A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes from Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multi-scale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler SAR (Synthetic Aperture Radar) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facetbased models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper (ATM) data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, roll) and sea ice properties (physical properties, roughness, presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm – potentially underestimating sea ice thickness by around 50 cm. We present a set of ‘ideal’ waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness and snow depth, from SAR altimeter observations

A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multi-scale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler SAR (Synthetic Aperture Radar) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facetbased models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper (ATM) data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, roll) and sea ice properties (physical properties, roughness, presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm – potentially underestimating sea ice thickness by around 50 cm. We present a set of ‘ideal’ waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness and snow depth, from SAR altimeter observations

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Department of Urban and Environmental Engineering (Environmental Science and Engineering)Sea ice closely interacts with the atmosphere and ocean systems. Land fast sea ice (fast ice) is a kind of sea ice attached to the shore, ice shelves, or grounded icebergs. It is widely distributed along the Antarctic coast and acts as an interface between the atmosphere and the ocean, affecting heat balance feedback, thermal insulation effects, and deep water formation depending on the temporal and spatial effects of the environmental conditions. It also plays an important role in the biological aspects of Antarctica. Attached to the Antarctic glacier is strongly associated with calving events of ice shelf as it is physically coupled with glaciers at the terminus. The existing Antarctic fast ice has been mainly focused on the East Antarctic, especially for the research on long-term fast ice. Several case studies for West Antarctic fast ice with satellite images were performed in local areas. Various types of satellite data and detection techniques were utilized to successfully detect fast ice. In addition, long-term fast ice maps specifically focused on the Amundsen sea of West Antarctica were generated to investigate the distribution and variability of fast ice. This thesis reports the results of fast ice detection algorithms that have been developed using various satellite images that can be used for fast ice detection. Along with the use of multiple satellite data, the proposed fast ice detection algorithms can more effectively detect fast ice, which then allows to obtain more accurate fast ice detection and produce long-term fast ice with high accuracy. Especially, the distribution and variability of time-series fast ice in West Antarctica, which is more concentrated in the Amundsen Sea, were analyzed together with bathymetry data and the distribution of glacier icebergs. In order to detect fast ice, machine learning techniques were basically used in this thesis. Two classes (i.e. fast ice and non-fast ice) were classified. Using MODIS images, there was a problem that fast ice was not produced in cloud cover areas and the polar night season, which is winter season in Antarctica. MODIS and AMSR-E satellite data were selectively used to solve the cloud contamination problem. Correlation-related variables were finally added based on the fact that fast ice is motionless for a certain period of time, and fast ice detection was performed at 15-day intervals using the improved input variables. Active microwave sensor data, ALOS PALSAR, was also used to detect fast ice and to validate fast ice detection results. Its high-spatial resolution allows to extract fast ice boundary more accurately. Fast ice detections showed good agreement with available ALOS PALSAR SAR images and MODIS reflectance images. Nearly decade-long fast ice extents were produced in the Amundsen Sea of West Antarctica and analyzed in terms of spatiotemporal variations with bathymetry and icebergs calved from ice shelves in study area. In addition, anomalous fast ice breakup events were examined, which suggests the importance of fast ice on the stability of ice shelves.clos

Electromagnetic backscatter modelling of icebergs at c-band in an ocean environment

This thesis outlines the development of an electromagnetic (EM) backscatter model of icebergs. It is a necessary first step for the generation of in-house synthetic aperture radar (SAR) data of icebergs to support optimum iceberg/ship classifier design. The EM modelling was developed in three stages. At first, an EM backscatter model was developed to generate simulated SAR data chips of iceberg targets at small incidence angles. The model parameters were set to mimic a dual polarized dataset collected at C-Band with the Sentinel-1A satellite. The simulated SAR data chips were compared with signatures and radiometric properties of the satellite data, including total radar cross section (TRCS). A second EM model was developed to mimic the parameters of a second SAR data collection with RADARSAT-2; this second data collection was at larger incidence angles and was fully polarimetric (four channels and interchannel phase). The full polarimetric SAR data allowed for a comparison of modelled TRCS and polarimetric decompositions. Finally, the EM backscatter models were tested in the context of iceberg/ship classification by comparing the performance of various computer vision classifiers using both simulated and real SAR image data of iceberg and vessel targets. This step is critical to check the compatibility of simulated data with the real data, and the ability to mix real and simulated SAR imagery for the generation of skilled classifiers. An EM backscatter modelling tool called GRECOSAR was used for the modelling work. GRECOSAR includes the ability to generate small scenes of the ocean using Pierson-Moskowitz spectral parameters. It also allows the placement of a 3D target shape into that ocean scene. Therefore, GRECOSAR is very useful for simulating SAR targets, however it can only model single layer scattering from the targets. This was found to be limiting in that EM scattering throughout volume of the iceberg could not be generated. This resulted in EM models that included only surface scattering of the iceberg. In order to generate realistic SAR scenes of icebergs on the ocean, 3D models of icebergs were captured in a series of field programs off the coast of Newfoundland and Labrador, Canada. The 3D models of the icebergs were obtained using a light detection and ranging (LiDAR) and multi-beam sonar data from a specially equipped vessel by a team of C-CORE. While profiling the iceberg targets, SAR images from satellites were captured for comparison with the simulated SAR images. The analysis of the real and simulated SAR imagery included comparisons of TRCS, SAR signature morphology and polarimetric decompositions of the targets. In general, these comparisons showed a good consistency between the simulated and real SAR scene. Simulations were also performed with varying target orientation and sea conditions (i.e., wind speed and direction). A wide variability of the TRCS and SAR signature morphology was observed with varying scene parameters. Icebergs were modelled using a high dielectric constant to mimic melting iceberg surfaces as seen during field work. Given that GRECOSAR could only generate surface backscatter, a mathematical model was developed to quantify the effect of melt water on the amount of surface and volume backscatter that might be expected from the icebergs. It was found that the icebergs in a high state of melt should produce predominantly surface scatter, thus validating the use of GRECOSAR for icebergs in this condition. Once the simulated SAR targets were validated against the real SAR data collections, a large dataset of simulated SAR chips of ships and icebergs were created specifically for the purpose of target classification. SAR chips were generated at varying imaging parameters and target sizes and passed on to an iceberg/ship classifier. Real and simulated SAR chips were combined in varying quantities (or targets) resulting in a series of different classifiers of varying skill. A good agreement between the classifier’s performance was found. This indicates the compatibility of the simulated SAR imagery with this application and provides an indication that the simulated data set captures all the necessary physical properties of icebergs for ship and iceberg classification

Forward Modelling of Multifrequency SAR Backscatter of Snow-Covered Lake Ice: Investigating Varying Snow and Ice Properties Within a Radiative Transfer Framework

Lakes are a key feature in the Northern Hemisphere landscape. The coverage of lakes by ice cover has important implications for local weather conditions and can influence energy balance. The presence of lake ice is also crucial for local economies, providing transportation routes, and acting as a source of recreation/tourism and local customs. Both lake ice cover, from which ice dates and duration can be derived (i.e., ice phenology), and ice thickness are considered as thematic variables of lakes as an essential climate variable by the Global Climate Observing System (GCOS) for understanding how climate is changing. However, the number of lake ice phenology ground observations has declined over the past three decades. Remote sensing provides a method of addressing this paucity in observations. Active microwave remote sensing, in particular synthetic aperture radar (SAR), is popular for monitoring ice cover as it does not rely on sunlight and the resolution allows for the monitoring of small and medium-sized lakes. In recent years, our understanding of the interaction between active microwave signals and lake ice has changed, shifting from a double bounce mechanism to single bounce at the ice-water interface. The single bounce, or surface scattering, at the ice-water interface is due to a rough surface and high dielectric contrast between ice and water. However, further work is needed to fully understand how changes in different lake ice properties impact active microwave signals. Radiative transfer modelling has been used to explore these interactions, but there are a variety of limitations associated with past experiments. This thesis aimed to faithfully represent lake ice using a radiative transfer framework and investigate how changes in lake ice properties impact active microwave backscatter. This knowledge was used to model backscatter throughout ice seasons under both dry and wet conditions. The radiative transfer framework used in this thesis was the Snow Microwave Radiative Transfer (SMRT) model. To investigate how broad changes in ice properties impact microwave backscatter, SMRT was used to conduct experiments on ice columns representing a shallow lake with tubular bubbles and a deep lake without tubular bubbles at L/C/X-band frequencies. The Canadian Lake Ice Model (CLIMo) was used to parameterize SMRT. Ice properties investigated included ice thickness, snow ice bubble radius and porosity, root mean square (RMS) height of the ice-water interface, correlation length of the ice-water interface, and tubular bubble radius and porosity. Modelled backscatter indicated that changes in ice thickness, snow ice porosity, and tubular bubble radius and porosity had little impact on microwave backscatter. The property that had the largest impact on backscatter was RMS height at the ice-water interface, confirming the results of other recent studies. L and C-band frequencies were found to be most sensitive to changes in RMS height. Bubble radius had a smaller impact on backscatter, but X-band was found to be most sensitive to changes in this property and would be a valuable frequency for studying surface ice conditions. From the results of these initial experiments, SMRT was then used to simulate the backscatter from lake ice for two lakes during different winter seasons. Malcolm Ramsay Lake near Churchill, Manitoba, represented a shallow lake with dense tubular bubbles and Noell lake near Inuvik, Northwest Territories, represented a deep lake with no tubular bubbles. Both field data and CLIMo simulations for the two lakes were used to parameterize SMRT. Because RMS height was determined to be the ice property that had the largest impact on backscatter, simulations focused on optimizing this value for both lakes. Modelled backscatter was validated using C-band satellite imagery for Noell Lake and L/C/X-band imagery for Malcolm Ramsay Lake. The root mean square error values for both lakes ranged from 0.38 to 2.33 dB and Spearman’s correlation coefficient (ρ) values >0.86. Modelled backscatter for Noell Lake was closer to observed values compared to Malcolm Ramsay Lake. Optimized values of RMS height provided a better fit compared to a stationary value and indicated that roughness likely increases rapidly at the start of the ice season but plateaus as ice growth slows. SMRT was found to model backscatter from ice cover well under dry conditions, however, modelling backscatter under wet conditions is equally important. Detailed field observations for Lake Oulujärvi in Finland were used to parameterize SMRT during three different conditions. The first was lake ice with a dry snow cover, the second with an overlying layer of wet snow, and the third was when a slush layer was present on the ice surface. Experiments conducted under dry conditions continued to support the dominance of scattering from the ice-water interface. However, when a layer of wet snow or slush layer was introduced the dominant scattering interface shifted to the new wet layer. Increased roughness at the boundary of these wet layers resulted in an increase in backscatter. The increase in backscatter is attributed to the higher dielectric constant value of these layers. The modelled backscatter was found to be representative of observed backscatter from Sentinel-1. The body of work of this thesis indicated that the SMRT framework can be used to faithfully represent lake ice and model backscatter from ice covers and improved understanding of the interaction between microwave backscatter and ice properties. With this improved understanding inversion models can be developed to retrieve roughness of the ice-water interface, this could be used to build other models to estimate ice thickness based on other remote sensing data. Additionally, insights into the impact of wet conditions on radar backscatter could prove useful in identifying unsafe ice locations