New SMOS Sea Surface Salinity with reduced systematic errors and improved variability - Sorbonne Université Access content directly
Journal Articles Remote Sensing of Environment Year : 2018

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability


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.
Fichier principal
Vignette du fichier
Boutin_debias_vquasi_final.pdf (3.39 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01844300 , version 1 (19-07-2018)



Jacqueline Boutin, Jean-Luc Vergely, Stéphane Marchand, Francesco d'Amico, Audrey Hasson, et al.. New SMOS Sea Surface Salinity with reduced systematic errors and improved variability. Remote Sensing of Environment, 2018, 214, pp.115-134. ⟨10.1016/j.rse.2018.05.022⟩. ⟨hal-01844300⟩
291 View
176 Download



Gmail Facebook X LinkedIn More