ESA's Soil Moisture and Ocean Salinity (SMOS) mission, launched 2-Nov-2009, has been in orbit for over 6 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps working well. The calibration strategy remains overall as established after the commissioning phase, with a few improvements. The data for this whole period has been reprocessed with a new fully polarimetric version of the Level-1 processor which includes a refined calibration schema for the antenna losses. This reprocessing has allowed the assessment of an improved performance benchmark. An overview of the results and the progress achieved in both calibration and image reconstruction is presented in this contribution.Peer ReviewedPostprint (author's final draft

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( ${50}^{\circ }$ N– ${90}^{\circ }$ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of BEC global SMOS SSS product v2.0, as well as an extensive quality assessment. Three SMOS SSS fields are distributed: a high-resolution level-3 product (with DOI https://doi.org/10.20350/digitalCSIC/12601, Olmedo et al., 2020 a ) consisting of binned SSS in 9 d maps at $\mathrm{0.25}{}^{\circ }\times \mathrm{0.25}{}^{\circ }$ ; low-resolution level-3 SSS computed from the binned salinity by applying a smoothing spatial window of 50 km radius; and level-4 SSS (with DOI https://doi.org/10.20350/digitalCSIC/12600, Olmedo et al., 2020 b ) consisting of daily $\mathrm{0.05}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$ maps that are computed by multifractal fusion with sea surface temperature maps. For the validation of BEC SSS products, we have applied a battery of tests aimed at the assessment of quality of the products both in value and in structure. First, we have compared BEC SSS products with near-to-surface salinity measurements provided by Argo floats. Secondly, we have assessed the geophysical consistency of the products characterized by singularity analysis, and the effective spatial resolutions are also estimated by means of power density spectra and singularity density spectra. Finally, we have calculated full maps of SSS errors by using correlated triple collocation. We have compared the performance of the BEC SMOS product with other satellite SSS and reanalysis products. The main outcomes of this quality assessment are as follows. (i) The bias between BEC SMOS and Argo salinity is lower than 0.02 psu at a global scale, while the standard deviation of their difference is lower than 0.34 and 0.27 psu for the high- and low-resolution level-3 fields (respectively) and 0.24 psu for the level-4 salinity. (ii) The effective spatial resolution is around 40 km for all SSS products and regions. (iii) The results from triple collocation show the BEC SMOS level-4 product as the product with the lowest estimated salinity error in most of the global ocean and the BEC SMOS high-resolution level-3 as the one with the lowest estimated salinity error in regions strongly affected by rainfall and continental freshwater discharge.

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.

The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.

Measuring salinity from space is challenging since the sensitivity of the brightness temperature (T_B) to sea surface salinity (SSS) is low (about 0.5 K psu⁻¹), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K). Moreover, the sensitivity of the T_B to SSS at cold waters is even lower (0.3 K psu⁻¹), making the retrieval of the SSS in the cold waters even more challenging. Due to this limitation, the ESA launched a specific initiative in 2019, the Arctic+Salinity project (AO/1-9158/18/I-BG), to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product (Martínez et al., 2019) . The product consists of 9 d averaged maps in an EASE 2.0 grid of 25 km. The product is freely distributed from the Barcelona Expert Center (BEC, http://bec.icm.csic.es/, last access: 25 January 2022) with the DOI number https://doi.org/10.20350/digitalCSIC/12620 (Martínez et al., 2019). The major change in this new product is its improvement of the effective spatial resolution that permits better monitoring of the mesoscale structures (larger than 50 km), which benefits the river discharge monitoring.