Cédric Jamet

Retrieving the vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: A method based on a neural network with potential for global‐scale applications

A neural network-based method is developed to assess the vertical distribution of (1) chlorophyll a concentration ([Chl]) and (2) phytoplankton community size indices (i.e., microphytoplankton, nanophytoplankton, and picophytoplankton) from in situ vertical profiles of chlorophyll fluorescence. This method (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm) uses as input only the shape of the fluorescence profile associated with its acquisition date and geo-location. The neural network is trained and validated using a large database including 896 concomitant in situ vertical profiles of HighPerformance Liquid Chromatography (HPLC) pigments and fluorescence. These profiles were collected during 22 oceanographic cruises representative of the global ocean in terms of trophic and oceanographic conditions, making our method applicable to most oceanic waters. FLAVOR is validated with respect to the retrieval of both [Chl] and phytoplankton size indices using an independent in situ data set and appears to be relatively robust spatially and temporally. To illustrate the potential of the method, we applied it to in situ measurements of the BATS (Bermuda Atlantic Time Series Study) site and produce monthly climatologies of [Chl] and associated phytoplankton size indices. The resulting climatologies appear very promising compared to climatologies based on available in situ HPLC data. With the increasing availability of spatially and temporally wellresolved data sets of chlorophyll fluorescence, one possible global-scale application of FLAVOR could be to develop 3-D and even 4-D climatologies of [Chl] and associated composition of phytoplankton communities. The Matlab and R codes of the proposed algorithm are provided as supporting information.

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Knowing that coastal areas concentrate about 60% of the worlds population (within 100 km from the coast), that 75-90% of the global sink of suspended river load takes place in coastal waters in which about 15% of the primary production occurs, the ecological, societal and economical value of these areas are obvious (fish resources, aquaculture, water quality information, recreation areas management, global carbon budget, etc). In that context, precise assessment of suspended particulate matter (SPM) concentrations and of the phenomena controlling its temporal variability is a key objective for many research fields in coastal areas. SPM which encompasses organic (living and non-living) and inorganic matter controls the penetration of light into the water and brings new nutrients into the system, both key parameters influencing phytoplankton primary production. Concentrations and availability of SPM are also known to control rates of food intake, growth and reproduction for various filter feeder organisms. Phytoplankton is highly sensitive to environmental perturbations (such as nutrient inputs, light, and turbulence). The abundance, biomass and dynamics of phytoplankton in coastal areas therefore reflect the prevailing environmental conditions and represent key parameters for assessing information on the ecological conditions, as well as on the coastal water quality. Because phytoplankton is highly sensitive to environmental perturbations [1], its distribution patterns and temporal variability represent good indicators of the ecological conditions of a defined region [2, 3]. Coastal waters also host complex ecosystems and represent important fishery areas that support industry and provide livelihood to coastal settlements. The food chain in the coastal ocean is generally short (especially in upwelling systems, having as low as three trophic levels) whereas the open ocean food web presents up to six trophic levels [4]. As a result, when compared to the open ocean, a relative lower fraction of the primary production gets respired in the coastal ocean while a higher fraction reaches the uppermost trophic level (fish) [5] or is exported to adjacent areas (coastal or open sea)...

Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province, Argentina

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R2) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s−1, R2 is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the ‘skin and sub-skin layer’ when wind speed is below 6 m s−1. The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002–December 2010. The results show that inter-annual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains.

Validation of chlorophyll-α concentration maps from Aqua MODIS over the Gulf of Gabes Tunisia: comparison between MedOC3 and OC3M bio-optical algorithms

Few studies have focused on the use of ocean colour remote sensors in the Gulf of Gabes (southeastern Tunisia). This work is the first study to evaluate the ocean colour chlorophyll-a product in this area. Chlorophyll-a concentrations were measured during oceanographic cruises performed off the Gulf of Gabes. These measurements were used to validate satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. First, two atmospheric correction procedures (standard and shortwave infrared) were tested to derive the remote-sensing reflectance, and then a comparison between two bio-optical (OC3M and MedOC3) algorithms were realized using the in situ measurements. Both atmospheric correction procedures gave similar results when applied to our study area indicating that most pixels were non-turbid. The comparison between bio-optical algorithms shows that using the regional bio-optical algorithm MedOC3 improves chlorophyll-a estimation in the Gulf of Gabes for the low values of this parameter.

A neural network‐based method for merging ocean color and Argo data to extend surface bio‐optical properties to depth: Retrieval of the particulate backscattering coefficient

The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.

Empirical nonlinear determination of the diffuse attenuation coefficient Kd(490) in coastal waters from ocean color images

The fine-scale study of the diffuse attenuation coefficient, Kd(λ), of the spectral solar downward irradiance is only feasible by ocean color remote sensing. Several empirical and semi-analytical methods exist. However, most of tthese models are generally applicable for clear open ocean waters. They show limitations when applied to coastal waters. A new empirical method based on neural networks has been developed using a relationship between the remote-sensing reflectances between 412 and 670 nm and Kd(490), for the SeaWiFS ocean color remote sensor. The architecture of the neural network has been defined using synthetical and in situ dataset and the optimal design is a tow hidden layer neural network with 4 neurons of the first layer and three on the second layer. The comparison with the SeaWiFS empirical algorithms shows similar retrievals accuracies for low values of Kd(490) (i.e. <0.20 m-1) and better estimates for greater values of and Kd(490). The new model is suitable for open water but also for turbid waters and does not show the limitations of the empirical method. The new model is more general that the empirical methods.

Estimation of the diffuse attenuation coefficient Kd(lambda) with a neural network inversion

The fine-scale study of the diffuse attenuation coefficient, Kd(λ), of the spectral solar downward irradiance is only feasible by ocean color remote sensing. Several empirical and semi-analytical methods exist. However, most of these models are generally applicable for clear open ocean waters and estimate this coefficient only at 490 nm. They show limitations when applied to coastal waters. A new empirical method based on neural networks has been developed using a relationship between the remote-sensing reflectances between 412 and 670 nm and Kd(λ) between 412 and 490 nm, for the SeaWiFS ocean color remote sensor. The first results concern the estimation of Kd(490). The architecture of the neural network has been defined using synthetical and in situ dataset The comparison with the SeaWiFS empirical algorithms shows similar retrievals accuracies for low values of Kd(490) (i.e. &#60;0.20 m−1) and better estimates for greater values of Kd(490). The new model is suitable for open water but also for turbid waters and does not show the limitations of the empirical method.

Neuro-variational inversion of ocean color imagery

This paper presents a neuro-variational method to invert satellite ocean color signal. The method is based on a combination of neural networks and classical variational inversion. The radiative transfer equations are modeled by neural networks whose input are the oceanic and atmospheric parameters and output the top of the atmosphere reflectance at several wavelengths. The procedure consists in minimizing a quadratic cost function which is the distance between the satellite observed reflectance and the neural network computed reflectance, the control parameters being the oceanic and atmospheric parameters. The method allows us to retrieve atmospheric and oceanic parameters. We present a feasibility experiment. We show we can retrieve Chl-a with an error of 19.7% if we can obtain a perfect knowledge of three atmospheric parameters. Finally, an inversion of one SeaWiFS image is presented. The Chl-a give coherent spatial structures.