Abstract

Spaceborne microwave sensors are powerful tools for monitoring the impacts of global climate change on the Greenland ice sheet. This dissertation focuses on refining methods for applying microwave data in Greenland studies by using new simple theoretical and empirical models to investigate (1) azimuth anisotropies in the data, (2) the microwave signature of the snow surface, (3) detection of snow melt, and (4) classification of snow melt. The results are applicable for identifying geophysical properties of the snow surface and monitoring changes on the ice sheet in relation to melt duration/extent, accumulation, and wind patterns. Azimuth dependence of the normalized radar cross-section (sigma-0) over the Greenland ice sheet is modeled with a simple surface scattering model. The model assumes that azimuth anisotropy in 1-100 meter scale surface roughness is the primary mechanism driving the azimuth modulation. This model is inverted to estimate snow surface properties using sigma-0 measurements from the C-band European Remote Sensing Advanced Microwave Instrument (ERS) in scatterometer mode. The largest roughness estimates occur in the lower portions of the dry snow zone. Estimates of the preferential direction in surface roughness are highly correlated with katabatic wind fields over Greenland. A new observation model is introduced that uses a limited number of parameters to characterize the snow surface based on the dependence of radar backscatter on incidence angle, azimuth angle, spatial gradient, and temporal rate of change. The individual model parameters are discussed in depth with examples using data from the NASA Scatterometer (NSCAT) and from the ERS. The model may be applied for increased accuracy in scatterometer, SAR, and wide-angle SAR studies. Examples illustrating the use of the model are included with one application focusing on analysis of inter-annual change and another focusing on increased sensitivity in studies of intra-annual change. Six different melt detection method/sensor combinations are compared using data for the summer of 2000. The sensors include the Special Spectral Microwave Imager (SSM/I), SeaWinds on QuikSCAT (QSCAT), and ERS. A new method of melt detection is introduced that is based on a simple physical model relating the moisture content and depth of a layer of wet surface snow to a single channel melt detection threshold. The model can be applied to both active and passive sensors. Model-based melt estimates from different sensors are highly correlated and do not exhibit the unnatural phenomenon observed with previous methods. Trends in SSM/I channel ratios are used to differentiate subsurface and surface melt. For ablation estimation, this separation is important due to expected differences in the ablation rate for the two melt types. Evidences of the daily melt refreeze cycle are observed in the diurnal variation of the different brightness temperature channel ratios. The polarization ratio increases during periods of surface melt while the frequency ratio remains relatively constant. The frequency ratio increases during periods of expected subsurface melt. Similar trends are observed in brightness temperature measurements from in situ data collected by other investigators.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

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