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http://dx.doi.org/10.15681/KSWE.2015.31.3.272

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

Lee, Hyuk (Water Quality Assessment Research Division, National Institute of Environmental Research)
Kang, Taegu (Water Quality Assessment Research Division, National Institute of Environmental Research)
Nam, Gibeom (Water Quality Assessment Research Division, National Institute of Environmental Research)
Ha, Rim (Water Quality Assessment Research Division, National Institute of Environmental Research)
Cho, Kyunghwa (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Journal of Korean Society on Water Environment / v.31, no.3, 2015 , pp. 272-285 More about this Journal
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.
Keywords
Chlorophyll-a Concentration; Inherent Optical Properties (IOPs); Inversion Model for Deriving IOPs; Red-NIR 3-band Model;
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