Mapping tidal and seasonal movements of the maximum turbidity zone in estuarine waters from remotely sensed (SPOT, LANDSAT) data. A semi-empirical approach.|
David Doxaran, Samantha J. Lavender and Jean-Marie Froidefond
The study concerns the quantification of
suspended sediments in turbid estuarine waters from high spatial resolution
remotely sensed data (SPOT-HRV, Landsat-ETM+). It is applied to the following
areas: the Gironde and the Loire estuaries (France), where suspended sediment
concentrations (SPM) in surface waters vary from 10 to 2000 mg l-1.
The methodology is essentially empirical. Based
on in situ optical measurements, calibration relationships are established
between the remote sensing reflectance (Rrs)
signal and SPM. These relationships,
obtained using reflectance ratios between near-infrared (NIR) and visible (VIS)
wavebands, are relatively independent of the sediment grain-size and
mineralogy, and of the illumination conditions (e.g. the cloud cover).
Consequently, they can be applied to satellite images, even if no simultaneous
in situ measurements were carried out concurrently with the satellite overpass.
A bio-optical model is used to explain the
obtained empirical results. It relates the inherent and apparent optical
properties of the considered estuarine waters. Scattering by sediments is modelled
using the Mie theory. Absorption by sediments is modelled according to the
existing knowledge in the domain. The modelled optical properties are validated
by comparison with in situ measurements.
The established calibration relationships allow an accurate estimation
of concentrations, once satellite data are corrected for atmospheric effects. Resulting
SPM maps permit to observe the seasonal
movements of the maximum turbidity zone (MTZ) and the resuspension phenomena in
the Gironde estuary.
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Submitted: 07 June 2003
Revised: 18 January 2004
Accepted: 24 January 2004
Doxaran D, S J Lavender & J-M Froidefond, 2004. Mapping tidal and seasonal movements of the maximum turbidity zone in estuarine waters from remotely sensed (SPOT, LANDSAT) data. A semi-empirical approach. EARSeL eProceedings 3(1), 54-68