|Author(s)||Zine S1, Boutin J1, Waldteufel P2, Vergely J.L.3, Pellarin T4, Lazure Pascal5|
|Affiliation(s)||1 : Univ Paris 06, Inst Pierre Simon Laplace,Inst Rech Dev, Lab Oceanog & Climat Experimentat & Approches Num, UMR ,Ctr Natl Rech Sci,Museum Natl Hist Nat, F-75252 Paris, France.
2 : Institut Pierre-Simon Laplace, Service d'Aéronomie du Centre National de la Recherche Scientifique, 91731 Verrières le Buisson, France.
3 : ACRI ST, F-91731 Verrieres Le Buisson, France.
4 : Lab Etud Transferts & Hydrol & Environm, F-38041 Grenoble, France.
5 : IFREMER, F-29280 Plouzane, France.
|Source||Transactions on geoscience an remote sensing IEEE (0196-2892) (IEEE), 2007-07 , Vol. 45 , N. 7 , P. 2061-2072|
|WOS© Times Cited||30|
|Keyword(s)||Soil Moisture and Ocean Salinity (SMOS) mission, sea surface salinity (SSS) retrieval, L band radiometry, Coastal areas|
|Abstract||This paper aims at studying the quality of the sea surface salinity (SSS) retrieved from soil moisture and ocean salinity (SMOS) data in coastal areas. These areas are characterized by strong and variable SSS gradients [several practical salinity units (psu) on relatively small scales: the extent of river plumes is highly variable, typically at kilometric and daily scales. Monitoring this variability from SMOS measurements is particularly challenging because of their resolution (typically 30-100 km) and because of the contamination by the nearby land. A set of academic tests was conducted with a linear coastline and constant geophysical parameters, and more realistic tests were conducted over the Bay of Biscay. The bias of the retrieved SSS has been analyzed, as well as the root mean square (rms) of the bias, and the retrieved SSS compared to a numerical hydrodynamic model in the semirealistic case. The academic study showed that the Blackman apodization window provides the best compromise in terms of magnitude and fluctuations of the bias of the retrieved SSS. Whatever the type of vegetation cover, a strong negative bias, greater than 1 psu, was found when nearer than 36 km from the coast. Between 44 and 80 km, the type of vegetation cover has an impact of less than a factor 2 on the bias, and no influence further than 80 km from the coast. The semirealistic study conducted in the Bay of Biscay showed a bias over ten days lower than 0.2 psu for distances greater than 47 km, due to an averaging over various geometries (coastline orientation, swath orientation, etc.). The bias showed a weak dependence on the location of the grid point within the swath. Despite the noise on the retrieved SSS, contrasts due to the plume of the Loire River and the Gironde estuary remained detectable on ten-day averaged maps with an rms of 0.57 psu. Finally, imposing thresholds on the major axis of the measurements brought little improvement to the bias, whereas it increased the rms and- could lead to strong swath restriction: a 49-km threshold on the major axis resulted in an effective swath of 800-900 km instead of 1200 km. NOT CONTROLLED OCR|
Zine S, Boutin J, Waldteufel P, Vergely J.L., Pellarin T, Lazure Pascal (2007). Issues About Retrieving Sea Surface Salinity in Coastal Areas From SMOS Data. Transactions on geoscience an remote sensing IEEE, 45(7), 2061-2072. Publisher's official version : https://doi.org/10.1109/TGRS.2007.894934 , Open Access version : https://archimer.ifremer.fr/doc/00000/3643/