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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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 |
work_keys_str_mv |
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