The Soil Moisture and Ocean Salinity (SMOS) and Aquarius satellite missions have produced the first sea-surface salinity (SSS) maps from space. The quality of the retrieved SSS must be assessed, in terms of its validation against sparse ground truth, but also in terms of its ability to detect and characterize geophysical processes, such as mesoscale features. Such characterization is sometimes elusive due to the presence of noise and processing artifacts that continue to affect state-of-the-art remote sensing SSS maps. A new method, based on singularity analysis, is proposed to contribute to the assessment of the geophysical characteristics of such maps. Singularity analysis can be used to directly assess the spatial consistency of the SSS fields and to improve the estimation of the wavenumber spectra slope through a new method, the singularity power spectra (SPS). To demonstrate the SPS performance and utility, we applied SPS to different gridded SSS maps, such as SMOS and Aquarius high-level products, the output of a numerical simulation, in situ reanalysis, and climatology, as well as to other sea-surface temperature products for reference. The singularity analysis and SPS methods reveal that both the SMOS level 4 and the Aquarius combined active passive products are both able to describe the geometry of the existing geophysical structures and provide consistent spectral slopes. This paper demonstrates that beyond the remaining sources of uncertainty in remote sensing SSS products, valuable dynamical information on the ocean state can be extracted from these SSS products.