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High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Title
High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
Authors
Pyo, Jong CheolLigaray, MayzoneeKwon, Yong SungAhn, Myoung-HwanKim, KyunghyunLee, HyukKang, TaeguCho, Seong BeenPark, YongeunCho, Kyung Hwa
Ewha Authors
안명환
SCOPUS Author ID
안명환scopus
Issue Date
2018
Journal Title
REMOTE SENSING
ISSN
2072-4292JCR Link
Citation
REMOTE SENSING vol. 10, no. 8
Keywords
Hyperspectral imageatmospheric correctionbio-optical algorithmphycocyaninchlorophyll-a
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.
DOI
10.3390/rs10081180
Appears in Collections:
일반대학원 > 대기과학공학과 > Journal papers
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