David Doxaran

Bio: David Doxaran is an academic researcher from University of Bordeaux. The author has contributed to research in topics: Spectral bands & Colored dissolved organic matter. The author has an hindex of 5, co-authored 7 publications receiving 742 citations. Previous affiliations of David Doxaran include Plymouth Marine Laboratory.

Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios

Abstract: Variations of sediment type (grain size and refractive index) and changing illumination conditions affect the reflectance signal of coastal waters and limit the accuracy of sediment-concentration estimations from remote-sensing measurements. These effects are analyzed from numerous in situ remote-sensing measurements carried out in the Gironde and Loire Estuaries and then reduced and partly eliminated when reflectance ratios between the near infrared and the visible are considered. These ratios showed high correlation with the sediment concentration. On the basis of the obtained relationships, performing correspondence functions were established that allow an accurate estimation of suspended sediments in the estuaries from Systeme Probatoire d'Observation de la Terre, Landsat, and Sea-Viewing Wide Field-of-View Sensor data, independently of the date of acquisition.

Apparent and inherent optical properties of turbid estuarine waters: measurements, empirical quantification relationships, and modeling

Abstract: Spectral measurements of remote-sensing reflectance (Rrs) and absorption coefficients carried out in three European estuaries (Gironde and Loire in France, Tamar in the UK) are presented and analyzed. Typical Rrs and absorption spectra are compared with typical values measured in coastal waters. The respective contributions of the water constituents, i.e., suspended sediments, colored dissolved organic matter, and phytoplankton (characterized by chlorophyll-a), are determined. The Rrs spectra are then reproduced with an optical model from the measured absorption coefficients and fitted backscattering coefficients. From Rrs ratios, empirical quantification relationships are established, reproduced, and explained from theoretical calculations. These quantification relationships were established from numerous field measurements and a reflectance model integrating the mean values of the water constituents' inherent optical properties. The model's sensitivity to the biogeochemical constituents and to their nature and composition is assessed.

Use of a Spectralon panel to measure the downwelling irradiance signal: case studies and recommendations.

Abstract: Field determinations of the remote sensing reflectance signal are necessary to validate ocean color satellite sensors. The measurement of the above-water downwelling irradiance signal Ed(0+) is commonly made with a reference plaque of a known reflectance. The radiance reflected by the plaque (L(dspec)) can be used to determine Ed(0+) if the plaque is assumed to be near Lambertian. To test this assumption, basic experiments were conducted on a boat under changing sky conditions (clear, cloudy, covered) and with different configurations for simultaneous measurements of L(dspec) and Ed(0+). For all measurement configurations, results were satisfactory under a clear sky. Under cloudy or covered skies, shadow effects on the plaque induced errors up to 100% in the determination of Ed(0+). An appropriate measurement configuration was defined, which enabled Ed(0+) to be determined with an accuracy of better than +/- 15% regardless of the sky conditions.

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.

Abstract: Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters

Abstract: The empirical approach of remote sensing has a proven capability to provide timely and accurate information on inland and near-coastal transitional waters. This article gives a thorough review of empirical algorithms for quantitatively estimating a variety of parameters from space-borne, airborne and in situ remote sensors in inland and transitional waters, including chlorophyll-a, total suspended solids, Secchi disk depth (z SD), turbidity, absorption by coloured dissolved organic matter (a CDOM) and other parameters, for example, phycocyanin. Current remote-sensing instruments are also reviewed. The theoretical basis of the empirical algorithms is given using fundamental bio-optical theory of the inherent optical properties (IOPs). Bands, band ratios and band arithmetic algorithms that could be used to produce common biogeophysical products for inland/transitional waters are identified. The article discusses the potential role that empirical algorithms could play alongside more advanced model-based algo...