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Remote Sens. 2017, 9(10), 1070;

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, Estonia
Centre for Limnology, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia
Department of Ecology and Genetics/Limnology, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden
Department of Marine Systems, School of Science, Tallinn University of Technology, Akadeemia Road 15a, 12618 Tallinn, Estonia
Author to whom correspondence should be addressed.
Received: 2 August 2017 / Revised: 22 September 2017 / Accepted: 12 October 2017 / Published: 20 October 2017
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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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. View Full-Text
Keywords: OLCI; Sentinel-3; Baltic Sea; C2RCC OLCI; Sentinel-3; Baltic Sea; C2RCC

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Toming, K.; Kutser, T.; Uiboupin, R.; Arikas, A.; Vahter, K.; Paavel, B. Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea. Remote Sens. 2017, 9, 1070.

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