Enhancing SMOS brightness temperatures over the ocean using the nodal sampling image reconstruction technique

Abstract

Abstract Abrupt changes in the Soil Moisture and Ocean Salinity (SMOS) brightness temperatures, such as those produced by land/sea/ice transitions and Radio-Frequency Interference (RFI) sources, produce artificial rippling patterns (i.e. the so-called Gibbs-like contamination) that propagate through the SMOS-reconstructed image. A nodal sampling technique, focused on the reduction of this kind of contamination by sampling at the points where the perturbation cancels, was introduced by Gonzalez-Gambau et al. (2015). In this work we show that the quality of nodal sampling can be largely improved by refining the determination of the nodal grid. In addition, we have carried out an extensive validation of the resulting data over the ocean. Nodal sampling reduces sidelobe levels and ripples in the reconstructed images leading to brightness temperatures in better agreement with the theoretically modeled ones. Validation of the salinity retrievals against close-to-surface Argo salinity observations shows that nodal sampling leads to improved salinity retrievals in open ocean, while close to the coast land–sea contamination seems to deteriorate the quality. Besides, spectral analysis shows that nodal sampled salinities become closer to what is geophysically expected without loss of effective spatial resolution.