Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

Abstract

Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between in-situ information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more toward phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated with specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current in-situ ecological knowledge.

A Global Plankton Diversity Monitoring Program

“…Batten et al (2003b) and Raitsos et al (2013) used satellite fluorescence data to positively validate the CPR's Phytoplankton Colour Index, showing that although a simple index, it reveals seasonal and long-term trends in phytoplankton communities. Rêve-Lamarche et al (2017) used CPR diatom taxonomic data to associate diatom assemblages with specific spectral anomalies (from PHYSAT) for regions of the English Channel and North Sea. The ability to ground-truth satellite-derived phytoplankton functional groups from different regions around the world sampled with CPRs is an attractive idea.…”

Plankton Imagery Data Inform Satellite‐Based Estimates of Diatom Carbon

“…Historically available data on phytoplankton assemblages typically do not have the spatial and temporal coverage needed for obtaining sufficient matches with remote sensing data, with the exception of continuous plankton recorder (CPR) data. CPR data have been applied to improve and develop algorithms for the detection of diatoms from remote sensing data (Raitsos et al., 2008; Rêve‐Lamarche et al., 2017); however, the data are semi‐quantitative. In addition, the CPR data likely underestimate diatom biomass given the 270 μm mesh size it utilizes (Richardson et al., 2006).…”

“…Previous studies have successfully linked satellite ocean color and reflectance data with phytoplankton groups assessed using flow cytometry (Thyssen et al., 2015; Zubkov and Quartly, 2003). Regarding diatom presence or abundance, information retrieved from remote sensing data include the analysis of multispectral water leaving radiance anomalies (Alvain et al., 2005, 2008; Rêve‐Lamarche et al., 2017), remote sensing reflectance ( R rs (λ)) band ratios (Kramer et al., 2018; Sathyendranath et al., 2004), a neural network approach incorporating environmental data (Raitsos et al., 2008), empirical orthogonal functions between R rs (λ) bands and phytoplankton groups (Xi et al., 2020), and a method of differential optical absorption spectroscopy (DOAS) that requires high spectral resolution of water‐leaving radiation measurements in the blue wavelengths (Bracher et al., 2009; Losa et al., 2017; Sadeghi et al., 2012). Phytoplankton pigment concentrations obtained from high performance liquid chromatography (HPLC) measurements are used in the construction and evaluation of the majority of these algorithms.…”

Dynamic Satellite Seascapes as a Biogeographic Framework for Understanding Phytoplankton Assemblages in the Florida Keys National Marine Sanctuary, United States

“…These studies have relied on empirical relationships between phytoplankton biomass (as chlorophyll-a concentration) and relative abundances of broad phytoplankton functional types (PFT; e.g., silicifiers, calcifiers, and nitrogen fixers) and size classes (Uitz et al, 2006;Hardman-Mountford et al, 2008;Brewin et al, 2010;Hirata et al, 2011). More recently, empirical orthogonal functions and machine learning methods have been applied to in situ pigment data and satellite retrievals to examine the biogeography and succession of taxonomic groups (e.g., diatoms, cyanobacteria, and nanoeucaryotes) within regional domains and globally (Alvain et al, 2008;Taylor et al, 2011;Rêve-Lamarche et al, 2017;Catlett and Siegel, 2018;El Hourany et al, 2019a;Xi et al, 2020). These efforts have improved our understanding of the affinity of phytoplankton groups to static biogeographic provinces and phytoplankton responses to climate forcings (Alvain et al, 2008;Catlett and Siegel, 2018).…”