FN Archimer Export Format
PT J
TI New SMOS Sea Surface Salinity with reduced systematic errors and improved variability
BT 
AF BOUTIN, J.
   VERGELY, J. L.
   MARCHAND, S.
   D'AMICO, F
   HASSON, A.
   KOLODZIEJCZYK, Nicolas
   REUL, Nicolas
   REVERDIN, G.
   VIALARD, J.
AS 1:1;2:2;3:1;4:1;5:1;6:3;7:4;8:1;9:1;
FF 1:;2:;3:;4:;5:;6:;7:PDG-ODE-LOPS-SIAM;8:;9:;
C1 Sorbonne Univ, IRD, MNHN, LOCEAN, F-75005 Paris, France.
   ACRI St, Guyancourt, France.
   Univ Brest, Ifremer, CNRS, LOPS,IRD, Brest, France.
C2 UNIV PARIS 06, FRANCE
   ACRI ST, FRANCE
   UBO, FRANCE
   IFREMER, FRANCE
SI TOULON
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-univ-france
IF 8.218
TC 125
UR https://archimer.ifremer.fr/doc/00441/55254/56819.pdf
LA English
DT Article
DE ;SMOS;Sea Surface Salinity;SMAP
AB Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico. 
PY 2018
PD SEP
SO Remote Sensing Of Environment
SN 0034-4257
PU Elsevier Science Inc
VL 214
UT 000436204300009
BP 115
EP 134
DI 10.1016/j.rse.2018.05.022
ID 55254
ER

EF