Phytoplankton communities quantified from hyperspectral ocean reflectance correspond to pigment-based communities

Area of Science:

• Oceanography
• Remote Sensing
• Phytoplankton Ecology

Background:

• Phytoplankton community composition influences ocean biogeochemical cycles.
• Hyperspectral remote sensing reflectance (Rrs) offers a global perspective on ocean color.
• High-performance liquid chromatography (HPLC) provides in-situ pigment data for phytoplankton identification.

Purpose of the Study:

• To investigate the similarity between phytoplankton communities detected using optical Rrs data and in-situ HPLC pigment data.
• To assess the efficacy of network-based community detection analysis for both data types.
• To establish a novel method for global phytoplankton community assessment using satellite Rrs observations.

Main Methods:

• Paired global dataset of in-water hyperspectral Rrs spectra and HPLC pigment concentrations.
• Network-based community detection analysis applied to Rrs residual (δRrs) to identify optical communities.
• Network-based community detection analysis applied to HPLC pigment ratios to identify pigment-based communities.

Main Results:

• Three distinct phytoplankton communities were successfully separated from both Rrs and HPLC data.
• A majority of samples (74%) were consistently assigned to the same communities across both methods.
• Differences in community assignments were identified and potential sources of discrepancy were discussed.

Conclusions:

• Optical communities derived from hyperspectral Rrs data correspond well with phytoplankton communities identified by HPLC pigment analysis.
• Network-based community detection is a viable method for analyzing both optical and pigment data.
• This study presents a new tool for global-scale phytoplankton community assessment using hyperspectral Rrs data, relevant for missions like NASA's PACE.