CEC-Locean L3 Debiased v3

De-biased SMOS SSS L3 v3 maps generated by LOCEAN/ACRI-ST Expertise Center
J. Boutin (jb@locean-ipsl.upmc.fr)– JL. Vergely (Jean-Luc.Vergely@acri-st.fr)
 5 July 2018


LOCEAN and ACRI-st work as Ocean Salinity Center of Expertise for CATDS (CATDS CEC-OS) in order to improve methodologies to be implemented in the future in the near real time CATDS processing chain (CATDS-CPDC). They have derived a methodology for correcting systematic SSS biases. Feedbacks from users about the quality of these new products are very welcome, as they are experimental.

This third version of Level 3 SMOS SSS corrected from systematic biases uses an improved ‘de-biasing’ technique: with respect to version 2 (see a full description of the method in Boutin et al. RSE  2018), the adjustment of the long term mean SMOS SSS in very dynamical areas, like in river plumes, and the bias correction at high latitudes have been improved.

The successive evolutions of the corrections are recalled below:


Main evolutions

Main improvements


Corresponding CATDS Near Real TIME products


= V2 + SSS natural variability varying seasonally; latitudinal bias correction applied everywhere; SSS correction at low SST; improved absolute correction

=V2 +  improved adjustment of land-sea biases close to coast; adjustment of high latitudinal biases

In development (to be released soon; global ocean coverage)


= V1 + SSS natural variability varying spatially; no latitudinal bias correction outside 47S-47N

= V1 + improved land-sea contamination in very dynamic areas

Boutin et al., RSE, 2018

L3Q products (RE05 and real time CPDC processings; limited to 47°N-47°S)


= V0 + seasonal latitudinal correction (same SSS natural variability everywhere)

= V0+ Reduced latitudinal biases



Reduced land-sea contamination

Kolodziejczyk et al., 2016


Introduction to the ‘De-biasing’ method:

When considering monthly SSS anomalies, with respect to a SMOS monthly climatology, the precision of SMOS SSS monthly anomalies is on the order of 0.2 pss (Boutin et al. 2016); working in terms of monthly anomalies, removes most of the biases occurring around continents and varying latitudinally. In view of these good results, we have developed a method that corrects SMOS SSS systematic biases by preserving the temporal SMOS SSS dynamic. We recall at the end of this note the principle of the method. In version 3, the correction has been updated by taking into account the seasonal variability of SSS natural variability, the non-Gaussianity of the SSS distribution and by refining the latitudinal correction at high latitude. Hence main changes with respect to version 2 are expected in fresh areas and at high latitudes. 9day and 18day products are delivered. These maps are provided every 4 days from 01/2010 to 12/2017 and are derived from a combination of ascending and descending orbits. Debiased SSS are temporally averaged using a slipping Gaussian kernel with a full width at half maximum of 9 days (9 day product) and of 18 days (18 days product). Maps are at a spatial resolution 25x25km2; a median filtering over nearest neighbors is applied. They also contain an estimation of the mean error of the salinities (field eSSS) obtained from the spatial standard deviation of the SSS in the 50km radius around each grid node. This error estimate also contains spatial natural variability and should only be considered as a qualitative indicator (e.g. larger error expected in areas contaminated by RFI).

Summary of the methodology:

The SMOS sea surface salinities (SSS) are affected by biases coming from various unphysical contaminations such as the so-called land-sea contamination and latitudinal biases likely due to the thermal drift of the instrument. These biases are relatively weak and have almost no impact on soil moisture retrieval. On the contrary, for salinity estimation, the impact is non-negligible and can reach more than 1 salinity unit in some regions close to the coasts.

These biases are not easy to characterize because they exhibit very strong spatial gradients and they depend on the coast orientation in the Field Of View (FOV). Moreover, these biases are dependent on the position on the swath.

The zero order bias is the so-called Ocean Target Transformation (OTT) which is a correction applied at brightness temperature level. Here, we consider remaining biases on the SSS retrieved from brightness temperatures corrected with an OTT. SSS maps are obtained from a correction applied at salinity level. This correction is determined using the January 2011-December 2017 period of SMOS observations. Indeed, it is possible to build salinity time series for each grid point obtained in various observation conditions (depending on the orbit direction and at various distance from the center of the track) and check, from a statistical point of view, the consistency of the salinities.

The first step of this empirical approach is to characterize as accurately as possible these biases as a function of the dwell line position. We first characterize the seasonal variation of the latitudinal biases using SSS further than 800km from the coast (in the Pacific Ocean up to v2, in the Atlantic Ocean in v3). We look for the dwell line (i.e. across track position) the least affected by latitudinal biases (at XXkm for the center of the swath) and we adjust all the SSS for a latitude and time varying bias estimated from biases averages with respect to the reference dwell line. The second step is to correct for biases in the vicinity of land. We have found that these biases vary little in time, and can be characterized according to the grid point geographical location (latitude, longitude) and to its location across track. If we assume that the salinity at a given grid point varies within a given range (defined by the SSS natural variability plus the SMOS SSS noise) during a given period, then, the different satellite passes crossing the same pixel during the given period should give consistent salinities. Additionally, assuming that the bias does not vary temporally for a given grid point implies that the relative salinity variation over the whole period should be the same whatever the distance to the center of the track. It is then possible to estimate the relative biases between the various distances across track and to obtain, with a least squares approach, a time series of relative salinity variations obtained from all the satellite passes. In the CATDS CEC LOCEAN debiased products version 0 (delivered in March 2015) only systematic biases near continents were removed. Version 1 (delivered in July 2016), has been updated to remove a latitudinal bias. The main difference between the debias_v1 version and the debias_v2 version (delivered in May 2017), is the SSS natural variability between the various SMOS SSS measured within 18 days at the same latitude, longitude: in debias_v2 version, we take into account an estimate of the natural variability expected from SMOS observed SSS while in debias_v1 version only a geographical constant noise on SMOS SSS was considered. In version 3, the natural SSS variability vary spatially and seasonally. Hence the debias_v3 version better preserves SSS natural variability especially close to river plumes. Note that the across track relative bias estimate does not use any external climatology. It allows minimizing relative biases between SMOS SSS retrieved at various distances across track and on ascending or descending orbits.

These relative salinity variations are then converted, in a last step, to salinities by adding a single constant determined, in each pixel, from SSS statistical distribution over the whole period (SMOS SSS distribution compared to ISAS SSS (see a description of ISAS methodology on http://www.umr-lops.fr/SNO-Argo/Products/ISAS-T-S-fields). This last step only determines the absolute SSS calibration in each grid point; the SMOS SSS temporal variation is independent of this adjustment. Up to version 2, the median of SMOS SSS over the whole study period was adjusted to the median of ISAS SSS. In version 3, in order to avoid incorrect adjustments in very dynamical river plumes not well captured by Argo floats and hence by ISAS optimal interpolation, the adjustment is made using upper quantiles of ISAS and SMOS SSS distributions over 2011-2017.


Boutin, J., N. Martin, N. Kolodziejczyk, and G. Reverdin, 2016, Interannual anomalies of SMOS sea surface salinity, Remote Sensing of Environment, doi:http://dx.doi.org/10.1016/j.rse.2016.02.053.

Boutin J., Vergely J.L., Marchand S., D'Amico F., Hasson A., Kolodziejczyk Nicolas, Reul Nicolas, Reverdin G., Vialard J. (2018). New SMOS Sea Surface Salinity with reduced systematic errors and improved variability. Remote Sensing of Environment, 214, 115-134. Publisher's official version : http://doi.org/10.1016/j.rse.2018.05.022 , Open Access version : http://archimer.ifremer.fr/doc/00441/55254/

Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin Mitigation of systematic errors in SMOS sea surface salinity, 2016, Remote Sensing of Environment, doi:http://dx.doi.org/10.1016/j.rse.2016.02.061.

Data policy

The CATDS data are freely distributed. However, when using these data in a publication, please use the following reference and acknowledgement :

Boutin Jacqueline, Vergely Jean-Luc, Khvorostyanov Dmitry (2018). SMOS SSS L3 maps generated by CATDS CEC LOCEAN. debias V3.0. SEANOE. http://doi.org/10.17882/52804#57467

Boutin J., Vergely J.L., Marchand S., D'Amico F., Hasson A., Kolodziejczyk Nicolas, Reul Nicolas, Reverdin G., Vialard J. (2018). New SMOS Sea Surface Salinity with reduced systematic errors and improved variability. Remote Sensing of Environment, 214, 115-134. Publisher's official version : http://doi.org/10.1016/j.rse.2018.05.022 , Open Access version : http://archimer.ifremer.fr/doc/00441/55254/

"The L3_DEBIAS_LOCEAN_v3 Sea Surface Salinity maps have been produced by LOCEAN/IPSL (UMR CNRS/UPMC/IRD/MNHN) laboratory and ACRI-st company that participate to the Ocean Salinity Expertise Center (CECOS) of Centre Aval de Traitement des Donnees SMOS (CATDS). This product is distributed by the Ocean Salinity Expertise Center (CECOS) of the CNES-IFREMER Centre Aval de Traitement des Donnees SMOS (CATDS), at IFREMER, Plouzane (France)."

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The CATDS-CEC Locean research products are freely available on FTP :
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password : catds2010

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