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The Soil Moisture and Ocean Salinity (SMOS) Ice project explored the potential of retrieving sea-ice information from the SMOS satellite, a polar-orbiting L-band radiometer successfully launched in November 2009. Toward this end, radiance measurements were collected over the Northern Baltic during the Pol-Ice campaign. We test a simple ice-concentration retrieval algorithm on these data and compare the results with ARTIST Sea Ice (ASI) maps derived from the Advanced Microwave Scanning Radiometer on the Earth Observing System. All operational ice-concentration algorithms are based on the same principle which, for the campaign data, reduces to a linear scaling of the radiances because, effectively, only one channel was available. Because of biases introduced by the different footprint sizes of the two radiometers (airborne and satellite), the linear flight path, and pilot selection of preferred surface type, Pol-Ice and ASI concentrations were compared using three different levels of averaging. In the first case, the individual measurements from the airborne radiometer were compared with interpolated ASI values; in the second, they were averaged over the pixels in the ASI maps; and in the third, they were averaged by binning the ASI values in 1% intervals. The correlations were 0.59, 0.67, and 0.76, respectively. Because of the unique operating principle of SMOS, each ground point will be viewed at multiple effective angles within a short time span. It is proposed to exploit this extra information by interpolating to a single effective viewing angle.
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Retrieving ice concentration from SMOS
Peter Mills, Georg Heygster, Member, IEEE,
Abstract—The SMOS-Ice project explored the potential of
retrieving sea ice information from the Soil Moisture and Ocean
Salinity satellite, a polar-orbiting, L-band radiometer successfully
launched in November 2009. Towards this end, radiance mea-
surements were collected over the Northern Baltic during the
Pol-Ice campaign. We test a simple ice concentration retrieval
algorithm on this data and compare the results to ARTIST
Sea Ice (ASI) maps derived from the Advanced Microwave
Scanning Radiometer on EOS (AMSR-E). All operational ice
concentration algorithms are based on the same principle which,
for the campaign data, reduces to a linear scaling of the
radiances, because effectively only one channel was available.
Because of biases introduced by the differing footprint sizes of
the two radiometers (airborne and satellite), the linear flight
path and pilot selection of preferred surface type, Pol-Ice and
ASI concentrations were compared using three different levels of
averaging. In the first case, the individual measurements from the
airborne radiometer were compared to interpolated ASI values,
in the second, they were averaged over the pixels in the ASI
maps and in the third, they were averaged by binning the ASI
values in one percent intervals. Correlations were 0.59, 0.67 and
0.76 respectively. Because of the unique operating principle of
SMOS, each ground point will be viewed at multiple effective
angles within a short time-span. It is proposed to exploit this
extra information by interpolating to a single effective viewing
Index Terms
The Soil Moisture and Ocean Salinity (SMOS) instrument
is a new satellite microwave radiometer launched in November
2009. It measures in the L-band range at 1.4 GHz with the
capability of rendering all four Stokes parameters. To produce
a reasonably small footprint size at such a low frequency while
keeping antenna weight down, an array of detectors fold out
upon deployment to produce an effective aperture equivalent
to the span of the array—i.e. much larger than the individual
While the chief focus of the instrument, as its name implies,
is the ocean surface and dry land, it will still return useful
information when it is pointing at the cryosphere—the sea ice
and glacial ice pack. Even more so than existing microwave
radiometers, SMOS will have the advantage of negligible
atmospheric contribution to the signal providing true all-
weather performance (since radiation at such low frequencies
has too little energy to interact strongly with most non-
magnetic and non-conducting materials, especially one as thin
as the atmosphere.) As preparation for the SMOS project,
the Pol-Ice field campaign was conducted in the Northern
Baltic in March 2007. This comprised airborne measurements
P. Mills and G. Heygster are both with the Institute of Environmental
Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany,
Manuscript received July 21, 2010
of sea ice brightness temperature using the EMIRAD L-band
radiometer [1] and helicopter measurements of ice thickness
using the E-M Bird ice thickness detection instrument [2].
Since brightness temperatures over open water tend to be
quite distinct from those over ice, the most obvious retrievable
quantity from SMOS data is ice concentration, defined as the
fraction of ice relative to the total area. Here we test a simple
concentration retrieval algorithm on Pol-Ice campaign data and
compare the results with satellite-based retrievals.
Most ice concentration algorithms are predicated on the
dual obervation that: 1. different surface types have different,
strongly clustered, radiometric signatures and 2. the final
radiometric signature at the instrument head is a linear com-
bination of that of the surface types found in the footprint,
with weighting factors taking on the values of the relative
concentrations. If we form a vector-space from the measure-
ments in which the signatures of the different surface types
are assumed invariant and all but one are linearly independent,
then it becomes a straightforward matter to derive the relative
ice concentrations [2], [3].
We could express this mathematically as follows:
(Tbi Tb0)Ci(1)
where Tbis the vector of brightness temperatures at the
instrument head, Tbi are the brightness temperatures of the
ith surface type, or tie-point, Ciare the relative concentra-
tions and Tb0are the brightness temperatures of the nominal
background surface type, i.e., of open water. The NASA team
algorithm uses a slight variation on this principle: the radiance
measurements are transformed by taking the difference of
two channels and dividing by their sum, producing a slightly
nonlinear retrieval [4], [5]. The influence of ice temperature
is thus mitigated since, all other things being equal, brightness
temperature varies roughly linearly with temperature (see
Equation (2)) and since sea ice brightness temperatures at
different microwave channels are strongly correlated [3].
We will use a simple radiative transfer model to simulate
the brightness temperature of sea ice at 1.4 GHz. Sea ice is a
complex composite comprised mainly of ice crystals, included
brine pockets and air bubbles. Because of the small size of
the scatterers relative to the wavelength, volume scattering
at L-band can be neglected [6]–[9]. In the case of uniform
properties within the ice sheet and plane-parallel geometry, the
radiative transfer equation for discontinuous media reduces to
the following, closed-form equation, which will henceforth be
referred to as the three-layer (air, ice and water) model:
(Ria 1) {[Rwiτ2+ (1 Rw i)τ1]Tice +
(Rwi 1)τ Tw+ (Ria 1)Rwi τ2Tsky }
(RiaRwi τ2
1) ,
where Tbis the modelled brightness temperature, Ria and
Rwi are the reflection coefficients at the ice-air and water-ice
interfaces, calculated via the Fresnel equations (see (5) and
(6), below), Tice is the temperature of the ice, Twis the water
temperature, Tsky is the downwelling brightness temperature
from the sky and τis the transmission coefficient:
τ= exp (
ccos θt)(3)
where νis the frequency, his ice thickness, nice is the complex
refractive index of the ice, cis the speed of light and θtis
the angle of the radiation as it is transmitted through the ice,
calculated from Snell’s law:
n1sin θ1=n2sin θ2(4)
where n1is the refractive index of the first medium, n2is
the refractive index of the second medium, θ1is the angle of
the ray in the first medium (relative to a normal drawn with
respect to the interface) and θ2is the angle of the ray in the
second medium. Once we have the two angles, the Fresnel
equations follow:
n2cos θ1n1cos θ2
n2cos θ1+n1cos θ2
n1cos θ1n2cos θ2
n1cos θ1+n2cos θ2
where Rvand Rhare the reflection coefficients at vertical and
horizontal polarization, respectively.
Model results summarizing the approximate behaviour of
the signal as the temperature, salinity and thickness are varied
can be seen in Figure 1. The figure shows brightness tem-
peratures computed for ice thicknesses ranging from 0 to 2
meters, correcting for the fact that a standard three layer model
does not converge to the open water case [10]. The salinity is
modelled as an exponential function of ice thickness [11]–[14]
for parent waters with two different salinities: the world oceans
and for the Baltic sea. Salinity and temperature determine the
complex permittivity through the mixture models of Vant et
al. [7]. The results suggest that it may be possible to retrieve
ice thickness simultaneously with ice concentration only at
temperatures close to melting.
For a more complete description of the model, see [2].
The Pol-Ice campaign was conducted in March 2007 in
the Northern Baltic and comprised fully polarimetric measure-
ments from an aircraft-mounted, L-band radiometer as well as
ice-thickness measurements [2] which will not be used in this
study. It is the only reliable source of L-band measurements
over sea ice to date. The radiometer used was the EMIRAD
which has an angular field-of-view (full-width, half-maximum)
Fig. 1. Model curves for ice at different temperatures in the Baltic (S=5)
and in the ocean (S=35). Thickness is varied between 0 and 2 m and marked
at intervals by solid dots. Salinity varies as an exponential function of ice
thickness. X-axis is the polarization difference or second Stokes component,
Q=Tbv Tbh.
Fig. 2. Map of all Pol-Ice EMIRAD measurements. Flights are labelled by
date, name and number.
Fig. 3. Measured L-band brightness temperatures from the Pol-Ice field
campaign compared to ice concentrations interpolated from ASI ice maps.
of 13.16 degrees [1]. Since the aircraft was flying between 500
m and 600 m, this translates to a footprint size on the order of
250 m. Figure 2 shows most of the radiometer flights labelled
by date and time, flight number and flight name.
Ice concentrations for comparison with those derived from
the field data are derived from the ASI (ARTIST Sea Ice)
algorithm which uses the 89 GHz channel of the Advanced
Microwave Scanning Radiometer on EOS (AMSR-E) to derive
ice concentrations [15]. These are averaged once a day to
maps rectangularly-gridded on a polar-stereographic projection
centered at 70 degrees latitutde. The 89 GHz channel is
used because it is the highest frequency, therefore it has
the best resolution with the final averaging resulting in a
mean grid-cell size of 6.25 by 6.25 km. The full record of
SSM/I and AMSR-E derived sea ice maps is available online: index.html
It was found that the vertical polarization of the aft-looking
radiometer was mal-functioning. Since the third and fourth
Stokes components are generated by correlating the hand v
components, this makes these two channels suspect also. Thus,
there is only one channel to work with and the concentration
retrieval in (1) reduces to a linear rescaling of a single
brightness temperature. Figure 3 compares ice concentration
interpolated from ASI ice maps with EMIRAD brightness
temperatures from the Pol-Ice campaign. The correlation, at
0.59, is relatively low, however the radically different footprint
sizes—over 3 km for AMSR-E, while for EMIRAD it will
be less than 300m—make direct comparison difficult. In par-
ticular, the small footprint of the EMIRAD instrument means
that pure signals will be more common, thus ice concentration
becomes more of an on-off value—either there is ice or there
is water. This can be clearly seen in Figure 3 and allows us
to easily pick out the two tie-points.
The ice concentrations algorithm will use tie-points of 80 K
and 200 K for open water and ice, respectively. Values lower
Fig. 4. Ice concentration retrieved from Pol-Ice EMIRAD measurements
compared with ASI ice maps. Retrieved concentrations are first averaged over
each pixel of the map.
Fig. 5. Ice concentration retrieved from Pol-Ice EMIRAD measurements
compared with ASI ice maps. Retrieved concentrations are binned in one
percent invervals based on the ASI values and averaged.
than 80K and higher than 200K are set to 0% and 100% ice
concentration, respectively.
The justification for these two tie points is further reinforced
by the model results in Figure 1. The model results suggest that
sea ice brightness temperatures converge to roughly the same
value independent of either the temperature or salinity of the
parent water. The values are also close to those chosen for the
retrieval, however the retrieval uses more moderate values for
the tie points because of the variability in the signatures of pure
ice types around the mean. We want to push concentrations
Fig. 6. Viewing angle dependence of sea-ice emissivity simulated using
three-layer RT model for three different scenarios of ice thickness, complex
permittivity and concentration.
close to the pure types over the border to a pure type if the
radiance signature is within this variability.
To address the issue of different footprint sizes, we average
all the EMIRAD measurements within each pixel of the ice
map and compare ice concentrations pixel-by-pixel. The ASI
ice maps are averaged daily to bins that are regularly-gridded,
along a polar-stereographic projection. This averaging does
not significantly reduce the resolution of the instrument and
is done mainly to take advantage of the many overlapping
swaths at high latitudes. EMIRAD measurements from Pol-
Ice are similarly averaged by first converting measurement
locations to the projection coordinates and then searching for
the matching ASI grid point. Results are shown in Figure 4.
Even though we have matched the instrument resolution to
the lower of the two, results may still be biased because the
pilot may be deliberately searching out either ice-covered or
open water surface and because the linear flight path traces
out only a narrow line of measurements across the broad
satellite footprint. To address this, we further average the
results by collecting them in one percent bins based on ASI
ice concentration. This generates the graph seen in Figure 5
which shows a much improved correlation.
The unique operating principle of the SMOS instrument
means that each point on the ground will be viewed from
multiple angles by overlapping measurements —each mea-
surement from the instrument will consist of a single large
footprint comprising multiple pixels of varying size and ef-
fective viewing angle [16]. With a high sampling rate, these
footprints will overlap. Can extra information be gained by
having measurements from different viewing angles? Results
from the model described in Section III suggest that the best
use of these extra measurements is to interpolate them to a
single effective viewing angle.
Figure 6 shows modelled sea ice emissivity as a function
of viewing angle for three scenarios of varying ice concentra-
tion, thickness and complex permittivity. These scenarios are
designed to be degenerate at an angle of 25 degrees. Although
Fig. 7. Viewing angle dependence of sea-ice emissivity simulated using
three-layer RT model and of fitted, re-scaled Fresnel equations. For RT model,
complex permittivity is ϵ= 3.5 + 0.05iand ice thickness is h=0.5m. Fresnel
equations are for a real refractive index of n= 1.91 and have been rescaled
using e
p=aep+bwhere epis emissivity at polarization pand a= 0.901
and b= 0.050 are constants.
we would expect them to diverge at other viewing angles,
they are almost identical, certainly to within the instrument
noise. It was found that the viewing angle dependence of ice
emissivity models of the type described in Equation (2) can be
well approximated by Fresnel equations that have been linearly
where e
pis the fitted emissivity at polarization p(hor v) and
ep= 1 Rpis the emissivity as calculated from the Fresnel
equations (5) and (6). Figure 7 demonstrates the procedure
which works as well if brightness temperature is modelled
with more than one ice layer.
Ice concentrations derived from Pol-Ice campaign data were
compared with ASI satellite- based retrievals. Correlations
were between 0.59 and 0.76 depending on the level of av-
eraging empoyed. Several influences make the averaging pro-
cedures necessary: the two instruments (satellite and airborne)
have very different footprint sizes as well as different temporal
resolutions; the airborne radiometer had a linear path, during
which the pilot may have been searching out specific surface
types. Another possible error source: the ASI algorithm is
designed for the high salinity of the open ocean rather than
the brackish waters of the Baltic Sea.
This comparison exercise was conducted as part of the
SMOSIce project, which aims to prepare retrieval algorithms
for data collected over sea ice by the Soil Moisture and Ocean
Salinity (SMOS) satellite which was successfully launched in
November 2009. The SMOS instrument will sample the same
stretch of ground at multiple effective viewing angles within
a short time span. Ice emissivity models suggest that this data
will provide little extra information. An effective use of it,
however, would be to interpolate to a single effective viewing
angle, thus helping to stabilize the measurements.
The authors would like to thank the following people
involved in the SMOS-Ice project for valuable discussions:
Catherine Bouzinac, Mark Drinkwater. Matthias Drusch, Ste-
fan Hendricks, Lars Kaleschke, Christian Maetzler and Ras-
mus Tonboe. This work was supported by ESA contract
21130/08/NL/EL, L-Band Radiometry for Sea Ice Applica-
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Peter Mills was born on December 22, 1973 in
Montreal, Canada. He received a Bachelor’s Degree
in Physics from the University of Waterloo in On-
tario, Canada and a Master’s Degree in Environmen-
tal Physics from the University of Bremen, Germany.
Peter has worked most of his adult life in the fields
of climate and remote-sensing research, including
some of the most hotly discussed areas such El
Ninio/Southern Oscillation and ozone detection. He
has a passion for all forms of rational inquiry,
particularly those that cross disciplines. Examples
include chaos theory, artificial life, complexity theory and cognitive science.
He has authored two papers on chaos theory.
Georg Heygster Georg Heygster (M’00) received
the Diploma degree in solid-state physics and the
Ph.D. degree in digital image processing from the
University of Goettingen, Goettingen, Germany, in
1976 and 1979, respectively.
He served as a Consultant with the Computer
Center of the University of Bremen, Bremen, Ger-
many, from 1979 to 1988. Since then, after working
for a year on the imaging mechanisms of scanning
acoustic microscopes, he has been Head of the
Geophysical Analysis of Satellite Images group at
the Institute of Environmental Physics, University of Bremen. His research
activities include passive and active microwave remote sensing, particularly of
both surface and atmospheric parameters in the high latitudes, various aspects
of the hydrological cycle, long-term trends, and retrieval techniques. He was
or still is Principal Investigator or Coinvestigator of many research projects
funded by the European Union, the European Space Agency, the German
Research Council, and the Japan Aerospace Exploration Agency. These
projects include the development of sensor soft- and hardware, conducting
campaigns, the final data analysis from multi- and single-sensor data to
geophysical parameters, and the interpretation and application of these results
in many areas such as meteorology, climatology, and oceanography.
... See for example Ivanova et al. (2015) for a review of a sample of thirteen of those algorithms. Although some authors (e.g., Mills and Heygster, 2011a;Kaleschke et al., 2013) have recently explored the feasibility of SIC determination using an aircraftmounted L-band radiometer, a method that extends satellite-based SIC retrievals down to L-band (i.e., SMOS) frequencies has been missing. We therefore set out to develop a new method, which we present here. ...
... Thanks to that increased penetration in sea ice (about 60 cm depending on ice conditions), the SMOS L-band radiometer is also sensitive to ice thickness (Kaleschke et al., 2012;Huntemann et al., 2014). In fact, ideally one would want to estimate both SIC and sea ice thickness 20 simultaneously (e.g., Mills and Heygster, 2011a), which is left for future work. Wilheit (1978) analyzed the sensitivity of microwave emissivity to a variety of geophysical variables such as atmospheric water vapor, sea surface temperature, wind speed, and salinity as function of frequency ( Figure 1). ...
... Hereafter, AD and PD will be evaluated at the incidence angles indicated above. to zero (e.g., Kaleschke et al., 2010;Mills and Heygster, 2011a;Maaß, 2013;Kaleschke et al., 2013). Also shown in Figure 5 are theoretical AD and PD values for the incidence angles indicated above. ...
Full-text available
We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness ~0.6 m). This result is expected because thin ice is partially transparent at L-band thus causing sea ice concentration to be underestimated. We therefore argue that SMOS estimates can be a compelling complement to estimates of ice concentration of both thick and thin sea ice from other satellite sensors such as the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR-2) or SSMIS, enabling a synergistic monitoring of pan-Arctic sea ice conditions.
... Although some authors (e.g. Mills and Heygster, 2011a;Kaleschke et al., 2013) have recently explored the feasibility of SIC determination using an aircraft-mounted L-band radiometer, a method that extends satellite-based SIC retrievals down to L-band (i.e. SMOS) frequencies has been missing. ...
... Notice that T B estimates start at an ice thickness of 5 cm because there is a discontinuity in the Burke model as the thickness of ice tends to zero (e.g. Kaleschke et al., 2010;Mills and Heygster, 2011a;Maaß, 2013;Kaleschke et al., 2013). Compared with T B , the total variation of both AD and PD with ice thickness is significantly smaller, and they are therefore better suited to estimate sea ice concentration. ...
Full-text available
Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100g sea ice and 100g seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness ≲g 0.6ĝm), the method underestimates the actual sea ice concentration. Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor pan-Arctic sea ice conditions.
... Two satellite missions dedicated to soil moisture monitoring currently make use of radiometric observations at L-band (1.41 GHz): The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched in 2009, [3] and the Soil Moisture Active and Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA), launched in 2015 [4]. Further applications of L-band radiometric observations include the estimation of sea surface salinity [5], soil freeze/thaw conditions [6] as well as characterizations of sea ice, polar ice cover and permafrost [7][8][9]. ...
Full-text available
This study investigates the sensitivity of L-band (1.41 GHz) polarimetric brightness temperature signatures to oriented permittivity patterns, which can occur for example in the case of row and interrow soil moisture differences in agricultural fields. A field experiment and model simulations are conducted to verify the effects of such patterns on all four Stokes parameters. We find that for an artificial target resembling idealized model conditions, permittivity patterns lead to systematic brightness temperature modulations in dependency of the azimuthal look angle. For the specific field setup, modulations reach amplitudes of ∼ 4 K and mostly affect h-polarized brightness temperatures as well as the first, second, and third Stokes parameters. Simulations of soil moisture patterns under idealized model conditions indicate even higher amplitudes (up to 60 K for extreme cases). However, the effects occur only for permittivity layer widths of up to 8 cm (given the observing wavelength of 21 cm), which is lower than the row and interrow widths typically observed in agricultural settings. For this reason, and due to the idealized model geometry investigated here, future studies are needed to transfer the findings of this study to potential applications such as the sensing of oriented soil moisture patterns. Particular interest might lie in radiometry and reflectometry in lower frequency ranges such as P-band, where according to the threshold established here (8/21 wavelengths), permittivity layer widths of up to ∼ 45 cm could be observed.
... Two algorithms for retrieval of sea ice thickness of thin sea ice during the Arctic freeze up, were initially developed independently in the University of Hamburg and the University of Bremen using disjunct data from the SMOS from different observation angle regimes [Kaleschke et al., 2012, Tian-Kunze et al., 2014. In addition to sea ice thickness, other potentials of SMOS data have been investigated like sea ice concentration [Mills and Heygster, 2011a] and snow thickness on multiyear ice [Maaß et al., , 2015a. ...
In this study we have developed an empirical retrieval for thickness of young and first-year ice during the freeze up period for the L-band passive microwave radiometer Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on the Soil Moisture and Ocean Salinity (SMOS) satellite. The retrieval is based on intensity and polarization difference using the incidence angle range of 40° to 50° and is validated using data from airborne EM-Bird, Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery, and self consistency checks for ice thicknesses up to 50 cm with an error of 30 % on average. In addition, we modeled the microwave emission for Arctic first-year ice using the sea ice version of the Microwave Emission Model of Layered Snowpacks (MEMLS). The sea ice conditions used as input for MEMLS were generated using a thermodynamic energy balance model (based on the Crocus model) driven by reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF). From unexpected features in the modeled microwave emission and disagreements with the empirically trained SMOS retrieval several shortcomings of the energy balance model and MEMLS were identified and corrected. The corrections include a treatment of mismatch of layer definition between the energy balance model and MEMLS, an adaptation of the reflection coefficient for lossy media in MEMLS, and several smaller corrections. For comparison, two simple models ignoring volume scattering, one incoherent and one coherent, were set up and were found to be able to reproduce the results of the more complex MEMLS model on average. With the simple models, the effects of thin coherent layers, the snow cover, the interface roughness and three different dielectric mixture models for sea ice were explored. It was found that the choice of the mixture model is essential for the relation of sea ice thickness to brightness temperatures in L-band, suggesting sea ice thickness sensitivities from few centimeters to several meters for salinity conditions of the global oceans. The interface properties, especially at the sea ice bottom, were found to be a major uncertainty source when modeling the microwave emission of thin sea ice. In addition, the variability in snow depth, the interface roughness, and the ice surface salinity and temperature were found to have a similar influence on the resulting brightness temperatures, with a strong effect on horizontally (up to 30 K) and weak effect on vertically polarized radiation (up to 10 K) for temperatures below 260 K. A model for simulating coherent microwave emission for thickness distributions of ice and snow was prepared to overcome weaknesses from the single thickness coherent and incoherent models. Comparison to the incoherent model showed that for realistic snow depth distributions obtained from Operation IceBridge (OIB) coherence effects can change the brightness temperatures on the scale of a SMOS footprint up to 10 K in horizontal polarization. These findings suggest that the retrieval for the thickness of thin sea ice with satellite based L-band sensors yield higher uncertainties than expected from earlier studies.
... The thermodynamic ice growth model, the Canadian Ice Growth Model (CLIMo), is described in Duguay et al. (2003). Mills and Heygster (2011a) provide a much simpler, but nonetheless conceptually similar example of how to model ice growth. Cox and Weeks (1988) also model sea ice growth using such a thermodynamic model. ...
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Lake ice within three Advanced Microwave Scanning Radiometer on EOS (AMSR-E) pixels over the Great Bear and Great Slave Lakes have been simulated with the Canadian Lake Ice Model (CLIMo). The resulting thicknesses and temperatures were fed to a radiative transfer-based ice emissivity model and compared to the satellite measurements at three frequencies---6.925 GHz, 10.65 GHz and 18.7 GHz. Excluding the melt season, the model was found to have strong predictive power, returning a correlation of 0.926 and a residual of 0.78 Kelvin at 18 GHz, vertical polarization. Discrepencies at melt season are thought to be caused by the presence of dirt in the snow cover which makes the microwave signature more like soil rather than ice. Except at 18 GHz, all results showed significant bias compared to measured values. Further work needs to be done to determine the source of this bias.
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Although sea ice remote sensing has reached the level of operational exploitation with well established retrieval methods, several important tasks are still unsolved. In particular during freezing and melting periods with mixed ice and water surfaces, estimates of ice concentration with passive and active microwave sensors remain challenging. Newly formed thin ice is also hard to distinguish from open water with radiometers for frequencies above 8 GHz. The SMOS configuration (planned launch 2009) with a radiometer at 1.4 GHz is a promising technique to complement observations at higher microwave frequencies. ESA has initiated a project to investigate the possibilities for an additional Level-2 sea ice data product based on SMOS. In detail, the project objectives are (1) to model the L band emission of sea ice, and to assess the potential (2) to retrieve sea ice parameters, especially concentration and thickness, and (3) to use cold water regions for an external calibration of SMOS. Modelling of L band emission: Several models have are investigated. All of them work on the same basic principles and have a vertically-layered, plane-parallel geometry. They are comprised of three basic components: (1) effective permittivities are calculated for each layer based on ice bulk and micro-structural properties; (2) these are integrated across the total depth to derive emitted brightness temperature; (3) scattering terms can also be added because of the granular structure of ice and snow. MEMLS (Microwave Emission Model of Layered Snowpacks (Wiesmann and Matzler 1999)) is one such model that contains all three elements in a single Matlab program. In the absence of knowledge about the internal structure of the sea ice, three-layer (air, ice and water) dielectric slab models which take as input a single effective permittivity for the ice layer are appropriate. By ignoring scattering effects one can derive a simple analytic expression for a dielectric slab as shown by Apinis and Peake (1976). This expression was used by Menashi et al. (1993) to derive the thickness of sea ice from UHF (0.6 GHz) radiometer. Second, retrieval algorithms for sea ice parameters with emphasis on ice-water discrimination from L-band observations considering the specific SMOS observations modes and geometries are investigated. A modified Menashi model with the permittivity depending on brine volume and temperature suggests a thickness sensitivity of up to 150 cm for low salinity (multi year or brackish) sea ice at low temperatures. At temperatures approaching the melting point the thickness sensitivity reduces to a few centimetres. For first year ice the modelled thickness sensitivity is roughly half a meter. Runs of the model MEMLS with input data generated from a 1-d thermodynamic sea ice model lead to similar conclusio. The results of the forward model may strongly vary with the input microphysical details. E.g. if the permittivity is modelled to depend in addition on the sea ice thickness as supported by several former field campaigns for thin ice, the model predictions change strongly. Prior to the launch of SMOS, an important source of observational data is the SMOS Sea-Ice campaign held near Kokkola, Finland, March 2007 conducted as an add-on of the POL-ICE campaign. Co-incident L-band observations taken with the EMIRAD instrument of the Technical University of Denmark, ice thickness values determined from the EM bird of AWI and in situ observations during the campaign are combined. Although the campaign data are to be use with care, for selected parts of the flights the sea ice thickness can be retrieved correctly. However, as the instrumental conditions and calibration were not optimal, more in situ data, preferably from the Arctic, will be needed before drawing clear conclusions about a future the sea ice thickness product based on SMOS data. Use of additional information from other microwave sensors like AMSR-E might be needed to constrain the conditions, e.g. on sea ice concentration and temperature. External calibration: to combine SMOS ice information with statistics on temperature and salinity variations derived from a suitable ocean model to identify ocean targets for a vicarious target calibration of the SMOS radiometer. Such a target can be identified most reliably in cold waters as suggested by Ruf (2000) before. At higher microwave frequencies the advantage of the Ruf method is that the absolute minimum of the observed brightness temperatures is a universal constant and can be used for external calibration. However, in the L band the salinity variations may shift the minimum to both directions so that suitable regions of low salinity variations need to be identified. For finding areas with fairly stable, at least known cold temperatures, one has to analyze existing prior (external) knowledge available from ocean observations (in situ and satellite) and from numerical models. From statistics based on daily AMSR SST fields and model simulations, the best area seems to be between Svalbard and Ocean Weather Ship Station (OWS) Mike, at 66N, 02E. However, variations in SST are still comparably large and the area can hardly be used for instrument calibration. It is suggested to deploy a number of drifters in a limited area representing a SMOS footprint to obtain accurate estimates of SSS and SST.
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Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002–2006) between ASI and NASA-Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.
Conference Paper
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Recent progress in spatial resolution enhancement of sea ice concentrations obtained by microwave remote sensing has been stimulated by two new developments: First, the new sensors AMSR (Advanced Microwave Scanning Radiometer) on MIDORI-II and AMSR-E on AQUA offer horizontal resolutions of 6×4 km at 89 GHz. This is nearly three times the resolution of the standard sensor SSM/I at 85 GHz (15×13 km). The sampling distance at the high frequencies is 12.5km at SSM/I and 5km at the AMSR-E instrument. Second, a new algorithm enables the estimation of sea ice concentrations from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows to fully exploit their horizontal resolution which is two to three times finer than the one of the channels near 19 and 37 GHz. These frequencies are used by the most widespread algorithms for sea ice retrieval, the NASA Team and Bootstrap algorithms. These two developments are combined to determine operationally sea ice concentration maps. The used ASI (Artist Sea Ice) algorithm combines a model for retrieving the sea ice concentration from SSM/I 85 GHz data proposed by Svendsen et al. [1] with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using two weather filters and the Bootstrap Algorithm. The AMSR-E sea ice concentration data are projected into grids of sampling sizes down to 3km. Hemispherical and regional maps are provided daily at
The physical, structural, and optical properties of newly formed ice types were studied in the Cape Bathurst polynya (71°N, 127°W) during fall freezeup in October to early November 2003. Variable meteorological conditions with occasional snowfall resulted in the formation of numerous ice types and surface conditions. Ice samples were collected from horizontally homogeneous surfaces representative of the area. Crystallographic analysis on 33 ice cores revealed highly variable growth conditions and formation mechanisms in the area. The mean fraction of granular ice was 33%, while intermediate granular-columnar and columnar ice contributed 37% and 30%, respectively. Salinity profiles in the ice were C-shaped and as the ice grew thicker, bulk salinities decreased according to 4.582 + 13.358/h i (cm). These conditions resulted in brine volumes ranging from 4% to 46%. Bare ice surfaces commonly formed a high salinity brine skim layer due to brine expulsion. Salinities up to 400/00 were observed in this layer. Under suitable conditions frost flowers formed on the ice, and their presence was related to characteristic ice microstructure with crystals that appeared disc-like in shape. Fine-grained snow-ice was formed when snow merged with surface brine to create a complex hypersaline surface at the snow/ice interface. The spectral reflectance for the thin ice types was most strongly related to surface conditions. The presence of frost flowers significantly increased the reflectance independent of snow precipitation. Any increase in ice thickness was found to have little effect on the reflectance once a 20-30 mm thick snow layer was present.
The 10 channels of scanning multichannel microwave radiometer data for the Arctic are examined by correlation, multiple regression, and principal component analyses. Data from April, August, and December 1979 are analyzed separately. Correlations are greater than 0.8 for all pairs of channels except some of those involving the 37-GHz channels. Multiple regression shows a high degree of redundancy in the data; three channels can explain between 94.0 and 99.6% of the total variance. A principal component analysis of the covariance matrix shows that the first two eigenvalues contain 99.7% of the variance. Only the first two principal components contain variance due to the mixture of surface types. Three component mixtures (water, first-year ice, and multiyear ice) can be resolved in two dimensions. The presence of other ice types, such as second-year ice or wet ice, makes determination of ice age ambiguous in some geographic regions. Winds and surface temperature variations cause variations in the first three principal components. The confounding of these variables with mixture of surface types is a major source of error in resolving the mixture. The variance in principal components 3 through 10 is small and entirely due to variability in the pure type signatures. Determination of winds and surface temperature, as well as other variables, from this information is limited by instrument noise and presently unknown large-scale variability in the emissivity of sea ice.
A comprehensive and unique set of measurements of the complex‐dielectric constant of sea ice, performed at several frequencies in the range 0.1–7.5 GHz, is described. In addition, a brief survey of previously published results is given and a set of dielectric models describing the complex‐dielectric behavior of sea ice, over the frequency range 0.1–40 GHz, is discussed.
The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux (e.g., heat, salt, and water) calculations to small changes in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in winter but larger disagreements in the seasonal regions and in summer. In some areas in the Antarctic, the Bootstrap technique shows ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.
The microwave emission properties of first-year sea ice were investigated from the R/V Polarstern during the Antarctic Winter Weddell Gyre Project in 1989. Radiometer measurements were made at 611 MHz and 10 GHz and were accompanied by video and visual observations. Using the theory of radiometric emission from a layered medium, a method for deriving sea ice thickness from radiometer data is developed and tested. The model is based on an incoherent reflection process and predicts that the emissivity of saline ice increases monotonically with increasing ice thickness until saturation occurs.
A model is presented for estimating salinity profiles for the first-year sea ice during the growth season, in which ice growth equations were coupled with salt entrapment and brine drainage relations to obtain the relationship between the initial ice salinity and the ice-growth velocity and seawater salinity, as well as the subsequent drainage of brine from the ice. The results obtained were found to be in reasonable agreement with field observations in that they showed characteristic C-shaped profiles similar to natural profiles. The average ice salinity values were also in reasonable agreement with field data. The predicted ice property profiles give composite plate properties that are significantly different from bulk property estimates that would result by assuming that sea ice could be represented as a homogeneous plate.