Bourgneuf Bay is a macrotidal bay along the French Atlantic coast, mostly constituted of mudflats, and widely used for shellfish aquaculture (oyster annual yield was 5,330 tons in 2010, Dessinges et al., 2012). In the present study, a focus was made on a shellfish farming site located at the northern limit of the oyster aquaculture zone (Figures 1, 2). Due to tidal resuspension, SPM concentration seldom decreases below 50 g m⁻³ and regularly exceeds 500 g m⁻³. As a too high SPM concentration impacts oyster clearance rate and other physiological functions (Barillé et al., 1997), oysters grown in this farming site are negatively impacted by high SPM concentration (Gernez et al., 2014). Daily mean chl a concentration was reported to vary between 4 and 14 mg m⁻³ (Dutertre et al., 2009), and monthly means between 5 and 30 mg m⁻³ were previously reported at the study site (Barillé-Boyer et al., 1997).

Field data were acquired during two bio-optical cruises in Bourgneuf Bay from 08 to 12 April 2013 and from 12 to 13 April 2016 in the frame of the ANR GIGASSAT and FP7 HIGHROC projects, respectively. During both cruises, water sampling and radiometric measurements were performed following the same protocol. Sampling stations were located nearshore, mostly within the intertidal zone and in the vicinity of farming sites (Figure 1). Sampling took place at different times of the tidal cycle in order to acquire reflectance spectra over a wide range of SPM and chl a concentration. The same flat-bottomed barge was used during both cruises. This kind of vessel makes it possible to navigate throughout the shallow intertidal waters, even during low tide. Some stations were visited when the water depth was as low as 0.5 m. The bottom was never visible from above surface, even at the shallowest station due to the extremely high turbidity.

In situ hyperspectral marine reflectance spectra show some typical characteristics of coastal turbid waters (Figure 3A). From 400 to 580 nm ρ_w(λ) is relatively insensitive to changes in SPM and chl a concentration, preventing the use of a blue to green ratio algorithm to derive chl a concentration. From 600 to 900 nm the changes in the magnitude and spectral composition of the marine reflectance are mainly driven by variation in SPM concentration, notably as a result of particulate scattering.

In the red and NIR spectral region, the ρ_w spectra also display several features associated with the presence of pigment-bearing particles. An inflection in the reflectance slope is visible around 632 nm, attributable to the absorption by both chl a and chl c, a pigments association specific to diatoms (Méléder et al., 2005). The most striking feature is however the trough at 675 nm associated with chl a absorption, and the resulting reflectance shift between 675 and 700 nm, generally referred to as the NIR/red edge (Gons et al., 2002). Significant chl a concentration was confirmed by the analysis of HPLC data. It most likely originates from the tide-driven resuspension of benthic microalgae resuspended together with surface sediments.

Though a significant loss of information results from the downscaling of the TriOS hyperspectral reflectance to S2/MSI spectral resolution, a NIR/red edge between 665 and 705 nm is still noticeable on several S2-simulated ρ_w spectra (Figure 3B), making it possible to apply the Gons et al. (2005) algorithm in Bourgneuf Bay's intertidal waters.

The SPM and chl a in situ data acquired concomitantly with the ρ_w(λ) were used to calibrate the bio-optical algorithms using a fitting procedure (Figure 4). In situ SPM concentration ranged from 10.92 to 700.83 g m⁻³, with a mean of 146.53 g m⁻³. Over this range, a significant relationship was obtained between simulated and measured SPM concentration (p-value < 10⁻⁵, correlation coefficient of 0.96, a slope of 0.93 and an intercept of 0.32). For the SPM retrieval, the root mean square error was 56.26 g m⁻³.

The range of chl a concentration was from 3.97 to 52.51 mg m⁻³, with a mean of 18.48 mg m⁻³. In the initial algorithm of Gons et al. (2005), the chlorophyll-retrieval parameters were originally set to p = 1.05 and aphy*(665) = 0.014 m² (mg Chl a)⁻¹ using observations performed in a variety of inland, estuarine and coastal waters over a range of chl a concentration from 1 to 181 mg m⁻³ (Gons, 1999; Gons et al., 2002). For the present study p was recalibrated to 1.02. A mean aphy*(665) of 0.013 m² (mg Chl a)⁻¹ was obtained, a value consistent with previously reported specific absorption coefficients for the Saint Laurent Estuary (Bricaud et al., 1995), and for the Baltic and North seas (Babin et al., 2003). The comparison between the marine reflectance- and HPLC-derived chl a concentration was satisfactory (Figure 4A). The obtained linear regression shows a significant correlation coefficient of 0.93 (p-value < 10⁻⁵), a slope of 1.02 and an intercept of −0.07. For the chl retrieval, the root mean square error was 3.05 mg m⁻³.

The high spatial resolution (20 m) and spectral characteristics of Sentinel2 made it possible to quantify the distribution of SPM and chl a concentration at the scale of an oyster farm (Figures 5–8). Three main features characterized SPM and chl a spatial distribution in the shellfish farming site. First, both SPM and chl a concentrations are generally high, often exceeding 200 g m⁻³ and 10 mg m⁻³, respectively. Second, their spatial structure depends on the bathymetry. SPM and chl a concentrations generally increase coastward, and the changes observed in their spatial distribution are more or less parallel to the isobaths (see for examples Figure 6C where the change in [chl a] from <5 to >10 mg m⁻³ occurred around the 1 m isobath, and Figure 7D where [SPM] displayed a clear gradient coastward). Third, the tidal cycle is a significant driver of SPM and chl a dynamics. Due to the tidal resuspension of benthic microalgae together with the other particles of the sedimentary surface, the chl a distribution was generally associated with SPM, a common feature of intertidal mudflats (Koh et al., 2006). Spatial fronts of highest chl a concentration generally move seaward during ebb tide (Figures 6A–C, 8A–C), and coastward during flow tide (Figures 6D–F, 8D–F).

SPM and chl a concentrations exhibited similar spatial pattern during neap tide and spring tides, but the amplitude of the changes in their concentrations varied markedly between neaps and springs. For example, within the oyster farming zone SPM concentration varied from <10 to 300 g m⁻³ during neap tides and from 50 to >1,000 g m⁻³ during spring tides. Chl a concentration varied from <5 to 25 mg m⁻³ during neap tides and from 10 to 40 mg m⁻³ during spring tides.

The tide-driven variability was confirmed by the analysis of SPM and chl a concentration at the three selected fixed locations (black circled crosses in Figures 5–8). Both SPM and chl a concentration increased during ebb tide, reaching their maximum value during low tide and the start of the flow tide, and eventually decreasing during the end of the flow tide (Figures 9A,C, 10A,C). The temporal correlation between SPM and chl a concentration is attributable to the tide-driven resuspension of surface sediments, which contain both mineral particles and benthic microalgae. As expected, the amplitude of the tide-driven changes in SPM and chl a was higher during spring than during neap tides.

The temporal changes in clearance and chl consumption rates were then analyzed in order to quantify the influence of the tide-driven SPM and chl a dynamic on the oyster physiological responses.

During neap tides the simulated chl consumption rate mirrored the changes observed in chl a concentration (Figures 9C,D). This is attributable to the limited negative impact of SPM concentration on the clearance rate (Figures 9A,B). Generally SPM concentration remained below 100 g m⁻³ throughout the composite tidal cycle, except during flow tide where SPM concentration increased up to 175 g m⁻³ due to the erosion of surface sediments by tidal currents. The clearance rate mostly fluctuated between 4 and 5 L h⁻¹, and the decrease which occurred just after low tide was too small to counterbalance the increase in chl consumption.

During spring tides the simulated oyster physiological functions were more complexly affected by SPM and chl a tidal variability (Figure 10). First, a significant part the intertidal zone rapidly became emerged, and after mid-ebb the oysters could no longer filter seawater nor consume particles (see plain lines in Figure 10). In the waters just outside the intertidal zone, SPM concentration rapidly exceeded 200 g m⁻³ due to the strong tidal currents occurring from mid-ebb to the end of flow tide (dashed and dotted lines in Figure 10A). Such high SPM concentrations are known to saturate the oyster gills (Barillé et al., 1997), and it resulted in the dramatic collapse of the clearance rate from mid-ebb to the end of flow tide (Figure 10B). Meanwhile, the positive effects of the tide-driven increase in chl a concentration were rapidly counterbalanced by the collapse of the clearance rate, and the chl consumption rate dropped to zero from mid-ebb to the end of the flow (Figure 10D).

In summary, during neap tide, the tidal cycle in SPM concentration does not negatively impact oyster physiological response very much, and the low-tide increase in chl a concentration was directly translated into an increase in chl consumption for the farmed oysters. As most of the intertidal zone remains immerged during neap tides, the altitudinal location of the oyster farms has little influence on the tidal cycle of the clearance and chl consumption rate (Figure 9). During spring tides on the contrary, a significant fraction of the intertidal zone is emerged. In the waters adjacent to the intertidal zone and in the intertidal areas still under water, the high SPM concentration negatively impacts both clearance and chl consumption rate, whatever the chl a concentration, thus further limiting oyster physiological activity during a significant fraction of the tidal cycle (Figure 10).

The Gons et al. (2005) algorithm, recalibrated to Sentinel2/MSI, was fitted to in situ measurement (Figure 4) before being applied to Bourgneuf Bay. The fit was satisfactory, as demonstrated by the consistency of the value of aphy*(665) with the literature (Bricaud et al., 1995; Gons et al., 2002; Babin et al., 2003). Other algorithms could have been used. Besides the Gons et al. (2005) method, various approaches were previously developed for turbid and/or eutrophic coastal and inland waters, including the 2-, and 3-band models (Dall'Olmo et al., 2005; Gitelson et al., 2008; Le et al., 2013), the fluorescence line height (FLH, Gower et al., 1999), the maximum chlorophyll index (MCI, Gower et al., 2005), and the 705 nm peak height (Toming et al., 2016). These algorithms are all based on a NIR/red edge either associated with chl a absorption at 675 nm or with sun-induced chl a fluorescence. An obvious limitation of these NIR/red algorithms is the lack of sensitivity in waters where the reflectance trough associated with chl a absorption around 675 nm is hardly pronounced (see for example the lowest ρ_w(λ) spectrum in Figure 3). In less eutrophic waters (i.e., chl a concentration smaller than ~4 mg m⁻³), the use of blue-green wavelengths would be more relevant to retrieve chl a concentration.

A recent study demonstrated that the 2- and 3-band models (Dall'Olmo et al., 2005) worked well with simulated Sentinel2/MSI-like imagery (Beck et al., 2016). In our algorithm inter-comparison (see Supplementary Information), the most performant method for our study site was the Gons et al. (2005) algorithm. Besides its good performance, another advantage of the Gons et al. (2005) algorithm is that the p and aphy*(665) parameters seem to be relatively stable over a variety of coastal waters, including the numerous inland, estuarine, and coastal sites initially sampled by Gons (1999), and the intertidal waters of Bourgneuf Bay. Additional field data are however needed to assess the geographical robustness of a common set of parameters, as well as its seasonal stability. While the range of chl a concentration retrieved from Sentinel2 data in our study site was consistent with previous field measurements (Barillé-Boyer et al., 1997), the accuracy of the chl a concentration maps is more difficult to quantitatively appraise due to the lack of validation data. More in situ and match-up data are needed to improve the method.

The retrieval of chl a concentration using a NIR/red algorithm is relevant in eutrophic waters, but at lower chl a concentration the accuracy would probably decrease. It is then generally advised to switch toward shorter wavelengths in the blue-green parts of the visible spectrum. As far as Sentinel2 is concerned, the lack of a spectral band at around 412 nm will certainly limit the performance of chl a inversion methods, as it has been long demonstrated that such a spectral band improves the deconvolution of chl a, PIM, and CDOM absorption (Carder et al., 1999). For sensors equipped with a spectral band at 412 nm such as MERIS, the ocean color 5 (OC5) algorithm (Gohin et al., 2002) has been recently recommended in recent intercomparison studies of the North West European (Tilstone et al., 2017) and Vietnamese (Loisel et al., 2017) coastal waters. As already indicated by Vanhellemont and Ruddick (2016), several other limitations specific to Sentinel2 can also arise, due to its relatively wide bands, low signal-to-noise ratio, and lack of vicarious calibration.

Despite these issues, Sentinel2 offers four main advantages for the remote-sensing of shellfish farming ecosystems. First, its high spatial resolution (20 m) made it possible to observe aquaculture sites located nearshore, in narrow bays and estuaries (Gernez et al., 2014), and to analyze within-farm spatial variability. Second, its relatively small revisit time (which is now 5 days since the launch of Sentinel-2B) increases the probability of acquiring cloud-free data over a given site. Sentinel2 acquisition frequency also limits subsampling and observation biases for the study of rapidly varying environments. There is no doubt that the Sentinel2 time-series will strengthen observation robustness and statistical descriptors of the very dynamic and changing coastal waters. Third, its SWIR spectral band facilitates atmospheric correction over turbid waters (Vanhellemont and Ruddick, 2015). Fourth, its spectral resolution in the red and NIR spectral regions made it possible to apply a variety of chlorophyll inversion algorithms (see previous section). Altogether, these characteristics represent a significant improvement for the remote sensing of turbid oyster farming ecosystems, and more generally for coastal zone observation.

The combination of EO and shellfish physiological models opens new perspectives for aquaculture management, shellfish farming ecosystems studies (Gernez et al., 2014), and more broadly for a better understanding of the coastal ocean response to global changes. For example, the poleward extent of the Pacific oyster (a well-known invasive species, Herbert et al., 2016) along the European coasts has been recently quantitatively analyzed using an original coupling of EO with mechanistic physiological oyster modeling (Thomas et al., 2016). In another recent study the EO time-series archive has been used with climatic, biological and energetics models to better understand predicted changes in growth, reproduction and mortality risk for commercially and ecologically important bivalves in the Mediterranean Sea (Montalto et al., 2016).

Concurrently with the increase of EO aquaculture applications, the development of improved satellite products should not be neglected. The detection of phytoplankton species causing harmful algal blooms (HABs) is a major concern for fisheries and shellfish farming management (Sourisseau et al., 2016), and recent algorithm developments have proved useful to provide early warnings (Davidson et al., 2009) or statistical estimation of HAB-related risks (Kurekin et al., 2014). Enhanced characterization of the composition of the particulate assemblage could also be used to improve satellite-derived aquaculture products. For example, as oysters have the ability to preferentially select organic rather than mineral particles before ingestion (Barillé et al., 1997; Dutertre et al., 2009), estimation of the organic fraction of the particulate assemblage (Woźniak et al., 2010) could be used to better constrain shellfish physiological models.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.