Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(A.), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m(-3), concentrations of non-algal particles, NAP, from 0 to 1,500g m(-3), and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m(-1). A second, independent, smaller dataset of simulated R-rs(lambda) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by ODOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ R-rs(lambda) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Micracystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on R-rs(lambda) spectra.