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...

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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:
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
North Atlantic
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
Description
Summary: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

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