Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (<i>Chl-a</i>). Alas, ocean color remote sensing applications to estimate <i>Chl-a</i> in this brackish basin, characterized by large gradients in salinity and dissolved or...

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Main Authors: Vittorio E. Brando, Michela Sammartino, Simone Colella, Marco Bracaglia, Annalisa Di Cicco, Davide D’Alimonte, Tamito Kajiyama, Seppo Kaitala, Jenni Attila
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/16/3071
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spelling doaj-900720d975994a71b5f6480053a1ad1d2021-08-26T14:17:11ZengMDPI AGRemote Sensing2072-42922021-08-01133071307110.3390/rs13163071Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a RetrievalsVittorio E. Brando0Michela Sammartino1Simone Colella2Marco Bracaglia3Annalisa Di Cicco4Davide D’Alimonte5Tamito Kajiyama6Seppo Kaitala7Jenni Attila8Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133 Rome, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133 Rome, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133 Rome, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133 Rome, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133 Rome, ItalyAequora, 1600-774 Lisbon, PortugalAequora, 1600-774 Lisbon, PortugalFinnish Environment Institute (SYKE), 00790 Helsinki, FinlandFinnish Environment Institute (SYKE), 00790 Helsinki, FinlandA relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (<i>Chl-a</i>). Alas, ocean color remote sensing applications to estimate <i>Chl-a</i> in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents <i>Chl-a</i> retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (<i>R<sub>rs</sub></i>) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of <i>Chl-a</i> observed in the literature for this basin. The <i>R<sub>rs</sub></i> and <i>Chl-a</i> time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea.https://www.mdpi.com/2072-4292/13/16/3071ocean colorregional algorithmsmultilayer perceptron neural netensemble approachphytoplankton phenologyoptically complex waters
collection DOAJ
language English
format Article
sources DOAJ
author Vittorio E. Brando
Michela Sammartino
Simone Colella
Marco Bracaglia
Annalisa Di Cicco
Davide D’Alimonte
Tamito Kajiyama
Seppo Kaitala
Jenni Attila
spellingShingle Vittorio E. Brando
Michela Sammartino
Simone Colella
Marco Bracaglia
Annalisa Di Cicco
Davide D’Alimonte
Tamito Kajiyama
Seppo Kaitala
Jenni Attila
Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
Remote Sensing
ocean color
regional algorithms
multilayer perceptron neural net
ensemble approach
phytoplankton phenology
optically complex waters
author_facet Vittorio E. Brando
Michela Sammartino
Simone Colella
Marco Bracaglia
Annalisa Di Cicco
Davide D’Alimonte
Tamito Kajiyama
Seppo Kaitala
Jenni Attila
author_sort Vittorio E. Brando
title Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
title_short Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
title_full Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
title_fullStr Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
title_full_unstemmed Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
title_sort phytoplankton bloom dynamics in the baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-08-01
description A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (<i>Chl-a</i>). Alas, ocean color remote sensing applications to estimate <i>Chl-a</i> in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents <i>Chl-a</i> retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (<i>R<sub>rs</sub></i>) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of <i>Chl-a</i> observed in the literature for this basin. The <i>R<sub>rs</sub></i> and <i>Chl-a</i> time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea.
topic ocean color
regional algorithms
multilayer perceptron neural net
ensemble approach
phytoplankton phenology
optically complex waters
url https://www.mdpi.com/2072-4292/13/16/3071
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