Monitoring snow layers of the Antarctic ice-sheet
using reflected signals: GNSS and the Sun as
sources of opportunity
E.Cardellach1, F. Fabra1, A. Rius1
S. Pettinato2, Giovanni Macelloni2
S. D'Addio3
1
Institut de Ciències de l'Espai (ICE/IEEC-CSIC), Spain
2
“Nello Carrara” Institute of Applied Physics (IFAC-CNR)
3 ESTEC/ESA
Outline
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Background and introduction
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Experimental campaigns:
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GNSS-R: GPS-SIDS
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L-band radiometry: DOMEX
GNSS-R:
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“Strange” features in the data
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Possible explanations and final hypothesis

Model to validate hypothesis
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Results
Sun Fringes:
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Availabe data sets

Adaptation of the model (GNSS-R to radiometry)
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Preliminary results
Summary & Conclusions
Background & Introduction
• Information on internal layer structures of the dry-snow zones is
required to better estimate snow accumulation rates. In turn, the snow
accumulation in ice sheet (Greenland and Antarctica) is a key factor
for estimating Earth’s ice mass balance.
• In the ice-sheets snow layering could also provide useful information
for a better understanding of the past climate history
• EO data, acquired by microwave sensors, together electromagnetic
models, are useful for estimating snow accumulation because volume
scattering is the dominant mechanism. Therefore the snow layering is
one of the unknowns that limit the retrieval capability
• Because of the high-penetration, it is possible to use low frequency
microwave signal to infer information on ice sheet layering?
Sources of Opportunity
L band
GNSS + R
Radiometer + Sun
ICE SHEET
•
How Deep L-Band Signals Penetrate into the Dry-Snow?
•
Can they be used to sense snow sub-structure?
•
Which physical infromation/parmeters can be extracted?
Experimental Campaigns
© CSA
Within the framework
(related to SMOS
experimental campaigns
at DOME-C Antarctica
period.
of ESA projects
and GNSS-R)
were conducted
in 2009 -2010
•The site is view on a sub-daily
frequency by polar-orbiting satellites,
at a variety of incidence and azimuth
angles.
Dome C
• Homogeneity of snow surface at the
100 km scale.
• Small surface roughness relative to
other ice sheets.
• Low snow accumulation rate
(around 3.7cm/yr).
Concordia Station (Dome C):
75.125 S, 123.25 E - 3270 a.s.l
• Clear sky, and extremely dry and
stable atmosphere.
• Well known topography and
environmental condition
Experimental Campaigns
Both campaigns were conducted at
the American Tower, observing a
pristine snow area
45 m
GPS-SIDS CAMPAIGN (2009):
→ GPS antenna (LHCP+RHCP) mounted
on top of the American Tower, ~45 m
altitude
→ Antenna pointing towards the horizon,
NW direction
→ GPS –Receiver: GOLD-RTR,
dedicated GNSS-R hardware receiver, L1,
10 parallel complex (I/Q) waveforms every
millisecond, 64 lags each, ~15 m inter-lag
spacing
DOMEX CAMPAIGN
L-band radiometer installed for SMOS calibration (2009-2011)

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Frequency : 1413 MHz
Bandwidth: 27 MHz
Sensitivity = 0.2 K (Ti =2 sec)
Polarization: H and V
HPBW: 20°
15 m
L – band Antenna
IR - Radiometer
Complementary Snow measurements
Summer
Snow deposition:
Grains shape and Size
Classification(precipitati
on, hoar,wind, etc.)
Winter
Snow layers:
Temperature
Hardness
Density
Grains shape and Size
Dielectric Constant
GNSS-R: data features
LAG 22
GNSS-features:
For specular
surfaces
• Specular reflections tend to generate
waveforms with the shape of the
signal modulation's auto-correlation
function,
with
no
further
deformations (in opposition to
diffuse scattering).
LAG 37
• The signal modulation used in this
experiment, has an autocorrelation
• function shaped as a triangle.
• The received waveforms do not show
the expected triangle shape, but a
series of distorted triangles, with
added tails and secondary peaks.
Same oscillation over four days  no noise!
GNSS-R: data features
In order to interpret the data a new observable was defined :
THE LAG-HOLOGRAM
→ A reference wave field (“direct” ray, no reflection) is used to reveal the
spectra from the total received field, at each lag independently:
Direct Ray
Snow Surface
-10 m
-70 m
Specular
direction = 45°
GNSS-R: data features
→ Lag-holograms show high temporal repeatability:
17-Dec.
19-Dec.
18-Dec.
20-Dec.
GNSS-R: external multipath?
Are the results due to multipath interferences ?
→ Planar horizontal
reflecting surfaces:
GNSS-R: external multipath?
- Frequency of the first (less negative) stripe in the lag-holograms
(dots) and theoretical frequency of the air-snow surface reflection
interference (solid grey line):
REFLECTORS ABOVE SNOW SURFACE
Snow Surface
REFLECTORS BELOW SNOW SURFACE
Main contribution comes from reflector placed below snow surface
but external reflector (i.e. building) are to far from the tower
GNSS-R: external multipath?
HYPOTHESIS: frequency fringes found
in the data correspond to interferences
between direct signals and reflections
off the snow surface and sub-surface
snow layers.
GNSS-R: model
Different components of the signal
coming from the different layers
Total signal
Snow model
Ice sheet is modeled as a multilayer media .
Model inputs:
-ground data collected at Concordia station 0-10 m
• Temperature
• Layer depth
• Later density
-theoretical models/measurements 10 – 700 m (density
saturates at 250 m)
For density and layering more
realizations were performed
(monte carlo approach).
GNSS-R: model results
Waveform
LagHologram
GNSS-R: model results
Waveform
Simulated Data
Real Data
Comparison with Real Data
Example of comparison between real and simulated data
Simulated Data
Real Data
Inc. angle = 45 °
GNSS-R: results
→ A set of layers presented reflectivity
stripes consistently in most of the lagholograms:
Depth (m)
10
70
130
240
→ Reflections down to ~300 meter depth
→ Vertical resolution: 5-10 meter
→ Full inversion procedures (lagholograms to density profiles) were not
yet successful
DOMEX-2 data
Observing Radiometer data (at fixed incidence
angle) fringes appear in the data when sun is in front
of the radiometer (specular reflection)
Sun in the specular direction !
Because of layering the sun effect is observed as «fringes» in the
signal. Since we observe at q = 42° (≈ Brewster angle) the effect is
evident at H polarization
Sun Fringes: data
Interference fringes found when Sun's reflection azimuth/elevation within the
radiometer's antenna pattern :
→ ~September-March  summer season
Very high temporal repeatability, slowly changing with Sun's coordinates
Sun Fringes: model
The model developed for dry-snow GNSS-R is adapted to
radiometric emissivity with Sun internal reflections:
- Cross-polar circular polarization → power of linear
polarized reflections HH, VV
- lag-structure → peak power solely
- Radiometer's antenna pattern included
Sun Fringes: data&model
Comparison between data and model for strong Sun signals (Feb 16, and Oct 16, 2010):
SUMMER PERIOD - DOTS: DATA, SOLID LINE: MODEL
Horizontal Polarization
February
October
Vertical Polarization
February
October
Sun Fringes: data&model
Comparison between data and model for vanishing Sun signals (March 16, and Sep 16, 2010):
WINTER PERIOD - DOTS: DATA, SOLID LINE: MODEL
Horizontal Polarization
March
Sept.
Vertical Polarization
March
Sept.
Sun Fringes: data&model
→ Data could be also represented in a different way :
JAN-MAR
Intensity is represented
in an arbritary scale:
Green = high sun reflection
Purple = low sun reflection
SEP-NOV
If we assume homogeneous vertical profiles of snow density, horizontal parallel layers (elevation
dependence solely) azimuthal symmetry is expected in the signal.
→ Sun's data polar plot slightly disagree → TILTED LAYERS?
Sun Fringes: extract info
→ Very coarse adaptation of the model to tackle non-homogeneous density profiles, introducing
layers' slopes along the azimuthal direction (and antenna pattern disabled to facilitate pattern
recognition):
COARSE TILTED MODEL
→ Need to refine slopes' model: RAY TRACER approach (work on going)
Conclusions
→ GNSS and L-band Sun signals penetrate into Antarctic dry-snow,
down to ~300 meter, and reflect off internal layers;
→ These reflections interfere between them, generating sets of
fringes, detectable in the frequency domain;
→ The lag-holograms have been created to visualize the fringes,
affecting different delay-lags of the GNSS-R waveforms. Their
temporal repeatability is significantly high;
→ Reflections have been identified, consistently, from a set of
depths;
→ The vertical resolution of the technique is 5-10 meter depth;
→ The Sun reflection fringes captured by a L-band radiometer are
also qualitatively explained using the same hypothesis/model;
→ Discrepancy found in the global behavior, it seems to indicate
that the vertical profiles are not homogeneous, possible internal
tilted slopes. Checked with a coarse model. A ray-tracer model is
being implemented to further validate this hypothesis.
Thank you for your attention !