title: Discrimination of phytoplankton functional groups using an ocean reflectance inversion model.
abstract: Ocean reflectance inversion models (ORMs) provide a mechanism for inverting the color of the water observed by a satellite into marine inherent optical properties (IOPs), which can then be used to study phytoplankton community structure. Most ORMs effectively separate the total signal of the collective phytoplankton community from other water column constituents; however, few have been shown to effectively identify individual contributions by multiple phytoplankton groups over a large range of environmental conditions. We evaluated the ability of an ORM to discriminate between Noctiluca miliaris and diatoms under conditions typical of the northern Arabian Sea. We: (1) synthesized profiles of IOPs that represent bio-optical conditions for the Arabian Sea; (2) generated remote-sensing reflectances from these profiles using Hydrolight; and (3) applied the ORM to the synthesized reflectances to estimate the relative concentrations of diatoms and N. miliaris. By comparing the estimates from the inversion model with those from synthesized vertical profiles, we identified those conditions under which the ORM performs both well and poorly. Even under perfectly controlled conditions, the absolute accuracy of ORM retrievals degraded when further deconstructing the derived total phytoplankton signal into subcomponents. Although the absolute magnitudes maintained biases, the ORM successfully detected whether or not Noctiluca miliaris appeared in the simulated water column. This quantitatively calls for caution when interpreting the absolute magnitudes of the retrievals, but qualitatively suggests that the ORM provides a robust mechanism for identifying the presence or absence of species.
authors.forename: P Jeremy
authors.initials: PJ
authors.lastname: Werdell
authors.forename: Collin S
authors.initials: CS
authors.lastname: Roesler
authors.forename: Joaquim I
authors.initials: JI
authors.lastname: Goes
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D009792:Oceans and Seas; D010783:Photometry / Q000379:methods*; D010839:Phytoplankton / Q000145:classification / Q000302:isolation & purification*; D058998:Remote Sensing Technology / Q000379:methods*; D013057:Spectrum Analysis / Q000379:methods*; D014871:Water Microbiology