|Author(s)||Zine S1, Boutin J1, Font J2, Reul Nicolas3, Waldteufel P4, Gabarro C2, Tenerelli Joseph3, Petitcolin F5, Vergely J5, Talone M2, Delwart S6|
|Affiliation(s)||1 : Univ Paris 06, CNRS, Inst Rech Dev, Museum Natl Hist Nat,UMR,LOCEAN, F-75252 Paris, France.
2 : CSIC, CMIMA, Inst Ciencies Mar, E-08003 Barcelona, Spain.
3 : IFREMER, F-29280 Plouzane, France.
4 : CNRS, Serv Aeron, F-91371 Verrieres Le Buisson, France.
5 : ACRI ST, F-06904 Sophia Antipolis, France.
6 : European Space Agcy, European Space Res & Technol Ctr, NL-2200 AG Noordwijk, Netherlands.
|Source||IEEE-Transactions on geoscience and remote sensing (0196-2892) (IEEE), 2008-03 , Vol. 46 , N. 3 , P. 621-645|
|WOS© Times Cited||95|
|Keyword(s)||Salinity, Oceanography, Microwave radiometry|
|Abstract||The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15 degrees C. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 m. s(-1), the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. For this reason, the use of wind components, which typically exhibit Gaussian error distributions, may be preferred in the retrieval. However, the use of noisy wind components creates WS and SSS biases at low WSs (0.1 psu at 3 m . s(-1)). At a sea surface temperature (SST) of 15 degrees C, the retrieved SSS is weakly sensitive to the SST biases, with the SSS bias always lower than 0.3 psu for SST biases ranging from -0.5 degrees C to -2 degrees C. In DP mode, biases in the vertical total electron content (TEC) of the atmosphere result in SSS biases smaller than 0.2 psu. The pseudofirst Stokes mode is insensitive to TEC. Failure to fully account for sea surface roughness scattering effects in the computation of sky radiation contribution leads to a maximum SSS bias of 0.2 psu in the selected configuration, i.e., a descending orbit over the Northern Pacific in February. To achieve SSS biases that are smaller than 0.2 psu, special care must be taken to correct for biases at low WS and to ensure that the bias on the mean WS (averaged over 200 km x 200 km and ten days) remains smaller than 0.5 m. s(-1).|
Zine S, Boutin J, Font J, Reul Nicolas, Waldteufel P, Gabarro C, Tenerelli Joseph, Petitcolin F, Vergely J, Talone M, Delwart S (2008). Overview of the SMOS sea surface salinity prototype processor. IEEE-Transactions on geoscience and remote sensing, 46(3), 621-645. Publisher's official version : https://doi.org/10.1109/TGRS.2008.915543 , Open Access version : https://archimer.ifremer.fr/doc/00000/3914/