Showing papers by "David Doxaran published in 2022"
TL;DR: In this article , the authors used in situ and satellite Ocean Colour Remote Sensing (SOCRS) data to show the strong seasonal dynamics of the Mackenzie River plume and the spatial distribution of associated terrigenous DOC on the Beaufort Sea Shelf.
6 citations
TL;DR: The PANGAEA dataset as discussed by the authors provides paired data of optical and biogeochemical properties to support research into optical monitoring of aquatic systems, including inherent optical properties, such as absorption, scattering and beam attenuation coefficients, near-forward volume scattering function, turbidity, apparent optical properties (Secchi disc depth, substrate and water-leaving Lambert-equivalent bi-hemispherical reflectance), and bio-ochemical properties (suspended particulate matter, mineral fraction of particle mass, particle size distribution, pigment concentration, DNA metabarcoding, flow microscopy counts, and bottom type classification).
Abstract: Abstract. From 2017 to 2019, an extensive sampling campaign was conducted in Belgian inland and coastal waters, aimed at providing paired data of optical and biogeochemical properties to support research into optical monitoring of aquatic systems. The campaign was focused on inland waters, with sampling of four lakes and a coastal lagoon during the growth season, in addition to samples of opportunity from other four lakes. Campaigns also included the Scheldt estuary over a tidal cycle and two sampling campaigns in the Belgian coastal zone. Measured parameters include inherent optical properties (absorption, scattering and beam attenuation coefficients, near-forward volume scattering function, turbidity), apparent optical properties (Secchi disc depth, substrate and water-leaving Lambert-equivalent bi-hemispherical reflectance), and biogeochemical properties (suspended particulate matter, mineral fraction of particle mass, particle size distribution, pigment concentration, DNA metabarcoding, flow microscopy counts, and bottom type classification). The diversity of water bodies and environmental conditions covered a wide range of system states. The chlorophyll a concentration varied from 0.63 to 382.72 mg m−3, while the suspended particulate matter concentration varied from 1.02 to 791.19 g m−3, with mineral fraction varying from 0 to 0.95. Depending on system and season, phytoplankton assemblages were dominated by cyanobacteria, green algae (Mamiellophyceae, Pyramimonadophyceae), or diatoms. The dataset is available from https://doi.org/10.1594/PANGAEA.940240 (Castagna et al., 2022).
5 citations
TL;DR: The HYPERMAQ dataset as discussed by the authors provides biogeochemical parameters (i.e., turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (e.g., water reflectance from above water measurements) and inherent optical properties such as absorption and attenuation coefficients) from six different study areas.
Abstract: Abstract. Because of the large diversity of case 2 waters ranging
from extremely absorbing to extremely scattering waters and the complexity
of light transfer due to external terrestrial inputs, retrieving main
biogeochemical parameters such as chlorophyll-a or suspended particulate
matter concentration in these waters is still challenging. By providing
optical and biogeochemical parameters for 180 sampling stations with
turbidity and chlorophyll-a concentration ranging from 1 to 700 FNU and from
0.9 to 180 mg m−3 respectively, the HYPERMAQ dataset will contribute to
a better description of marine optics in optically complex water bodies and
can help the scientific community to develop algorithms. The HYPERMAQ
dataset provides biogeochemical parameters (i.e. turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical
properties (i.e. water reflectance from above water measurements) and inherent optical properties (i.e. absorption and attenuation coefficients)
from six different study areas. These study areas include large estuaries
(i.e. the Rio de la Plata in Argentina, the Yangtze estuary in China, and the Gironde estuary in France), inland (i.e. the Spuikom in Belgium and
Chascomùs lake in Argentina), and coastal waters (Belgium). The dataset is
available from Lavigne et al. (2022) at https://doi.org/10.1594/PANGAEA.944313.
3 citations
10 Feb 2022
TL;DR: Castagna et al. as mentioned in this paper conducted an extensive sampling campaign in Belgian inland and coastal waters, aimed at providing paired data of optical and biogeochemical properties to support research into optical monitoring of aquatic systems.
Abstract: Abstract. From 2017 to 2019, an extensive sampling campaign was conducted in Belgian inland and coastal waters, aimed at providing paired data of optical and biogeochemical properties to support research into optical monitoring of aquatic systems. The campaign was focused on inland waters, with sampling of four lakes and a coastal lagoon along the growth season, in addition to samples of opportunity of other four lakes. Campaigns also included the Scheldt estuary over a tidal cycle and two sam- pling campaigns in the Belgian coastal zone. Measured parameters include inherent optical properties (absorption, scattering and attenuation coefficients, near-forward volume scattering function, turbidity), apparent optical properties (Secchi disk depth, substrate and water-leaving Lambert-equivalent bi-hemispherical reflectance), and biogeochemical properties (suspended particulate matter, mineral fraction of particle mass, particle size distribution, pigment concentration, DNA metabarcoding, flow microscopy counts, and bottom type classification). The diversity of water bodies and environmental conditions covered a wide range of system states. The chlorophyll a concentration varied from 0.63 mgm−3 to 382.72 mgm−3, while the suspended particulate matter concentration varied from 1.02 gm−3 to 791.19 gm−3, with mineral fraction varying from 0 to 0.95. Depending on system and season, phytoplankton assemblages were dominated by cyanobacteria, green algae (Mamiellophyceae, Pyramimonadophyceae) or diatoms. The dataset is available from Castagna et al. (2022), https://www.pangaea.de/tok/c67200d99ea9bbbeadd9edec9690f937b5bacbff.
3 citations
TL;DR: In this paper , an existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over a very wide range (from 1 to more than 1000 g) encountered over such a continuum.
Abstract: This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.
3 citations
TL;DR: In this article , a simulation based on a hydro-sedimentary model was conducted for the period between summer 2010 and spring 2012 in the Gulf of Lion (northwestern Mediterranean Sea) to understand the spatial and temporal variability of sediment transport, erosion and deposition on the continental shelf and slope.
2 citations
10 Feb 2022
TL;DR: The HYPERMAQ dataset as discussed by the authors provides biogeochemical parameters (i.e., turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties, and inherent optical properties such as absorption and attenuation coeffi-25 cients) from six different study areas.
Abstract: . Because of the large diversity of case 2 waters ranging from extremely absorbing to extremely scattering 17 waters and the complexity of light transfer due to external terrestrial inputs, retrieving main biogeochemical pa- 18 rameters such as chlorophyll-a or suspended particulate matter concentration in these waters is still challenging. 19 By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a 20 concentration ranging from 1 to 700 FNU and from 0.9 to 180 mg m -3 respectively, the HYPERMAQ dataset will 21 contribute to a better description of marine optics in optically complex water bodies and can help the scientific 22 community to develop algorithms. The HYPERMAQ dataset provides biogeochemical parameters (i.e. turbidity, 23 pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (i.e. water re- 24 flectance from above water measurements) and inherent optical properties (i.e. absorption and attenuation coeffi- 25 cients) from six different study areas. These study areas include large estuaries (i.e. the Rio de la Plata in Argentina, 26 the Yangtze Estuary in China and the Gironde Estuary in France), inland (i.e. the Spuikom in Belgium and Chasco- 27 mùs lake in Argentina) and coastal waters (Belgium). The dataset is available from Lavigne et al (2022), 28 https://doi.pangaea.de/10.1594/PANGAEA.944313. 29 and located over three continents (i.e. Europe, South America and Asia). The HYPERMAQ 306 dataset includes big river estuaries characterized by high turbidity, inland lagoons with productivity ranging from 307 moderate to extreme and finally Belgian coastal waters in the North Sea characterized by the high spatio-temporal 308 variability of optical properties (Vantrepotte et al., 2012). The parameters shared in the HYPERMAQ dataset 309 include descriptors of biogeochemical conditions (i.e. [Chl-a], SPM, turbidity) as well as AOPs (i.e. water reflec- 310 tance) and IOPs (a nw and c nw ). Although this dataset does not aim to cover the whole variability of case 2 waters, 311 it provides valuable information to describe turbid and even extremely turbid waters and has the potential to help 312 the development of remote sensing algorithms. It can also contribute to the production of a larger optical database, 313 based on in situ measurements for a comprehensive description of case 2 waters.