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.

Woźniak, Sławomir B.; Meler, Justyna; Lednicka, Barbara; Zdun, Agnieszka; Stoń-Egiert, Joanna

doi:10.5697/oc.53-3.691

Cited by 32 publications

(25 citation statements)

References 25 publications

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“…2012). In other areas of the Baltic Sea, such as the Vistula Lagoon, Gulf of Gdansk, Gulf of Riga, and Gulf of Finland, the in situ SPM concentrations range from 0.4-16.0, 10.0-24.2, 0.8-6.0, and 0.8-2.6 g m −3 , respectively (Woźniak et al 2011;Raag, Sipelgas, and Uiboupin 2014;Vazyulya et al 2014;Koponen et al 2007). Ohde, Siegel, and Gerth (2007) measured a range of 0.4-9.0 g m −3 in the Arkonian Sea, the Bornholm Sea, and the Gotland Sea.…”

Section: Discussionmentioning

confidence: 99%

Retrieval of suspended particulate matter from turbidity – model development, validation, and application to MERIS data over the Baltic Sea

Kari

Kratzer

Beltrán-Abaunza

et al. 2016

International Journal of Remote Sensing

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(2017) Retrieval of suspended particulate matter from turbidity -model development, validation, and application to MERIS data over the Baltic Sea, International Journal of Remote Sensing, 38:7, 1983, DOI: 10.1080/01431161.2016 Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method.Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R 2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m -3which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian dataset with r = 0.94 (RMS = 29.5% and MNB = −1.5%; n = 35). Then, the turbidity was derived from the MERIS standard SPM product using the new in situ SPM model, but retrieving turbidity from SPM instead. The derived image was then compared to existing in situ data and showed to be in the right range of values for each sub-area. The new SPM model REMOTE SENSING, 2017 VOL. 38, NO. 7, 1983 http://dx.doi.org/10.1080/01431161.2016 © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.provides a robust and cost-efficient method to determine SPM from in situ turbidity measurements (or vice versa). The developed SPM model...

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Section: Discussionmentioning

confidence: 99%

Retrieval of suspended particulate matter from turbidity – model development, validation, and application to MERIS data over the Baltic Sea

Kari

Kratzer

Beltrán-Abaunza

et al. 2016

International Journal of Remote Sensing

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“…Particulate beam attenuation (c p ), backscattering (b bp ), and absorption (a p ) coefficients are IOPs used as optical proxies of POC (Gardner et al, 1993;Stramski et al, 1999;Wozniak et al, 2011). The advantage of applying the IOPs as POC proxies is that these optical properties are retrievable from space and/or by autonomous platforms (Lee et al, 1996;Bishop et al, 2002), which allows prediction of POC-fluxes with a greater temporal and spatial resolution than conventional methods (e.g., Bishop et al, 2004;Bishop and Wood, 2009;Briggs et al, 2011;Estapa et al, 2013;Dall'Olmo and Mork, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Optical Proxies of Particulate Organic Carbon across the Surface Atlantic Ocean

et al. 2017

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Empirical relationships between particulate organic carbon (POC) and inherent optical properties (IOPs) are required for estimating POC from ocean-color remote sensing and autonomous platforms. The main relationships studied are those between POC and particulate attenuation (c p) and backscattering (b bp) coefficients. The parameters of these relationships can however differ considerably due to differences in the methodologies applied for measuring IOPs and POC as well as variations in particle characteristics. Therefore, it is important to assess existing relationships and explore new optical proxies of POC. In this study, we evaluated empirical relationships between surface POC and IOPs (c p , b bp and the particulate absorption coefficient, a p) using an extensive dataset collected during two Atlantic Meridional Transect (AMT 19 and 22) cruises spanning a wide range of oceanographic regimes. IOPs and POC were measured during the two cruises using consistent methodologies. To independently assess the accuracy of the POC-IOPs relationships, we predicted surface POC for AMT-22 using relationships developed based on independent data from AMT-19. We found typical biases in predicting POC ranging between 2-3, 4-9, and 6-13% for c p , b bp , and a p , respectively, and typical random uncertainties of 20-30%. We conclude that (1) accurate POC-c p and POC-b bp relationships were obtained due to the consistent methodologies used to estimate POC and IOPs and (2) a p could be considered as an alternative optical proxy for POC in open-ocean waters, only if all physiological variability in the POC:chl ratio could be modeled and used to correct a p .

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“…Moreover, the concentration of SPM influences the optical properties of the sea water. According to Wozniak et al, 28 SPM concentrations vary between 0.36 and 15.7 g∕m 3 with a mean value of 2.42 g∕m 3 .…”

Section: Study Areamentioning

confidence: 99%

Comparison of satellite chlorophyll a algorithms for the Baltic Sea

Woźniak¹,

Bradtke²,

Krężel³

2014

J. Appl. Remote Sens

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Chlorophyll a (chl-a) products calculated using medium resolution imaging spectrometer (MERIS) data were tested. The satellite products were compared to chl-a concentrations measured in surface waters between 2003 and 2011 throughout the Baltic Sea. Image processing was performed with two neural-network-based MERIS data processors: the Case-2 Water Properties processor developed at the Freie Universität Berlin (FUB) and the Case-2 Regional processor of the German Institute for Coastal Research (C2R). Additionally, two algorithms for deriving chl-a concentrations from atmospherically corrected reflectances originally designed for Moderate Resolution Imaging Spectroradiometer and Sea-viewing Wide Field-of-view Sensor radiometers and adapted to Baltic Sea conditions were tested (algorithms denoted further as MD and SW respectively). The effectiveness of the Improved Contrast between Ocean and Land (ICOL) processor was also verified. Our results showed that the accuracy of chl-a concentration retrieval from satellite data varies depending on the location of the area. The difference in the statistical error between results from optically different coastal and open sea waters was as high as 200%. The most accurate results for the coastal zone were noted for the standard chl-a FUB processor product, while in open sea waters the highest accuracy was noted for the MD and SW algorithms with reflectance derived from the FUB processor. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Cited by 32 publications

References 25 publications

Retrieval of suspended particulate matter from turbidity – model development, validation, and application to MERIS data over the Baltic Sea

Retrieval of suspended particulate matter from turbidity – model development, validation, and application to MERIS data over the Baltic Sea

Evaluating Optical Proxies of Particulate Organic Carbon across the Surface Atlantic Ocean

Comparison of satellite chlorophyll a algorithms for the Baltic Sea

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