High-spatial resolution monitoring of phycocyanin and chlorophyll-a using airborne hyperspectral imagery

Jong Cheol Pyo, Mayzonee Ligaray, Yong Sung Kwon, Myoung Hwan Ahn, Kyunghyun Kim, Hyuk Lee, Taegu Kang, Seong Been Cho, Yongeun Park, Kyung Hwa Cho

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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 R2 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.

Original languageEnglish
Article number1180
JournalRemote Sensing
Issue number8
StatePublished - 1 Aug 2018


  • Atmospheric correction
  • Bio-optical algorithm
  • Chlorophyll-a
  • Hyperspectral image
  • Phycocyanin


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