Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity

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We are pleased to inform you that our paper “Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity” has recently appeared in Remote Sensing of Environment.

In the paper, we present a new method to process SMOS data in order to obtain more precise, less biased values of Sea Surface Salinity (SSS). With the new methodology, we do not only improve the overall quality of SSS data, but we also obtain valid retrievals in areas previously deemed as inaccessible, such as the Mediterranean.

In the standard SSS retrieval algorithm used so far at SMOS DPGS, all the values of TB referred to a specific geographic location and satellite overpass are processed together for the retrieval of the associated SSS, usingĀ a single, Bayesian cost function. The new method, in contrast, is based in the individualized processing of each value of brightness temperature TB obtained for any specific geographic location, because depending on the location in the antenna plane TB values suffer different biases. The biases are characterized, then corrected, using SMOS-based climatologies.

SMOS-based-climatologiesThe new method leads to a more than remarkable increase in quality over the global ocean, and an improvement of the coverage of SSS data, especially in coastal areas.


Compare_nonBayes-BayesThis method is currently being applied for the generation of our advanced products, an update of those products is forthcoming.


The BEC Team