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David Doxaran

Researcher at Centre national de la recherche scientifique

Publications -  78
Citations -  3013

David Doxaran is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Ocean color & Colored dissolved organic matter. The author has an hindex of 27, co-authored 66 publications receiving 2431 citations. Previous affiliations of David Doxaran include Pierre-and-Marie-Curie University & University of Paris.

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Marine ecosystems' responses to climatic and anthropogenic forcings in the Mediterranean

X. Durrieu de Madron, +93 more
TL;DR: In this article, a review of current functioning and responses of Mediterranean marine biogeochemical cycles and ecosystems with respect to key natural and anthropogenic drivers and to consider the ecosystems' responses to likely changes in physical, chemical and socio-economical forcings induced by global change and by growing anthropogenic pressure at the regional scale.
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A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters

TL;DR: In this paper, a semi-empirical single-band turbidity retrieval algorithm using the near infrared (NIR) band at 859 nm in highly turbid waters is assessed.
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Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data

TL;DR: In this article, field and satellite measurements of surface water turbidity were combined in order to study the dynamics of the turbidity maximum zone (TM) in a macrotidal estuary (the Gironde, France).
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Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery

TL;DR: In this paper, regional empirical algorithms based on in-situ data were tested to retrieve the concentration of total suspended matter and turbidity from the remote sensing reflectance, and the respective sensitivity of MODIS surface reflectance bands 1 and 2 for water quality application was investigated as well as the quality of atmospheric corrections.
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Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters

TL;DR: The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions.