scholarly journals Analysis of riverine suspended particulate matter fluxes (Gulf of Lion, Mediterranean Sea) using a synergy of ocean color observations with a 3-D hydrodynamic sediment transport model

2015 ◽  
Vol 120 (2) ◽  
pp. 942-957 ◽  
Author(s):  
Vincent Le Fouest ◽  
Malik Chami ◽  
Romaric Verney
Keyword(s):  
Particulate Matter ◽  
Sediment Transport ◽  
Mediterranean Sea ◽  
Suspended Particulate Matter ◽  
Transport Model ◽  
Ocean Color ◽  
Sediment Transport Model ◽  
Suspended Particulate ◽  
Gulf Of Lion

scholarly journals Annual cycles of chlorophyll-<i>a</i>, non-algal suspended particulate matter, and turbidity observed from space and in-situ in coastal waters

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 705-732 ◽  
Author(s):  
F. Gohin
Keyword(s):  
Particulate Matter ◽  
Continental Shelf ◽  
Mediterranean Sea ◽  
North Sea ◽  
Suspended Particulate Matter ◽  
Chlorophyll Concentration ◽  
Statistical Characteristics ◽  
Monitoring Networks ◽  
Suspended Particulate

Abstract. Sea surface temperature, chlorophyll, and turbidity are three variables of the coastal environment commonly measured by monitoring networks. The observation networks are often based on coastal stations, which do not provide a sufficient coverage to validate the model outputs or to be used in assimilation over the continental shelf. Conversely, the products derived from satellite reflectance generally show a decreasing quality shoreward, and an assessment of the limitation of these data is required. The annual cycle, mean, and percentile 90 of the chlorophyll concentration derived from MERIS/ESA and MODIS/NASA data processed with a dedicated algorithm have been compared to in-situ observations at twenty-six selected stations from the Mediterranean Sea to the North Sea. Keeping in mind the validation, the forcing, or the assimilation in hydrological, sediment-transport, or ecological models, the non-algal Suspended Particulate Matter (SPM) is also a parameter which is expected from the satellite imagery. However, the monitoring networks measure essentially the turbidity and a consistency between chlorophyll, representative of the phytoplankton biomass, non-algal SPM, and turbidity is required. In this study, we derive the satellite turbidity from chlorophyll and non-algal SPM with a common formula applied to in-situ or satellite observations. The distribution of the satellite-derived turbidity exhibits the same main statistical characteristics as those measured in-situ, which satisfies the first condition to monitor the long-term changes or the large-scale spatial variation over the continental shelf and along the shore. For the first time, climatologies of turbidity, so useful for mapping the environment of the benthic habitats, are proposed from space on areas as different as the southern North Sea or the western Mediterranean Sea, with validation at coastal stations.

scholarly journals An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

2020 ◽  
Vol 12 (13) ◽  
pp. 2172 ◽  
Author(s):  
Juliana Tavora ◽  
Emmanuel Boss ◽  
David Doxaran ◽  
Paul Hill
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Ocean Color ◽  
Major Constituent ◽  
Coastal Environments ◽  
Suspended Particulate ◽  
Spm Concentration

Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.

scholarly journals Qualitative Dynamics of Suspended Particulate Matter in the Changjiang Estuary from Geostationary Ocean Color Images: An Empirical, Regional Modeling Approach

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4186 ◽  
Author(s):  
Dinghui Shang ◽  
Huiping Xu
Keyword(s):  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Ocean Color ◽  
Changjiang Estuary ◽  
Separate Analysis ◽  
High Concentration ◽  
The Changjiang Estuary ◽  
Band Ratio ◽  
Sea Bed ◽  
Suspended Particulate

The suspended particulate matter (SPM) in Changjiang Estuary is characterized by a high concentration of significant diurnal dynamics. With a higher temporal resolution (eight images obtained per day), Geostationary Ocean Color Imager (GOCI) was selected as the primary remote sensor for the dynamics monitoring in this paper, instead of other satellite sensor working in polar orbit. Based on the characteristics of the field spectra measured in the estuary, an empirical model was established with the band ratio of Rrs745 divided by Rrs490 and proven effective in Suspended Particulate Matter (SPM) estimation (R2 = 0.9376, RMSE = 89.32 mg/L). While, Validation results showed that the model performed better in coastal turbid waters than offshore clear waters with higher chlorophyll-a concentration, stressing the importance of partitioning SPM into its major components and doing separate analysis. The hourly observations from GOCI showed that the diurnal variation magnitudes exhibited clear regional characteristics, with a maximum in the turbidity belt near the mouth and a minimum in the offshore deeper areas. In addition, comparing the monthly averaged SPM distribution with the amount of sediment discharged into the estuary, the variation in estuarine turbidity maximum zone is more likely contributed by the sediments resuspended from the sea bed that has already accumulated in the estuarine delta.

scholarly journals Influence of the Suspended Particulate Matter on the Satellite Radiance in the Sunglint Observation Geometry in Coastal Waters

2020 ◽  
Vol 12 (9) ◽  
pp. 1445
Author(s):  
Malik Chami ◽  
Morgane Larnicol ◽  
Audrey Minghelli ◽  
Sebastien Migeon
Keyword(s):  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Coastal Waters ◽  
Satellite Image ◽  
Ocean Color ◽  
Optical Measurements ◽  
Strong Wind ◽  
Turbid Waters ◽  
Suspended Particulate ◽  
Observation Geometry

The analysis of satellite ocean color data that are acquired over coastal waters is highly relevant to gain understanding of the functioning of these complex ecosystems. In particular, the estimation of the suspended particulate matter (SPM) concentrations is of great interest for monitoring the coastal dynamics. However, a high number of pixels of satellite images could be affected by the surface-reflected solar radiation, so-called the sunglint. These pixels are either removed from the data processing, which results in a loss of information about the ocean optical properties, or they are subject to the application of glint correction techniques that may contribute to increase the uncertainties in the SPM retrieval. The objective of this study is to demonstrate the high potential of exploiting satellite observations acquired in the sunglint viewing geometry for determining the water leaving radiance for SPM dominated coastal waters. For that purpose, the contribution of the water leaving radiance Lw to the satellite signal LTOA is quantified for the sunglint observation geometry using forward radiative transfer modelling. Some input parameters of the model were defined using in-situ bio-optical measurements performed in various coastal waters to make the simulations consistent with real-world observations. The results showed that the sunglint radiance is not sufficiently strong to mask the influence of the oceanic radiance at the satellite level, which oceanic radiance remains significant (e.g., 40% at 560 nm for a SPM concentration value of 9 g m−3). The influence of the sunglint radiance is even weaker for highly turbid waters and/or for strong wind conditions. In addition, the maximum radiance simulated in the sunglint region for highly turbid waters remains lower than the saturation radiances specified for the current ocean color sensors. The retrieval of Lw and SPM should thus be feasible from radiances measured in the sunglint pattern by satellite sensors, thus increasing the number of exploitable pixels within a satellite image. The results obtained here could be used as a basis for the development of inverse ocean color algorithms that would interestingly use the radiance measured in sunglint observation geometry as it has been done for other topics than the field of ocean color research.

Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager (GOCI)

2012 ◽  
Vol 47 (3) ◽  
pp. 331-345 ◽  
Author(s):  
Kevin Ruddick ◽  
Quinten Vanhellemont ◽  
Jing Yan ◽  
Griet Neukermans ◽  
Guomei Wei ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Bohai Sea ◽  
Ocean Color ◽  
The Bohai Sea ◽  
Suspended Particulate
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