Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing sea surface salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.

The accuracy of primary satellite ocean color data products from the Moderate Resolution Imaging Spectroradiometer on-board Aqua (MODIS-A) and the Visible/Infrared Imager/Radiometer Suite (VIIRS) is investigated in the Western Black Sea using in situ measurements from the Gloria site included in the ocean color component of the Aerosol Robotic Network (AERONET-OC). The analysis is also extended to an additional well-established AERONET-OC site in the northern Adriatic Sea characterized by optically complex coastal waters exhibiting similarities to those observed at the Gloria site. Results from the comparison of normalized water-leaving radiance LWN indicate biases of a few percent between satellite-derived and in situ data at the center wavelengths relevant for the determination of chlorophyll a concentrations (443–547 nm, or equivalent). Remarkable is the consistency between the annual cycle determined with time series of satellite-derived and in situ LWN ratios at these center wavelengths. Contrarily, the differences between in situ and satellite-derived LWN are pronounced at the blue (i.e., 412 nm) and red (i.e., 667 nm, or equivalent) center wavelengths, confirming difficulties in confidently applying satellite-derived radiometric data from these spectral regions for quantitative analysis in optically complex waters.

A deconvolution-based model has been developed in an attempt to improve the spatial resolution of future soil moisture and ocean salinity (SMOS) data. This paper is devoted to the analysis and evaluation of different algorithms using brightness temperature images obtained from an upgraded version of the SMOS end-to-end performance simulator. Particular emphasis is made on the use of least-square-derived Lagrangian methods on the Fourier and wavelet domains. The possibility of adding suitable auxiliary information in the reconstruction process has also been addressed. Results indicate that, with these techniques, it is feasible to enhance the spatial resolution of SMOS observations by a factor of 1.75 while preserving the radiometric sensitivity simultaneously.

Stray light perturbations are unwanted distortions of the measured spectrum due to the nonideal performance of optical radiometers. Because of this, stray light characterization and correction is essential when accurate radiometric measurements are a necessity. In agreement with such a need, this study focused on stray light correction of hyperspectral radiometers widely applied for above-water measurements to determine the remote-sensing reflectance ($R_{\mathrm{RS}}$). Stray light of sample radiometers was experimentally characterized and a correction algorithm was developed and applied to field measurements performed in the Mediterranean Sea. Results indicate that mean stray light corrections are appreciable, with values generally varying from $-1\%$ to $+1\%$ in the 400–700 nm spectral region for downward irradiance and sky radiance, and from $-1\%$ to $+4\%$ for total radiance from the sea. Mean corrections for data products such as $R_{\mathrm{RS}}$ exhibit values that depend on water type varying between $-0.5\%$ and $+1\%$ in the blue–green spectral region, with peaks up to 9% in the red in eutrophic waters. The possibility of using one common stray light correction matrix for the analyzed class of radiometers was also investigated. Results centered on $R_{\mathrm{RS}}$ support such a feasibility at the expense of an increment of the uncertainty typically well below 0.5% in the blue–green and up to 1% in the red, assuming sensors are based on spectrographs from the same production batch.

The Three-Component Reflectance Model (3C) was primarily developed to improve the determination of the remote-sensing reflectance (R_rs) from above-water radiometric hyperspectral measurements performed during sub-optimal conditions (i.e., cloudy sky, variable viewing geometry, high glint perturbations, low illumination conditions). In view of further validating the model and showing its broad range of uses, this work presents the application of 3C to above-water radiometry data collected in oceanic and coastal waters with a variety of measurement conditions. R_rs derived from measurements performed during optimal and slightly sub-optimal conditions exhibit equivalence with R_rs obtained with an established above-water method that is commonly used to support ocean color validation activities. Additionally, the study shows that 3C can still provide relevant R_rs retrievals from field data characterized by low-light illumination, high glint perturbations and variable measurement geometries, for which the established method cannot be confidently applied. Finally, it is shown that the optimization residual returned by the 3C full-spectrum inversion procedure can be a potential relative indicator to assess the quality of derived R_rs.

More than two years of soil moisture data derived from the Advanced SCATterometer (ASCAT) and from the Soil Moisture and Ocean Salinity (SMOS) radiometer are analysed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture product derived from ASCAT. The available database covers a large part of the SMOS mission lifetime (2010, 2011 and partially 2012) and both Europe and North Africa are considered. A specific strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. Results demonstrate that the two products show a fairly good degree of correlation. Their consistency has some dependence on season, geographical zone and surface land cover. Additional factors, such as spatial property features, are also preliminary investigated