DOCUMENT OVERVIEW
Title: Fully Polarimetric Airborne SAR and ERS SAR
Observations of Snow: Implications For Selection of ENVISAT
ASAR Modes
Journal: International Journal of Remote Sensing, 2003, Vol. 24,
No. 19, 3839-3854
Authors: Tore Guneriussen and Harold Johnsen
Prepared by: Joey Boggess
Date: December 1, 2004
ABSTRACT:
Over the past several decades snow cover has had a substantial impact on the processes
involved in the interaction between atmosphere and surface, and studies have shown that
the working knowledge of snow parameters are important in both climatologic studies as
well as weather forecasting. The authors of this article utilized the launch of the
Advanced Synthetic Aperture Radar (ASAR) instruments on Envisat, to enhance their
snow mapping capabilities. The authors then fully discuss polarimetric C- and L-band
airborne SAR data, European Earth Resource Satellite (ERS) SAR and auxiliary data
from various snow conditions in the mountainous areas and analyze them in order to
determine the optimum ASAR modes for snow monitoring. The authors used seven
different ASAR image modes in their studies, which had incidence angles that ranged
from 15 - 45°, which are approximately the same variation as the Radarsat Standard beam
mode data that is frequently used. With the modes in place the authors used their data
and the theory of backscattering from snow cover to determine the optimum polarization
and incidence angle combinations to successfully monitor the snow coverage of their
point of interest.
INTRODUCTION:
Over the past several decades snow cover has had a substantial impact on the processes
involved in the interaction between atmosphere and surface, and studies have shown that
the working knowledge of snow parameters are important in both climatologic studies as
well as weather forecasting. As in many areas of the United States, the mountainous
areas in the whole of Northern Europe annual snowfall is a substantial portion of the
overall precipitation recorded. In Norway alone approximately 50% of the country’s
precipitation recorded in the mountainous areas falls as snow (Hanssen-Bauer, Førland,
Haugen, and Tveito). As a result, knowledge of snow spatial distribution is an important
issue for hydropower production and planning and flood predictions.
During the past few years the understanding of the interaction between microwaves with
snow and the ground, have improved dramatically, which have improved the capabilities
to map the snow cover using the SAR instruments (Guneriussen and Johnsen). From my
readings I have learned that SAR is actual a method of microwave remote sensing where
the motion of the radar is used to improve the image resolution in the direction of the
moving radar antenna. I also read that the SAR instruments can penetrate through clouds,
haze, smoke, and vegetation. The active nature of SAR sensors means they can operate
equally well in all lighting conditions, not requiring the smoothing normally necessary
for optical imaging due to sun position or sun glint off reflective surfaces making this
sensor perfect for this study. I believe the authors chose to use the SAR instruments
solely because by using the SAR instruments they would be able to achieve very fine
resolution from great distances while covering large areas of the Earth.
Space-borne single parameter SAR such as ERS and Radarsat have demonstrated the
capabilities of detecting the extent of wet snow cover in mountainous areas (Haefner et
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al. Rott and Nagler, and Guneriussen). Several algorithms for deriving the extent of wet
snow from single parameter SAR data have been proposed (Koskinen et al.). Imaging
radar C- and X-band SAR (SIR –C/X-SAR) have demonstrated the capabilities of
estimating the wetness of the top layer of the snow pack (Shi and Dozier), and promising
results using C-band SAR data for Snow Water Equivalent (SWE) have been reported
(Bernier and Fortin). The enhanced separation capabilities using multipolarization SAR
instruments have been demonstrated by SIR-C/X shuttle mission data (Shi et al. and
Matzler et al.) and airborne instruments (Guneriussen and Johnsen).
Even with such advances the authors realized that the variation in scattering properties of
ground and snow can give rise to larger variations in the image intensity. The variation
in the image intensity can make the development of consistent repeated snow
classification difficult. Image distortions are introduced by the relief of the mountains,
which affect both the radiometry and the geometry of the radar images which in turn
complicates the task of snow classification. The authors related the observed SAR data
to the existing microwave signatures and used the signatures to enhance the classification
accuracy. The SAR data had to be geocoded and recalibrated in order for the signatures
to work. Using the geocoded SAR data the authors had to use a Digital Elevation Model
(DEM) to correct some of the relief distortions.
The purpose of this study was to contribute to the growing understanding of the
interaction between snow cover and microwaves. The authors used seven different
ASAR image modes, which had incidence angles that ranged from 15 - 45°, which are
approximately the same variation as the Radarsat Standard beam mode data frequently
used. With the modes in place the authors used their data and the theory of
backscattering from snow cover to determine the optimum polarization and incidence
angle combinations to successfully monitor the snow coverage of their point of interest.
STUDY AREA & DATA:
The author’s study area and source of data came from the Norwegian part of the snow
and ice experiment within the European Multi-sensor Airborne Campaign (EMAC’95)
acquired in the Kongsfjellet area, located in Norway. The snow test field covered
altitudes from approximately 400 meters to 1,100 meter and the size of the area was
approximately 60 square kilometers (Guneriussen and Johnsen). The vegetation in the
study area varied from sparsely forest peat land, forested area, to areas where the
underlying rock was exposed.
Data stemmed from the combination of three remote sensing and in situ campaigns that
were conducted in 1995. Fully polarimetric C- and L-band SAR data was gathered from
the ElectroMagnetic Institute SAR (EMISAR), which is an airborne instrument operated
by the Danish Center for Remote Sensing (DCR). The data gather from the DCR was
attained in the months of March, May and July of 1995. The in situ data included the
snow density, snow grain size and snow liquid water content, which was acquired from
several positions in range with 100 meter height intervals (see Figure 1). The field
measurement sites and the corner reflectors were georeferenced using a global poisoning
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system (GPS) with p-code, giving a localization error less than 10 meters. Additional
data was also acquired from several ERS SAR, field and auxiliary data, and airborne
photos (Guneriussen and Johnsen). The remote sensing data is available in Table 1.
Table 1
EMAC’1995, Kongsfjellet, Norway Remote Sensing Data
Emisar
Date (1995)
March 22
March 23
March 29
May 1
May 3
June 7
July 5
July 6
July 11
July 12
July 14
Time (UTC)
Band
14:21
15:31
L
15:38
12:45
L
C
12:12
08:40
L
C
ERS-1 Time
Field Data
Airphoto
Xxx
Xx
Descending
Xxx
Xxx
Descending
Descending
Xx
Xxx
Ascending
Descending
xxx
Guneriussen and Johnsen
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THEORY:
The authors used the theory for backscattering from snow cover to guide their test results.
The theory states that backscattering from a snow covered terrain depends on 1) sensor
parameters which includes frequency, polarization and viewing geometry, and 2)
snowpack and ground parameters which includes snow density, liquid water content, ice
particle size and shape, surface roughness parameters, and stratification. Scattering from
a snow cover is the sum of the scattering from the snow surface, the snow volume and the
scattering from the underlying ground and is given by:
σo (θ) = σoss (θ) + ψ (θ)2 [σosv(θ’) + σosg (θ’)L -2 (θ’)]
where σoss (θ) = snow surface backscattering coefficient, ψ (θ)= transmissivity of the
snow pack, σosv(θ’) = backscattering coefficient of the snow volume, σosg (θ’)= the
backscattering coefficient of the underlying ground, and L(θ’) the one way propagation
loss in the snow volume (Guneriussen, Johnsen, and Lauknes).
RESULTS:
The first of two results acquired focused on the backscattering angular dependency of
snow and bare ground from ERS and EMISAR. Seven ASAR image modes that had
incidence angles ranging from 15-45° were used because they are approximately the
same variation as the Radarsat Standard beam mode. Using the optimum incidence angle
for discrimination of snow is important (Guneriussen and Johnsen). The authors used
statistical outputs to visually display their results as see in Figure 2. Figure 2 presents the
EMISAR C-VV, July 6, mean backscattering coefficient with respect to the local
incidence angle, probability distribution function (PDF) for local incidence angle and the
PDF of the EMISAR backscattering coefficient for Area 1 and Area 2, both for wet snow
and bare ground (Guneriussen and Johnsen). By reviewing their statistical outputs the
authors noticed that the angular dependencies for the bare ground in Area 1 (local
incidence angles ranging from 35-55º) and Area 2 (local incidence angles ranging from
45-65º) are very similar, but the values reported for Area 2 seem to be a little higher. The
authors assume that the bare ground may be regarded a rough surface with small
incidence angle dependency ranging from 35-65º. Furthermore, the test results show
only small incidence angle variation were observed, which may be due to the fact that the
surface was wet since precipitation measurements showed that it had rained nearly every
day before the test was ran.
The test results based on backscattering angular dependency of snow and bare ground
from ERS and EMISAR showed the authors that at high incidence angles the EMISAR
backscattering corresponded to volume scattering, while at low local incidence angles
that data corresponded more with surface scattering. By referring to their data the
authors assumed that the greatest distinction between the snow and bare ground was to be
expected from SAR instruments with large incidence angles.
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Figure 2. EMISAR C-VV statistics for Area 1 and Area2: (a) mean backscattering
coefficient versus local incidence angle; (b) local incidence angle PDF; (c) backscattering
PDF.
The second set of results focused on the angular dependency of polarization features from
snow and bare ground. The authors used the mean backscattering coefficient versus the
local incidence angle for C- and L-band HH, VV, and HV polarization for the bare
ground for July, May, and March, shown below in Figure 3.
Figure 3. (a) March, (b) May, and (c) July data
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The authors used their data to enhance the differences between VV and HH polarizations
by increasing the incidence angles. The enhanced difference between VV and HH shown
in the results were consistent with the theoretical results for the first-order solution of the
radiative transfer equation for a randomly rough surface for which multiple scattering can
be ignored (Fung).
CONCLUSION:
The authors concluded their study by analyzing the fully polarimetric L- and C-band
SAR data, ERS SAR, in situ measurements of the snow properties and auxiliary data in
their study area. The authors used both airborne and space-borne SAR data that they
geometrically corrected by using DEMs. The conclusion was drawn that the best
separation between wet snow and the ground was found using the C-band data. The
authors discovered that the highest contrast between bare ground and wet snow was
observed for high incidence angles compared to lower incidence angles in the EMISAR
C-VV data. The authors concluded from their studies that when the snow properties
changed the C-band proved to be more affected than the L-band in the month when the
snow cover was wettest, noting that a decrease in backscattering was observed for all the
polarizations.
ACKNOWLEDGMENTS:
Part of this work was carried out within SNOW TOOLS, an Environmental and Climate
project funded by the Commission of the European Community Contract no.
ENV4-CT96-0304, Norwegian research Council, ENFO, Statkraft and Norwegian Water
and Energy administration (Guneriussen and Johnsen).
Special thanks to Dr. Hongjie Xie of the Department of Earth and Environmental
Sciences at the University of Texas at San Antonio for making Remote Sensing, ES 5053
a fun, challenging, and exciting course. Happy Holidays.
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