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
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: final response (author comments only)
RC1: 'Comment on egusphere-2022-430', Anonymous Referee #1, 01 Aug 2022
- AC1: 'Reply on RC1', Tihomir Kostadinov, 13 Nov 2022
RC2: 'Comment on egusphere-2022-430', Emmanuel Boss, 14 Aug 2022
- AC2: 'Reply on RC2', Tihomir Kostadinov, 13 Nov 2022
Tihomir Sabinov Kostadinov et al.
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. 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 https://doi.org/10.5281/zenodo.6354654
Tihomir Sabinov Kostadinov et al.
Viewed (geographical distribution)
This study attempts to retrieve from space (ocean color satellite data) information on particle size distribution and carbon-based phytoplankton size classes in open ocean waters. This significant piece of work is actually the extension of previous studies (Kostadinov et al. 2007-2022) which includes validation results. The manuscript is well organized, written and illustrated.
Unfortunately, these validation results are not convincing, most probably as several assumptions made in the methodology are not valid. The authors should carefully revise the assumptions made notably to model the particle size distributions and discuss the impact on the resulting satellite-derived products. Detailed comments are provided hereafter to clarify the methodology and discuss the validation results.
Line 45, Equation 1:
To my knowledge this very convenient power law size distribution of particles does not apply to phytoplankton particles in oceanic waters. Can you please provide relevant references to support your statement?
Again, probably the main/major issue in this study: phytoplankton cells in oceanic waters DO NOT follow a power-law PSD. If I am wrong please prove it based of already published quality field data.
“a single population of particles (approximated by homogeneous spheres)”
This is another strong assumption which definitely does not apply to phytoplankton cells in in marine waters. Please discuss it and say what is the impact in your methodology.
Where do minerogenic particles come from in open ocean waters?
Line 93 ‘an initial effort of validation’:
Such an effort to at least first validate the assumptions made in your recent and present studies and notably validate the PSD algorithm should have been made already, before going forward applying non-validated algorithms to satellite data and interpret the results obtained
Line 98 ‘backscattering are modeled using Mie theory (Mie, 1908) for homogeneous spherical particles and the Aden-Kerker (Aden and Kerker, 1951) method for coated spheres.’:
Is Mie theory well adapted to your study?
What not considering also the more realistic case of non-spherical particles?
Line 138 ‘The two key assumptions are: 1) Phytoplankton and NAP have a power-law PSD (Eq. 1) with the same slope ξ’
Once again, I do not agree for phytoplankton. Moreover why the same slope?
Tables 1 and 2:
Please justify the choice of the minimum, mean and maximum values considered here as inputs. Are your computations realistic??
Figure 8 ‘PSD validation results’:
Thank you for showing these validation results which are not satisfactory, as could be expected considering that several assumptions made are (most probably) not valid.
While there is somehow an agreement (or at least a trend) between the satellite and situ No (number of particles), there is no correlation for the slope, therefore no validation of the satellite-derived PSD, assuming the PSD is a power-law.
These poor validation results must be discussed so as its implication on the whole methodology. What would be the results if another (more realistic) function was used to model the PSD?
These validation results are more convincing. Please specify in the figure legend what you mean by ‘empirical tuning’.
As in Figure 8, poor validation results.