorithm based on
the findings in the previous sections. The proposed
algorithm will be evaluated over the whole study
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area. It is also evaluated temporally over the selected
test sites. The results are compared with that of
other algorithms such as Chang’s global algorithm
and Goodison-Walker regional algorithm.
Development of the New Algorithm
Considering the analysis and results detailed in
the previous sections, we propose:
1. Using SSI (22V-85V) for shallow snow estimations.
2. Using GTV (19V-37V) for deeper SWE ⁄ snow
depth estimations along with the corresponding
regression slope.
3. Using NDVI value to derive the regression slope
for GTV
The decision-tree algorithm is illustrated in
Figure 10. Where SWE is the snow water equivalent
in mm, GTV, and SSI are SSM ⁄ I spectral scattering
signatures. Winter time NDVI was obtained from a
FIGURE 10. Algorithm to Estimate Snow Water Equivalent
(SWE) Using SSM ⁄ I Data in the Great Lakes Area.
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IN
ESTIMATING SNOW WATER EQUIVALENT
10-day composite image for January 1994. A and B
are derived from the scatter plots of regression slope
and NDVI. Coefficients C and D are determined from
the scatter plots of SWE vs. SSI using the average of
the best fitted line to the scatter plots. The Values of
coefficients A, B, C, and D entering the above
formula were found equal to 37, 1.8, 1.03, and )3.27
respectively.
Algorithm Validation
The new algorithm was examined over by the
whole dataset of matched satellite retrieval and SWE
estimates in Great Lakes region. All of the test site,
the new algorithm showed significant improvement
in reducing the RMSE as compared with GoodisonWalker or Chang’s algorithm. Figure 11 shows the
results obtained with the new algorithm over test
Site 10 as compared to Goodison-walker and Chang
algorithms. The tests Site 10 is located in latitude
46.8N and longitude )88.46W in the area covered
with mixed forest. The results indicate decrease of
Root Mean Square Error (RMSE) over the test Site
10 which is the result of introducing NDVI value into
the equations. In order to derive snow depth, the estimated SWE values were multiplied by the average
snow density (0.23 gr ⁄ cm3).
The new algorithm was validated for the three
winter periods (December 01-February 28) 20012004. In winter 2003-2004, both SWE (from
NOHRSC) and snow depth (from NCDC) data were
available. For winter seasons 2001-2002 and
2002-2003, only snow depth data was available.
The results indicate 28 mm, 33 mm of reduction in
RMSE for SWE estimations as compared with
results from Chang and Goodison-Walker algorithms consecutively. Similarly, estimated snow
depth with the new algorithm is more accurate that
the other two algorithms. The source of error for
Chang and Goodison-walker algorithms is mainly
underestimation of SWE and snow depth (Figure 11). This underestimation is due to attenuation
of microwave scattering in the forested area which
is reflected in reduction of brightness temperature
(Tb). By introducing NDVI to the algorithm equation, the new algorithm takes vegetation effect into
account for snow estimations.
Besides the temporal validation, the new algorithm was spatially validated for the whole study
area (Latitudes: 41-49N and Longitudes: )87W to
)98W) excluding the areas covered by the lakes.
The EASE-Grid pixels covered or in the vicinity of
the lakes, were filtered out. There were 11 days
(3 days December, 4 days January, and 4 days February) with full coverage of SSM ⁄ I data in winter
1357
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FIGURE 11. Scatter Plots of Estimated vs. True Snow Depth ⁄ SWE for Different
Algorithms for Test Site 10 (Lat = 48.6N, Lon = )88.46W, and NDVI = 0.2).
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IN
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FIGURE 12. Comparison of Spatial Distribution of Estimated SWE by Various Algorithms With
Ground Truth Data for January 25, 2004 for the Study Area (Lat: 41N-49N and Lon: )87W to )98W).
FIGURE 13. NDVI Image and Results of Estimated SWE vs. Ground Truth for January 25, 2004.
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FIGURE 14. Scatter Plots of Estimated SWE by Chang and Goodison-Walker Algorithms vs. Ground Truth for January 25, 2004.
2003-2004. The ground truth data was obtained by
averaging NOHRSC SNODAS dataset. The new
algorithm was used to estimate SWE spatial distribution over the study area. Figure 12 shows the
ground truth and estimated SWE for January 25,
2004.
The NDVI image of the study area (Figure 13)
shows higher values of NDVI around the lake.
This is the area that both Chang and GoodisonWalker algorithms highly underestimate the SWE
(Figure 12). In contrast, the algorithm can estimate
SWE in the area in the vicinity of the lake with
much higher accuracy (Figures 12 and 13). The
calculated RMSE and correlation coefficient (R2) are
shown for all the three algorithms. The use of
NDVI in the new algorithm results in a decrease of
the RMSE and the increase of the correlation
coefficient. It also increases the range for the estimated SWE. Table 4 demonstrates a consistent
improvement in the accuracy of the estimated SWE
for the winter season of 2003-2004. In average,
compared with Chang global algorithm, the correlation coefficient is improved about 0.20 and the
RMSE is decreased about 4 mm of SWE over the
study area.
For all days, application of the new developed
algorithm results in the highest correlation coefficient between SSM ⁄ I and SWE. At the same time,
the RMSE of SWE derived with the new algorithm
is lower for all days but those in February (Table 5).
There is a decreasing trend of in correlations and
increasing trend in SWE in February. The most
probable reason for this trend is snow melt. In
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TABLE 5. Variations of RMSE and Correlation
Coefficients for Selected Days in Winter 2003-2004.
Correlation
Coefficients
RMSE (mm)
Days ⁄ Algorithm
Chang
GW
NEW
Alg.
Chang
GW
NEW
Alg.
December 6, 2003
December 13, 2003
December 20, 2003
January 4, 2004
January 11, 2004
January 18, 2004
January 25, 2004
February 1, 2004
February 8, 2004
February 16, 2004
February 23, 2004
0
0.35
0
0.47
0.10
0.33
0.52
0.5
0.32
0.30
0.30
0
0
0
0.39
0.12
0.28
0.50
0.46
0.20
0.23
0.12
0.29
0.30
0.30
0.69
0.59
0.67
0.70
0.60
0.48
0.43
0.48
22
21
33
22
21
28
25
30
37
42
55
13
14
20
19
16
25
25
31
38
52
54
11
12
15
17
14
18
19
32
47
54
54
February, the study area and especially its southern
part experienced several melt and refreeze of snow.
The higher brightness temperature and reported
surface temperatures over the study area supports
the existence of wet snow for those days (Tedesco
et al., 2006). Estimates of snow depth and SWE with
satellite observations in microwave become practically impossible when snow is wet. The results presented in Figure 15, confirms the existence of the
wet ⁄ melting snow for the days in February. There
are many points on the vertical axis of the scatter
plots of microwave estimated SWE vs. SWE from
NOHRSC which indicate the snow is not detected by
the microwave-based algorithm.
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FIGURE 15. Validation of the New Algorithm Over the Great Lakes Area on Different
Days (dates are selected based on full coverage of the study area by SSM ⁄ I data).
CONCLUSIONS
A new algorithm was developed to estimate SWE
using SSM ⁄ I scattering Signatures and NDVI over
Great Lakes area of United States. Current linear
algorithms such as Goodison-Walker and Chang algorithms are not sufficient for accurate estimations of
SWE in Great Lakes area. In order to resolve this
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problem three winter seasons were studied. SSM ⁄ I
data with corresponding snow depth, and snow water
equivalent (SWE) were used to examine the sensors
response to the changes in snow pack properties.
SSM ⁄ I response in GTV (19V-37V), GTH (19H-37H),
and SSI (22V-85V) to snow depth or water equivalent
changes were analyzed. In order to minimize
wet ⁄ melting snow conditions, in which microwave signatures cannot be used to estimate SWE ⁄ snow
1361
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depth, only the periods between December 1 and February 28 were considered.
In more southerly areas where snow is mostly
shallow, SSI has the highest correlation with SWE.
In northern part of the study area, GTV and GTH
are better estimators of SWE. Also, the scatter plots
of SWE vs. GTV and GTH shows that the slope of the
regression line between the spectral signatures and
SWE varies with location. This variation of the slope
was found to be correlated to NDVI and was
employed in the new algorithm for estimating SWE
using SSM ⁄ I data over the Great Lakes area. The
new algorithm was spatially validated for the whole
study area, excluding the areas covered by the lakes.
The use of NDVI in the new algorithm results in a
decrease of the RMSE and the increase of the correlation coefficient. It also increases the range for the
estimated SWE. In average, compared with Chang
global algorithm, the correlation coefficient is
improved about 0.20 and the RMSE is decreased
about 4 mm of SWE over the study area.
ACKNOWLEDGMENTS
The authors express their gratitude to Meteorological Service of
Canada (MSC). Thanks to NOHRSC and NSIDC for providing
SNODAS-SWE and SSM ⁄ I dataset. This study was supported and
monitored by National Oceanic and Atmospheric Administration
(NOAA) under Grant NA06OAR4810162. The views, opinions, and
findings contained in this report are those of the author(s) and
should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or
decision.
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