Open Access
31 December 2022 Evaluation of remote sensing and modeled chlorophyll-a products of the Baltic Sea
Tuuli Soomets, Kaire Toming, Birgot Paavel, Tiit Kutser
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Abstract

The Baltic Sea is an optically very complex study object for watercolor remote sensing because of the high quantity of colored dissolved organic matter, two optically distinct phytoplankton seasons, high variability in concentrations of optically active substances, and low sun angles. Despite this, there are numerous remote sensing and modeled chlorophyll-a (Chl-a) products publicly available for the Baltic Sea. Sixteen openly accessible Chl-a products were tested with 267 in situ Chl-a measurements that were carried out in Estonian marine waters during 2016 to 2021. All modeled products and about half of the remote sensing products failed to produce reliable results. The best-performing remote sensing Chl-a product was Case2/Regional CoastColour produced from Sentinel-3 ocean and land color imager (OLCI) reflectance with R2 = 0.55, root mean squared error ( RMSE ) = 4.5 mg m − 3, mean absolute percentage error (MAPE) = 74%. In addition, eight different band ratio algorithms were applied on Sentinel-3 OLCI and Sentinel-2 multispectral instrument data. The best remote sensing band ratio algorithm was derived from top-of-atmosphere reflectance of Sentinel-3 data using 665, 709, and 754 nm bands (R2 = 0.67, RMSE = 3.9 mg m − 3, and MAPE = 63%). Our results show good suitability of Sentinel-3 for Chl-a retrieval. However, the high uncertainties suggest for the further product development and validation needs.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Tuuli Soomets, Kaire Toming, Birgot Paavel, and Tiit Kutser "Evaluation of remote sensing and modeled chlorophyll-a products of the Baltic Sea," Journal of Applied Remote Sensing 16(4), 046516 (31 December 2022). https://doi.org/10.1117/1.JRS.16.046516
Received: 28 September 2022; Accepted: 19 December 2022; Published: 31 December 2022
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Remote sensing

Water

Data modeling

Polymers

Reflectivity

Ocean optics

Atmospheric corrections

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