Optical discrimination of harmful Cochlodinium polykrikoides blooms in Korean coastal waters.

We investigated the possibility of optically discriminating harmful algal blooms (HABs), focusing on Cochlodinium polykrikoides, the major HAB causative dinoflagellate species in Korean waters. We produced a large data set of simulated remote sensing reflectance (Rrs) spectra in a wide range of bio-optical conditions using Hydrolight software and bio-optical data provided by the International Ocean-Color Coordinating Group. The two Rrs band ratios (Rrs(555)/Rrs(531) and Rrs(488)/Rrs(443)) were determined to be effective in discriminating high-density C. polykrikoides blooms. The results were consistent with in situ observations and seem applicable to diverse coastal environments. Our findings provide theoretical and quantitative criteria upon which in-water HAB detecting algorithms can be developed.

Remote estimation of phytoplankton size fractions using the spectral shape of light absorption.

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of phytoplankton size structure using a(ph)(λ) derived from satellite observations or rapid field instrument measurements in the future.