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Monitoring suspended matter fluxes in coastal waters using ocean colour remote sensing and transport modelling. An integrated approach


The project aims to develop an operational monitoring system for turbid estuarine and coastal waters. The system combines in-situ observations, ocean colour remote sensing measurements and numerical transport modelling. In-situ and remote sensing (satellite and/or airborne) observations are directly integrated into a sediment transport model.

This integration approach is used to calibrate the model and calculate sedimentary fluxes. The integrity of new quantification algorithms for ocean colour remote sensing sensors is first assessed in two European estuarine environments. These algorithms include recently developed atmospheric correction methods and suspended particulate matter (SPM) quantification relationships.

The integrity assessment is based on multiple match-ups (simultaneous remote sensing and in-situ measurements). Once assessed, algorithms are routinely applied to multi-scale satellite data in order to observe the tidal and seasonal movements of surface SPM concentrations in a selected study area. The retrieved concentrations are associated to a quantified percentage of error. Remote sensing observations are complemented by numerous in-situ measurements providing information on the continuous variations and vertical profiles of turbidity.

The generated database covers one full year and provides information on the tidal and seasonal SPM dynamics. Data (SPM concentrations) are integrated into a three-dimensional sediment transport model. In-situ and remote sensing data are first integrated separately to assess their respective influence in terms of model calibration (calibration of the empirical parameters used in the sediment transport model).

The full dataset is then integrated. The final product (sediment transport model with data-integration) is used to obtain a better understanding of hydrodynamic/transport processes and quantify tidal/seasonal sedimentary fluxes.

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