FN Archimer Export Format
PT J
TI Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties
BT 
AF Thouvenin-Masson, Clovis
   Boutin, Jacqueline
   Vergely, Jean-Luc
   Reverdin, Gilles
   Martin, Adrien C.H.
   Guimbard, Sebastien
   Reul, Nicolas
   Sabia, Roberto
   Catany, Rafael
   Hembise Fanton-d’Andon, Odile
AS 1:1,2,3;2:1;3:2;4:1;5:4;6:5;7:6;8:7;9:8;10:2;
FF 1:;2:;3:;4:;5:;6:;7:PDG-ODE-LOPS-SIAM;8:;9:;10:;
C1 LOCEAN/IPSL Laboratory, Sorbonne University, SU-CNRS–IRD–MNHN, 75005 Paris, France
   ACRI-st, 06904 Sophia-Antipolis, France
   CNES (Centre National des Études Spatiales), 31401 Toulouse, France
   National Oceanography Centre, Southampton SO14 3ZH, UK
   Ocean Scope, 29200 Brest, France
   IFREMER (Institut Français de Recherche Pour l’Exploitation de la Mer), 29280 Plouzané, France
   Telespazio-UK for ESA, ESRIN, 00044 Frascati, Italy
   ARGANS Ltd., Plymouth PL6 8BU, UK
C2 UNIV SORBONNE, FRANCE
   ACRI-ST, FRANCE
   CNES, FRANCE
   NOC, UK
   OCEAN SCOPE, FRANCE
   IFREMER, FRANCE
   TELESPAZIO UK, ITALY
   ARGANS LTD, UK
SI TOULON
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   DOAJ
   copubli-france
   copubli-europe
   copubli-univ-france
IF 5
TC 3
UR https://archimer.ifremer.fr/doc/00765/87739/93285.pdf
   https://archimer.ifremer.fr/doc/00765/87739/93286.zip
LA English
DT Article
DE ;sea surface salinity;sampling mismatch;sub footprint variability;uncertainty;validation
AB alidation 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. 
PY 2022
PD APR
SO Remote Sensing
SN 2072-4292
PU MDPI AG
VL 14
IS 8
UT 000787400100001
DI 10.3390/rs14081878
ID 87739
ER

EF