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Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument Imagery in the Baltic Sea
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Limnology. Univ Tartu, Estonian Marine Inst, Maealuse 14, EE-12618 Tallinn, Estonia.;Estonian Univ Life Sci, Ctr Limnol, Kreutzwaldi 5, EE-51014 Tartu, Estonia.ORCID iD: 0000-0001-5190-2459
Univ Tartu, Estonian Marine Inst, Maealuse 14, EE-12618 Tallinn, Estonia..
Tallinn Univ Technol, Dept Marine Syst, Sch Sci, Akad Rd 15a, EE-12618 Tallinn, Estonia..
Tallinn Univ Technol, Dept Marine Syst, Sch Sci, Akad Rd 15a, EE-12618 Tallinn, Estonia..
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2017 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 9, no 10, article id 1070Article in journal (Refereed) Published
Abstract [en]

The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

Place, publisher, year, edition, pages
2017. Vol. 9, no 10, article id 1070
Keywords [en]
OLCI, Sentinel-3, Baltic Sea, C2RCC
National Category
Ecology
Identifiers
URN: urn:nbn:se:uu:diva-341673DOI: 10.3390/rs9101070ISI: 000414650600100OAI: oai:DiVA.org:uu-341673DiVA, id: diva2:1182184
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-02-12Bibliographically approved

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