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Water 2018, 10(10), 1428; https://doi.org/10.3390/w10101428

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

1
Department of Physics and Technology, UiT the Arctic University of Norway, P.O. Box 6050 Langnes, NO-9037 Tromsø, Norway
2
Balaton Limnological Institute, Hungarian Academy of Science, Centre for Ecological Research, Klebelsberg K. street 3, 8237 Tihany, Hungary
*
Author to whom correspondence should be addressed.
Received: 23 July 2018 / Revised: 24 September 2018 / Accepted: 4 October 2018 / Published: 11 October 2018
(This article belongs to the Special Issue Novel Lake Water Quality Monitoring Strategies)
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Abstract

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology. View Full-Text
Keywords: shallow lake; Chl-a; CDOM; TSM; Gaussian process regression; automatic model selection algorithm shallow lake; Chl-a; CDOM; TSM; Gaussian process regression; automatic model selection algorithm
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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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Blix, K.; Pálffy, K.; R. Tóth, V.; Eltoft, T. Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI. Water 2018, 10, 1428.

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