WP4_20140204

Report
www.photofromtheworld.com
WP4 : Satellite remote sensing of wave in ice
SWARP KO Bergen, 4 Feb. 2014
OceanDataLab (ODL)
• IFREMER spin-off incorporated in Brest, april 2013.
• Principal objective: Develop tools for multimodal
synergy analysis (multisensors, models, in-situ)
• 4 persons : 3 research engineers and 1 software
engineer.
• Role in SWARP : provide SAR wave spectra retrieval in
the marginal ice zone and participate to the
validation effort.
SWARP KO Bergen, 4 Feb. 2014
WP4 Outline
Objective
To develop and implement remote sensing methods for observation and
model validation of waves in the MIZ.
Tasks
1.
2.
3.
4.
5.
Ice type recognition from coarse resolution scatterometry (Ifremer)
Ice type recognition from high resolution SAR and optical images
(NIERSC)
Waves-in-ice retrieval methodology review and implementation (ODL)
Acquisition and analysis of collocated SAR, optical and CryoSat altimeter
data (NERSC)
Analysis of observed waves-in-ice evolution relative to sea ice type (ODL)
SWARP KO Bergen, 4 Feb. 2014
Task 4.1 : Ice type recognition from
coarse resolution scatterometry
Fanny GIRARD ARDHUIN, IFREMER
Sea ice roughness from scatterometer sensors
Ifremer/CERSAT unique time series
18-02-2007
ERS-1&2 : 1991-2001
NSCAT : 1996-1997
QuikSCAT : 1999-2009
ASCAT : 2007-present
Grid. resolution : 12.5 km, 25 km
Daily (weekly for ERS)
Since 1991
for both Arctic & Antarctic areas
Low res. scatterometer data :
daily maps for both pole areas
Canada
QuikSCAT
Ice type from scatterometer
Roughness is linked with
sea ice age
MY/FY ice can be
detected
18-02-2007
QuikSCAT
FY
Robinson (2004)
Multi year ice (MY)
First year ice (FY)
Example of backscatter time series during a winter
OCT
DEC
MAR
MAY
MY
MY extent time series
Example of MY area time series with QuikSCAT data (19992009) (consistent with Kwok's results)
Example of QuikSCAT backs. time series and
ice type classification
Swan & Long, 2009
FY and MY areas can be quantified applying
a moving back. threshold value
Backscatter values over sea ice depend on frequencies, polarisation, incidence angle
but also on ice type, salinity in the ice, etc...
QuikSCAT method to adapt to ASCAT data for recent period (since 2009) for SWARP
project and to validate → need SAR ice type detection
Task 4.2 Sea ice classification
from SAR data
Nansen International Environmental
and Remote Sensing Centre, St.Petersburg, Russia
Vladimir Volkov
Natalia Zakhvatkina
SWARP KO Bergen, 4 Feb. 2014
Objective
 To develop sea ice classification algorithm using
high-resolution SAR images in order to classify
the MIZ in selected test areas
 To map ice edge and the details of the ice cover
like ice types, open water and various stages of
new and first-year ice, leads, polynyas, and
others;
 Implement the developed technique for the
determination of the zone of broken-up floes in
the MIZ, which will be used to validate the floe
size distribution given by sea ice models
SWARP KO Bergen, 4 Feb. 2014
Data
• I. Envisat’s ASAR (Advanced Synthetic Aperture Radar)
– ASAR operated in the C band in 5 modes; we worked mostly with Wide
Swath mode images of 405 km swath and 150 m resolution.
– ESA announced the end of Envisat's mission on 9 May 2012.
• II. Radarsat-2 SAR
–
–
–
–
multiple modes of operation,
HH, HV, VV and VH polarized data can be acquired,
its highest resolution is 3 m in Very High Resolution mode,
we work with data of ScanSAR Wide Beam mode that has a nominal
swath width of 500 km and an imaging resolution of 100 m.
• III. Optical data ?????????????
SWARP KO Bergen, 4 Feb. 2014
Radarsat-2 ScanSAR Wide mode images
• We use RADARSAT-2 data received in ScanSAR Wide (SCW) mode at HH
(horizontally transmitted and horizontally received) and HV (horizontally
transmitted, vertically received) polarizations. This mode assembles wide
SAR image from several narrower SAR beams, resulting to an image of 500
× 500 km with 100 m resolution.
OW rough
HH
HV
OWr
Ice
OW
Fram Strait, 20/02/2012
SWARP KO Bergen, 4 Feb. 2014
OW calm
Sea Ice
OWr
Ice
OW
Ice and water pixels
can be separated
Sea ice classification using SAR images
Two RADARSAT-2 SAR ScanSAR Wide images: HH and HV polarizations
Noise correction of HV dual-polarization SAR image
SAR images calibration, angular dependence correction
Image features calculation: mean backscatter, texture characteristics
Image classification using Support Vector Machines technique
Sea ice charts
SWARP KO Bergen, 4 Feb. 2014
Noise reduction in HV polarization
•
The effect is reduced by
subtracting the noise
floor level from the HV
image values.
•
Left image - raw HV
polarization image,
right image – noise
reduced image.
•
•
SWARP KO Bergen, 4 Feb. 2014
Blue curve shows the
sigma0 value profile of
the raw HV channel
image over the
horizontal line, the red
curve depicts the noise
floor level and the
green curve is the
result of subtraction
Support Vector Machines algorithm
TEACHING
(klusterization)
SWARP KO Bergen, 4 Feb. 2014
Support Vector Machines CLASSIFICATION
SWARP KO Bergen, 4 Feb. 2014
Automated classification of Radarsat-2
data
• The described technique has been used in the development
of automated sea ice / water classification.
SWARP KO Bergen, 4 Feb. 2014
18 Jan 2014
SWARP KO Bergen, 4 Feb. 2014
Vilkitskiy Stright, 23, 26 Aug 2013
HH
SWARP KO Bergen, 4 Feb. 2014
HV
SVM
28 Aug 2013
SWARP KO Bergen, 4 Feb. 2014
Task 4.3 Waves-in-ice retrieval
methodology review and
implementation
Fabrice Collard (OceanDataLab)
SWARP KO Bergen, 4 Feb. 2014
Wave modulation modification
MIZ
Incident swell
SWARP KO Bergen, 4 Feb. 2014
Wave modulation modification
MIZ
Incident swell
SWARP KO Bergen, 4 Feb. 2014
Wave modulation modification
MIZ
Incident swell
SWARP KO Bergen, 4 Feb. 2014
MIZ OPEN OCEAN
ASAR data © ESA 2011
SWARP KO Bergen, 4 Feb. 2014
Wave modulation modification
MIZ
MIZ
SWARP KO Bergen, 4 Feb. 2014
Wave modulation modification
MIZ
MIZ
SWARP KO Bergen, 4 Feb. 2014
Work to be done
• Update Modulation transfer functions to cope
with ice roughness and dynamical properties
• Validate retrieved SAR wave spectra using
wave buoys in the MIZ.
SWARP KO Bergen, 4 Feb. 2014
Task 4.4 Acquisition and analysis
of collocated SAR, optical and
CryoSat altimeter data (NERSC)
SWARP KO Bergen, 4 Feb. 2014
SMOS ice Thickness
2013 02 19
Task 4.5 Analysis of observed
waves-in-ice evolution relative to
sea ice type (ODL)
SWARP KO Bergen, 4 Feb. 2014
Attenuation analysis
WARNING !!! : Uncalibrated retrieved wave height in the MIZ
MIZ
OPEN OCEAN
SWARP KO Bergen, 4 Feb. 2014

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