As part of the preparation for the European Space Agency SMOS (Soil Moisture and Ocean Salinity) satellite mission, empirical sea surface emissivity (forward) models have been applied to retrieve sea surface salinity from L-band brightness temperature (TB) measurements. However, the salinity inversion is not straightforward and an important effort is required to define the most appropriate cost function (inversion algorithm).
Different Bayesian-based configurations of the cost function are examined, depending on whether prior information is used in the inversion or not. It is important to properly balance all the terms of the cost function, as well as to have a good knowledge of the quality of the prior information. A sensitivity analysis shows that the instrument has low sensitivity to the geophysical parameters that modulate the Tb (including salinity). As such, the inversion needs to be constrained with prior information. Simulations are also performed using the SMOS simulator to assess the retrieval errors produced by the different cost function configurations. In line with the sensitivity analysis, the errors are very large when no prior information is used in the cost function. The lowest errors are obtained when the inversion is constrained with the full prior information, i.e., information from all the auxiliary (geophysical) parameters. As such, it is concluded that the use of prior information is essential for a successful salinity retrieval from SMOS measurements.
C Gabarró, M Portabella, M Talone, J Font. Analysis of the SMOS ocean salinity inversion algorithm
2007, 2007, IEEE