Phytoplankton community composition significantly influences important biogeochemical processes, particularly the biological carbon pump. Assessing the global distribution and dynamics of main phytoplankton groups is therefore of the utmost importance. Taking advantage of the synoptic view of satellite ocean color and altimetry observations combined with vertically-resolved proles of chlorophyll fluorescence collected by the global BioGeoChemical-Argo (BGC-Argo) fleet, we previously developed a neural network-based approach to infer a global tridimensional (3D) gridded product of chlorophyll a (Chla), i.e. the SOCA-Chla method. Expanding upon SOCA-Chla, we introduce SOCA-PFT, a novel method for deriving a global 3D product of phytoplankton functional types (PFT). SOCA-PFT follows the same principle as SOCA-Chla but requires an initial step to enrich the training BGC-Argo database with the PFT information that would not otherwise be available. This step involves developing a neural network trained on a large-scale database of concurrent shipborne measurements of vertical proles of pigments determined by High Performance Liquid Chromatography (HPLC), fluorescence and temperature/salinity (T/S). Applied to the BGC-Argo database, this intermediate method yields a PFT-enriched BGC-Argo database, which is further matched up with satellite observations to train the SOCA-PFT method. The resulting global PFT product provides depth-resolved Chla associated with pico-, nano-, and microphytoplankton as well as concentrations of pigment biomarkers representing major phytoplankton groups. This new product is expected to be useful for various applications, from understanding the response of phytoplankton communities to environmental conditions, to improving the quantification of biogeochemical budgets or validating biogeochemical models that explicitly incorporate multiple phytoplankton groups.

<p>To better understand the global ocean biogeochemical processes, it is crucial to strengthen the spatial coverage of high-quality biogeochemical variables. In this context, we provide high-quality nutrients (nitrate, phosphate and silicate) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH and partial pressure of carbon dioxide) profiles for BGC-Argo floats equipped with oxygen sensors and data qualified in delayed mode. These variables are derived using neural network models called CANYON-B and CONTENT for nutrients and carbonate system variables, respectively. For the Mediterranean Sea, we deliver these variables from a regional dedicated model called CANYON-MED. These variables are distributed from September 2002 to August 2022 as part of CMEMS MOBTAC service. The last update of the product will be available in CMEMS portal from March 2023.</p> <p>At the global scale, nitrate, phosphate and silicate are retrieved with an accuracy (from the root mean squared difference) of 0.68, 0.051, 2.3 µmol kg^-1_, respectivelyand the carbonate system variables, i.e., total alkalinity, dissolved inorganic carbon, pH and partial pressure of carbon dioxide are retrieved with an accuracy of 6.2 µmol kg^-1, 6.9 µmol kg^-1, 0.013 (unitless), and 15 µatm, respectively. The global models (CANYON-B and CONTENT) have also been validated with independent data collected from recent various oceanic cruises not used for the development of the methods (from GLODAPv2.2021 database) and the Hawaii Ocean Time series (HOT). These independent validations demonstrate the validity of these models for global ocean applications. The nitrate and pH products were again validated against measured nitrate and pH from BGC-Argo floats equipped with oxygen sensors. Overall validation results were quite satisfactory at global and regional spatial scales.</p> <p>Currently, the profiles are available for BGC-Argo floats with concurrent profiles of temperature, salinity and oxygen qualified in delayed mode. This product will be also available from near real-time observations from 2024.</p>

The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., Rrs, PAR, and kd(490)) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m−2 s−1 for PAR and 0.04, 0.08, and 0.09 W m−2 nm−1 for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.

<div> <p><span data-contrast="none">Producing comprehensive information about the ocean has become a top priority to monitor and predict the ocean and climate change.</span><span data-contrast="none"> Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is developed thought the Copernicus Marine Service MultiOBservation Thematic Assembly (</span><span data-contrast="auto">MOB TAC). Recent advances in data fusion techniques and use of machine-learning approach open the possibility of producing estimators of ocean physic and biogeochemistry (BGC) operationally, using input data from diverse sensors, satellites and in-situ programs.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:160,&quot;335559740&quot;:259}"> </span></p> </div> <div> <p><span data-contrast="auto">MOB TAC provides the following multi observations products at global scale: </span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:60,&quot;335559740&quot;:259}"> </span></p> </div> <div> <p><span data-contrast="auto">Blue ocean</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:60,&quot;335559740&quot;:259}"> </span></p> </div> <div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">3D temperature, salinity, geopotential height and geostrophic current fields, both in near-real-time (NRT) and as long time series (REP=Reprocessing) in delayed-mode;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">2D sea surface salinity and sea surface density fields, both in NRT and as REP;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">2D total surface and near-surface currents, both in NRT and as REP;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="4" data-aria-level="1"><span data-contrast="auto">3D Vertical velocity fields as REP;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="5" data-aria-level="1"><span data-contrast="auto">L2Q and L4 sea surface salinity from SMOS in REP and NRT (only L2Q)</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> </ul> </div> </div> <div> <div> <p><span data-contrast="auto">Green ocean</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:0,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></p> </div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">2D surface carbon data sets of FCO2, pCO2, DIC, Alkalinity, saturation states of surface waters with respect to calcite and aragonite as REP;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">Nutrient and Carbon vertical distribution (including Nitrates, Phosphates, Silicates, pH, pCO2, Alkalinity, DIC) profiles as REP and NRT;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Wingdings&quot;,&quot;469769242&quot;:[9642],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">3D Particulate Organic Carbon (POC), particulate backscattering coefficient (bbp) and Chlorophyll a (Chl-a) fields as REP.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:200,&quot;335559740&quot;:276}"> </span></li> </ul> </div> <div> <p><span data-contrast="auto">Parallel to its portfolio, MOB TAC has and will further develop specific expertise about the integration of multiple satellites and in-situ based observations coming from the other CMEMS TACs and projects. </span><span data-contrast="none">Furthermore, MOB TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor and the global ocean carbon sink. </span></p> </div> </div>

Because of the large diversity of case 2 waters ranging from extremely absorbing to extremely scattering waters and the complexity of light transfer due to external terrestrial inputs, retrieving main biogeochemical parameters such as chlorophyll-a or suspended particulate matter concentration in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from 1 to 700 FNU and from 0.9 to 180 mg m −3 respectively, the HYPER-MAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms. The HYPERMAQ dataset provides biogeochemical parameters (i.e. turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (i.e. water reflectance from above water measurements) and inherent optical properties (i.e. absorption and attenuation coefficients) from six different study areas. These study areas include large estuaries (i.e. the Rio de la Plata in Argentina, the Yangtze estuary in China, and the Gironde estuary in France), inland (i.e. the Spuikom in Belgium and Chascomùs lake in Argentina), and coastal waters (Belgium). The dataset is available from Lavigne et al. (2022) at

As part of Copernicus Marine Environmental Monitoring Service (CMEMS), the multi-observations thematic assembly center aims to provide products based on observations and data fusion techniques (Guinehut et al., 2021). Sauzede et al., (2016) have demonstrated the potential of using hydrological measurements and ocean color satellite observations to infer the vertical distribution of backscattering coeffi cient, a proxy for the stock of particulate organic carbon (POC). The 'Satellite Ocean-Color merged with Argo data to infer bio-optical properties to depth' (SOCA) method is a neural-network-based method trained using the Biogeochemical-Argo database. SOCA has been upgraded to improve the POC retrieval and additionally retrieve the chlorophyll-a concentration (Chl). Using this method with CMEMS hydrological and satellite products, weekly 3-dimensional fi elds of POC and associated uncertainty were retrieved for the 1998-2018 period and made available from the CMEMS online portal since July 2020. The 3-dimensional products of SOCA-retrieved Chl will be made available by the end of 2021. Both of these products will be updated yearly as new input data become available. These new CMEMS products represent a most valuable source of data useful not only for supporting the quality control of Biogeochemical-Argo fl oat observations but also for data assimilation and initialization/validation of biogeochemical models.

The capacity to monitor suspended sediment concentrations (SSCs) in the ocean, from surface to bottom, using data acquired by the future Meteosat Third-Generation (MTG)/flexible combined imager (FCI) satellite sensor has been quantified by observing system simulation experiments (OSSEs). The ``true'' ocean state for these experiments is based on a 15-month numerical simulation of hydrodynamic and sediment transport, configured to represent the highly dynamical waters of the English Channel under the influences of tides and waves. Simulated MTG/FCI hourly averaged acquisitions at a given location near the Isle of Wight have been processed via hidden Markov model combined with a statistical classification--based on self-organizing maps--of predicted vertical SSC profiles. The resulting experiments demonstrated that MTG/FCI images, despite their high temporal resolution, and because of many gaps due to nights and clouds over the English Channel, still require spatial interpolations to enhance the amount of information available at a given location. For an accurate determination of particle concentrations, time series of the main forcing (wind, tides, and waves) need to be included in the process: 1) as a crucial parameter correlated with the dynamics of large particles (sands) and 2) as an equally important parameter as satellite data themselves in the correlation with the dynamics of fine particles (silts).

The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance (Rhow) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate Rhow in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ Rhow measurements is still required in highly turbid waters.

Regions Of Freshwater Influence (ROFI) are of particular interest in a source-to-sink approach in terms of sediment advection, settling, and deposition in the coastal zone. An experiment was carried out in the ROFI of the Rhône River in February 2016 to describe the properties of suspended particulate matter (SPM) during a flood event. A digital holographic camera (LISST-HOLO, 20-2000 μm) was used to estimate the variability of fine sediment floc properties (size, nature and shape) formed in the Rhône mouth. An automatic image toolbox was developed to classify the different constituents of the SPM (as diatoms, bubbles and flocs) and to describe the diversity of floc shapes existing in the material in suspension. We estimated the fractal dimension (DF<SUB>3D</SUB>), the aspect ratio (AR) and the settling velocity of flocs (W<SUB>s</SUB>). The estimated DF<SUB>3D</SUB> ranged between 2.0 and 2.5 highlights the complexity of floc shape, which was used as a proxy of the flocculation mechanism functioning in the Rhône mouth. Additionally, we performed a sensitivity analysis on the estimate of W<SUB>s</SUB> using different shape-related coefficients (α/β) and primary particle size (d<SUB>P</SUB>). The results highlighted the impact of the flocculation of fine sediments on the increase of W<SUB>s</SUB> from 0.01 to 3 mm s<SUP>-1</SUP> when floc sizes increase from 30 to 500 μm. W<SUB>s</SUB> showed a decrease of 41% considering the sphericity of flocs that emphasized the need to consider the floc shape to properly estimate their settling velocity. We showed that an increase of d<SUB>P</SUB> from 1 to 12 μm induces a fivefold increase of W<SUB>s</SUB> that showed the need for an adequate system to properly estimate the size of primary particles. These results emphasized the need to take into account such variability in future model of floc dynamics in ROFI to properly estimate plume sinking rate and SPM dynamics.

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled "Hidden" and "Observable". The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) from the Iberian-Biscay-Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error-NRMSE of less than 16%) is the first step in demonstrating the robustness of the method.

Satellite remote sensing observations allow the ocean surface to be sampled synopticallyover large spatio-temporal scales. The images provided from visible and thermal infraredsatellite observations are widely used in physical, biological, and ecological oceanography.The present work proposes a method to understand the multi-scaling properties of satelliteproducts such as the Chlorophyll-a (Chl-a), and the Sea Surface Temperature (SST), rarelystudied. The specific objectives of this study are to show how the small scale heterogeneitiesof satellite images can be characterised using tools borrowed from the fields of turbulence.For that purpose, we show how the structure function, which is classically used in theframe of scaling time series analysis, can be used also in 2D. The main advantage of thismethod is that it can be applied to process images which have missing data. Based on bothsimulated and real images, we demonstrate that coarse-graining (CG) of a gradient modulustransform of the original image does not provide correct scaling exponents. We show,using a fractional Brownian simulation in 2D, that the structure function (SF) can be usedwith randomly sampled couple of points, and verify that 1 million of couple of points providesenough statistics.

The impact of tidal current, waves, and turbulence on particles re-suspension over the sea bottom is studied through eulerian high frequency measurements of velocity and particle size distribution (PSD) during 5 tidal cycles (65 hours) in a coastal environment of the eastern English Channel. High frequency variability of PSD is observed along with the velocity fluctuations. Power spectral analysis shows that turbulent velocity and PSD parameters have similarities in their spectral behaviour over the whole range of examined temporal scales. The low frequency variability of particles is controlled by turbulence (beta =-5/3) and the high frequency is partly driven by dynamical processes impacted by the sea bottom interactions with turbulence (wall turbulence). Stokes number (St), rarely measured in situ, exhibits very low values, emphasizing that these particles can be considered as passive tracers. The effect of tide and waves on turbidity and PSD is highlighted. During slack tide, when the current reaches its minimum value, we observe a higher proportion of small particles compared to larger ones. To a lower extent, high significant wave heights are also associated with a greater concentration of suspended sediments and the presence of larger particles (larger Sauter's diameter D_A, and lower PSD slope xi).