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Retrieval of suspended particulate matter from turbidity - model development, validation, and application to MERIS data over the Baltic Sea
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
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Number of Authors: 5
2017 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 38, no 7, 1983-2003 p.Article in journal (Refereed) Published
Abstract [en]

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. 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(-3) which 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 provides a robust and cost-efficient method to determine SPM from in situ turbidity measurements (or vice versa). The developed SPM model predicts SPM concentration with high quality despite the high coloured dissolved organic matter (CDOM) range in the Baltic Sea. By applying the developed SPM model to already existing remote sensing data (MERIS/Envisat) and most importantly to a new generation of satellite sensors (in particular OLCI on board the Sentinel-3), it is possible to derive turbidity for the Baltic Sea.

Place, publisher, year, edition, pages
2017. Vol. 38, no 7, 1983-2003 p.
National Category
Biological Sciences
Research subject
Marine Ecology
Identifiers
URN: urn:nbn:se:su:diva-141264DOI: 10.1080/01431161.2016.1230289ISI: 000394652900012OAI: oai:DiVA.org:su-141264DiVA: diva2:1088144
Available from: 2017-04-11 Created: 2017-04-11 Last updated: 2017-04-11Bibliographically approved
In thesis
1. Bio-optics, satellite remote sensing and Baltic Sea ecosystems: Applications for monitoring and management
Open this publication in new window or tab >>Bio-optics, satellite remote sensing and Baltic Sea ecosystems: Applications for monitoring and management
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Earth observation satellites cover large areas with frequent temporal repetition and provide us with new insight into ocean and coastal processes. Ocean colour measurements from satellite remote sensing are linked to the bio-optics, which refers to the light interactions with living organisms and dissolved and suspended constituents in the aquatic environment. Human pressures have changed the aquatic ecosystems, by, for example, the increased input of nutrient and organic matter leading to eutrophication. This thesis aims to study and develop the link between bio-optical data and the remote sensing method to the monitoring and management of the Baltic Sea. The results are applied to the European Union’s Water Directives, and the Baltic Sea Action Plan from the Helsinki commission. In paper I indicators for eutrophication, chlorophyll-a concentration and Secchi depth were evaluated as a link to remote sensing observations. Chlorophyll-a measurements from an operational satellite service (paper I) were compared to conventional ship-based monitoring in paper II and showed high correlations to the in situ data. The results in paper I, II and IV show that the use of remote sensing can improve both the spatial and temporal monitoring of water quality. The number of observations increased when also using satellite data, thus facilitating the assessment of the ecological and environmental status within the European Union’s water directives. The spatial patterns make it possible to study the changes of e.g. algae blooms and terrestrial input on larger scales. Furthermore, the water quality products from satellites can offer a more holistic and easily accessible view of the information to decision makers and end-users. In paper III variable relationships between in situ bio-optical parameters, such as coloured dissolved organic matter (CDOM), dissolved organic carbon, salinity and Secchi depth, were found in different parts of the Baltic Sea. In paper IV an in situ empirical model to retrieve suspended particulate matter (SPM) from turbidity was developed and applied to remote sensing data. The use of Secchi depth as an indicator for eutrophication linked to the concentrations of chlorophyll-a and SPM and CDOM absorption was investigated in paper V. The variations in Secchi depth were affected differently by the mentioned parameters in the different regions. Therefore, one must also consider those when evaluating changes in Secchi depth and for setting target levels for water bodies. This thesis shows good examples on the benefits of incorporating bio-optical and remote sensing data to a higher extent within monitoring and management of the Baltic Sea.

Place, publisher, year, edition, pages
Stockholm: Department of Ecology, Environment and Plant Sciences, Stockholm University, 2015. 59 p.
Keyword
Bio-optics, Remote sensing, MERIS, Eutrophication, Baltic Sea, Monitoring, Management, WFD, MSFD, HELCOM, Chlorophyll-a, Secchi depth, Coloured Dissolved Organic Matter, Suspended Particulate Matter
National Category
Ecology
Research subject
Marine Ecology
Identifiers
urn:nbn:se:su:diva-119578 (URN)978-91-7649-219-2 (ISBN)
Public defence
2015-10-02, William-Olssonsalen, Geovetenskapens hus, Svante Arrhenius väg 14, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish National Space Board, Dnr. 165/11EU, European Research Council, SPICOSA 36992Nordic Council of Ministers, 80106 & 42041EU, FP7, Seventh Framework Programme, WaterS 251527Baltic Ecosystem Adaptive Management (BEAM), 4315403Ecosystem dynamics in the Baltic Sea in a changing climate perspective - ECOCHANGE, 4315403Swedish Environmental Protection Agency, WATERS
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Manuscript. Paper 5: Manuscript.

Available from: 2015-09-10 Created: 2015-08-17 Last updated: 2017-04-11Bibliographically approved

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