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

Abstract

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, R rs (λ), 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 10 5 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 1,500 g 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 (λ) 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 CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ R rs (λ) 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 Microcystis 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 (λ) spectra.

Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

“…Here, we used the condition number (n cond ) (MATLAB function cond) as a diagnostic for the degree of the well-conditioning of matrix C (a matrix with a high n cond is ill-conditioned, and vice versa). In addition, a similarity index (SI i,j ) [74,75] was used to represent the similarity between the absolute values of two specific spectra a i * (λ) and a j * (λ) (denoted as a * + (λ)).…”

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

“…Among these efforts, a derivative spectroscopy/similarity index (SI) approach is the most common method for identifying dominant phytoplankton species or groups [25][26][27]. However, because SI-based approaches assign unknown spectra to the reference spectra that have the largest SI, only dominant species or groups can be identified, so it is difficult to determine phytoplankton species composition using this method, and the algorithm degradation for coastal waters with high suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) is inevitable [28,29].Regarding data acquisition, most studies on phytoplankton community structures are based on the high-performance liquid chromatography-chemical taxonomy (HPLC-Chemtax) method [30]. On this foundation, Pan et al established third-order polynomial functions for individual pigment concentration inversion [31-33], further estimating phytoplankton community composition along the northeastern coast of the United States and northern South China Sea.…”

“…Among these efforts, a derivative spectroscopy/similarity index (SI) approach is the most common method for identifying dominant phytoplankton species or groups [25][26][27]. However, because SI-based approaches assign unknown spectra to the reference spectra that have the largest SI, only dominant species or groups can be identified, so it is difficult to determine phytoplankton species composition using this method, and the algorithm degradation for coastal waters with high suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) is inevitable [28,29].…”

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

“…Principal component regression analysis provides a powerful tool to retrieve optically-significant marine variables from hyperspectral radiometry by exploring spectral variations in R rs (λ) [33,35]. With regard to assessing phytoplankton community composition, this method has been implemented most frequently in areas of high phytoplankton biomass, where changes in phytoplankton composition and biomass provide significant changes in phytoplankton absorption that are reflected in spectral variations in R rs (λ) [33,35,60]. The highest picophytoplankton abundances occur in the stable oligotrophic ocean, where the spectral signature of water is influenced not only by the present cells but also by other seawater constituents that co-vary with their abundances such as the absorption of colored dissolved organic matter and backscattering of heterotrophic bacteria, both of which alter the magnitude and shape of R rs (λ).…”