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The Sentinel-3A satellite was launched on 16 February 2016 with the Ocean and Land Colour Instrument (OLCI-A) on-board for the study of ocean color. The accuracy of ocean color parameters depends on the atmospheric correction algorithm (AC). This processing consists of removing the contribution of the atmosphere from the total measured signal by the remote sensor at the top of the atmosphere. Five ACs: the baseline AC, the Case 2 regional coast color neural network AC, its alternative version, the Polymer AC, and the standard NASA AC, are inter-compared over two bio-optical contrasted French coastal waters. The retrieved water-leaving reflectances are compared with in situ ocean color radiometric measurements collected using an ASD FielSpec4 spectrometer. Statistical and spectral analysis were performed to assess the best-performing AC through individual (relative error (RE) at 412 nm ranging between 23.43 and 57.31%; root mean squared error (RMSE) at 412 nm ranging between 0.0077 and 0.0188) and common (RE(412 nm) = 24.15–50.07%; RMSE(412 nm) = 0.0081–0.0132) match-ups. The results suggest that the most efficient schemes are the alternative version of the Case 2 regional coast color neural network AC with RE(412 nm) = 33.52% and RMSE(412 nm) = 0.0101 for the individual and Polymer with RE(412 nm) = 24.15% and RMSE(412 nm) = 0.0081 for the common ACs match-ups. Sensitivity studies were performed to assess the limitations of the AC, and the errors of retrievals showed no trends when compared to the turbidity and CDOM.

Chlorophyll-a concentration (Chl-a) is a crucial parameter for monitoring the water quality in coastal waters. The principal aim of this study is to evaluate the performance of existing Chl-a band ratio inversion models for estimating Chl-a from Sentinel2-MSI and Sentinel3-OLCI observation. This was performed using an extensive in situ Rrs-Chl-a dataset covering contrasted coastal waters (N = 1244, Chl-a (0.03–555.99) µg/L), which has been clustered into five optical water types (OWTs). Our results show that the blue/green inversion models are suitable to derive Chl-a over clear to medium turbid waters (OWTs 1, 2, and 3) while red/NIR models are adapted to retrieve Chl-a in turbid/high-Chl-a environments. As they exhibited the optimal performance considering these two groups of OWTs, MuBR (multiple band ratio) and NDCI (Normalized Difference Chlorophyll-a Index)-based models were merged using the probability values of the defined OWTs as the blending coefficients. Such a combination provides a reliable Chl-a prediction over the vast majority of the global coastal turbid waters (94%), as evidenced by a good performance on the validation dataset (e.g., MAPD = 21.64%). However, our study further illustrated that none of the evaluated algorithms yield satisfying Chl-a estimates in ultra-turbid waters, which are mainly associated with turbid river plumes (OWT 5). This finding highlights the limitation of multispectral ocean color observation in such optically extreme environments and also implies the interest to better explore hyperspectral Rrs information to predict Chl-a.

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score Stot (16.89) and for the coefficient of correlation R2 (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score Stot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary.

Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.

The variability of the backscattering to scattering ratio of marine particles, $\"b\"_{{\\rm{bp}}}$: $\"b\"_{\\rm{p}}$, is examined from in situ measurements performed during the spring and early summer of 2004 in the eastern English Channel and southern North Sea. This area is characterized by a quasi-permanent background of mineral matter from direct inputs, or resuspension effects, and by relatively intense spring phytoplankton blooms (mainly diatoms and the prymnesiophyte \"Phaeocystis globosa\"). The $\"b\"_{{\\rm{bp}}}$: $\"b\"_{\\rm{p}}$ surface values range between 0.0024 and 0.0417, with a mean value of 0.0138 ± 0.0083. In order to interpret such a great variability, simultaneous water samples were collected for the biogeochemical characterization of the bulk suspended particle population. We show that the $\"b\"_{{\\rm{bp}}}$: $\"b\"_{\\rm{p}}$ variability is related to the composition of the particulate assemblage expressed by Chi \"a\", the POC : SPM and POC : Ch1 \"a\" ratios, where Chi \"a\", POC, and SPM are the concentrations of chlorophyll \"a\", particulate organic carbon, and suspended particulate matter, respectively. Low $\"b\"_{{\\rm{bp}}}$: $\"b\"_{\\rm{p}}$ values are observed for a particle population dominated by low refractive index material such as phytoplankton, whereas high $\"b\"_{{\\rm{bp}}}$: $\"b\"_{\\rm{p}}$ values are generally observed in presence of relatively high concentration of inorganic particles. The amount of organic material (both living and nonliving, including phytoplankton) relative to phytoplankton has a strong (and sometimes the greatest) effect on the backscattering-to-scattering ratio. Assuming that phytoplankton and detritus have similar refractive index, this pattern is interpreted as resulting from changes in the particle size distribution as well as by aggregation of mineral and nonliving organic detrital material.

Studying how natural phytoplankton adjust their photosynthetic properties to the quantity and quality of underwater light ( i.e . light climate) is essential to understand primary production. A wavelength-dependent photoacclimation strategy was assessed using a multi-color pulse-amplitude-modulation chlorophyll fluorometer for phytoplankton samples collected in the spring at 19 locations across the English Channel. The functional absorption cross section of photosystem II, photosynthetic electron transport (PET λ ) parameters and non-photochemical quenching were analyzed using an original approach with a sequence of three statistical analyses. Linear mixed-effects models using wavelength as a longitudinal variable were first applied to distinguish the fixed effect of the population from the random effect of individuals. Population and individual trends of wavelength-dependent PET λ parameters were consistent with photosynthesis and photoacclimation theories. The natural phytoplankton communities studied were in a photoprotective state for blue wavelengths (440 and 480 nm), but not for other wavelengths (green (540 nm), amber (590 nm) and light red (625 nm)). Population-detrended PET λ values were then used in multivariate analyses (partial triadic analysis and redundancy analysis) to study ecological implications of PET λ dynamics among water masses. Two wavelength ratios based on the microalgae saturation parameter E k (in relative and absolute units), related to the hydrodynamic regime and underwater light climate, clearly confirmed the physiological state of microalgae. They also illustrate more accurately that natural phytoplankton communities can implement photoacclimation processes that are influenced by in situ light quality during the daylight cycle in temporarily and weakly stratified water. Ecological implications and consequences of PET λ are discussed in the context of turbulent coastal ecosystems.

After the exponential growth phase, variability in the scattering efficiency of phytoplankton cells over their complete life cycle is not well characterised. Bulk measurements are impacted by senescent cells and detritrus. Thus the analysis of the evolution of the optical properties thanks to their morphological and/or intra-cellular variations remains poorly studied. Using the Cytosense flow cytometer (CytoBuoy b.v., NL), the temporal course of the forward and sideward efficiencies of two phytoplankton species (Thalassiosira pseudonana and Chlamydomonas concordia) were analyzed during a complete life-cycle. These two species differ considerably from a morphological point of view. Over the whole experiment, the forward and sideward efficiencies of Thalassiosira pseudonana were, on average, respectively 2.2 and 1.6 times higher than the efficiencies of Chlamydomonas concordia. Large intra-species variability of the efficiencies were observed over the life cycle of the considered species. It highlights the importance of considering the optical properties of phytoplankton cells as a function of the population growth stage of the considered species. Furthermore, flow cytometry measurements were combined with radiative transfer simulations and biogeochemical and optical measurements. Results showed that the real refractive index of the chloroplast is a key parameter driving the sideward signal and that a simplistic two-layered model (cytoplasm-chloroplast) seems particularly appropriate to represent the phytoplankton cells.

This study analyzes relationships between concentration of suspended particles represented by dry mass, [SPM], or area, [AC], and optical properties including particulate beam attenuation (cp ), side scattering (bs ), and backscattering (bbp ), obtained from an intensive sampling program in coastal and offshore waters around Europe and French Guyana. First-order optical properties are driven by particle concentration with best predictions of [SPM] by bbp and bs , and of [AC] by cp . Second-order variability is investigated with respect to particle size, apparent density (dry weight-to-wet-volume ratio), and composition. Overall, the mass-specific particulate backscattering coefficient, b b p m (=bbp : [SPM]), is relatively well constrained, with variability of a factor of 3–4. This coefficient is well correlated with particle composition, with inorganic particles having values about three times greater ( b b p m = 0.012   m 2   g − 1 ) than organic particles ( b b p m = 0.005   m 2   g − 1 ). The mass-specific particulate attenuation coefficient, c p m (= cp : [SPM]), on the other hand, varies over one order of magnitude and is strongly driven (77% of the variability explained) by particle apparent density. In this data set particle size does not affect c p m and affects b b p m only weakly in clear (case 1) waters, despite size variations over one order of magnitude. A significant fraction (40–60%) of the variability in b b p m remains unexplained. Possible causes are the limitation of the measured size distributions to the 2–302-μm range and effects of particle shape and internal structure that affect bbp more than cp and were not accounted for.

The variability of inherent optical properties is investigated in the ultra-oligotrophic waters of the Mediterranean Sea sampled during the BOUM experiment performed during early summer 2008. Bio-optical relationships found for ultra-oligotrophic waters of the three anticyclonic gyres sampled significantly depart from the mean standard relationships provided for the global ocean, confirming the peculiar character of these Mediterranean waters. These optical anomalies are diversely related to the specific biological and environmental conditions occurring in the studied ecosystem. Specifically, the surface specific phytoplankton absorption coefficient exhibits values lower than those expected from the general relationships mainly in relation with a high contribution of relatively large sized phytoplankton. Conversely, the particulate backscattering coefficient, bbp, values are much higher than the mean standard values for a given chlorophyll-a concentration, TChl-a. This feature can presumably be related to the relevant influence of highly refractive submicrometer particles of Saharan origin in the surface layer of the water column. The present measurements also show that the Mediterranean Sea is greener than TChl-a alone indicates, as already stressed in previous studies. This color anomaly is partly explained by the estimated colored dissolved organic matter and submicrometer particles absorption coefficients, and to a greater extent by the high bbp/TChl-a values assuming that these particles backscatter light similarly in the green and blue parts of the visible spectrum. The diel variation of both the particulate matter attenuation and backscattering coefficients were also investigated specifically. Despite some differences in the timing and the magnitude of the daily oscillations found for these optical parameters, potential for the backscattering coefficient daily oscillation to be used, similarly to that for the attenuation coefficient, as a proxy for estimating carbon community production budget has been highlighted for the first time. This result is particularly relevant for present and future geostationary spatial ocean color missions.