How Representative Are European AERONET-OC Sites of European Marine Waters?

Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrum...

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Published in:Remote Sensing
Main Authors: Cazzaniga I., Mélin F.
Other Authors: Cazzaniga, I, Mélin, F
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2024
Subjects:
Online Access:https://hdl.handle.net/10281/496479
https://doi.org/10.3390/rs16101793
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record_format openpolar
spelling ftunivmilanobic:oai:boa.unimib.it:10281/496479 2024-09-15T17:35:12+00:00 How Representative Are European AERONET-OC Sites of European Marine Waters? Cazzaniga I. Mélin F. Cazzaniga, I Mélin, F 2024 ELETTRONICO https://hdl.handle.net/10281/496479 https://doi.org/10.3390/rs16101793 eng eng MDPI country:CH info:eu-repo/semantics/altIdentifier/wos/WOS:001231581300001 volume:16 issue:10 journal:REMOTE SENSING https://hdl.handle.net/10281/496479 doi:10.3390/rs16101793 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85194274493 AERONET-OC classification ocean color OLCI optical water type remote sensing reflectance validation info:eu-repo/semantics/article 2024 ftunivmilanobic https://doi.org/10.3390/rs16101793 2024-07-30T23:35:47Z Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance (Formula presented.) from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, (Formula presented.) spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters. Article in Journal/Newspaper Aerosol Robotic Network Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive) Remote Sensing 16 10 1793
institution Open Polar
collection Università degli Studi di Milano-Bicocca: BOA (Bicocca Open Archive)
op_collection_id ftunivmilanobic
language English
topic AERONET-OC
classification
ocean color
OLCI
optical water type
remote sensing reflectance
validation
spellingShingle AERONET-OC
classification
ocean color
OLCI
optical water type
remote sensing reflectance
validation
Cazzaniga I.
Mélin F.
How Representative Are European AERONET-OC Sites of European Marine Waters?
topic_facet AERONET-OC
classification
ocean color
OLCI
optical water type
remote sensing reflectance
validation
description Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance (Formula presented.) from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, (Formula presented.) spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters.
author2 Cazzaniga, I
Mélin, F
format Article in Journal/Newspaper
author Cazzaniga I.
Mélin F.
author_facet Cazzaniga I.
Mélin F.
author_sort Cazzaniga I.
title How Representative Are European AERONET-OC Sites of European Marine Waters?
title_short How Representative Are European AERONET-OC Sites of European Marine Waters?
title_full How Representative Are European AERONET-OC Sites of European Marine Waters?
title_fullStr How Representative Are European AERONET-OC Sites of European Marine Waters?
title_full_unstemmed How Representative Are European AERONET-OC Sites of European Marine Waters?
title_sort how representative are european aeronet-oc sites of european marine waters?
publisher MDPI
publishDate 2024
url https://hdl.handle.net/10281/496479
https://doi.org/10.3390/rs16101793
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_relation info:eu-repo/semantics/altIdentifier/wos/WOS:001231581300001
volume:16
issue:10
journal:REMOTE SENSING
https://hdl.handle.net/10281/496479
doi:10.3390/rs16101793
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85194274493
op_doi https://doi.org/10.3390/rs16101793
container_title Remote Sensing
container_volume 16
container_issue 10
container_start_page 1793
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