Location
University of Utah
Start Date
5-8-2000 10:00 AM
Description
Polar sea ice characteristic provide important inputs to models of several geophysical processes. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section, oo, from sea ice characteristics. These models are based on very small scale ice features and generally assume that the region of interest is spatially homogeneous. Unfortunately, spaceborne scatterometer footprints are very large (5-50 km) and usually contain very heterogeneous mixtures of sea ice surface parameters. In this paper, we apply scatterometer data to large scale inverse modeling. Given the limited resolution, we adopt a simple geometric optics forward scattering model to analyze surface and volume scattering contributions to observed Ku-band signatures. A model inversion technique based on recursive optimization of an objective function is developed. Simulations demonstrate the performance of the method in the presence of noise. The inverse model is implemented using Ku-band image reconstructed data collected by the NASA scatterometer. The results are used to analyze and interpret a oo phenomenon occurring in the Arctic.
Large-Scale Inverse Microwave Backscatter Modeling of Sea Ice
University of Utah
Polar sea ice characteristic provide important inputs to models of several geophysical processes. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section, oo, from sea ice characteristics. These models are based on very small scale ice features and generally assume that the region of interest is spatially homogeneous. Unfortunately, spaceborne scatterometer footprints are very large (5-50 km) and usually contain very heterogeneous mixtures of sea ice surface parameters. In this paper, we apply scatterometer data to large scale inverse modeling. Given the limited resolution, we adopt a simple geometric optics forward scattering model to analyze surface and volume scattering contributions to observed Ku-band signatures. A model inversion technique based on recursive optimization of an objective function is developed. Simulations demonstrate the performance of the method in the presence of noise. The inverse model is implemented using Ku-band image reconstructed data collected by the NASA scatterometer. The results are used to analyze and interpret a oo phenomenon occurring in the Arctic.