Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)

Abstract

Models based on the multivariate partial least squares (PLS) regression technique are developed for the retrieval of phytoplankton size structure from measured light absorption spectra (BOUSSOLE site, northwestern Mediterranean Sea). PLS-models trained with data from the Mediterranean Sea showed good accuracy in retrieving, over the nine-year BOUSSOLE time series, the concentrations of total chlorophyll a [Tchl a], of the sum of seven diagnostic pigments and of pigments associated with micro, nano, and picophytoplankton size classes separately. PLS-models trained using either total particle or phytoplankton absorption spectra performed similarly, and both reproduced seasonal variations of biomass and size classes derived by high performance liquid chromatography. Satisfactory retrievals were also obtained using PLS-models trained with a data set including various locations of the world's oceans, with however a lower accuracy. These results open the way to an application of this method to absorption spectra derived from hyperspectral and field satellite radiance measurements.

Diel variability of the beam attenuation and backscattering coefficients in the northwestern Mediterranean Sea (BOUSSOLE site)

“…Note that microphytoplankton dominate the biomass only during the spring bloom in 2006 while nanophytoplankton dominate during the spring bloom from 2007 to 2010. The switch between nano and microphytoplankton dominated blooms is also a feature of this area: microphytoplankton dominated during the bloom of years 2000 and 2005 [Organelli et al, 2013] while the bloom was dominated by nanophytoplankton during the other years.…”

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

“…The results of this study indicate that four additional bands (381, 473, 532, and 594 nm) for the Ocean and Land Colour Instrument (OLCI) would potentially enable absorption-based quantitative retrievals of diatoms, cyanobacteria, and coccolithophores. Recent methods have been developed to retrieve PG from in situ hyperspectral algal or particulate absorption coefficients, and validated using in situ measurements (Moisan et al, 2013;Organelli et al, 2013;Zhang et al, 2015). As absorption coefficients can be estimated from satellite measurements using inverse bio-optical models, this opens the way to applications of these methods to satellite data.…”

“…Improved models on phytoplankton photoacclimation combined with new approaches in determining cell size should assist in improving our understanding of how pigment packaging influences the spectral signature of natural phytoplankton assemblages. Efforts inverting hyperspectral reflectance and absorption spectra to obtain PG have shown limited success, leading to identification of certain PT with no quantification (Werdell et al, 2014;Kudela et al, 2015;Xi et al, 2015) of PSC fractions (Organelli et al, 2013) or quantification of accessory pigments in addition to chl-a Moisan et al, 2013;Bracher et al, 2015b). PT specific absorption properties are available but large spectral variability is related to algal culturing and variations in size, pigment composition and pigment packaging due to physiological responses of PT.…”

Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra

“…The QAA is simple and quick to apply, as its calculation efficiency is similar to that of empirical models, but its accuracy has been shown to be similar to that of optimization methods [39]. Both absorption coefficient spectra, NAP and CDOM, are characterized by an absorption exponentially decreasing with wavelength without pronounced maxima or minima, thus their absorption will have very little influence on the spectral shape of the fourth derivative spectrum [19]. Compared to that, the inversions using bio-optical models for aph(λ), aCDOM(λ) and aNAP(λ) usually include uncertainties and errors due to a series of assumptions and empirical relationships between the absorption coefficient and wavelength.…”

“…Recently, different bio-optical and ecological algorithms have been developed for identifying and differentiating between phytoplankton functional types (PFTs) or size class (PSCs), and taxonomic composition of phytoplankton at the ocean surface, including remote sensing algorithms for monitoring and detecting harmful algal blooms, and for identifying specific phytoplankton species [4][5][6][7][8]. These methods can be summarized into four main types: (1) methods using information on chlorophyll or light absorption to distinguish between PFTs or PSCs [9][10][11]; (2) spectral response methods based on reflectance anomalies for different PFTs/PSCs (e.g., the PHYSAT approach by Alvain et al [12][13][14]); (3) absorption-based spectral approaches by deriving a phytoplankton size factor [15,16], through look-up tables [17], by a phytoplankton size discrimination model [18], by the partial least squares regression method [19], or by Differential Optical Absorption Spectroscopy (PhytoDOAS) [20,21]; and (4) a backscatter-based method to infer particle size distribution (PSD) and PSCs [22]. Most approaches mentioned above have been tested globally and applications for using these satellite products have been started.…”