HOW EFFICIENT CAN SENTINEL-2 DATA HELP SPATIAL MAPPING OF MUCILAGE EVENT IN THE MARMARA SEA?

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B3-2022; pp. 181 - 186
• Main Authors: Sunar, F., Dervisoglu, A., Yagmur, N., Colak, E., Kuzyaka, E., Mutlu, S.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 01-01-2022; Copernicus Publications
• Subjects: Anthropogenic factors; Chlorophyll; Chlorophyll a; Climate change; Feasibility studies; Imagery; Marine pollution; Mathematical models; Mucilages; Parameters; Pollution monitoring; Remote sensing; Research facilities; Satellite imagery; Sea pollution; Spaceborne remote sensing; Spatial data; Spatial distribution; Statistical methods; Turbidity; Urbanization; Water quality; Water quality measurements
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLIII-B3-2022-181-2022

With the repetition of mucilage event, which is triggered by many different anthropogenic, climatic and microbiological factors, in the Marmara Sea in 2021, the importance of water quality in the seas has come to the fore again. To present the spatial distribution of the mucilage, a feasibility study has been carried out with point-based water quality measurements and remote sensing data. In-situ measurements are collected routinely within the scope of the Integrated Marine Pollution Monitoring Program (DEN-IZ) which was conducted in cooperation with the Ministry of Environment, Urbanisation and Climate Change and the Scientific and Technological Research Council of Turkey - Marmara Research Center (TUBITAK-MAM). In this preliminary study, 16 in-situ measurements, 5 of which were taken from water containing mucilage, on 29 April 2021 in the Gulf of Gemlik were used. Then, univariate regression analyzes were performed in two different scenarios (i.e. 5 mucilage points and all in-situ points) with Sentinel-2 satellite imagery and in-situ water quality measurements for 2 different parameters (i.e. chlorophyll-a – Chl-a) and turbidity). According to R2 and accuracy assessment measures (f- and t- statistics etc.), the most suitable models were determined for two scenarios and two parameters. Finally, the performances of the selected models were tested with 2 different in-situ measurements and satellite images (dated 22 and 27 April) taken from dates close to the data set used; and it was concluded that the models created with 16 points were successful for both Chl-a and turbidity estimation for this preliminary study.