Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

peer reviewed A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean a...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Olmedo, Estrella, Taupier-Letage, Isabelle, Turiel, Antonio, Alvera Azcarate, Aida
Other Authors: FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2018
Subjects:
Online Access:https://orbi.uliege.be/handle/2268/221425
https://orbi.uliege.be/bitstream/2268/221425/1/olmedo2018.pdf
https://doi.org/10.3390/rs10030485
id ftorbi:oai:orbi.ulg.ac.be:2268/221425
record_format openpolar
spelling ftorbi:oai:orbi.ulg.ac.be:2268/221425 2024-10-20T14:10:39+00:00 Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis Olmedo, Estrella Taupier-Letage, Isabelle Turiel, Antonio Alvera Azcarate, Aida FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège 2018-03 https://orbi.uliege.be/handle/2268/221425 https://orbi.uliege.be/bitstream/2268/221425/1/olmedo2018.pdf https://doi.org/10.3390/rs10030485 en eng MDPI http://www.mdpi.com/2072-4292/10/3/485/html urn:issn:2072-4292 https://orbi.uliege.be/handle/2268/221425 info:hdl:2268/221425 https://orbi.uliege.be/bitstream/2268/221425/1/olmedo2018.pdf doi:10.3390/rs10030485 open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Remote Sensing, 10 (3), 485 (2018-03) sea surface salinity remote sensing Mediterranean Sea SMOS alboran sea data processing quality assessment Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique journal article http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article peer reviewed 2018 ftorbi https://doi.org/10.3390/rs10030485 2024-09-27T07:01:35Z peer reviewed A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR). Improving sea surface salinity estimates through multivariate and multisensor analyses Article in Journal/Newspaper North Atlantic University of Liège: ORBi (Open Repository and Bibliography) Remote Sensing 10 3 485
institution Open Polar
collection University of Liège: ORBi (Open Repository and Bibliography)
op_collection_id ftorbi
language English
topic sea surface salinity
remote sensing
Mediterranean Sea
SMOS
alboran sea
data processing
quality assessment
Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
spellingShingle sea surface salinity
remote sensing
Mediterranean Sea
SMOS
alboran sea
data processing
quality assessment
Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
Olmedo, Estrella
Taupier-Letage, Isabelle
Turiel, Antonio
Alvera Azcarate, Aida
Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
topic_facet sea surface salinity
remote sensing
Mediterranean Sea
SMOS
alboran sea
data processing
quality assessment
Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
description peer reviewed A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR). Improving sea surface salinity estimates through multivariate and multisensor analyses
author2 FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège
format Article in Journal/Newspaper
author Olmedo, Estrella
Taupier-Letage, Isabelle
Turiel, Antonio
Alvera Azcarate, Aida
author_facet Olmedo, Estrella
Taupier-Letage, Isabelle
Turiel, Antonio
Alvera Azcarate, Aida
author_sort Olmedo, Estrella
title Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
title_short Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
title_full Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
title_fullStr Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
title_full_unstemmed Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
title_sort improving smos sea surface salinity in the western mediterranean sea through multivariate and multifractal analysis
publisher MDPI
publishDate 2018
url https://orbi.uliege.be/handle/2268/221425
https://orbi.uliege.be/bitstream/2268/221425/1/olmedo2018.pdf
https://doi.org/10.3390/rs10030485
genre North Atlantic
genre_facet North Atlantic
op_source Remote Sensing, 10 (3), 485 (2018-03)
op_relation http://www.mdpi.com/2072-4292/10/3/485/html
urn:issn:2072-4292
https://orbi.uliege.be/handle/2268/221425
info:hdl:2268/221425
https://orbi.uliege.be/bitstream/2268/221425/1/olmedo2018.pdf
doi:10.3390/rs10030485
op_rights open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.3390/rs10030485
container_title Remote Sensing
container_volume 10
container_issue 3
container_start_page 485
_version_ 1813450625734672384