Large-scale inverse Ku-band backscatter modeling of sea ice
Polar sea ice characteristics provide important inputs to models of several geophysical processes. Microwave scatterometers are ideal for monitoring these regions due to their sensitivity to ice properties and insensitivity to atmospheric distortions. Many forward electromagnetic scattering models h...
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ftbrighamyoung:oai:scholarsarchive.byu.edu:facpub-1482 2023-07-23T04:13:04+02:00 Large-scale inverse Ku-band backscatter modeling of sea ice Long, David G. Remund, Quinn P. 2003-08-01T07:00:00Z application/pdf https://scholarsarchive.byu.edu/facpub/483 https://scholarsarchive.byu.edu/context/facpub/article/1482/viewcontent/IR_CISOPTR_692.pdf English eng BYU ScholarsArchive https://scholarsarchive.byu.edu/facpub/483 https://scholarsarchive.byu.edu/context/facpub/article/1482/viewcontent/IR_CISOPTR_692.pdf Faculty Publications backscatter oceanographic techniques radar cross-sections radar theory remote sensing by radar sea ice water Electrical and Computer Engineering text 2003 ftbrighamyoung 2023-07-03T22:20:31Z Polar sea ice characteristics provide important inputs to models of several geophysical processes. Microwave scatterometers are ideal for monitoring these regions due to their sensitivity to ice properties and insensitivity to atmospheric distortions. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section (σ˚) 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 use scatterometer data in a large-scale inverse modeling experiment. Given the limited data 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. The result is a least squares estimate of three surface parameters: the power reflection coefficient at nadir, the rms surface slope, and the volume scattering albedo. 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 National Aeronautics and Space Administration scatterometer. The results are used to analyze and interpret σ˚ phenomena occurring in the Antarctic and the Arctic. Text albedo Antarc* Antarctic Arctic Sea ice Brigham Young University (BYU): ScholarsArchive Antarctic Arctic The Antarctic |
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Open Polar |
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Brigham Young University (BYU): ScholarsArchive |
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ftbrighamyoung |
language |
English |
topic |
backscatter oceanographic techniques radar cross-sections radar theory remote sensing by radar sea ice water Electrical and Computer Engineering |
spellingShingle |
backscatter oceanographic techniques radar cross-sections radar theory remote sensing by radar sea ice water Electrical and Computer Engineering Long, David G. Remund, Quinn P. Large-scale inverse Ku-band backscatter modeling of sea ice |
topic_facet |
backscatter oceanographic techniques radar cross-sections radar theory remote sensing by radar sea ice water Electrical and Computer Engineering |
description |
Polar sea ice characteristics provide important inputs to models of several geophysical processes. Microwave scatterometers are ideal for monitoring these regions due to their sensitivity to ice properties and insensitivity to atmospheric distortions. Many forward electromagnetic scattering models have been proposed to predict the normalized radar cross section (σ˚) 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 use scatterometer data in a large-scale inverse modeling experiment. Given the limited data 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. The result is a least squares estimate of three surface parameters: the power reflection coefficient at nadir, the rms surface slope, and the volume scattering albedo. 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 National Aeronautics and Space Administration scatterometer. The results are used to analyze and interpret σ˚ phenomena occurring in the Antarctic and the Arctic. |
format |
Text |
author |
Long, David G. Remund, Quinn P. |
author_facet |
Long, David G. Remund, Quinn P. |
author_sort |
Long, David G. |
title |
Large-scale inverse Ku-band backscatter modeling of sea ice |
title_short |
Large-scale inverse Ku-band backscatter modeling of sea ice |
title_full |
Large-scale inverse Ku-band backscatter modeling of sea ice |
title_fullStr |
Large-scale inverse Ku-band backscatter modeling of sea ice |
title_full_unstemmed |
Large-scale inverse Ku-band backscatter modeling of sea ice |
title_sort |
large-scale inverse ku-band backscatter modeling of sea ice |
publisher |
BYU ScholarsArchive |
publishDate |
2003 |
url |
https://scholarsarchive.byu.edu/facpub/483 https://scholarsarchive.byu.edu/context/facpub/article/1482/viewcontent/IR_CISOPTR_692.pdf |
geographic |
Antarctic Arctic The Antarctic |
geographic_facet |
Antarctic Arctic The Antarctic |
genre |
albedo Antarc* Antarctic Arctic Sea ice |
genre_facet |
albedo Antarc* Antarctic Arctic Sea ice |
op_source |
Faculty Publications |
op_relation |
https://scholarsarchive.byu.edu/facpub/483 https://scholarsarchive.byu.edu/context/facpub/article/1482/viewcontent/IR_CISOPTR_692.pdf |
_version_ |
1772176394490478592 |