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Because the earth's cryosphere influences global weather patterns and climate, the scientific community has had great interest in monitoring this important region. Microwave remote sensing has proven to be a useful tool in estimating sea and glacial ice surface characteristics with both scatterometers and radiometers exhibiting high sensitivity to important ice properties. This dissertation presents an array of studies focused on extracting key surface features from multisensor microwave data sets. First, several enhanced resolution image reconstruction issues are addressed. Among these are the optimization of the scatterometer image reconstruction (SIR) algorithm for NASA scatterometer (NSCAT) data, an analysis of Ku-band azimuthal modulation in Antarctica, and inter-sensor European Remote Sensing Satellite (ERS) calibration. Next, various methods for the removal of atmospheric distortions in image reconstruction of passive radiometer observations are considered. An automated algorithm is proposed which determines the spatial extent of sea ice in the Arctic and Antarctic regions from NSCAT data. A multisensor iterative sea ice statistical classification method which adapts to the temporally varying signatures of ice types is developed. The sea ice extent and classification algorithms are adopted for current SeaWinds scatterometer data sets. Finally, the automated inversion of large-scale forward electromagnetic scattering of models is considered and used to study the temporal evolution of the scattering properties of polar sea ice.

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This thesis reports on my Ph.D. research in the area of microwave remote sensing of the Arctic. The main objective of this research is to reconstruct the dielectric profile of the snow-covered sea ice, and indirectly retrieve some of its geophysical and thermodynamic properties. To meet this objective, a nonlinear electromagnetic inverse scattering algorithm is developed that consists of forward and inverse solvers. The input to this algorithm is the normalized radar cross section (NRCS) data collected by radar systems from the snow-covered sea ice profile. The proposed inversion algorithm iteratively minimizes a discrepancy between the measured and simulated NRCS data to achieve an accurate reconstruction. Two main challenges associated with this inverse problem are its ill-posedness and its limited available scattering data. To tackle these, the utilization of appropriate regularization and weighting schemes as well as the incorporation of prior information into the inversion algorithm are employed. These include the utilization of (i) appropriate weighting factors for the misfit cost function, (ii) more sensitive NRCS data with respect to the unknown parameters, (iii) further parametrization of the profile based on the expected distribution, (iv) time-series NRCS data to better initialize the inversion process, and (v) NRCS data collected by the satellite and on-site scatterometer to be inverted simultaneously for profile reconstruction. The experimental data utilized are collected by the author in collaboration with the Centre for Earth Observation Science. These measurements are performed on (i) the artificially-grown sea ice in the Sea-ice Environmental Research Facility, located at the University of Manitoba during winter 2014, and (ii) the landfast sea ice located in the Arctic (Cambridge Bay, Nunavut) during May 2014. The measurement procedure includes NRCS data collection through an on-site C-band scatterometer and a spaceborne SAR satellite and physical sampling of the snow and sea ice. The proposed electromagnetic inverse scattering algorithm is utilized to invert these experimental data sets, as well as some synthetic data sets. It will be shown that the use of various techniques developed in this thesis in conjunction with the developed inversion algorithm results in reasonable snow-covered sea ice profile reconstruction. / October 2016

By the end of 2016 surveillance and reconnaissance satellites will have been monitoring Arctic-wide sea ice conditions for decades. Situated at the boundary between atmosphere and ocean, Arctic sea ice retreat has been one of the most conspicuous indication of climate change, especially in the two most recent decades. The 2001 annual minimum extent of Arctic sea ice marks the last year above the 1981 -- 2012 long-term average extent. Ever since then only lower than average Arctic sea ice has been observed at the end of each summer's melt season. For more than a century climate scientists have postulated that the darkening of the Arctic due to retreating sea ice and therefore more exposed open ocean would be the consequence of global warming. In the first decade of the 2000s the human influence on that warming in the Arctic was indeed detected in observations and attributed to increasing atmospheric greenhouse-gas concentrations. In this study we direct our attention to a potential offsetting effect from other anthropogenic (OANT) forcing agents, mainly aerosols, that has potentially out masked a fraction of greenhouse-gas induced warming by a combined cooling effect. We acknowledge that multiple sources of uncertainty exist in our method, in particular in the observed records of Arctic sea ice and corresponding simulations from climate models. No formal detection and attribution (DA) analysis has yet been carried out to try to detect the combined cooling effect from aerosols in observations of Arctic sea ice extent. We use three publicly available observational data sets of Arctic sea ice and climate simulations from eight models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). In our detection and attribution study observations are regressed on model-derived climate response pattern, or fingerprints, under all known historical (ALL), greenhouse-gas only (GHG) and known natural-only (NAT) forcing factors using an optimal fingerprinting method. We estimate regression coefficients (scaling factors) for each forcing group that scale the fingerprints to best match the observed record. From the scaled ALL, GHG and NAT fingerprints we calculate the relative contribution of the observed sea ice decline attributable to OANT forcing agent. Based on our DA results we show that the simulated climate response patterns to changes in GHG, OANT and NAT forcing are detected in the observed records of September Arctic sea ice extent for the 1953 to 2012 period. / Graduate

Sea ice surface roughness is a geophysical property which can be defined and quantified on a variety scales, and consequently affects processes across various scales. The sea ice surface roughness influences various mass, gas, and energy fluxes across the ocean-sea ice-atmosphere interface. Utilizing synthetic aperture radar (SAR) data to understand and map sea ice roughness is an active area of research. This thesis provides new techniques for the estimation of sea ice surface roughness in the Canadian Arctic Archipelago using synthetic aperture radar (SAR). Estimating and isolating sea ice surface properties from SAR imagery is complicated as there are a number of sea ice and sensor properties that influence the backscattered energy. There is increased difficulty in the melting season due to the presence of melt ponds on the surface, which can often inhibit interactions from the sensor to the sea ice surface as shorter microwaves cannot penetrate through the melt water. An object-based image analysis is here used to quantitatively link the winter first-year sea ice surface roughness to C-band RADARSAT-2 and L-band ALOS-2 PALSAR-2 SAR backscatter measured at two periods: winter (pre-melt) and advanced melt. Since the sea ice in our study area, the Canadian Arctic Archipelago, is landfast, the same ice can be imaged using SAR after the surface roughness measurements are established. Strong correlations between winter measured surface roughness, and C- and L-band SAR backscatter acquired during both the winter and advanced melt periods are observed. Results for winter indicate: (1) C-band HH-polarization backscatter is correlated with roughness (r=0.86) at a shallow incidence angle; and (2) L-band HH- and VV-polarization backscatter is correlated with roughness (r=0.82) at a moderate incidence angle. Results for advanced melt indicate: (1) C-band HV/HH polarization ratio is correlated with roughness (r=-0.83) at shallow incidence angle; (2) C-band HH-polarization backscatter is correlated with roughness (r=0.84) at shallow incidence angle for deformed first-year ice only; and (3) L-band HH-polarization backscatter is correlated with roughness (r=0.79) at moderate incidence angle. Retrieval models for surface roughness are developed and applied to the imagery to demonstrate the utility of SAR for mapping roughness, also as a proxy for deformation state, with a best case RMSE of 5 mm in the winter, and 8 mm during the advanced melt. / Graduate

Polar sea ice measurements are an important contribution to global climate models. Passive and active microwave remote sensing instruments are used to track global trends in polar sea ice growth and retreat from day to day. A scatterometer sea ice extent data set is valuable for comparison with other radiometer data sets and ground based measurements. This scatterometer sea ice record began with the NASA Scatterometer (NSCAT) and continued with the Quick Scatterometer (QuikSCAT) data set. The Ku-band Oceansat-2 scatterometer (OSCAT) is very similar to the Quick Scatterometer, which operated from 1999 to 2009. OSCAT continues the Ku-band scatterometer data record through 2014 with an overlap of eighteen days with QuikSCATs mission in 2009. This thesis discusses a particular climate application of the time series for sea ice extent observation. In this thesis, a QuikSCAT sea ice extent algorithm is modified for OSCAT. Gaps in OSCAT data are accounted for using a reverse time processing approach. The data gaps are filled in to support sea ice extent mapping. The data set is validated with overlapping data from QuikSCAT as well as the sea ice extent data set calculated from Special Sensor Microwave Imager data by the NASA Team algorithm.Data from the Advanced Scatterometer (ASCAT), which operates at C-band, are processed using a Bayesian classification algorithm for a stand-alone C-band sea ice extent product to continue scatterometer sea ice extent observation past 2014. ASCAT azimuth dependence data is developed for use as a parameter in the ASCAT sea ice extent algorithm. Image dilation and erosion techniques are employed to smooth the sea ice edge and correct misclassifications. ASCAT sea ice extent data is validated to overlapping OSCAT data.

When light intensities become supersaturating for photosynthesis, phytoplankton must be able to protect the photosynthetic machinery from potential damage by excess energy absorption. One of the most important photoprotective mechanisms involves the nonradiative dissipation of excess light energy by the interconversion of the carotenoid pigments of the so-called xanthophyll cycle. Very little is known about how the xanthophyll cycle of natural communities of phytoplankton responds to high light conditions and the relationship of this photoprotective mechanism to the surrounding physical environment. The purpose of this thesis was to examine the functioning, activation and relationship to the physical environment of the xanthophyll cycle in phytoplankton from the Antarctic ecosystem and the Southern Ocean. Experiments in Antarctica were conducted in austral spring under various natural and artificial light regimes including the use of a newly developed light mixing simulator (LMS). Photoprotective carotenoid pigment concentrations were determined using a carotenoid specific protocol for High Performance Liquid Chromatography (HPLC). The photoprotective xanthophyll cycle was not active in Antarctic sea ice algae under the low light conditions under the annual sea ice. When sea ice algae are exposed to high irradiance, there was an initial rapid deepoxidation of the xanthophyll pigment diadinoxanthin (DD) to diatoxanthin (DT). With on-going irradiance exposure, slower deepoxidation of DD continued. The recovery of DD in the dark or under low light was found to be significantly faster than in temperate algal communities, and is likely a particular adaptation to the unique light environment in Antarctica. The temporal accumulation of pigments of the violaxanthin (VX) xanthophyll cycle was observed for the first time in a natural phytoplankton population. It is hypothesized that the VX cycle may function as a pathway to maintain the pool of DD cycle pigments rather than as a separate photoprotective pathway as observed in higher plants. The high irradiances of ultraviolet - B (290 - 320 nm) radiation (UVB) as a result of stratospheric ozone depletion over Antarctica in spring was found to significantly impact on the DD cycle. Exposure to high levels of both ultraviolet-A (320- 400 nm) radiation (UVA) and UVB reduced the photoprotective xanthophyll pigment pool with the greatest reduction occurring after exposure to high levels of UVB. The reduction in the amount of cellular DD after exposure to high levels of UVB was greater than can be explained by deepoxidation activity, which implies that high UVB exposure can lead to a loss of DD from the community. The first-order kinetic rates of the DD cycle were found to be similar to other studies and did not vary with light intensity. Simulations under natural light using the LMS demonstrated that the response of the DD cycle to static in situ incubations and when subject to vertical mixing was not similar, and that static incubations overestimate DD-cycle activity Over the long term, algae in a simulated vertically mixed environment were able to increase the pool of xanthophyll pigments compared to static conditions where the pool remained the same or decreased. Oceanographic observations from the subantarctic waters south-east of New Zealand in austral autumn provided the physical background for new insights into the xanthophyll cycle of Southern Ocean phytoplankton. The circulation flow and water masses between the Bounty Plateau and Bollons Seamount was resolved and shown to differ from numerical models. Relatively little of the warm and salty Subantarctic Mode Water (SAMW) from the Tasman Sea is carried in the flow of the Subantarctic Front (SAF). The spatial distribution of photoprotective xanthophyll pigments showed higher than expected concentrations in the surface mixed layer of the region. The high concentration of photoprotective pigments is considered to be a consequence of the low iron concentrations in southern waters and the highly variable light and vertical mixing environment. The high cellular concentrations of photoprotective pigments constrains photosynthetic activity implying that the photoprotective pigments may play a more significant role in controlling phytoplankton production in the Southern Ocean than previously thought. Analysis of the xanthophyll pigments and physical oceanography with a Self-Organising map (SOM) Artificial Neural Network (ANN) showed that the photophysiological index DT/ (DD+DT) can be used to resolve a change in water type properties. A simple numerical model was developed which can be used to provide a quantitative index of the relative magnitudes of vertical mixing and phytoplankton photoprotection in the water column. This approach may be useful to identify the effects of physical changes in the surface mixed layer of the Southern Ocean as predicted by climate change modelling.