scholarly journals Inherent optical properties of suspended particulate matter in the southern Baltic Sea**Financial support for this study was provided by research project grant No. N306 2838 33 awarded to S.B. Woźniak by the Polish Ministry of Science and Higher Education and by Statutory Research Programme No. I.1 at the Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland.

Oceanologia ◽  
2011 ◽  
Vol 53 (3) ◽  
pp. 691-729 ◽  
Author(s):  
Sławomir B. Woźniak ◽  
Justyna Meler ◽  
Barbara Lednicka ◽  
Agnieszka Zdun ◽  
Joanna Stoń-Egiert
2019 ◽  
Vol 19 (5) ◽  
pp. 2580-2593 ◽  
Author(s):  
Paul A. Bukaveckas ◽  
Marija Katarzyte ◽  
Anne Schlegel ◽  
Renalda Spuriene ◽  
Todd Egerton ◽  
...  

2020 ◽  
Vol 12 (13) ◽  
pp. 2172 ◽  
Author(s):  
Juliana Tavora ◽  
Emmanuel Boss ◽  
David Doxaran ◽  
Paul Hill

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.


Oceanology ◽  
2018 ◽  
Vol 58 (6) ◽  
pp. 856-869 ◽  
Author(s):  
V. N. Lukashin ◽  
V. A. Krechik ◽  
A. A. Klyuvitkin ◽  
D. P. Starodymova

2019 ◽  
Vol 11 (19) ◽  
pp. 2283 ◽  
Author(s):  
Nariane Bernardo ◽  
Alisson do Carmo ◽  
Edward Park ◽  
Enner Alcântara

Suspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tietê River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m3, turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since Kd is not susceptible to the saturation issue encountered when using remote sensing reflectance (Rrs), we estimate SPM concentrations via Kd. Kd was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using Kd(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%.


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