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Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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Remote Estimation Models for Deriving Chlorophyll-a Concentration using Optical Properties in Turbid Inland Waters: Application and Valuation

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Title
Remote Estimation Models for Deriving Chlorophyll-a Concentration using Optical Properties in Turbid Inland Waters: Application and Valuation
Other Titles
분광특성을 이용한 담수역 클로로필-a 원격 추정 모형의 적용과 평가
Author
Lee, HyukKang, TaeguNam, GibeomHa, RimCho, Kyung Hwa
Issue Date
2015-05
Publisher
한국물환경학회
Citation
JOURNAL OF KOREAN SOCIETY ON WATER ENVIRONMENT, v.31, no.3, pp.272 - 285
Abstract
Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.
URI
https://scholarworks.unist.ac.kr/handle/201301/20616
DOI
10.15681/KSWE.2015.31.3.272
ISSN
1229-4144
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