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Comparative Study on Hyperspectral and Satellite Image for the Estimation of Chlorophyll a Concentration on Coastal Areas
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연안 해역의 클로로필 농도 추정을 위한 초분광 및 위성 클로로필 영상 비교 연구

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Type
Academic journal
Author
Journal
The Korean Society Of Remote Sensing 대한원격탐사학회지 대한원격탐사학회지 제36권 제2호 KCI Accredited Journals
Published
2020.1
Pages
309 - 323 (15page)

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Comparative Study on Hyperspectral and Satellite Image for the Estimation of Chlorophyll a Concentration on Coastal Areas
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Estimation of chlorophyll a concentration (CHL) on coastal areas using remote sensing has been mostly performed through multi-spectral satellite image analysis. Recently, various studies using hyperspectral imagery have been attempted. In particular, airborne hyperspectral imagery is composed of hundreds of bands with a narrow band width and high spatial resolution, and thus may be more effective in coastal areas than estimation of CHL through conventional satellite image. In this study, comparative analysis of hyperspectral and satellite-based CHL images was performed to estimate CHL in coastal areas. As a result of analyzing CHL and seawater spectrum data obtained by field survey conducted on the south coast of Korea, the seawater spectrum with high CHL peaked near the wavelength bands of 570 and 680 nm. Using this spectral feature, a new band ratio of 570 / 490 nm for estimating CHL was proposed. Through regression analysis between band ratio and the measured CHL were generated new CHL empirical formula. Validation of new empirical formula using the measured CHL showed valid results, with R2 of 0.70, RMSE of 2.43 mg m-3, and mean bias of 3.46 mg m-3. As a result of applying the new empirical formula to hyperspectral and satellite images, the average RMSE between hyperspectral imagery and the measured CHL was 0.12 mg m-3, making it possible to estimate CHL with higher accuracy than multi-spectral satellite images. Through these results, it is expected that it is possible to provide more accurate and precise spatial distribution information of CHL in coastal areas by utilizing hyperspectral imagery.

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