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Since its launch in 2009, the European Space Agency mission Soil Moisture and Ocean Salinity (SMOS) has provided valuable information on soil moisture, sea surface salinity data, and other geophysical variables. Due to its innovative instrument, an L-band 2-D synthetic aperture interferometric radiometer, SMOS is able to provide high-resolution L2 data (as compared with other L-band missions). However, SMOS processing is complex, giving rise to the emergence of some unexpected biases. In this paper, we have analyzed the spatial structure of two-point correlations owing to the SMOS synthetic antenna, finding that they are not negligible. Those correlations can be characterized by means of effective point spread functions (PSFs). This paper indicates that the SMOS PSF matrix can be computed in a fast way from measured data without the need for any model or auxiliary data. Furthermore, this matrix can be described in terms of a convolution kernel. The knowledge of that convolution kernel can be used to improve the quality of the SMOS image and to assess the effect of changes of processing procedures, including calibration methods. [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]

Arctic sea ice is going through a dramatic change in its extent and volume at an unprecedented rate. Sea-ice thickness (SIT) is a controlling geophysical variable that needs to be understood with greater accuracy. For the first time, a SIT-retrieval method that exclusively uses only airborne SIT data for training the empirical algorithm to retrieve SIT from Soil Moisture Ocean Salinity (SMOS) brightness temperature (TB) at different polarization is presented. A large amount of airborne SIT data has been used from various field campaigns in the Arctic conducted by different countries during 2011–15. The algorithm attempts to circumvent the issue related to discrimination between TB signatures of thin SIT versus low sea-ice concentration. The computed SIT has a rms error of 0.10 m, which seems reasonably good (as compared to the existing algorithms) for analysis at the used 25 km grid. This new SIT retrieval product is designed for direct operational application in ice prediction/climate models. [ABSTRACT FROM AUTHOR]

Abstract Sea surface salinity (SSS) retrieved from SMAP radiometer measurements are validated against in situ salinity from Argo floats, tropical moored buoys and ship-based thermosalinograph (TSG) data. SMAP SSS achieved an accuracy of 0.2 PSU on a monthly basis in comparison with Argo gridded data in the tropics and mid-latitudes. In the tropical oceans, time series comparison of salinity measured at 1 m by moored buoys indicates that SMAP can track large salinity changes occurred within a month. Synergetic analysis of SMAP, SMOS and Argo data allows us to identify and exclude erroneous jumps or drift in some real-time buoy data from the assessment of the satellite data. The resulting SMAP-buoy matchup analysis gives an average standard deviation of 0.22 PSU and correlation coefficient of 0.73 on weekly scale. On monthly time scales, the average standard deviation reduced to 0.17 PSU and the correlation coefficient improved to 0.8. SMAP L3 daily maps reveals salty water intrusions from the Arabian Sea into the Bay of Bengal during the Indian summer monsoon, consistent with the daily measurements collected from Argo floats deployed during the Bay of Bengal Boundary Layer Experiment (BoBBLE) project field campaign. In the Mediterranean Sea, the spatial pattern of SSS from SMAP is confirmed by the ship-based TSG. Comparison with individual Argo floats suggests the SMAP retrieval algorithm performs better in the Western Mediterranean region, but suffers from radio-frequency interference (RFI) and land contamination in the Eastern Mediterranean region and Adriatic Sea. [ABSTRACT FROM AUTHOR]

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 local oscillators (LOs) of the Soil Moisture and Ocean Salinity mission payload are used to shift the operating frequency of the 72 receivers to an optimal intermediate frequency needed for the signal processing. The LO temperature variations produce phase errors in the visibility, which result in a blurring of the reconstructed brightness temperature (Tb) image. At the end of the commissioning phase, it was decided to calibrate the LO every 10 min while waiting for a more in-depth analysis. During short periods of time, the LO calibration has been performed every 2 min to assess the impact of a higher calibration rate on the quality of the data. In this paper, by means of a decimation experiment, the relative errors of 6- and 10-min calibration interval data sets are estimated using the 2 min as a reference. A noticeable systematic across- and along-track pattern of amplitude \pm0.3 K is observed for Tb differences between 10 and 2 min, whereas this is reduced between 6 and 2 min. A simulation experiment confirms that the nature of such systematic pattern is due to the visibility phase errors induced by the LO calibration rate. Such pattern is propagated into the sea surface salinity (SSS) retrievals. Overall, the SSS error increase (relative to the 2 min SSS data) is about 0.39 and 0.14 psu for the 10- and 6-min data sets, respectively. This paper shows that a LO calibration rate of at least 6 min would noticeably improve the SSS retrievals. [ABSTRACT FROM AUTHOR]

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between − 0.72 and − 0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km 2 , with a typical freshwater content of 11.84–14.02 km 3 . The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km 2 and a freshwater content of 10.15–12.44 km 3 . The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season. [ABSTRACT FROM AUTHOR]

ESA SMOS satellite [1] has been providing Sea Surface Salinity (SSS) measurements, for the first time from space, since November 2009. Until recently, inherent algorithm limitations or external interferences hampered a reliable provision of satellite SSS data in semi-enclosed basin such as the Mediterranean. This has however been overcome through different strategies in the processing chain and data filtering approach which allowed a consistent SSS data stream in the Mediterranean Sea [2, 3]. This recent capability has been in turn used to infer the spatial and temporal distribution of Total Alkalinity (TA - a crucial parameter of the marine carbonate system) in this basin, exploiting some direct relationships existing between salinity and Alkalinity. Preliminary results [4] focused on the differences existing in several parameterizations [e.g, 5] relating these two variables, and how they vary over one-year timeframe. This is now been expanded over a larger timeframe to assess quantitatively the seasonal to interannual variability of the TA estimates. Moreover, to verify the consistency and accuracy of the derived products, these data are being properly validated against a proper ensemble of in-situ, climatology and model outputs within the Mediterranean basin. An error propagation exercise is also being planned to assess how uncertainties in the satellite data would translate into the final products accuracy.The resulting preliminary estimates of Alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of Ocean Acidification assessment and marine carbon cycle.[1] J. Font et al., "SMOS: The Challenging Sea Surface Salinity Measurement From Space," in Proceedings of the IEEE, vol. 98, no. 5, pp. 649-665, May 2010. doi: 10.1109/JPROC.2009.2033096[2] Olmedo, E., J. Martinez, A. Turiel, J. Ballabrera-Poy, and M. Portabella, "Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity". Remote Sensing of Environment 193, 103-126 (2017).[3] Alvera-Azcárate, A., A. Barth, G. Parard, J.-M. Beckers, Analysis of SMOS sea surface salinity data using DINEOF, In Remote Sensing of Environment, Volume 180, 2016, Pages 137-145, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.02.044.[4] Sabia, R., E. Olmedo, A. Turiel, J. Martinez, A. Alvera-Azcarate, G. Cossarini, SMOS Satellite Inference of Alkalinity over Mediterranean Basin, IGARSS '18 (International Geoscience and Remote Sensing Symposium), Valencia, Spain, July 23 –27, 2018.[5] Cossarini, G., Lazzari, P., and Solidoro, C.: Spatiotemporal variability of alkalinity in the Mediterranean Sea, Biogeosciences, 12, 1647-1658, https://doi.org/10.5194/bg-12-1647-2015, 2015. [ABSTRACT FROM AUTHOR]