# An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor

Frédéric Jourdin, Renosh Pannimpullath Remanan, Anastase Alexandre Charantonis, Nicolas Guillou, Sylvie Thiria, Fouad Badran, Thierry Garlan

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Frédéric Jourdin, Renosh Pannimpullath Remanan, Anastase Alexandre Charantonis, Nicolas Guillou, Sylvie Thiria, et al.. An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59 (7), pp.5423 - 5433. 10.1109/TGRS.2020.3011742. hal-03054553

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# An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor

Frédéric Jourdin<sup>1, ∗</sup>, Pannimpullath Remanan Renosh<sup>1, †</sup>, Anastase Alexander Charantonis, Nicolas Guillou, Sylvie Thiria, Fouad Badran, and Thierry Garlan

**Abstract**—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).

**Index Terms**—English channel, geostationary satellite, hidden Markov model (HMM), machine learning, Meteosat Third Generation (MTG)/flexible combined imager (FCI), observing system simulation experiment (OSSE), particles, self-organizing maps (SOMs), suspended sediment.

Manuscript received November 26, 2019; revised June 29, 2020; accepted July 18, 2020. This work was supported by the French state agency Direc- tion Générale de l'Armement (DGA) through the project entitled Prevision Régionale Océanique des Turbidités, Ecolements, Vagues et Sédiments (PROTEVS). (*Corresponding author: Frédéric Jourdin*.)

Frédéric Jourdin and Thierry Garlan are with the Service Hydrographique et Océanographique de la Marine (SHOM), 29228 Brest, France (e-mail: jourdin@shom.fr).

Pannimpullath Remanan Renosh is with the Laboratoire d'Océanographie de Villefranche (LOV), 06230 Villefranche-sur-Mer, France.

Anastase Alexander Charantonis is with the Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), 91000 Evry, France.

Nicolas Guillou is with the Laboratoire de Génie Côtier et Environnement, CEREMA, 29280 Brest, France.

Sylvie Thiria is with the Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), 75005 Paris, France.

Fouad Badran is with the Conservatoire National des Arts et Métiers (CNAM), 75003 Paris, France.

Color versions of one or more of the figures in this article are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2020.3011742

## I. INTRODUCTION

After processing images acquired over the eastern English Channel and the southern North Sea by the SEVIRI sensor onboard the Meteosat Second-Generation (MSG) satellite, Neukermans et al. [1], [2] first demonstrated the potential of meteorological satellites in observing the diurnal dynamics of suspended particulate matter in shelf waters. The next-generation satellite sensor, flexible combined imager (FCI) onboard the Meteosat Third Generation (MTG), is planned for operational service in the next couple of years. This sensor will have increased temporal, spatial, and spectral resolutions with potentially extended applications in bio-optical oceanography [3]–[5]. MTG will thus deliver the large volumes of data that will need also increased processing. For the purpose of operational oceanography, the exploitation of these data might be evolving rapidly in relation to recent advances in probabilistic modeling [6] and applications in characterizing suspended particulate matter in the ocean from data-driven assimilation schemes [7]–[10], [11]–[13] or model-driven assimilation schemes [14]–[18].

In this context, the present study proposes the feasibility experiments of type observing system simulation experiment (OSSE) [19] about the potential use of MTG/FCI in the observation of suspended sediment concentrations (SSCs) over different grain size classes and through the whole water column. To achieve such a challenging aim, a particularly robust data-driven assimilation scheme has been employed, derived from Charantonis et al. [20]. This scheme is dedicated to the time series analysis of vertical profiles of oceanographic state parameters. More precisely, the method is similar to the one described in [13] but generalized to different sediment size classes. The machine learning process benefits from the stochastic analysis of a time series of SSC profiles issued from a past simulation of a hydrodynamic and sediment transport numerical model. The main assumption in these OSSE experiments is that the sediment transport model provides surface values that are statistically consistent with ocean color remote sensing data (in other words that the model configuration has been calibrated with satellite observations). Moreover, the model results, represented with a finite number of states, are supposed to well reproduce the statistics (in terms
<figure><img src="image_2.png" /><figcaption>Fig. 1. Water depths in the English Channel with location of the point of interest (black star).</figcaption></figure>

of transition probabilities between two subsequent states) of physical processes within time scales ranging from hourly to yearly dynamics. The results obtained in these OSSE experiments will be interpreted in light of recent findings obtained after the launch, by the Korean Aerospace Research Institute (KARI), of the first geosynchronous equatorial orbit (GEO) satellite sensor, entitled GOCI, dedicated to the high-frequency ocean color observation from a geostationary platform [21]. In particular, it has been demonstrated that GOCI is able to observe the diurnal dynamics of suspended matter observed in the tidal waters around Korea [21]–[24]. The study area is located in the English Channel (northwestern European shelf seas), a part of the future MTG sighting view, where the main processes governing the fate of suspended matter are also driven by wind, waves, and tides.

## II. STUDY AREA

In the English Channel, the wind, waves, and tides are the main drivers of the general circulation [25]. The point of interest (of coordinates 0°51'W in longitude and 50°36'N in latitude) is located off the United Kingdom's coast, approximately 15 miles southeast of the Isle of Wight (Fig. 1). It is entitled M3 with respect to the reference study [27]. This location belongs to a prominent turbid area of subsurface SSC with an average value within 10–15 mg/L. The suspended load transport displays local residual gyres on both the sides of the Isle of Wight [26], [27]. The temporal evolution of

the turbidity area is furthermore characterized by a bimodal contribution of silts and sands which contrasts with the nearly uniform grain-size distribution found offshore [28].

## III. DATA

The met-ocean data used in the OSSE experiments are presented in Section III-A. In particular, the hydrodynamic numerical simulation is based on a 3-D Regional Ocean Modeling System (ROMS) [29] configuration of the English Channel with a spatial horizontal resolution of 3 km and a sigma-coordinate (terrain following) system composed of 11 layers. This ROMS model is combined with a multicomponent sediment transport referenced in Section III-B, whose simulation serves as ocean truth data for the OSSE experiments. The characteristics of the MTG/FCI satellite sensor are described in Section III-C also by comparison with the present MSG/SEVIRI sensor. The exact way MTG/FCI data are simulated in the OSSE experiments, which are described in Section III-D.

### A. Meteorologic and Hydrodynamic Forcing

The model is driven by the tidal free-surface elevation recomposed from the dominant ten harmonic constituents of the TPXO.6 (TOPEX/Poseidon) database [30]. Wind speeds at 10 m above the free surface are provided by the meteorological model ALADIN [31]. Moreover, the wave forcing
<table><caption>TABLE I
NAMES OF ANCILLARY DATA SETS USED IN OSSE EXPERIMENTS</caption><thead><tr><th>Name</th><th>Notation</th><th>Mean parameter name</th></tr></thead><tbody><tr><td>ALADIN</td><td>U<sub>10</sub></td><td>Wind speed at 10 m</td></tr><tr><td>NORGAS or HOMERE</td><td>H<sub>s</sub></td><td>Significant wave height</td></tr><tr><td>ROMS</td><td>U<sub>b</sub>, V<sub>b</sub></td><td>Northward and eastward components of the barotropic current velocity</td></tr></tbody></table>

on the bottom boundary layer derives from the Wave-Watch3 model configured for the English Channel and the North Bay of Biscay with a 2' regular square grid called “NORGAS 2mn” [32].

Mean hydrodynamic parameter values that quantify the magnitudes of wind, waves, and currents can be included in the process to help the machine learning method in deriving SSC. These data become then “Ancillary data” of the method. They are the wind speed at 10 m above the sea surface $U_{10}(t)$, the significant wave height $H_s(t)$, and the two northward $U_b(t)$ and eastward $V_b(t)$ components of the barotropic current. All these parameters can be obtained from either numerical modeling or satellite products. In the present OSSE experiments, barotropic currents were taken from the ROMS simulation, the wind from ALADIN, and the significant wave height from either the WW3/NORGAS [32] or the WW3/HOMERE [33] configurations. The latter uses a finite-element spatial grid which allows to test the impact on the machine learning process from a different wave input data for this main hydrodynamic forcing. Therefore, the twin experiments will keep data from the NORGAS configuration, whereas more realistic experiments will take values from HOMERE. These ancillary observable data are shown in Table I.

## B. ROMS Suspended Sediment Simulation

The initial state of the simulated seabed sediments is obtained with an original statistical interpolation approach dedicated to the multivariable feature of the grain size distribution of bottom samples [26]. One class of silt and five classes of sands are taken into account (Table II). The hydrosedimetary ROMS numerical model simulates the fate of these noncohesive sediments. The simulated data exploited (excluding the spin-up period) extend from October 2007 to December 2008, with a time step of 1 h. The numerical simulation retained here is entitled ROMS-E and corresponds to the fifth simulation reported in [27], denominated “Configuration E” in this reference article. This simulation benefits from improved boundary conditions derived from a least squares analysis of satellite and mean hydrodynamic data [34]. By comparison with ocean color satellite measurements, the simulation ROMS-E is the most accurate. ROMS-E results for the geographical point M3 were thus extracted, and this extraction corresponds to the time series of SSC profiles from bottom to surface over the 11 sigma levels.

<table><caption>TABLE II
MODELED GRAIN SIZE CLASSES IN THE ROMS-E SIMULATION</caption><thead><tr><th>Notation</th><th>Name</th><th>Diameter range (mm)</th></tr></thead><tbody><tr><td>C<sub>1</sub>(z<sub>k</sub>, t)</td><td>Silt (Sand 1)</td><td>0 - 0.05</td></tr><tr><td>C<sub>2</sub>(z<sub>k</sub>, t)</td><td>Sand 2</td><td>0.05 - 0.1</td></tr><tr><td>C<sub>3</sub>(z<sub>k</sub>, t)</td><td>Sand 3</td><td>0.1 - 0.2</td></tr><tr><td>C<sub>4</sub>(z<sub>k</sub>, t)</td><td>Sand 4</td><td>0.2 - 0.5</td></tr><tr><td>C<sub>5</sub>(z<sub>k</sub>, t)</td><td>Sand 5</td><td>0.5 - 1</td></tr><tr><td>C<sub>6</sub>(z<sub>k</sub>, t)</td><td>Sand 6</td><td>1 - 2</td></tr></tbody></table>

In the present OSSE experiments, this ROMS simulation is used to derive the statistics for the so-called “hidden data” according to hidden Markov model (HMM) terminology. “Hidden data” are the unknowns (or targets) we want to recover. These hidden data correspond to concentrations denoted $C_i(z_k, t)$ in Table II, for all grain size class $i$ at each sigma depth $z_k$, with $k = 1$ at the bottom and $k = 11$ at the surface. $t$ is the time. These data provide, furthermore, the following information:

1) the “true” state of SSC at all time in the vertical column;

2) the base in deriving ergodic statistics for the HMM;

3) surface values in deriving simulated MTG/FCI as observable data;

4) a small data subset reserved for the validation of the OSSE experiments.

## C. MSG/SEVIRI and MTG/FCI Data Characteristics

The spatial resolution of the present MSG/SEVIRI is 3 km, but the future MTG/FCI will benefit from a resolution of 1 km, at nadir. Then, in the area of interest located around 50° of latitude, the ground spatial resolution of an MTG/FCI frame image will be about 2 km. This is not far from the ROMS model configuration with a resolution of 3 km. The acquisition frame rate of MTG/FCI is 10 mn (instead of 15 mn for MSG/SEVIRI), but the observation of turbidity will require increasing signal-to-noise ratio (SNR) via hourly product averaging similar to the processing of MSG/SEVIRI data [4]. Products obtained are typical L2 satellite products of SSC retrievals. From these L2 products, L4 products of SSC can also be derived by applying statistical interpolation methods which complete data in cloudy areas. For instance, a Data Interpolating Empirical Orthogonal Function (DINEOF) has been applied using MSG/SEVIRI data [37]. DINEOF is a nowcast reconstruction of missing data using spatial correlations from historical data located in the vicinity. However, the obvious consequence is that the whole accuracy of SSC values of L4 products is degraded compared to L2 products.

MTG/FCI has three VIS bands (blue, green, and red) and two NIR bands (865 and 915 nm) instead of one VIS band and one NIR band for MSG/SEVIRI. It has also a higher SNR than MSG/SEVIRI [5]. Hence, it can be assumed that MTG/FCI will observe turbidity with similar or more likely better accuracy than MSG/SEVIRI. Hereafter, there are accuracy observations of MSG/SEVIRI L2 and L4 products in the area of interest. These observations are compared with a lognormal random noise error model of the measured SSC
<table><caption>TABLE III
PERCENTILES OF THE PREDICTION ERROR FOR MSG/SEVIRI L2/SSC PRODUCT [2] COMPARED WITH LOGNORMAL DISTRIBUTIONS</caption><thead><tr><th rowspan="2">Satellite sensor or Simulated error</th><th rowspan="2">product or parameters</th><th colspan="3">Percentiles</th></tr><tr><th>5<sup>th</sup></th><th>50<sup>th</sup></th><th>95<sup>th</sup></th></tr></thead><tbody><tr><td>MSG/SEVIRI</td><td>L2/SSC</td><td>3</td><td>29</td><td>80</td></tr><tr><td>Common lognormal error distribution with</td><td><i>m</i>=-0.07<br/><i>σ</i>=0.2</td><td>3</td><td>31</td><td>82</td></tr><tr><td>Common lognormal error distribution with</td><td><i>m</i>=0.0<br/><i>σ</i>=0.2</td><td>3</td><td>30</td><td>113</td></tr></tbody></table>

<table><caption>TABLE IV
SSC CORRELATIONS BETWEEN MSG/SEVIRI AND In Situ DATA AT WARP ANCHORAGE SITE (OFF THAMES ESTUARY) [37]</caption><thead><tr><th>Satellite sensor</th><th>product</th><th>Correlation</th></tr></thead><tbody><tr><td>MSG/SEVIRI</td><td>L2/SSC</td><td>89%</td></tr><tr><td>MSG/SEVIRI</td><td>L4/DINEOF</td><td>74%</td></tr></tbody></table>

<table><caption>TABLE V
SSC CORRELATIONS BETWEEN SIMULATED MSG/SEVIRI AND In Situ DATA WITH SIMILAR SSC RANGE AS AT WARP ANCHORAGE</caption><thead><tr><th>Simulated error</th><th>parameters</th><th>Correlation</th></tr></thead><tbody><tr><td>Common lognormal error distribution with</td><td><i>m</i>=0 and <i>σ</i>=0.2</td><td>88%</td></tr><tr><td></td><td><i>m</i>=0 and <i>σ</i>=0.3</td><td>76%</td></tr></tbody></table>

according to the following equation:

$$ \log_{10}(C_S(t)) = N(m, \sigma) + \log_{10}(C_{\text{True}}(t)) \quad (1) $$

where $C_{\text{True}}$ and $C_S$ are the “true” and remote sensed values of SSC measured at the sea surface, respectively. $m$ and $\sigma$ are the mean and standard deviation of a normal probability distribution, respectively.

According to this definition, Table III shows that a lognormal distribution with $\sigma = 0.2$ approaches fairly well the relative prediction error distribution of MSG/SEVIRI L2 SSC products obtained by Neukermans et al. [2], between L2 resulting values and in situ measurements in the North Sea.

In a similar way, Tables IV and V show that a sigma value of 0.3 is consistent with correlations obtained between SSC values derived from MSG/SEVIRI L4/DINEOF [37] and the same in situ observations in the North Sea.

### D. Simulated MTG/FCI Data

ROMS-E hourly outputs are used to simulate MTG/FCI L2 and L4 products of SSC. They are the so-called “observable data” according to HMM terminology. First, $C_{\text{True}}$ is obtained by computing the sum of all size class concentrations from the ROMS-E data taken at the surface of the ocean only ($z = 0$) since the satellite sensor can only observe the surface SSC, which results in the following formulation:

$$ C_{\text{True}}(t) = \sum_{i=1}^{6} C_i(z_{11} = 0, t). \quad (2) $$

<table><caption>TABLE VI
NAMES AND CHARACTERISTICS OF THE DIFFERENT MTG/FCI SIMULATED DATA SETS</caption><thead><tr><th>Name of Data</th><th>Type of product</th><th>Noise mean (m)</th><th>Noise standard deviation (σ)</th><th>Presence of Nights</th><th>Clouds</th></tr></thead><tbody><tr><td>Raw</td><td>L2/SSC</td><td>0</td><td>0.2</td><td>YES</td><td>YES</td></tr><tr><td>DINEOF</td><td>L4/DINEOF</td><td>0</td><td>0.3</td><td>YES</td><td>NO</td></tr><tr><td>Ceaseless</td><td>errorless</td><td>0</td><td>0.0</td><td>NO</td><td>NO</td></tr></tbody></table>

Second, to obtain the satellite values $C_S$, a lognormal noise is added to $C_{\text{True}}$ according to (1) in a way to simulate the data errors from sensor noise and processing imperfections. This noise is assumed to fully correlated during 1 day of acquisition in order to reproduce typical errors in processing GEO satellite data as found in [2]. However, between two different days of acquisition, noise values are assumed, on the contrary, uncorrelated. Moreover, to simulate a satellite recording during daytime, all ROMS-E data during night (between 17h00 and 09h00 UTM) were discarded. The remaining data were also further obstructed by generating an hourly mask using a Bernoulli distribution that follows the cloud cover climatology in the English Channel defined in the International Satellite Cloud Climatology Project [35]. The resulting set of data is then called “raw satellite data” in this article even though they are typical L2 satellite products.

Another set of data is called “DINEOF,” on the basis that all pixels of satellite data in a geographical region can be processed using the DINEOF method. This set of data (typical L4 product) is generated by keeping all daytime $C_S$ values (cloudy areas are covered but nights are still discarded).

Finally, for the purpose of better testing the method, the third set of data is generated. This set is simply the total surface concentration taken from hourly ROMS-E outputs irrespective of day, night or cloud, and without any noise added. This set of data is then called “ceaseless satellite data.” Of course, such satellite data are not realistic, but they allow twin experiments for testing the algorithm.

Table VI shows the parameters of the three sets of data retained for all OSSE experiments. Noise characteristics are assumed consistent with MSG/SEVIRI product errors.

### IV. METHODS

The machine learning process used here is based on a combination of a time series analysis with HMMs and a spatial classification with self-organizing maps (SOMs). The principle of the method, called “PROFHMM,” is described in Section IV-A. This method has been tested on different sets of observable data in order to quantify the importance of each of them. The corresponding experimental design is specified in Section IV-B.

#### A. PROFHMM

The purpose is to assess the evolution of size class particle concentrations in the water column from satellite imaging without having to run a dedicated sediment transport model
<figure><img src="image_3.png" /><figcaption>Fig. 2. Schematic of PROFHMM as applied in this study.</figcaption></figure>

except for a hindcast simulation. The statistical methodology is called PROFHMM after [20] and described as a diagram in Fig. 2. PROFHMM does not directly use the hindcast simulation, but its statistical properties required to establish an HMM. PROFHMM means “Profiles HMM” because it has been designed for in-depth ocean profile determination parameters from an incomplete time series of surface observations. To perform all these tasks, PROFHMM combines two statistical algorithms, such as HMMs and SOMs. The HMM process requires the estimation of “transition” and “emission” conditional probabilities between “hidden” and “observable” data sets, corresponding to the outputs and inputs of the method. Before running the Markov model, PROHMM first reduces data sets in a limited amount of classes through SOM classification, for both the observable (labeled SOMobs) and the hidden (labeled SOMhid) data sets. PROFHMM then learns the probabilities of transitioning from one hidden class to another, as well as the conditional probabilities of one hidden class from a given observable class. Knowing this a priori information, PROFHMM is then able to reconstruct the most likely sequence of hidden states from the sole sequence of new observations.

The statistical properties—described by the transition and emission probabilities—are derived by counting the number of transitions and emissions from the sequences of states obtained with the SOM classification in the *a priori* simulation time series (here, ROMS-E) in order to deduce the conditional probabilities between two consecutive time steps and between hidden and observable data. This statistical approach allows further synchronization with different inputs (SSC measurements at the ocean surface, ocean currents, and so on) and the identification of dependences on these different inputs, by including them in the classification of the “observable” states.

During the study, we noticed the necessity to separate the application of PROFHMM for the fine (silts) and coarse (sands) particles since they resulted from different dynamics. Furthermore, during the application, it was made clear that the fine and coarse particles could not be classified as a whole because the SOMhid containing both of them proved to under represent the silts, due to the larger proportion of sand types. This could be resolved either by creating a larger SOMhid, which would hinder the estimation of the HMM probabilities, or to create two sets of SOMhids and HMM probabilities; one devoted to the fine particles, and the other one devoted to the coarse. We chose the second option and, therefore, have generated two hidden maps: SOMhid-silt and SOMhid-sand, with their corresponding emissions and transitions matrices. As such, the results correspond to the application of PROFHMM in two steps: one step for the fine particles and the other one for the coarse particles (Fig. 2). For the sake of simplicity, we continue to refer to the hidden maps with the same name. The results for total SSC are the sum of results for all $C_i$ determined after separate processing (here, PROFHMM has not been applied directly to total concentrations).

## B. Experimental Design

The performance of the PROFHMM method is tested with different combinations of observable data sets. These experiments are a way in quantifying the importance of each type of data in using either the surface concentration from the satellite only, defining the experiments of type “SAT” shown in Table VII, or a combination of both satellite and ancillary data, defining the experiments of type “DYN” presented in Table VIII (and including the experiment “Dyn” based on ancillary data only). Hence, a set of seven experiments in total
<table><caption>TABLE VII
NAMES FOR THE EXPERIMENTS OF TYPE SAT (WITH SATELLITE DATA)</caption><thead><tr><th rowspan="2">Name of<br>Experiment</th><th rowspan="2">Name of<br>Satellite data</th><th colspan="3">Ancillary Data sets</th></tr><tr><th>Wind</th><th>Waves</th><th>Currents</th></tr></thead><tbody><tr><td>Sat-Cls</td><td>Ceaseless</td><td>None</td><td>None</td><td>None</td></tr><tr><td>Sat-Din</td><td>DINEOF</td><td>None</td><td>None</td><td>None</td></tr><tr><td>Sat-Raw</td><td>Raw</td><td>None</td><td>None</td><td>None</td></tr></tbody></table>

<table><caption>TABLE VIII
NAMES FOR THE EXPERIMENTS OF TYPE DYN (WITH SATELLITE AND ANCILLARY DATA)</caption><thead><tr><th rowspan="2">Name of<br>Experiment</th><th rowspan="2">Name of<br>Satellite data</th><th colspan="3">Ancillary Data sets</th></tr><tr><th>Wind</th><th>Waves</th><th>Currents</th></tr></thead><tbody><tr><td>Dyn</td><td>None</td><td>ALADIN</td><td>HOMERE</td><td>ROMS</td></tr><tr><td>Dyn-Cls</td><td>Ceaseless</td><td>ALADIN</td><td>NORGAS</td><td>ROMS</td></tr><tr><td>Dyn-Din</td><td>DINEOF</td><td>ALADIN</td><td>HOMERE</td><td>ROMS</td></tr><tr><td>Dyn-Raw</td><td>Raw</td><td>ALADIN</td><td>HOMERE</td><td>ROMS</td></tr></tbody></table>

has been designed. The first experiment called Sat-Cls and the fifth, called Dyn-Cls, which are typically twin experiments because they use not noisy and not missing satellite data (the Ceaseless data), and the mean hydrodynamic parameters come from the initial hydrodynamic models (ROMS and NORGAS data). These experiments should then give the best results, in particular, Dyn-Cls which maximizes the amount of input information. The other remaining experiments correspond to more realistic situations, where observable data are noisy or missing, and ancillary data for the waves come from another model (HOMERE).

The results of all these experiments were validated against 10% of ROMS-E profile data that have been reserved for “independent” validation. The term “independent” here means that these 10% of data were neither used in the classification (SOM) nor in the learning phase (HMM). These 10% of profiles were chosen to be regularly spaced throughout the whole time series (15 months) and thus define a typically known cross-validation scheme (e.g., [38]).

## V. RESULTS

This section presents the qualitative and quantitative comparisons between results obtained by the seven OSSE experiments (defined in Tables VII and VIII) and the ROMS-E simulation considered as the “true” ocean state. The qualitative comparisons are in the form of graphic displays of total concentration profile patterns for the whole period or for a spring tidal period. They are, respectively, presented in Sections V-A and V-B. The quantitative comparisons are error budgets for total and size class concentrations. They are, respectively, presented in Sections V-C and V-D.

### A. Monthly Patterns

All the seven experiments described in Tables VII and VIII last for the same 15 months period, from October 2007 to December 2008. The results obtained are shown in Fig. 3. The ROMS-E simulation, considered as the real ocean, is added on top of them for a direct comparison. Only total concentrations

<figure><img src="image_4.png" /><figcaption>Fig. 3. Temporal evolution of the vertical profiles of total SSC (in mg/L) at point M3 from (a) ROMS-E simulation, (b) Sat-Cls, (c) Sat-Din, (d) Sat-Raw, (e) Dyn, (f) Dyn-Cls, (g) Dyn-Din, and (h) Dyn-Raw experiments during all their 15 months' period: from October 2007 to December 2008.</figcaption></figure>

are displayed. This shows that all experimental results broadly reproduce the resuspension events of winter storms and spring tides. However, Sat-Raw appears extremely noisy and fails to reproduce properly the three main signal peaks appearing in winter from December 2007 to February 2008. This failure is expected since the input data in Sat-Raw bring very little information to the system. Indeed, those data come only from the satellite, and they are scarce because there are no data during nights and few during cloudy weather. Sat-Din appears better but is still noisier than the rest of experiments. Results for Sat-Cls, having continuous and most accurate satellite data, are correct. All experiments of type DYN defined in Table VIII (benefiting from a continuous time series of ancillary data) display correct patterns (compared to ROMS-E). Even the Dyn experiment, which does not have any satellite information, produces correct shapes and shows only few discrepancies, especially in February, with ROMS-E.

### B. Daily Patterns

Fig. 4 shows the performance of each experiment in reproducing the daily patterns of total concentration profiles during
<figure><img src="image_5.png" /><figcaption>Fig. 4. Temporal evolution of vertical profiles of total SSC (in mg/L) at point M3 from (a) ROMS-E simulation, (b) Sat-Cl s, (c) Sat-Din, (d) Sat- Raw, (e) Dyn, (f) Dyn-Cl s, (g) Dyn-Din, and (h) Dyn-Raw experiments for a spring tide period: from June 1 to June 10, 2008.</figcaption></figure>

a period of mean spring tide (with weak meteorological conditions: wind and waves), here lasting from June 1 to June 10, 2008. In all experiments, the resulting concentrations clearly exhibit semidiurnal variations in close correlation with the effects of the tide. The ability of the statistical method in reproducing this signal may come from the matrix of transition probabilities sorted out during the learning period. However, the regularity of this tidal signal, well displayed in the ROMS-E simulation, is largely perturbed in Sat-Din [Fig. 4(c)] and Sat-Raw [Fig. 4(d)] experiments. In these two experiments, satellite data are fragmentary, and this temporally loss of information impacts the periodicity of the signal. In par- ticular, the Sat-Raw experiment can completely lose the tidal cycle, as shown at the end of the section starting on June 9th. In Sat-Din, the daily tidal signal becomes simply uneven due to data gaps during nights. Among all experiments of type SAT, having only satellite data as input (Table VII), only Sat-Cl s (which benefits from a continuous time series of most accurate satellite data, but is an unrealistic twin experiment) well keeps the tidal cycle. Finally, all the experiments of type

DYN (having notably a continuous time series of ancillary data, see Table VIII) well display the correct tidal signal, including the Dyn experiment which does not incorporate any satellite data.

### C. Error Budget for Total Concentrations

The error budget presented here gives an idea of the whole performance of experiments by the comparison between the results obtained and the ROMS-E simulation over the 10% of data reserved for validation (these 10% of data have neither been included in the learning process nor in the classification). The absolute error parameter in mg/L retained is the root mean square error (RMSE), computed for each sigma depth level z_k, with k = 1 at the bottom and k = 11 at the surface, according to the following formulation:

$$RMSE_k = \sqrt{\frac{\sum_{j=1}^{N} (C(z_k, t_j)_{\text{Exp.}} - C(z_k, t_j)_{\text{True}})^2}{N}} \quad (3)$$

In the present investigation, N = 1080 is the number of time steps reserved for validation (corresponding to 10% of the total time steps) regularly spaced throughout the whole time series. The subscript “Exp.” stands for each of the seven simulation experiments, and “True” stands for the so-called “true” ocean state ROMS-E simulation.

The chosen relative error parameter is WAPE, rather than the most often used NRMSE, because, when applied on size class concentrations, absolute values for large particles can become extremely small near the sea surface, which causes singularities in the computation of NRMSE. WAPE errors are in % and computed as follows:

$$WAPE_k = 100 \cdot \frac{\sum_{j=1}^{N} |C(z_k, t_j)_{\text{Exp.}} - C(z_k, t_j)_{\text{True}}|}{\sum_{j=1}^{N} C(z_k, t_j)_{\text{True}}} \quad (4)$$

RMSE and WAPE values for all SAT and DYN experiments are shown in Fig. 5. In all experiments, RMSE increases from the surface to the bottom, having the shape of an exponential graph. This is expected since the studied process deals with resuspension, for which the maximum concentration arises at the bottom. In terms of relative errors, WAPE value increases almost linearly from the surface to the bottom in SAT experiments. In the DYN experiments, this increase is monotonic but not linear, and all values converge to nearly the same value round 20% at the bottom.

The SAT experiments result in distinct error budgets. In Sat- Raw, as expected, WAPE has large values ranging from 40% at the surface to nearly 100% at the bottom. Accuracy is much better for Sat-Cl s with 7% at the surface and nearly 25% at the bottom. The performance of Sat-Din is in between, with accuracies almost 20% at the surface and 40% at the bottom.

On the contrary, all DYN experiments result to almost the same errors at the bottom. This is expected since these experiments benefit from similar information from ancillary data which mainly drives the SSC at the bottom. At the surface, errors deviate because they are also controlled by the amount and accuracy of satellite data. As expected the more accurate and numerous the satellite data are, the more accurate is the SSC recovery. Moreover, when satellite data are few and very
<figure><img src="image_6.png" /><figcaption>Fig. 5. (a) RMSE and (b) WAPE error deviations for total concentrations, between ROMS-E simulation (considered as the true state) and SAT experiments of Table VII. (c) RMSE and (d) WAPE error deviations for total concentrations for DYN experiments of Table VIII. All errors are given at all depth sigma levels with sigma level 11 at the surface and sigma level 1 near the bottom.</figcaption></figure>

noisy (Dyn-Raw), they do not bring any useful information to the system. Dyn-Raw is even slightly less accurate than Dyn because, probably, very noisy satellite data are considered statistically inconsistent with the ancillary (hydrodynamic) data.

## D. Error Budget for Each Size Class

RMSE and WAPE values are shown in Figs. 6 and 7 for all the experiments of type SAT and DYN, respectively. In each experiment, the WAPE series of profiles shows that the finer the particles are, the lower is the relative error. This result appears surprising since the dynamics of fine particles involves the advection process which adds complexity. On the other hand, except for the large values in Sat-Raw, all WAPE relative errors appear broadly constant on the vertical. Consistently, RMSE values display vertical profiles similar to their concomitant concentrations, say a constant value for silts and an exponential decay from bottom to surface for large particles (not displayed).

As expected, among SAT experiments (having satellite data only), the Sat-Cls twin experiment, with its continuous satellite data, gives the most accurate results. On the contrary, satellite data are few in Sat-Raw and give WAPE errors often far beyond 100%. Compared to it, Sat-Din (having DINEOF processed satellite data) appears much better, nonetheless having WAPE errors around 50%.

In all DYN experiments (benefiting from continuous ancillary data), WAPE errors are below 50%. When comparing results of all these experiments for a given sand size class, errors vary little, except for silts. Effectively, RMSE and WAPE graphs for silts display vertical lines with values differing among experiments. Again, the more accurate satellite data are processed, and the more accurate is the recovery of the silts (Sand1). For these fine particles, Dyn and Sat-Din have almost

<figure><img src="image_7.png" /><figcaption>Fig. 6. Error deviations between ROMS-E simulation (considered as the true state) and SAT experiments of Table VII, for all sand size classes. (a) RMSE and (b) WAPE for Sat-Cls. (c) RMSE and (d) WAPE for Sat-Din. (e) RMSE and (f) WAPE for Sat-Raw. Errors are given at all depth sigma levels with sigma level 11 at the surface and sigma level 1 near the bottom.</figcaption></figure>

the same accuracies, having relative error values around 12% along each vertical profile. An improvement occurs in Dyn-din (in combining both data), leading to relative errors of about 8% [Fig. 7(f)].

For large particles (sands), Dyn has relative errors that are much better than Sat-Din, and satellite data do not improve the results when combined with ancillary (hydrodynamic) data, in all DYN experiments (Fig. 7). Hence, the contribution of satellite data has a weak impact on the determination of the large particles (Sand2 to Sand6). This is expected since the fate of these particles depends almost directly on the local dynamics which is given mostly by the ancillary data. The contribution of satellite data has the main impact on the determination of silts. This is expected since the dynamics of silts not only depends on local dynamics but also on the advection of silts, for which the satellite can bring additional information that helps for the determination of these fine particles.

## VI. DISCUSSION

The results show that a hidden Markov processing such as PROFHMM, working on time series, benefits from GEO satellite data only if these data are spatially interpolated in advance (through DINEOF processing for instance). The fact is that despite their high temporal resolution, MTG/FCI L2 data will have too many gaps due to clouds and nights
<figure><img src="image_8.png" /><figcaption>Fig. 7. Error deviations between ROMS-E simulation (considered as the true state) and Dyn experiments of Table VIII, for all sand size classes. (a) RMSE and (b) WAPE for Dyn. (c) RMSE and (d) WAPE for Dyn-Cls. (e) RMSE and (f) WAPE for Dyn-Din. (g) RMSE and (h) WAPE for Dyn-Raw. Errors are given at all depth sigma levels with sigma level 11 at the surface and sigma level 1 near the bottom.</figcaption></figure>

so that they bring too little information in the process. Using interpolated data, however, the gain in accuracy becomes significant [see the accuracy gain from Fig. 6(d) to (f)]. Monthly patterns of concentration profiles then appear much better [see Fig. 3(c)], but daily patterns still suffer from irregularities [see Fig. 4(c)]. The signal becomes regular when information on the hydrodynamics is also provided [see Fig. 4(g)]. Liu and Wang [36] obtained similar results processing GOCI data in the Yangtze and Yellow River mouth regions. They applied a DINEOF processing to 2 months of GOCI images and analyzed SSC time series, assuming that tides mainly explain the high-frequency variations of SSC. However, beyond the tidal signal, GEO satellites can also bring information on episodic events as well. For instance, He *et al.* [24] demonstrated, with an analysis of GOCI images in Hangzhou Bay, that a GEO satellite is able to well capture the rapid rise and fall of suspended sediments, in a half day only, following the passage of a weak typhoon.

In the following discussion, we will dismiss results from Sat-Raw and Dyn-Raw experiments because they deal with L2 satellite data that bring insufficient information. Moreover, the Sat-Cls and Dyn-Cls twin experiments will also be dismissed because they only serve to define the accuracy limits of the method and to check the algorithm. Then, in the following, we are comparing only Sat-Din, Dyn, and Dyn-Din. Doing so for the fine particles (silts), it appears that exploiting satellite data only (in Sat-Din) or hydrodynamic data only (in Dyn) lead to a similar accuracy (round 12%). Moreover, an improvement of about 4% occurs with the exploitation of both data at the same time (in Dyn-Din). For the large particles (sands), pure satellite data (in Sat-Din) give worse results than pure hydrodynamic data (in Dyn) and do not improve results when processed in combination with the hydrodynamics (in Dyn-Din). Hence, satellite data appear interesting only in the determination of fine particles.

Globally, these results emphasize the importance of assimilating data from numerical model in addition to satellite data. However, one must not forget that the main assumption of these OSSE experiments is that the ROMS sediment transport model is previously calibrated with satellite measurements themselves. These calibration measurements do not usually come from GEO satellites but from standard low-earth orbit (LEO) satellites, providing a sufficient long time hindcast model simulation for the calibration purpose. In retrospect, dealing with the aims of operational oceanography, this study shows that a proper calibrated hindcast simulation coupled with an operational hydrodynamic model could give acceptable forecasts, at least as a first guess. The addition of GEO satellite data into the system may lead to an improvement of the accuracy by a significant factor (here from 12% to 8% error).

## VII. CONCLUSION

The present OSSE experiment has quantified the capacity to monitor SSC in the ocean from data acquired by the future MTG/FCI satellite sensor. The test case is conducted in the dynamical waters of the English Channel, with the dominant forcing of wind, tides, and waves. The processing of simulated MTG/FCI data demonstrated that a GEO satellite, despite its high temporal acquisition rate, still needs spatial interpolation processing of images because of the presence of high cloud cover over the English Channel. The inclusion in the processed data set of times series of the main forcing parameters (wind, tides, and waves) appears to be of primary importance for an accurate determination of both fine (silts) and large (sands) particles.

The algorithm developed here can deliver analysis and forecast products on particulate matter concentrations, from the sea surface to its bottom. To achieve this, the processing makes use of a classification map of the different vertical states of particle concentrations, with their related ergodic properties, deduced from the statistical analysis of a hydrosedimentary model hindcast. Such an algorithm can be developed for the operational oceanography, but a prerequisite of paramount importance is the availability of hindcast simulations from
well-calibrated and configured hydrosedimentary model sim-
ulations of long periods of time (typically decadal duration).

REFERENCES

[1] G. Neukermans, K. Ruddick, E. Bernard, D. Ramon, B. Nechad, and P.-Y. Deschamps, "Mapping total suspended matter from geostationary satellites: A feasibility study with SEVIRI in the Southern Nnorth sea," *Opt. Express*, vol. 17, no. 16, pp. 14029–14052, Aug. 2009. [Online]. Available: http://www.opticsexpress.org/abstract.cfm?URI=oe-17-16-14029

[2] G. Neukermans, K. G. Ruddick, and N. Greenwood, "Diurnal vari-
ability of turbidity and light attenuation in the Southern North
sea from the SEVIRI geostationary sensor," *Remote Sens. Envi-
ron.*, vol. 124, pp. 564–580, Sep. 2012. [Online]. Available: http://
www.sciencedirect.com/science/article/pii/S0034425712002398

[3] K. Ruddick, G. Neukermans, Q. Vanhellemont, and D. Jolivet, “Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results,” *Remote Sens. Environ.*, vol. 146, pp. 63–76, Apr. 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0034425713003271

[4] E. J. Kwiatkowska et al., "Ocean colour opportunities from MeteoSat second and third generation geostationary platforms," *Ocean Sci.*, vol. 12, no. 3, pp. 703–713, May 2016. [Online]. Available: https://www.ocean-sci.net/12/703/2016/

[5] H. Lavigne and K. Ruddick, “The potential use of geostationary MTG/FCI to retrieve chlorophyll–a concentration at high temporal resolution for the open oceans,” *Int. J. Remote Sens.*, vol. 39, no. 8, pp. 2399–2420, Apr. 2018, doi: 10.1080/01431161.2017.1420935.

[6] S. Ouala et al., "Learning stochastic representations of geophysical dynamics," in *Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP)*, May 2019, pp. 3877-3881.

[7] X. Chen et al., "Integration of multi-source data for water quality classification in the Pearl river estuary and its adjacent coastal waters of Hong Kong," *Continental Shelf Res.*, vol. 24, no. 16, pp. 1827-1843, Oct. 2004. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S02924796311001862

[8] N. Margvelashvili and E. P. Campbell, "Sequential data assimila-
tion in fine-resolution models using error-subspace emulators: The-
ory and preliminary evaluation," *J. Mar. Syst.*, vol. 90, no. 1,
pp. 13–22, Feb. 2012. [Online]. Available: http://www.sciencedirect.
com/science/article/pii/S0924796311001862

[9] N. Margvelashvili, J. Andrewartha, M. Herzfeld, B. J. Robson, and V. E. Brando, "Satellite data assimilation and estimation of a 3D coastal sediment transport model using error-subspace emulators," *Environ. Model. Softw.*, vol. 40, pp. 191–201, Feb. 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364815212002411

[10] B. Saulquin et al., "Characterization of time-varying regimes in remote sensing time series: Application to the forecasting of satellite-derived suspended matter concentrations," *IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.*, vol. 8, no. 1, pp. 406–417, Jan. 2015.

[11] Y. H. Kim, J. Im, H. K. Ha, J.-K. Choi, and S. Ha, "Machine learning approaches to coastal water quality monitoring using GOCI satellite data," *GISci. Remote Sens.*, vol. 51, no. 2, pp. 158–174, Mar. 2014, doi: 10.1080/15481603.2014.900983.

[12] M. Nazeer, M. Alsahli, A. Waqas, M. Bilal, and M. Shahzad, "Evaluation of empirical and machine learning algorithms for estimation of coastal water quality parameters," *ISPRS Int. J. Geo-Inf.*, vol. 6, no. 11, p. 360, Nov. 2017. [Online]. Available: https://www.mdpi.com/2220-9964/6/11/360

[13] P. Renosh et al., "Construction of multi-year time-series profiles of suspended particulate inorganic matter concentrations using machine learning approach," *Remote Sens.*, vol. 9, no. 12, p. 1320, Dec. 2017. [Online]. Available: https://www.mdpi.com/2072-4929/9/12/1320

[14] P. Zhang, O. Wai, X. Chen, J. Lu, and L. Tian, "Improving sediment transport prediction by assimilating satellite images in a Tidal Bay Model of Hong Kong," *Water*, vol. 6, no. 3, pp. 642–660, Mar. 2014. [Online]. Available: https://www.mdpi.com/2073-4441/6/3/642

[15] V. Le Fouest, M. Chami, and R. Verney, “Analysis of riverine suspended particulate matter fluxes (Gulf of lion, Mediterranean sea) using a synergy of ocean color observations with a 3-D hydrodynamic sediment transport model,” *J. Geophys. Res., Oceans*, vol. 120, no. 2, pp. 942–957, Feb. 2015. [Online]. Available: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014JC010098

[16] X. Mao, D. Wang, J. Zhang, C. Bian, and X. Lv, "Dynamically constrained interpolation of the sparsely observed suspended sediment concentrations in both space and time: A case study in the Bohai sea," *J. Atmos. Ocean. Technol.*, vol. 35, no. 5, pp. 1151–1167, May 2018, doi: 10.1175/JTECH-D-17-0149.1.

[17] D. Wang, A. Cao, J. Zhang, D. Fan, Y. Liu, and Y. Zhang, "A three-dimensional cohesive sediment transport model with data assimilation: Model development, sensitivity analysis and parameter estimation," *Estuarine, Coastal Shelf Sci.*, vol. 206, pp. 87-100, Jun. 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0272771416302827

[18] D. Wang et al., "Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the hangzhou bay: Temporal variations and spatial distributions," *Ocean Model.*, vol. 121, pp. 34–48, Jan. 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1463500317301841

[19] C. P. Arnold and C. H. Dey, "Observing-systems simulation experiments: Past, present, and future," *Bull. Amer. Meteorological Soc.*, vol. 67, no. 6, pp. 687–695, 1986, doi: 10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2.

[20] A. A. Charantonis, F. Badran, and S. Thiria, "Retrieving the evolution of vertical profiles of chlorophyll-a from satellite observations using hidden Markov models and self-organizing topological maps," *Remote Sens. Environ.*, vol. 163, pp. 229–239, Jun. 2015. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0034425715001157

[21] J.-K. Choi, Y. J. Park, J. H. Ahn, H.-S. Lim, J. Eom, and J.-H. Ryu, "GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity," *J. Geophys. Res., Oceans*, vol. 117, no. C9, Sep. 2012, Art. no. C09004. [Online]. Available: https://agupubs. onlinelibrary.wiley.com/doi/abs/10.1029/

[22] K. Ruddick, Q. Vanhellemont, J. Yan, G. Neukermans, G. Wei, and S. Shang, "Variability of suspended particulate matter in the Bohai sea from the geostationary ocean color imager (GOCI)," *Ocean Sci. J.*, vol. 47, no. 3, pp. 331–345, Sep. 2012, doi: 10.1007/s12601-012-0032-4.

[23] M. Wang et al., "Ocean color products from the Korean geostationary ocean color imager (GOCI)," *Opt. Express*, vol. 21, no. 3, pp. 3835–3849, Feb. 2013. [Online]. Available: http://www.opticsexpress.org/abstract.cfm?URI=oe-21-3-3835

[24] X. He et al., "Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters," *Remote Sens. Environ.*, vol. 133, pp. 225–239, Jun. 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0034425713000503

[25] S. Jean-Claude and B. Marguerite, "An atlas of long-term currents in the channel," *Oceanologica Acta*, vol. 16, nos. 5-6, pp. 439–448, 1993. [Online]. Available: https://archimer.ifmerfr.fr/doc/00099/21068/

[26] N. Guillou and G. Chapalain, "Numerical simulation of tide-
induced transport of heterogeneous sediments in the english
channel," *Continental Shelf Res.*, vol. 30, no. 7, pp. 806–819,
Apr. 2010. [Online]. Available: http://www.sciencedirect.com/science/
article/pii/S0278434310000361

[27] N. Guillou, A. Rivier, F. Gohin, and G. Chapalain, "Modeling near-surface suspended sediment concentration in the English channel," *J. Mar. Sci. Eng.*, vol. 3, no. 2, pp. 193–215, May 2015. [Online]. Available: https://www.mdpi.com/2077-1312/3/2/193

[28] N. Guillou, A. Rivier, G. Chapalain, and F. Gohin, "The impact of tides and waves on near-surface suspended sediment concentrations in the English channel," *Oceanologia*, vol. 59, no. 1, pp. 28–36, Jan. 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S007832341630032X

[29] A. F. Shchepetkin and J. C. McWilliams, "The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model," *Ocean Model.*, vol. 9, no. 4, pp. 347–404, Jan. 2005. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1463500304000484

[30] G. D. Egbert and S. Y. Erofeeva, "Efficient inverse modeling of barotropic ocean tides," *J. Atmos. Ocean. Technol.*, vol. 19, pp. 183–204, Feb. 2002, doi: 10.1175/1520-0426(2002)019<0183 :EIMOBO>2.0.CO;2.

[31] P. Bénard et al., "Dynamical kernel of the aladin-NH spectral limited-area model: Revised formulation and sensitivity experiments," *Quart. J. Roy. Meteorological Soc.*, vol. 136, no. 646, pp. 155–169, Jan. 2010. [Online]. Available: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.522
[32] F. Ardhuin, "Prévisions de vagues (WW3) pour la façade golfe de gascogne/nord atlantique (grille régulière) (WW3-NORGAS-2MIN)," Tech. Rep., 2012. [Online]. Available: https://sextant.ifremer.fr/record/fde1f80f-b0a7-45ba-bad6-2343bb4e9a9f/

[33] E. Boudière, C. Maisondieu, F. Ardhuin, M. Accensi, L. Pineau-Guillou, and J. Lepesqueur, "A suitable metocean hindcast database for the design of marine energy converters," *Int. J. Mar. Energy*, vols. 3-4, pp. e40-e52, Dec. 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2214166913000362

[34] A. Rivier, F. Gohin, P. Bryère, C. Petus, N. Guillou, and G. Chapalain, "Observed vs. predicted variability in non-algal suspended particulate matter concentration in the English channel in relation to tides and waves," *Geo-Mar. Lett.*, vol. 32, no. 2, pp. 139-151, Apr. 2012, doi: 10.1007/s00367-011-0271-x.

[35] W. B. Rossow and R. A. Schiffer, "Advances in understanding clouds from ISCCP," *Bull. Amer. Meteorolog. Soc.*, vol. 80, no. 11, pp. 2261-2288, Nov. 1999, doi: 10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

[36] X. Liu and M. Wang, "Analysis of ocean diurnal variations from the Korean geostationary ocean color imager measurements using the DINEOF method," *Estuarine, Coastal Shelf Sci.*, vol. 180, pp. 230-241, Oct. 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0272771416302220

[37] A. Alvera-Azcárate, Q. Vanhellemont, K. Ruddick, A. Barth, and J.-M. Beckers, "Analysis of high frequency geostationary ocean colour data using DINEOF," *Estuarine, Coastal Shelf Sci.*, vol. 159, pp. 28-36, Jun. 2015. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0272771415001043

[38] T.-T. Wong, "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation," *Pattern Recognit.*, vol. 48, no. 9, pp. 2839-2846, Sep. 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0031320315000989

<figure><img src="image_9.png" /></figure>

**Anastase Alexander Charantonis** is an Assistant Professor in deep learning with the Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), Evry, near Paris, France. His research interest is learning in geosciences, with sparse and noisy data.

<figure><img src="image_10.png" /></figure>

**Nicolas Guillou** received the Ph.D. degree and the Habilitation to Supervise Research (HDR) in physical oceanography in 2007 and 2015, respectively.

He is a Research Engineer in physical oceanography and the Deputy Director of the Laboratory of Coastal Engineering and Environment, CEREMA, Brest, France. He is specialized in simulating and understanding the coastal hydrodynamics processes within continental shelves ecosystems and shallow-water environments (e.g., tidal straits, bays, or estuaries). His research pays particular attention to the local and global ecological impacts of human disturbances (including the effects of climate change) by providing further insights about the evolution of environmental parameters (e.g., temperature, salinity, and suspended sediment concentrations) and investigating the interactions with living organisms.

<figure><img src="image_11.png" /></figure>

**Sylvie Thiria** is a Professor Emeritus in artificial intelligence at the Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Paris, France. She mainly works on neural networks applied to geosciences. She was Co-I on the NASA NSCAT and Quick-Scat experiments and on the ESA ERS1-2 experiment. She has authored more than 90 scientific publications in international journals.

<figure><img src="image_12.png" /></figure>

**Fouad Badran** is a Professor Emeritus with the Conservatoire National des Arts et Métiers, Paris, France. His studies cover data mining methods for complex data, mainly in remote sensing and environmental sciences.

<figure><img src="image_13.png" /></figure>

**Thierry Garlan** received the Ph.D. degree in geology and the Habilitation to Supervise Research (HDR) degree in natural sciences from the University of Lille I, Lille, France, in 1985 and 2004, respectively.

He is a Senior Scientist whose research is focused on physical properties and modeling of sediments and sediment structure environments. In 1988, he established the Sedimentology Laboratory, SHOM, Brest, France, and since 2008, he has been the Head of the SHOM Department of Marine Geology. He is a Project Manager of three projects financed by the French Ministry of Research: 1) anthropic and seismic impacts of World War II mines and bombs clearing; 2) characterization and study of sediment dynamics in extreme tidal current environment for the installation of tidal turbines; and 3) hydraulic dunes dynamics and its impact on Marine Renewable Energy projects, and two projects financed by the French Ministry of Defence on sandy beaches dynamic modeling. He set up and co-organized the MARID conferences on marine and river dunes dynamics.

<figure><img src="image_14.png" /></figure>

**Frédéric Jourdin** received the Ph.D. degree in spatial oceanography and data assimilation.

His studies aim at forecasting the underwater sighting range (e.g., for divers). His studies include *in situ* marine optics with emphasis on light attenuation, coastal moorings with optical and acoustical sensors (ADCP in particular), and statistical learning methods applied to ocean color remote sensing.

<figure><img src="image_15.png" /></figure>

**Pannimpulath Remanan Renosh** received the B.Sc. degree in physics from the University of Calicut, Kerala, India, in 2003, the M.Sc. degree in oceanography from the Cochin University of Science and Technology (CUSAT), Kerala, in 2007, and the Ph.D. degree in oceanography from the Lille I University of Science and Technology, Lille, France, in 2015, funded by the Center National de la Recherche Scientifique (CNRS) and the Center National d'études Spatiales (CNES). His thesis focused on the "characterization of the coupling

between oceanic turbulence and the variability of coastal waters optical properties using *in situ* measurements and satellite data."

After the master's degree, he was a Research Scholar from the Department of Marine Sciences, Goa University, Taleigão, India, from 2008 to 2011. After the completion of his Ph.D., he was a Post-Doctoral Researcher with the Laboratoire d'Océanologie et de Géosciences (LOG), Wimereux, France, funded by the Université du Littoral-Côte-d'Opale (ULCO), Dunkrik, France. He was also a Post-Doctoral Researcher with SHOM, Brest, France, from 2016 to 2018, funded by the Conservatoire National des Arts et Métiers (CNAM), Paris, France. Since 2018, he has been a Post-Doctoral Researcher with the Laboratoire d'Océanographie de Villefranche (LOV), Sorbonne University, Villefranche-Sur-Mer, France. His research interests include oceanography, ocean color remote sensing, marine optics, time-series analysis, and machine learning techniques.