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

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) &mdash; 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.</p

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches

CoastColour Round Robin data sets: A database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters

Bio-optical properties of oceanic waters: A reappraisal

International audienceThe apparent optical properties (AOPs) of oceanic case 1 waters were previously analyzed [Morel, 1988] and statistically related to the chlorophyll concentration ([Chl]) used as a global index describing the trophic conditions of water bodies. From these empirical relationships a bio-optical model of the upper layer was developed. With objectives and structure similar to those of the previous study the present reappraisal utilizes AOPs determined during recent Joint Global Ocean Flux Study cruises, namely, spectral attenuation for downward irradiance Kd(X) and irradiance reflectance R(X). This revision also benefits from improved knowledge of inherent optical properties (lOPs), namely, pure water absorption coefficients and particle scattering and absorption coefficients, and from better pigment quantification (via a systematic use of highperformance liquid chromatography). Nonlinear trends, already observed between optical properties and algal biomass, are fully confirmed, yet with numerical differences. The previous Kd(X) model, and subsequently the R(X) model, is modified to account for these new relationships. The R(X) values predicted as a function of [Chl] and the predicted ratios of reflectances at two wavelengths, which are commonly used in ocean color algorithms, compare well with field values (not used when developing the reflectance model). This good agreement means that semianalytical ocean color algorithms can be successfully applied to satellite data. Going further into purely analytical approaches, ideally based on radiative transfer computations combined with a suite of relationships between the lOPs and [Chl], remains presently problematic, especially because of the insufficient knowledge of the phase function and backscattering efficiency of oceanic particles

Optimization of a semianalytical ocean color model for global-scale applications

Semianalytical (SA) ocean color models have advantages over conventional band ratio algorithms in that multiple ocean properties can be retrieved simultaneously from a single water-leaving radiance spectrum. However, the complexity of SA models has stalled their development, and operational implementation as optimal SA parameter values are hard to determine because of limitations in development data sets and the lack of robust tuning procedures. We present a procedure for optimizing SA ocean color models for global applications. The SA model to be optimized retrieves simultaneous estimates for chlorophyll (Chl) concentration, the absorption coefficient for dissolved and detrital materials [a cdm(443)], and the particulate backscatter coefficient [b bp(443)] from measurements of the normalized water-leaving radiance spectrum. Parameters for the model are tuned by simulated annealing as the global optimization protocol. We first evaluate the robustness of the tuning method using synthetic data sets, and we then apply the tuning procedure to an in situ data set. With the tuned SA parameters, the accuracy of retrievals found with the globally optimized model (the Garver-Siegel-Maritorena model version 1; hereafter GSM01) is excellent and results are comparable with the current Sea-viewing Wide Field-of-view sensor (SeaWiFS) algorithm for Chl. The advantage of the GSM01 model is that simultaneous retrievals of a cdm(443) and b bp(443) are made that greatly extend the nature of global applications that can be explored. Current limitations and further developments of the model are discussed

Global surface ocean HPLC phytoplankton pigments and hyperspectral remote sensing reflectance

This dataset is a global surface ocean compilation of high-performance liquid chromatography (HPLC) phytoplankton pigment concentrations and hyperspectral remote sensing reflectance (Rrs) data, with associated temperature and salinity measurements. The pigments measured include: total chlorophyll-a (Tchla), 19'-hexanoyloxyfucoxanthin (HexFuco), 19'-butanoyloxyfucoxanthin (ButFuco), alloxanthin (Allo), fucoxanthin (Fuco), peridinin (Perid), zeaxanthin (Zea), divinyl chlorophyll a (DVchla), monovinyl chlorophyll b (MVchlb), chlorophyll c1+c2 (Chlc12), chlorophyll c3 (Chlc3), neoxanthin (Neo), and violaxanthin (Viola). Rrs data are measured at 1 nm spectral resolution from 400-700 nm. The Rrs data from the ANT cruises were collected using a RAMSES hyperspectral radiometer, the Rrs data from the NAAMES, SABOR, Tara, RemSensPOC, BIOSOPE, and EXPORTS cruises were generated by a HyperPro (Satlantic, Inc.) hyperspectral radiometer. All samples presented in this dataset have previously been published and are publicly available, as referenced in the table: ANT: Bracher et al. (2015), https://doi.org/10.1594/PANGAEA.847820, NAAMES: Behrenfeld et al. (2014a), http://dx.doi.org/10.5067/SeaBASS/NAAMES/DATA001, Remote Sensing of POC: Cetinić (2013), http://dx.doi.org/10.5067/SeaBASS/REMSENSPOC/DATA001, SABOR: Behrenfeld et al. (2014b), http://dx.doi.org/10.5067/SeaBASS/SABOR/DATA001, Tara Oceans: Boss and Claustre (2009), http://dx.doi.org/10.5067/SeaBASS/TARA_OCEANS_EXPEDITION/DATA001, Tara Mediterranean: Boss and Claustre (2014), http://dx.doi.org/10.5067/SeaBASS/TARA_MEDITERRANEAN/DATA001, BIOSOPE: Claustre and Sciandra (2004), https://doi.org/10.17600/4010100 hosted at http://www.obs-vlfr.fr/proof/php/bio_open_access_data.php, EXPORTS: Behrenfeld et al. (2018), http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001. This compilation of these data is used in Kramer et al. (2021) to evaluate a model that reconstructs pigment concentrations from hyperspectral remote sensing reflectance

Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales

Phytoplankton community composition impacts food webs, climate, and fisheries on regional and global scales, and can be assessed at coarse taxonomic resolution from biomarker pigments measured using high-performance liquid chromatography (HPLC). Presently, satellite ocean color provides unprecedented coverage of the global surface ocean and offers reliable estimates of bulk biological properties; however, existing multispectral sensors have limited ability to provide information about phytoplankton community composition. Satellite ocean color at hyperspectral resolution (e.g., NASA's upcoming Plankton, Aerosol, Cloud, and ocean Ecosystem sensor, PACE) is expected to improve estimates of phytoplankton community composition from space. Phytoplankton impact ocean color via contributions to absorption and fluorescence (through phytoplankton pigments) and scattering, especially on narrow spectral scales (5–100 nm). Here, a global open ocean dataset of concurrent HPLC pigments and hyperspectral remote sensing reflectance (Rrs(λ)) observations is used to model phytoplankton pigment composition from optical data. Phytoplankton pigments are reconstructed from Rrs(λ) using optimized principal components regression modeling. This work demonstrates that thirteen phytoplankton pigments, representing five phytoplankton pigment groups (e.g., diatoms, dinoflagellates, haptophytes, green algae, and cyanobacteria), can be modeled from hyperspectral Rrs(λ). Spectral information needed to model each phytoplankton pigment concentration is found throughout the entire visible spectrum and the model results are best at high spectral resolution (≤5 nm). The resulting model recreates observed relationships among pigment concentrations, providing support for the designation of five pigment-based phytoplankton groups for the global open ocean. This work represents a step toward developing robust, global spectral models for phytoplankton pigment composition. However, more high-quality data from a wide range of ecosystems and environments are still needed to achieve this goal

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

Monthly global 4km satellite products spanning September 1997 to December 2020. The data contains Particle Size Distribution (PSD) parameters of an assumed power-law PSD, absolute and fractional size-partitioned phytoplankton carbon and associated variables such as particulate organic carbon (POC) and Chlorophyll-a as derived from the PSD algorithm. The retrieval is based on a backscattering bio-optical model using two particle populations and coated spheres for phytoplankton inherent optical properties (IOP) modeling, and a retrieval using spectral angle mapping (SAM - where satellite spectra are classified using a comparison to a collection of modeled end-member spectra, by treating spectra as vectors and using their dot product). Partial uncertainties are given as standard deviation and are estimated using a combination of Monte Carlo simulations and analytical error propagation. An empirical tuning factor is given for attaining more realistic estimated model concentrations of POC and Chlorophyll-a. The tuning factor is multiplicative, to be applied in linear space. This tuning factor has not been applied to the monthly data, users can choose whether or not to apply it to absolute carbon and Chlorophyll-a concentrations. The factor does not affect retrievals of fractional contributions of phytoplankton size classes to total phytoplankton carbon. Monthly climatologies files and an overall climatology file are also provided, and in those files, both untuned (tuning factor not applied) and tuned (tuning factor applied) variables are provided, for user convenience. Input remote-sensing reflectance data are v5.0 of the Ocean Colour -Climate Change Initiative (OC-CCI) of the European Space Agency. The OC-CCI general reference is Sathyendranath et al. (2019; doi:10.3390/s19194285), and for v5.0 of the dataset, the reference is Sathyendranath et al. (2021; doi:10.5285/1dbe7a109c0244aaad713e078fd3059a). More detailed metadata, including geospatial metadata, are given in the netCDF files. Variable names should be self-explanatory. Quick browse images are provided as well. Coastlines in these quick browse images are from v2.3.7 of the GSHHS data set - see Wessel and Smith (1996) (doi:10.1029/96JB00104). Modeling and data processing was done in MATLAB ®

A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance

International audienceWe present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(λ). The derived IOPs include the total (a(λ)), phytoplankton (aphy(λ)), and colored detrital matter (acdm(λ)), absorption coefficients, and the total (bb(λ)) and particulate (bbp(λ)) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a(λ) and bbp(λ) are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a(λ) and bb(λ) at any wavelength at which Rrs(λ) is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy(λ) and acdm(λ) from anw(λ).The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters

A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance

International audienceWe present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(λ). The derived IOPs include the total (a(λ)), phytoplankton (aphy(λ)), and colored detrital matter (acdm(λ)), absorption coefficients, and the total (bb(λ)) and particulate (bbp(λ)) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a(λ) and bbp(λ) are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a(λ) and bb(λ) at any wavelength at which Rrs(λ) is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy(λ) and acdm(λ) from anw(λ).The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters