4 Extraction of Intermediate Scale Sea Ice Deformation Parameters from SAR Ice Motion Products
71
the long dark period during winter in the Arctic may also significantly restrict the use
of AVHRR in such studies.
An optimal approach would be to couple the results of the buoy program, which can,
for a limited number of points, provide nearly continuous motion measurements in the
temporal sense, with a satellite remote sensing program that can provide less frequent
(every few days) but nearly continuous spatial observations. The remote sensing
requirements for such a program would appear deceptively simple; the system must produce imagery that is of a sufficiently high resolution to allow one to follow identifiable
ice features for a number of repeat periods. Unfortunately, this also means that the system must function under all-weather conditions so that data can be obtained whenever the satellite is over the region of interest. These requirements eliminate visual- and
near-infrared-range sensors (limited by darkness and clouds), thermal infrared sensors
(limited by clouds), and passive microwave sensors (limited by inadequate resolution).
Until the launch of the two radar satellites ERS-l (1991, ESA - the European Space
Agency) and JERS-l (1992, NASDA - the Japanese Space Agency), satellite-borne remote
sensing systems have not held up to their portion of this task. However, the difficulty has
now changed from a "no-suitable-sensor" problem to a problem of how to rapidly
extract the necessary geophysical information from the extensive SAR coverage that is
being obtained. This latter problem has become even more acute since the launch of the
Canadian Radarsat in 1995. The wide-swath observation mode of Radarsat results in
image strips that are 500 km wide (in contrast to present synthetic aperture radar satellites that provide image strips with widths of 100 km). Considering that ERS-l and JERS1 plus ERS-2, which is a follow-up of ERS-l, are still operating during the Radarsat time
period, the motivation for the work reported in the present chapter becomes clear.
Our main objective is to develop a method for screening large volumes of synthetic
aperture radar (SAR) products and extract significant ice deformation events without
having to look at either the SAR images or at the SAR-derived ice motion products on
which no significant ice deformation occurred. In the following sections, the background information on the satellite SAR derived ice motion products and the relationship between ice motion and ice deformation are given first. Then the details of the
actual algorithms used for derivation of ice deformation are presented, the resulting
ice deformation products are described and their accuracy discussed. Finally, as illustrations, two examples are given to demonstrate extraction of intermediate-scale sea
ice deformation parameters using the algorithm.
In one example, the temporal variations in ice deformation are obtained for a 6-month
period during the winter of 1991-1992, for a circular sampling area with a radius of 200
km centered at 81°N and 170 oW in the Chukchi Sea portion of the Arctic Ocean. In the
other example, the ice deformation as a function of time and latitude in the Beaufort
Sea is extracted during a period between January 23 and March 15, 1994, as part of the
SIMI (Sea Ice Mechanics Initiative) project.
4.2
Background
The following brief description provides some necessary background information
about the already available ice motion data and the mathematical relationships
between the ice motion and the ice deformation.
71
the long dark period during winter in the Arctic may also significantly restrict the use
of AVHRR in such studies.
An optimal approach would be to couple the results of the buoy program, which can,
for a limited number of points, provide nearly continuous motion measurements in the
temporal sense, with a satellite remote sensing program that can provide less frequent
(every few days) but nearly continuous spatial observations. The remote sensing
requirements for such a program would appear deceptively simple; the system must produce imagery that is of a sufficiently high resolution to allow one to follow identifiable
ice features for a number of repeat periods. Unfortunately, this also means that the system must function under all-weather conditions so that data can be obtained whenever the satellite is over the region of interest. These requirements eliminate visual- and
near-infrared-range sensors (limited by darkness and clouds), thermal infrared sensors
(limited by clouds), and passive microwave sensors (limited by inadequate resolution).
Until the launch of the two radar satellites ERS-l (1991, ESA - the European Space
Agency) and JERS-l (1992, NASDA - the Japanese Space Agency), satellite-borne remote
sensing systems have not held up to their portion of this task. However, the difficulty has
now changed from a "no-suitable-sensor" problem to a problem of how to rapidly
extract the necessary geophysical information from the extensive SAR coverage that is
being obtained. This latter problem has become even more acute since the launch of the
Canadian Radarsat in 1995. The wide-swath observation mode of Radarsat results in
image strips that are 500 km wide (in contrast to present synthetic aperture radar satellites that provide image strips with widths of 100 km). Considering that ERS-l and JERS1 plus ERS-2, which is a follow-up of ERS-l, are still operating during the Radarsat time
period, the motivation for the work reported in the present chapter becomes clear.
Our main objective is to develop a method for screening large volumes of synthetic
aperture radar (SAR) products and extract significant ice deformation events without
having to look at either the SAR images or at the SAR-derived ice motion products on
which no significant ice deformation occurred. In the following sections, the background information on the satellite SAR derived ice motion products and the relationship between ice motion and ice deformation are given first. Then the details of the
actual algorithms used for derivation of ice deformation are presented, the resulting
ice deformation products are described and their accuracy discussed. Finally, as illustrations, two examples are given to demonstrate extraction of intermediate-scale sea
ice deformation parameters using the algorithm.
In one example, the temporal variations in ice deformation are obtained for a 6-month
period during the winter of 1991-1992, for a circular sampling area with a radius of 200
km centered at 81°N and 170 oW in the Chukchi Sea portion of the Arctic Ocean. In the
other example, the ice deformation as a function of time and latitude in the Beaufort
Sea is extracted during a period between January 23 and March 15, 1994, as part of the
SIMI (Sea Ice Mechanics Initiative) project.
4.2
Background
The following brief description provides some necessary background information
about the already available ice motion data and the mathematical relationships
between the ice motion and the ice deformation.