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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]

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]

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 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]