Preprints
https://doi.org/10.5194/egusphere-2022-430
https://doi.org/10.5194/egusphere-2022-430
 
14 Jun 2022
14 Jun 2022
Status: this preprint is open for discussion.

Ocean Color Algorithm for the Retrieval of the Particle Size Distribution and Carbon-Based Phytoplankton Size Classes Using a Two-Component Coated-Spheres Backscattering Model

Tihomir Sabinov Kostadinov1, Lisl Robertson Lain2,a, Christina Eunjin Kong3, Xiaodong Zhang4, Stéphane Maritorena5, Stewart Bernard6, Hubert Loisel7, Daniel S. F. Jorge7, Ekaterina Kochetkova8, Shovonlal Roy9, Bror Jonsson3, Victor Martinez-Vicente3, and Shubha Sathyendranath3 Tihomir Sabinov Kostadinov et al.
  • 1Department of Liberal Studies, California State University San Marcos, 333 S. Twin Oaks Valley Rd., San Marcos, CA 92096, USA
  • 2Earth Observation, Smart Places, CSIR
  • 3National Centre for Earth Observation, Plymouth Marine Laboratory, UK
  • 4Division of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA
  • 5Earth Research Institute, University of California at Santa Barbara, Santa Barbara, CA 93106-3060, USA
  • 6SANSA, Enterprise Building, Mark Shuttleworth Street, Innovation Hub, Pretoria 0087, South Africa
  • 7Laboratoire d’Océanologie et de Géosciences, Université du Littoral-Côte-d’Opale, Université Lille, CNRS, IRD, UMR 8187, LOG, 32 avenue Foch, Wimereux, France
  • 8Department of Earth & Environmental Science, Hayden Hall, University of Pennsylvania, 240 South 33rd St., Philadelphia, PA 19104, USA
  • 9Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom
  • aformerly at: University of Cape Town, Department of Oceanography, Cape Town, South Africa

Abstract. The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g. in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs) and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations (particle assemblage categories) — phytoplankton and non-algal particles (NAP). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAP are modeled as homogeneous spheres. The forward model uses Mie and Aden-Kerker scattering computations, for homogeneous and coated spheres (for phytoplankton and NAP, respectively) to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via Spectral Angle Mapping (SAM) which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e. carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. The EAP-based formulation allows for the estimation of chlorophyll-a concentration via the retrieved PSD, as well as the estimation of the percent of backscattering due to NAP vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented, using PSD, POC, and pico-phytoplankton carbon in-situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of phytoplankton carbon and related variables to facilitate future algorithm improvements.

Tihomir Sabinov Kostadinov et al.

Status: open (until 09 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Tihomir Sabinov Kostadinov et al.

Data sets

Particle Size Distribution and Size-partitioned Phytoplankton Carbon Using a Two-Component Coated-Spheres Bio-optical Model: Monthly Global 4 km Imagery Based on the OC-CCI v5.0 Merged Ocean Color Satellite Data Set. Kostadinov, Tihomir Sabinov; Robertson-Lain, Lisl; Kong, Christina Eunjin; Zhang, Xiaodong; Maritorena, Stéphane; Bernard, Stewart; Loisel, Hubert; Jorge, Daniel S F; Kochetkova, Ekaterina; Roy, Shovonlal; Jönsson, Bror; Martinez-Vicente, Victor; Sathyendranath, Shubha https://doi.pangaea.de/10.1594/PANGAEA.939863

Model code and software

PSD_PhytoC_v2021: Ocean Color Algorithm for the Retrieval of the Particle Size Distribution and Size-Partitioned Phytoplankton Carbon: Algorithm Development and Operational Code Tihomir Sabinov Kostadinov; Lisl Roberston-Lain; Stewart Bernard; Xiaodong Zhang; Hubert Loisel https://doi.org/10.5281/zenodo.6354654

Tihomir Sabinov Kostadinov et al.

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Short summary
We present a remote sensing algorithm to retrieve the size and concentration of particles suspended in natural near-surface ocean water, using ocean color data. The algorithm can be used to estimate the concentration and carbon content of phytoplankton – photosynthesizing microorganisms that are at the basis of the marine food web and play an important role in Earth’s carbon cycle and climate. A merged, multi-sensor satellite data set and the model scientific code are provided.