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Introduction Model Results obtained Conclusions
An empirical approach towardscharacterization of Dry Snow layers using
GNSS-R
Fran Fabra1, E. Cardellach1, O. Nogues-Correig1, S. Oliveras1,S. Ribo1, A. Rius1, G. Macelloni2, S. Pettinato2 and S.
D’Addio3
1ICE-CSIC/IEEC, Spain2IFAC/CNR, Italy
3ESA-ESTEC, Netherlands
IEEE International Geoscience and Remote Sensing SymposiumJuly 24-29, 2011, Vancouver, Canada
Introduction Model Results obtained Conclusions
Outline
1 Introduction
2 Model
3 Results obtained
4 Conclusions
Introduction Model Results obtained Conclusions
Outline
1 Introduction
2 Model
3 Results obtained
4 Conclusions
Introduction Model Results obtained Conclusions
The frame of this work
GPS-SIDS project: Sea Ice - Dry Snow
GOAL: to investigate the use of reflected GPS signals to study sea iceand dry snow properties from Space
METHOD: to collect long term data sets from fixed platforms (2 fieldcampaigns) and then extrapolate the results
CHALLENGE: experimental campaigns under polar environmentalconditions
Many institutions involved: ICE-CSIC/IEEC, GFZ and IFAC/CNR(funded by ESA)
Introduction Model Results obtained Conclusions
Experimental campaign
Location
Dome Concordia (Antarctica)
Introduction Model Results obtained Conclusions
Experimental campaign
Location
Dome Concordia (Antarctica): American tower
Introduction Model Results obtained Conclusions
Experimental campaign
Introduction Model Results obtained Conclusions
Experimental campaign
Main aspects I
Instrument controlled in-situ by IFAC in coordination with IEEC.
Short campaign due to stability (DOMEX experiment [Macelloni et al., 2006])of the dry snow cover: 10th to 21st December 2009
Antennas on top of American tower with a height of 46m.
Not a single surface: reflected signal as a contribution from different layers.
Introduction Model Results obtained Conclusions
Experimental campaign
Main aspects II
Maximum elevation achievable through a GPS reflection of 65◦ due tolimitations of the satellites constellation at these Latitudes.
Minimum elevation of 5◦ given by GOLD-RTR’s internal GPS receiver.
Monitorization area of ∼400m x 400m: pristine snow.
255˚
27
0˚
285˚
300˚
315˚
330˚
345˚0˚
15˚
30˚0˚
10˚
20˚
30˚
40˚
50˚
60˚
70˚
4
8
12
16
20
24
28
32
PR
N
Introduction Model Results obtained Conclusions
The instrument employed: GOLD-RTR
GNSSR dedicated hardware receiver
GPS L1 (1575.45 MHz)
C/A code (Public and with simpleautocorrelation)
10 channels compute cross-correlations(waveforms) of 64 lags every millisecond
50 ns lagspacing ⇒∼ 15 meters
Scan the delay- and/or Doppler-space
3 radio front-ends
One Up-looking antenna for reference signal(internal GPS receiver)
A dual polarization (LHCP and RHCP)Down-looking antenna for reflected signals
Designed, manufactured, and tested at theICE-CSIC/IEEC
Introduction Model Results obtained Conclusions
Outline
1 Introduction
2 Model
3 Results obtained
4 Conclusions
Introduction Model Results obtained Conclusions
Motivation
Existing Model [Wiehl et al., 2003]: sub-surface contribution essentially given byvolumetric scattering.
But we consider volume scattering negligible compared to absorption loss.
Motivation: data shows clear interference fringes, better explained bymultiple-layer reflections.
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
0
5
10
15
20
Am
plitu
de (
a.u.
)
44.00 44.25 44.50 44.75 45.00
Elevation angle (deg)
⇒ Need to develop our own model
Introduction Model Results obtained Conclusions
Multi-layer Single-reflection model: MLSR
Multiple infinite parallel layers
1 single reflection per layer is considered
Only LHCP reflections reach the receiver
Expressions
ρi = ρ0 +k=i∑k=1
2nkHk
cos(θk )− (
k=i∑k=1
Dk )sin(θ0)
Dk = 2Hk tan(θk )
Uk = Rk,k+1Πi=ki=1Ti−1iTii−1e
−2αdi
Tco =1
2(T‖ + T⊥)
Rcross =1
2(R‖ − R⊥)
α =2π
λ|Ima{
√ε}|
Introduction Model Results obtained Conclusions
Methodology
Input of the forward model (MLSR)
Depths and permittivity of the dry snow layers are needed
Retrieved from in situ measurements of snow density
Introduction Model Results obtained Conclusions
Methodology
Complex waveform generated
Incident signal at surface with A=1
Direct signal set to lag 22 (RHCP toLHCP leakage with A=0.1)
Frequency of direct signal as areference
Several contributions
Introduction Model Results obtained Conclusions
Methodology
Complex waveform generated
Incident signal at surface with A=1
Direct signal set to lag 22 (RHCP toLHCP leakage with A=0.1)
Frequency of direct signal as areference
Several contributions
Example: elevation from 44.884 to 44.955
deg (128 samples)
Introduction Model Results obtained Conclusions
Methodology
Comparison of the modeled waveforms with real data
Introduction Model Results obtained Conclusions
Methodology
Comparison of the modeled waveforms with real data
Introduction Model Results obtained Conclusions
Methodology
Evolution of the different contributions wrt incidence angle
⇒ Interferometric frequency relates to depth of the layer
Introduction Model Results obtained Conclusions
Methodology
How do we retrieve information?
FFT to each of the lag-time series ⇒ elevation domain (cycles/deg)
Several bands of main contributions below the surface level appear,corresponding to depths with stronger gradients of snow density/permittivity
⇒ Proper inversion might determine dominant layers of L-band reflections
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
Lag-hologram from previous example
Introduction Model Results obtained Conclusions
Outline
1 Introduction
2 Model
3 Results obtained
4 Conclusions
Introduction Model Results obtained Conclusions
Lag-holograms from real data
First impressions
It show assymetrical behavior: consistentwith rotation due to interference fromdifferent contributions.
Apparently, wider frequency fringes thanexpected (high frequency bands at shortdelay lags).
Lag-hologram of 256 samples
PRN 19 at ∼ 40◦ of elevation from Dec-16.
Next processing step: averaging
To fade away those other features of the data which are not consistentlyrepeated (noise, atmospheric and instrumental induced features).
To identify the geometric and experimental parameters which essentially drivethe interferometric signal.
Introduction Model Results obtained Conclusions
Averaging in elevation
Next slides show a series of
averaged lag-holograms
(using all PRN’s) in
elevation steps of 5◦ from
December 16th.
5◦ ⇒ 65◦
5◦ - 10◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
10◦ - 15◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation
15◦ - 20◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
20◦ - 25◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
25◦ - 30◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation
30◦ - 35◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
35◦ - 40◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
40◦ - 45◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation
45◦ - 50◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
50◦ - 55◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
55◦ - 60◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation
55◦ - 60◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
60◦ - 65◦
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Apparently, the elevation
range that shows consistent
features from deep snow
layers goes from 20◦ to
45◦.
The elevation-rate also
plays an important role in
this analysis.
Introduction Model Results obtained Conclusions
Averaging in elevation-rate
Next slides show a series of
averaged lag-holograms
(using all PRN’s) in
elevation-rate steps of
0.001◦/s from December
16th.
0.001◦/s ⇒ 0.008◦/s
0.0◦/s - 0.001◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.001◦/s - 0.002◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation-rate
0.002◦/s - 0.003◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.003◦/s - 0.004◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.004◦/s - 0.005◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation-rate
0.005◦/s - 0.006◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.006◦/s - 0.007◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.007◦/s - 0.008◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Averaging in elevation-rate
0.006◦/s - 0.007◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
0.007◦/s - 0.008◦/s
−20
−15
−10
−5
0
5
10
15
20
Fre
quen
cy (
cycl
e/de
g−el
ev.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Consistent features from
deep snow layers are shown
from up to ∼0.005◦/s.
Expected result:
interferometric information
depends on the evolution of
the different paths followed
by each layer-contribution
during the time-series of
data (fixed).
Introduction Model Results obtained Conclusions
First conclusions from averaging
A minimum elevation and elevation-rate are needed for obtaining persistentfeatures in the lag-holograms.
The apparently bad results at higher elevations may be due to the impact of lowelevation-rate samples in the elevation averaging.
Test: Data set divided in cells of elevation and elevation-rate ⇒ Averaging overmost populated cell.
0.000
0.002
0.004
0.006
0.008
Ele
vatio
n ra
te (
deg−
elev
/s)
10 20 30 40 50 60 70
Elevation angle (deg)
5
10
15
20
25
30
PR
N
Cell division
0.000
0.002
0.004
0.006
0.008
Ele
vatio
n ra
te (
deg−
elev
/s)
10 20 30 40 50 60 70
Elevation angle (deg)
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Counts
Introduction Model Results obtained Conclusions
Averaging in cell: Repeatability
Dec 16th
−20
−15
−10
−5
0
5
10
15
20
Freq
uenc
y (cy
cle/de
g−ele
v.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Dec 18th
−20
−15
−10
−5
0
5
10
15
20
Freq
uenc
y (cy
cle/de
g−ele
v.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Dec 17th
−20
−15
−10
−5
0
5
10
15
20
Freq
uenc
y (cy
cle/de
g−ele
v.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Dec 19th
−20
−15
−10
−5
0
5
10
15
20
Freq
uenc
y (cy
cle/de
g−ele
v.)
10 20 30 40 50 60
Delay−lag (15 m.−lag)
Introduction Model Results obtained Conclusions
Depth of the contributing layers
Most of the reflected signals comes fromfirst 40 meters: especially 10 first meters–5 to 7 cycle/deg–, and from 30 to 40meters depth –around 9 cycle/deg–.
Remarkable contributions between 75 and100 meters –10 to 12 cycle/deg–, andaround 110 and 140 meters –14 and 16cycle/deg–.
Layers corresponding to depths withstronger gradients of snowdensity/permittivity from the in-situmeasurements.
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
0
100
200
300
Sno
w D
epth
(m
)
−18 −15 −12 −9 −6
Interf. Freq. (cycles/deg−el)
MODEL
DATA (averaged cell)
Introduction Model Results obtained Conclusions
Outline
1 Introduction
2 Model
3 Results obtained
4 Conclusions
Introduction Model Results obtained Conclusions
Conclusions
Persistent signatures have been found in the collected data-set of GPSreflections over dry snow.
The frequency-domain analysis shows interferometric behavior(asymmetrical spectrum) with contributions below the snow surface level.
No success when attempting a total inversion of the dry snow layersprofile from the data.
In spite of the poor results, the link between frequency bands in thelag-holograms and the depth of a reflecting interface is still a source ofinformation.
Alternative estimates
In [Hawley et al., 2006], the delays of radar measurements are not used to
estimate the snow density, but combined with a given density profile to
estimate the snow accumulation rates. A similar application can be
envisaged for this data set.
Introduction Model Results obtained Conclusions
Thank you for your attention
Introduction Model Results obtained Conclusions
References
Macelloni et al., 2006: G. Macelloni et al., ”DOMEX 2004: An Experimental Campaign at Dome-CAntarctica for the Calibration of Spaceborne Low-Frequency Microwave Radiometers”, IEEE Trans. Geosci.and Remote Sens., vol. 44, 2006.
Wiehl et al., 2003: M. Wiehl, R. Legresy, and R. Dietrich, ”Potential of reflected GNSS signals for icesheet remote sensing”, Progress in Electromagnetics Research, vol. 40, pp. 177–205, 2003.
Hawley et al., 2010: R.L. Hawley, E. M. Morris, R. Cullen, U. Nixdorf, A. P. Shepherd and D. J.Wingham, ”ASIRAS airborne radar resolves internal annual layers in the dry-snow zone of Greenland”,Geophysical Research Letters, vol. 33, 2006.