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Article

Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties

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LOCEAN/IPSL Laboratory, Sorbonne University, SU-CNRS–IRD–MNHN, 75005 Paris, France
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ACRI-st, 06904 Sophia-Antipolis, France
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CNES (Centre National des Études Spatiales), 31401 Toulouse, France
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National Oceanography Centre, Southampton SO14 3ZH, UK
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Ocean Scope, 29200 Brest, France
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IFREMER (Institut Français de Recherche Pour l’Exploitation de la Mer), 29280 Plouzané, France
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Telespazio-UK for ESA, ESRIN, 00044 Frascati, Italy
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ARGANS Ltd., Plymouth PL6 8BU, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Philippe Waldteufel, Yann H. Kerr and Christine Gommenginger
Remote Sens. 2022, 14(8), 1878; https://doi.org/10.3390/rs14081878
Received: 14 February 2022 / Revised: 22 March 2022 / Accepted: 28 March 2022 / Published: 13 April 2022
Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated. View Full-Text
Keywords: sea surface salinity; sampling mismatch; sub footprint variability; uncertainty; validation sea surface salinity; sampling mismatch; sub footprint variability; uncertainty; validation
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MDPI and ACS Style

Thouvenin-Masson, C.; Boutin, J.; Vergely, J.-L.; Reverdin, G.; Martin, A.C.H.; Guimbard, S.; Reul, N.; Sabia, R.; Catany, R.; Hembise Fanton-d’Andon, O. Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties. Remote Sens. 2022, 14, 1878. https://doi.org/10.3390/rs14081878

AMA Style

Thouvenin-Masson C, Boutin J, Vergely J-L, Reverdin G, Martin ACH, Guimbard S, Reul N, Sabia R, Catany R, Hembise Fanton-d’Andon O. Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties. Remote Sensing. 2022; 14(8):1878. https://doi.org/10.3390/rs14081878

Chicago/Turabian Style

Thouvenin-Masson, Clovis, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Adrien C.H. Martin, Sébastien Guimbard, Nicolas Reul, Roberto Sabia, Rafael Catany, and Odile Hembise Fanton-d’Andon. 2022. "Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties" Remote Sensing 14, no. 8: 1878. https://doi.org/10.3390/rs14081878

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