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Since the beginning of the soil moisture and ocean salinity mission, the pervading presence of radio frequency interferences (RFI) has been one of the most problematic issues. The effect of an RFI is not just a hot spot but also six tails along the three main axes, and the general presence of ripples which degrade the quality of L1 brightness temperature snapshots. The standard mitigation technique is to apply an apodization (Blackman), but such a low-pass filter leaves traces of the tails and spreads the signal of the main lobes. New RFI mitigation techniques, such as nodal sampling, are very effective in reducing the impact of tails and ripples, but in some cases they lead to the spread of the RFI main lobe, with a significant loss of data on the affected area. In this letter, we propose a new technique to reduce their spread by an adaptive thresholding on a bandpass filtered version of the snapshot, with a significant recovery of data. [ABSTRACT FROM AUTHOR]

Estimating sea surface salinity by European Space Agency's soil moisture and ocean salinity (SMOS) mission in coastal regions is still a challenging task. The brightness temperature measured by the SMOS interferometric radiometer is biased at coastal areas by the presence of land–sea contamination and other variable sources of error, mainly human-induced radio frequency interferences (RFI). In this paper, we will discuss on the benefits for retrieving salinity maps in coastal regions of applying two correction techniques that enhance the quality of brightness temperatures: the correction of residual multiplicative errors at calibration level (for the mitigation of systematic biases close to landmasses) and the nodal sampling at imaging level (for the reduction of Gibbs-like contamination). The joint application of both techniques leads to significant improvements in salinity retrievals not only in open ocean, but also in strongly contaminated coastal regions. Comparisons of SMOS salinity observations with in situ measurements show that biases of salinity differences are lower and standard deviations are significantly reduced with respect to those obtained from the current SMOS brightness temperatures. [ABSTRACT FROM PUBLISHER]

Soil moisture and ocean salinity (SMOS) brightness temperature (TB) images and calibrated visibilities are related by the so-called G-matrix. Due to the incomplete sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the TBs to diminish the amplitude of artifacts such as ripples, as well as other Gibbs-like effects. In this paper, a novel image reconstruction algorithm focused on the reduction of Gibbs-like contamination in TB images is proposed. It is based on sampling the TB images at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a significant reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This technique has been thoroughly validated using snapshots over the ocean, by comparing TBs reconstructed in the standard way or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the difference between the measurement and the model is reduced around 1 K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.7 K when considering the global ocean. This represents a crucial improvement in TB quality, which will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity. [ABSTRACT FROM PUBLISHER]

The Baltic Sea is one of the most challenging regions for the satellite Sea Surface Salinity (SSS) retrieval. Nowadays, the availability of SMOS SSS products is quite limited over this region (temporal and spatially) due to several technical limitations related to the low sensitivity of L-band TB at SSS changes in cold waters, land-sea and ice-sea contamination, and high contamination by Radio-Frequency Interferences (RFI) sources. The assessment of the quality of the SMOS SSS products in the Baltic Sea is also an issue. On one hand, the basin is strongly stratified and therefore, differences between the first centimetres (SMOS measurements) and the few meters (measurements provided by in situ) can be noticeable. On the other hand, the basin is very shallow and then, the near surface vertical salinity gradient must be estimated from different sources. There are also seasonal salinity variations in the surface, which are caused by melting of ice and spring floods from rivers. Thus, the representativeness of satellite salinity must be assessed. Besides, some parts of the Baltic Sea such as the Gulf of Finland have estuarine regimes and therefore exhibit relatively strong horizontal salinity gradients.In the recent years, the Barcelona Expert Center (BEC) team has developed innovative algorithms for improving the quality of brightness temperatures and sea surface salinities dealing with the main processing issues: (i) Nodal Sampling [1] to mitigate the RFI contamination, (ii) Debiased non-Bayesian SSS retrieval [2] to empirically mitigate systematic biases and improve the spatial coverage of SSS maps, and (iii) Multifractal Fusion technique [3] to improve the temporal and spatial resolution to better describe the salinity dynamics. The application of these techniques has led to the generation of regional SMOS SSS products over some critical areas such as the Mediterranean Sea (http://bec.icm.csic.es/ocean-experimental-dataset-mediterranean/). The Baltic+ Salinity Dynamics project is a recent European Space Agency funded initiative, which aims at advancing research and consolidating existing developments towards the generation of a dedicated SSS product suited for Baltic Earth research (https://www.baltic-earth.eu/organisation/bewg%5fsalinity/index.html). The project started in December 2018. We will present at the conference the up-to-date Baltic+ SSS products generated in this project. We will provide a quality assessment of the SMOS SSS dataset in the Baltic Sea and we will present the potential scientific applications of this product. [1] V. González-Gambau, E. Olmedo, A. Turiel, J. Martínez, J. Ballabrera-Poy, M. Portabella, and M. Piles, "Enhancing SMOS brightness temperatures over the ocean using the nodal sampling image reconstruction technique," Remote Sensing of Environment, vol. 180, pp. 205 – 220, 2016, special Issue: ESA's Soil Moisture and Ocean Salinity Mission - Achievements and Applications. [2] E. Olmedo, J. Martínez, A. Turiel, J. Ballabrera-Poy, M. Portabella, "Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity", Remote Sensing of Environment, vol. 193, no. Supplement C, Pages 103-126, 2017.[3] Olmedo, E., Martínez, J., Umbert, M., Hoare au, N., Portabella, M., Ballabrera-Poy, J., Turiel, A., 2016. "Improving time and space resolution of SMOS salinity maps using multifractal fusion". Remote Sens. Environ. 180, 246–263. [ABSTRACT FROM AUTHOR]