1 Recent Advances in the Analysis of SAR Data of the Polar Oceans
7
between the time series evolution of the microwave scattering coefficient and components of the surface energy balance. These statistical relationships are then exploited
utilizing a variety of image processing approaches.
Chapter 4, by S. Li, Z. Cheng, and W.P. Weeks, discusses how intermediate regional
scale sea ice deformation can help bridge the gap between the sea ice behavior on the
floe scale, which is measurable in situ, and the one on the climate scale, which is used
as a direct input to climate models. The chapter describes the algorithm that allows
tracking of sea ice motion using SAR data and a method for screening large volumes
of SAR products and extracting significant ice deformation events without having to
look at either the SAR images or the SAR-derived ice motion products on which no significant ice deformation occurred.
Chapter 5, by S. Beaven, and S.P. Gogineni, presents an approach that integrates SAR
analysis with an SSM!I sea ice concentration algorithm in an attempt to generate more
accurate sea ice type fractions. Dynamic thresholding is used on SAR sea ice data to
generate an estimate of multiyear ice, and this value is used by the SSM!I classification
algorithm as an initialization parameter. The authors show how the results of the integrated algorithm improved concentration measurement and matched better with in situ
observations.
Chapter 6, by A.K. Liu, and C. Y. Peng, discusses uses of the wavelet transform on sea
ice SAR images to provide spectral decompositions via the scale concept. The wavelet
analysis is used for ice feature tracking, for estimating the ice edge in the marginal ice
zone and in polynyas, and for identifying ice floes.
Chapter 7, by D. Winebrenner, D. Long, and B. Holt, demonstrates the use of automated methods to detect and map the seasonal transitions of Arctic sea ice using SAR
data. The chapter discusses how in situ and laboratory observations of the behavior
of SAR backscatter led to the development of algorithmic methods to identify and
map melt onset and freeze-up of Arctic sea ice. The authors also discuss possible
future uses of scatterometry data to complement SAR data for detection of transitions.
Chapter 8, by M. Drinkwater, discusses how in the last few years scientists have for
the first time had access to wide, repeat coverage of the Antarctic using SAR and scatterometer data. The main result of analysis of these data is an understanding of how
microwave signatures and their time-varying properties can be used as a basis for
understanding geophysical changes in the Antarctic sea ice cover. Furthermore, the
chapter describes algorithms that have been applied to these data and ultimately lead
to data products including ice kinematics fields and area flux information, with
which to address mass- or freshwater-balance questions in the Southern Ocean.
Chapter 9, by P. Carsey, R. Harding, and C. Wales, describes the Alaska SAR Facility
(ASF). ASF, established in 1985, is the facility that has collected, processed, and archived
ERS-1, ERS-2 and JERS-1 data in the past, and which will fulfill all data system aspects
for the US Government allocation of data from RADARSAT. ASF compiles and submits
data acquisition requests, schedules and receives data directly at antennas at Fairbanks
and McMurdo as well as receiving data by courier from other network ground stations
around the world, archives raw data, processes raw data into image data, generates a
number of higher level products (e.g., sea ice motion), and makes available to the user
community software tools to manipulate SAR imagery. ASF is an important resource
for scientists interested in the polar regions.
7
between the time series evolution of the microwave scattering coefficient and components of the surface energy balance. These statistical relationships are then exploited
utilizing a variety of image processing approaches.
Chapter 4, by S. Li, Z. Cheng, and W.P. Weeks, discusses how intermediate regional
scale sea ice deformation can help bridge the gap between the sea ice behavior on the
floe scale, which is measurable in situ, and the one on the climate scale, which is used
as a direct input to climate models. The chapter describes the algorithm that allows
tracking of sea ice motion using SAR data and a method for screening large volumes
of SAR products and extracting significant ice deformation events without having to
look at either the SAR images or the SAR-derived ice motion products on which no significant ice deformation occurred.
Chapter 5, by S. Beaven, and S.P. Gogineni, presents an approach that integrates SAR
analysis with an SSM!I sea ice concentration algorithm in an attempt to generate more
accurate sea ice type fractions. Dynamic thresholding is used on SAR sea ice data to
generate an estimate of multiyear ice, and this value is used by the SSM!I classification
algorithm as an initialization parameter. The authors show how the results of the integrated algorithm improved concentration measurement and matched better with in situ
observations.
Chapter 6, by A.K. Liu, and C. Y. Peng, discusses uses of the wavelet transform on sea
ice SAR images to provide spectral decompositions via the scale concept. The wavelet
analysis is used for ice feature tracking, for estimating the ice edge in the marginal ice
zone and in polynyas, and for identifying ice floes.
Chapter 7, by D. Winebrenner, D. Long, and B. Holt, demonstrates the use of automated methods to detect and map the seasonal transitions of Arctic sea ice using SAR
data. The chapter discusses how in situ and laboratory observations of the behavior
of SAR backscatter led to the development of algorithmic methods to identify and
map melt onset and freeze-up of Arctic sea ice. The authors also discuss possible
future uses of scatterometry data to complement SAR data for detection of transitions.
Chapter 8, by M. Drinkwater, discusses how in the last few years scientists have for
the first time had access to wide, repeat coverage of the Antarctic using SAR and scatterometer data. The main result of analysis of these data is an understanding of how
microwave signatures and their time-varying properties can be used as a basis for
understanding geophysical changes in the Antarctic sea ice cover. Furthermore, the
chapter describes algorithms that have been applied to these data and ultimately lead
to data products including ice kinematics fields and area flux information, with
which to address mass- or freshwater-balance questions in the Southern Ocean.
Chapter 9, by P. Carsey, R. Harding, and C. Wales, describes the Alaska SAR Facility
(ASF). ASF, established in 1985, is the facility that has collected, processed, and archived
ERS-1, ERS-2 and JERS-1 data in the past, and which will fulfill all data system aspects
for the US Government allocation of data from RADARSAT. ASF compiles and submits
data acquisition requests, schedules and receives data directly at antennas at Fairbanks
and McMurdo as well as receiving data by courier from other network ground stations
around the world, archives raw data, processes raw data into image data, generates a
number of higher level products (e.g., sea ice motion), and makes available to the user
community software tools to manipulate SAR imagery. ASF is an important resource
for scientists interested in the polar regions.