Active microwave remote sensing is an important tool for classification of sea ice in polar regions. The aim of this research is to improve the understanding of microwave scattering that occurs during the advanced melt season, with a focus on multiyear ice (MYI). This was done using a combination of in situ C-Band scatterometer measurements, geophysical characteristics of ice, and Radarsat-2 data. Results indicate that it is difficult to differentiate between first year ice (FYI) and MYI during advanced melt but combinations of incidence angle and polarization exist that assist with this. It is known that the presence of liquid water governs microwave scattering, therefore further research investigating the variation of microwave backscattered signatures over a diurnal time period was conducted. These results indicate an inverse relationship between temperatures and microwave signatures. The overall results from this research show that summer MYI signatures are extremely variable and difficult to classify.

Active microwave remote sensing is an important tool for classification of sea ice in polar regions. The aim of this research is to improve the understanding of microwave scattering that occurs during the advanced melt season, with a focus on multiyear ice (MYI). This was done using a combination of in situ C-Band scatterometer measurements, geophysical characteristics of ice, and Radarsat-2 data. Results indicate that it is difficult to differentiate between first year ice (FYI) and MYI during advanced melt but combinations of incidence angle and polarization exist that assist with this. It is known that the presence of liquid water governs microwave scattering, therefore further research investigating the variation of microwave backscattered signatures over a diurnal time period was conducted. These results indicate an inverse relationship between temperatures and microwave signatures. The overall results from this research show that summer MYI signatures are extremely variable and difficult to classify.

The seasonal cycle of snow and sea ice is a fundamental feature of the Arctic climate system. In the Northern Hemisphere, about 55 million km2 of sea ice and snow undergo complete melt and freeze processes every year. Because snow and sea ice are much brighter (higher albedo) than the underlying surface, their presence reduces absorption of incoming solar energy at high latitudes. Therefore, changes of the sea-ice and snow cover have a large impact on the Arctic climate and possibly at lower latitudes. One of the most important determining factors of the seasonal snow and sea-ice cover is the timing of the seasonal melt-freeze transitions. Hence, in order to better understand Arctic climate variability, it is key to continuously monitor these transitions. This thesis presents an algorithm for obtaining melt-freeze transitions using scatterometers over both the land and sea-ice domains. These satellite-borne instruments emit radiation at microwave wavelengths and measure the returned signal. Several scatterometers are employed: QuikSCAT (1999–2009), ASCAT (2009–present), and OSCAT (2009–present). QuikSCAT and OSCAT operate at Ku-band (λ=2.2 cm) and ASCAT at C-band (λ=5.7 cm), resulting in slightly different surface interactions. This thesis discusses these dissimilarities over the Arctic sea-ice domain, and juxtaposes the time series of seasonal melt-freeze transitions from the three scatterometers and compares them with other, independent datasets. The interactions of snow and sea ice with other components of the Arctic climate system are complex. Models are commonly employed to disentangle these interactions. But this hinges upon robust and well-formulated models, reached by perpetual testing against observations. This thesis also presents an evaluation of how well eleven state-of-the-art global climate models reproduce the Arctic sea-ice cover and the summer length—given by the melt-freeze transitions—using surface observations of air temperature. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: In press. Paper 4: Submitted.</p>

This thesis provides insight into techniques for the detection and classification of various marginal ice zone roughnesses in the southern Beaufort Sea using in situ and satellite-based microwave remote sensing. A proposed model of surface roughness shows the dependence of circular coherence, a discriminator of roughness, on the roughness and dielectrics. A relationship between ice slopes in azimuth and range direction is derived. Microwave brightness temperature of open water is significantly correlated with wave height but not with the wind speed, having the strongest correlations for the H-polarization at both 37 and 89 GHz. A modified formula for the relationship between non-dimensional form of energy and wave age at wind speeds 0−10 m/s is obtained. The brightness temperature (April−June) of sea ice at H-polarization of 89 GHz is found to decrease with increasing roughness, and is attributed to the dominant contributions from rapidly varying thermodynamic properties of snow-covered sea ice.

The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also made

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms.