Information content of in situ and remotely sensed chlorophyll-a: Learning from size-structured phytoplankton model

© 2018 Chlorophyll-a measurements in the form of in situ observations and satellite ocean colour products are commonly used in data assimilation to calibrate marine biogeochemical models. Here, a two size-class phytoplankton biogeochemical model, with a 0D configuration, was used to simulate the surface chlorophyll-a dynamics (simulated surface Chl-a) for cyclonic and anticyclonic eddies off East Australia. An optical model was then used to calculate the inherent optical properties from the simulation and convert them into remote-sensing reflectance (Rrs). Subsequently, Rrs was used to produce a satellite-like estimate of the simulated surface Chl-a concentrations through the MODIS OC3M algorithm (simulated OC3M Chl-a). Identical parameter optimisation experiments were performed through the assimilation of the two separate datasets (simulated surface Chl-a and simulated OC3M Chl-a), with the purpose of investigating the contrasting information content of simulated surface Chl-a and remotely-sensed data sources. The results we present are based on the analysis of the distribution of a cost function, varying four parameters of the biogeochemical model. In our idealized experiments the simulated OC3M Chl-a product is a poor proxy for the total simulated surface Chl-a concentration. Furthermore, our result show the OC3M algorithm can underestimate the simulated chlorophyll-a concentration in offshore eddies off East Australia (Case I waters), because of the weak relationship between large-sized phytoplankton and remote-sensing reflectance. Although Case I waters are usually characteristic of oligotrophic environments, with a photosynthetic community typically represented by relatively small-sized phytoplankton, mesoscale features such as eddies can generate seasonally favourable conditions for a photosynthetic community with a greater proportion of large phytoplankton cells. Furthermore, our results show that in mesoscale features such as eddies, in situ chlorophyll-a observations and the ocean colour products can carry different information related to phytoplankton sizes. Assimilating both remote-sensing reflectance and measurements of in situ chlorophyll-a concentration reduces the uncertainty of the parameter values more than either data set alone, thus reducing the spread of acceptable solutions, giving an improved simulation of the natural environment

Modelling the impact of phytoplankton cell size and abundance on inherent optical properties (IOPs) and a remotely sensed chlorophyll-a product

© 2020 Elsevier B.V. Ocean colour data are commonly used to quantify primary production, study phytoplankton dynamics and calibrate marine models, thus understanding the origin of errors in the retrieved chlorophyll-a (Chl-a) product is critical. One source of uncertainty in retrieved Chl-a products can be related to large photosynthetic cells, characterised by lower mass-specific absorption coefficients due to increased packaging effect. Here, we explore the relationship between phytoplankton size structure and an ocean colour product using optical simulations and in situ observations. Specifically, we use an optical model to explore how phytoplankton cell size and abundance influence phytoplankton absorption and backscattering coefficients and the implication this has for water leaving radiance and the estimated Chl-a derived from satellite ocean colour. The optical model simulations show phytoplankton cell size has a significant impact on the remote-sensing reflectance, with Chl-a packaged in 5 to 10 μm cells resulting in about 54 to 76% the simulated ocean colour Chl-a compared to 1 μm cells, as determined by an algorithm that converts reflectances to Chl-a. To support optical simulations, size-fractionated Chl-a samples were collected from several water masses to investigate the phytoplankton size contribution (i.e., 10 μm) to the total Chl-a. We focused on the offshore eastern Australian ocean region, largely characterised by oligotrophic waters in which phytoplankton dominate the optical properties of the water column. Of the 22 stations sampled, a total of ten in situ size fractionated Chl-a measurements were matched-up with the corresponding clear-sky satellite Chl-a product. The matched-up points revealed a systematic underestimation of in situ Chl-a. With the low amount of data, it was not possible to statistically relate the satellite underestimation to a specific phytoplankton size class, but the observations showed that the largest satellite Chl-a underestimates were found when phytoplankton larger than 10 μm represented more than 50% of the phytoplankton community. Additional measurements that combine in situ optical measurements with phytoplankton size distribution and carbon/Chl-a ratio would help to clarify the relationship between phytoplankton size structures and remotely sensed Chl-a product

Effect of phytoplankton community size structure on remote-sensing reflectance and chlorophyll a products

© 2020 Elsevier B.V. Remotely-sensed ocean colour is the main tool for estimating chlorophyll a (Chl-a) concentration and primary productivity on the global scale. In order to investigate the source of errors in remotely-sensed Chl-a concentration we obtained in situ bio-optical properties, in situ reflectances, satellite-derived reflectances and the Chl-a concentration satellite products of the Ocean and Land Colour Instrument (OLCI) Instrument on board Sentinel-3 A in waters off eastern Australia. The mesoscale eddies of these oligotrophic waters provide contrasting phytoplankton communities that allowed us to focus on the effect of phytoplankton size as a source of errors. In these waters, cold-core cyclonic eddies (CE) are dominated by large phytoplankton cells, while small cells dominate warm-core anticyclonic eddies (ACE). The chlorophyll-specific absorption and backscattering from contrasting sites show significant difference due to the differing package effect of phytoplankton size distributions. After normalising the absorption and backscattering spectra to Chl-a associated with just small phytoplankton, the spectra of optical properties become much more similar, showing that small-sized phytoplankton dominate IOPs even when large cells contain the greater fraction of Chl-a concentration of the phytoplankton community. Measured in situ reflectances agreed with reflectances calculated using a simple optical model based on measured IOPs. Furthermore, the in situ measured reflectances agreed well with the OLCI reflectance (mean normalised bias (MNB) of 7% for wavelengths <600 nm). However, a systematic underestimation of Chl-a concentrations by the OLCI algorithms was found in the region of cyclonic eddies characterised by increased Chl-a concentration and dominance of large-sized phytoplankton. A similar underprediction was found in Chl-a concentration calculated with the band-ratio OC4Me algorithm using in situ and IOP-calculated reflectance. Excluding Chl-a associated with large-sized phytoplankton, reduced the bias in the OC4Me from −31% to −0.1%. The large cells absorbed and scattered less per unit of chlorophyll resulting in a smaller impact on the reflectance, and therefore are less detectable by remotely-sensed band-ratio-based Chl-a algorithms than small cells