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A fully data-driven method for predicting Antarctic sea ice concentrations using temporal mixture analysis and an autoregressive model

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Title
A fully data-driven method for predicting Antarctic sea ice concentrations using temporal mixture analysis and an autoregressive model
Other Titles
원격탐사 자료 기반 남극 해빙농도 예측
Authors
Chi, Junhwa
Kim, Hyun-cheol
Subject
Imaging Science & Photographic Technology
Keywords
linear mixture modeling; pixel unmixing; remote sensing; sea ice; sea ice concentration; time series analysis; Remote Sensing
Issue Date
2017
Citation
Chi, Junhwa, Kim, Hyun-cheol. 2017. "A fully data-driven method for predicting Antarctic sea ice concentrations using temporal mixture analysis and an autoregressive model". REMOTE SENSING LETTERS, 8: 106-115.
Abstract
While sea ice dynamics have been gaining increased attention in climate change and global warming studies, remote sensing (RS) sensors, capable of detecting and characterizing detailed information on targets of interest, have come to play a critical role in acquiring image data over extended and inaccessible areas. Passive microwave sensors have been the most effective and consistent tool for characterizing daily sea ice cover at global scale. However, it is typically challenging to study temporally successive data acquired at high time frequencies, referred to as hypertemporal data. To address this issue, among various RS analysis techniques, temporal mixture analysis (TMA) approaches are often investigated for characterizing seasonal characteristics of environmental factors including sea ice concentration (SIC) in this study. The goal of the present study is to predict daily Antarctic SICs for one year through a combination of TMA results and time series analysis without incorporation of environmental factors. First, temporally most significant sea ice signals, referred to as temporal endmembers (EMs), were found using signal processing algorithms, and then corresponding fractional abundances (FAs) associated with each EM were calculated using least squares solution. Using these FAs, subsequently, a single autoregressive (AR) model that typically fits all Antarctic SIC data for the period 1979-2013 was applied to predict SIC values for 2014. Daily SIC data reconstructed using the proposed method were qualitatively and quantitatively compared to those of using real FAs derived from a spectral unmixing method. It was found that AR model trained by the proposed method successfully predicts new FAs for 2014 and the FAs should be used to reconstruct resulting 2014 daily SIC images.
URI
http://repository.kopri.re.kr/handle/201206/5819
DOI
http://dx.doi.org/10.1080/2150704X.2016.1234726
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