Polar Sea Ice Mapping for SeaWinds
type: CreativeWork
description: In recent years, the scientific community has expressed interest in the ability to observe global climate indicators such as polar sea ice. Advances in microwave remote sensing technology have allowed a large-scale and detailed study of sea ice characteristics. This thesis provides the analysis and development of sea ice mapping algorithms for the SeaWinds scatterometer. First, an in-depth analysis of the Remund Long (RL) algorithm for SeaWinds is performed. From this study, several improvements are made to the RL algorithm which enhance its performance. In addition, a new method for automated polar sea ice mapping is developed for the SeaWinds instrument. This method is rooted in Bayes decision theory, and incorporates an adaptive model for seasonally fluctuating sea ice and ocean microwave signatures. The new approach is compared to the RL algorithm, to passive microwave data, and to high-resolution SAR imagery for validation.
abstract: In recent years, the scientific community has expressed interest in the ability to observe global climate indicators such as polar sea ice. Advances in microwave remote sensing technology have allowed a large-scale and detailed study of sea ice characteristics. This thesis provides the analysis and development of sea ice mapping algorithms for the SeaWinds scatterometer. First, an in-depth analysis of the Remund Long (RL) algorithm for SeaWinds is performed. From this study, several improvements are made to the RL algorithm which enhance its performance. In addition, a new method for automated polar sea ice mapping is developed for the SeaWinds instrument. This method is rooted in Bayes decision theory, and incorporates an adaptive model for seasonally fluctuating sea ice and ocean microwave signatures. The new approach is compared to the RL algorithm, to passive microwave data, and to high-resolution SAR imagery for validation.
author: Anderson, Hyrum Spencer
language: English
keywords: polar, sea ice, mapping, SeaWinds, scatterometer, classification, remote sensing, QSCAT, QuikSCAT, Bayes, Brigham Young, electrical engineering, Electrical and Computer Engineering
date modified: 2026-07-24T01:27:16Z
provider: Brigham Young University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Brigham Young University - Provo