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VI Expanding Ocean Frontiers Conference (EOF 2021), 5-7 July 2021

Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission., SMOS es la primera misión capaz de medir la salinidad superficial del mar y la humedad del suelo desde el espacio. La novedad del instrumento que SMOS lleva a bordo (MIRAS, un radiómetro interferométrico en dos dimensiones que trabaja en banda L) ha implicado el desarrollo de nuevos algoritmos para el procesado de los datos obtenidos. Esto ha supuesto todo un reto, no sólo tecnológico sino también científico, puesto que ha sido necesario abordar diferentes cuestiones inherentes al uso de una nueva tecnología. A raíz del lanzamiento de SMOS, ha surgido una nueva comunidad de usuarios con la correspondiente demanda de nuevos productos y aplicaciones, y un interés creciente en este tipo de nuevos servicios satelitales. En este artículo, se detallan las funciones desempeñadas por el BEC (Barcelona Expert Centre) en la misión SMOS, bajo la dirección de Jordi Font, investigador principal de la parte de salinidad de la misión. Asimismo, también quedan reflejadas las principales actividades de investigación llevadas a cabo en este centro, los logros conseguidos y las futuras líneas de trabajo, destacando la importancia de las aplicaciones oceanográficas de los datos de SMOS.

European Geosciences Union General Assembly 2013, 7-12 April, Vienna, Austria, The Soil Moisture and Ocean Salinity (SMOS) mission was launched in November 2009 within the framework of the European Space Agency (ESA) Living Planet programme. Over the oceans, it aims at providing Sea Surface Salinity (SSS) maps with spatial and temporal coverage adequate for large scale oceanography. A comprehensive inversion scheme has been defined and implemented in the operational retrieval chain to allow proper SSS estimates in a single satellite overpass (L2 product) from the multi-angular brightness temperatures (TBs) measured by SMOS. Such SMOS operational L2 salinity processor minimizes the difference between the measured and modeled TBs, including additional constraints on Sea Surface Temperature (SST) and wind speed auxiliary fields. In particular, by adopting a maximum-likelihood Bayesian approach, the inversion scheme retrieves salinity under an iterative convergence loop. However, despite the implemented iterative technique is well established and robust, it is still prone to limitations; for instance, the presence of local minima in the cost function cannot be excluded. Moreover, previous studies have demonstrated that the background and observational terms of the cost function are not properly balanced and this is likely to introduce errors in the retrieval procedure. In order to overcome such potential drawbacks, in this study it is proposed a novel approach for the SSS estimation based on the >-insensitive Support Vector Regression (SVR), where both SMOS L1 measurements and auxiliary parameters are used as input. The SVR technique already proved capable of high generalization and robustness in a variety of different applications, with a limited complexity in handling the learning phase. Notably, instead of minimizing the observed training error, it attempts to minimize the generalization error bound so as to achieve generalized performance. For this purpose, the original input domain is mapped into a higher dimensionality space (where the function underlying the data is supposed to have increased flatness) and linear regression is performed. The SVR training is performed using suitable in situ SSS data (i.e. ARGO buoys data) collected in a representative region of the ocean. So far, in situ data coming from a match-up ARGO database in November 2010 over the South Pacific constitute the preliminary benchmark of the study. Ongoing activities point at extending this spatial and temporal frame to assess the robustness of the method. The in situ data have been collocated with SMOS TB measurements and additional parameters (e.g., SST and wind speed) in the learning phase of the SVR under various training/testing configurations. Afterwards, the SSS regression has been performed out of the SMOS TBs or emissivities. Estimated SVR salinity fields are in general (very) well correlated with ARGO data. The analysis of the different impact of the various features has been performed once a rigorous data filtering/flagging is applied, and misfit (SSSSVR-SSSARGO) statistics have been computed. For assessing the effectiveness of the proposed method, final results will be compared to those obtained using the official SMOS SSS retrieval algorithm

International audience; SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

17 pages, 13 figures, 1 table.-- Data availability: The product (Martínez et al., 2019) is freely distributed on the BEC (Barcelona Expert Center) web page (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) and on the Digital CSIC server: https://digital.csic.es/handle/10261/219679 (last access: 25 January 2022). Data can be downloaded from the FTP service: http://bec.icm.csic.es/bec-ftp-service/ (last access: 25 January 2022). The maps are distributed in the standard grid EASE-Grid 2.0, which has a spatial resolution of 25 km. In addition to the product validated in this work (L3 with temporal resolution of 9 d), L3 products having a temporal resolution of 3 and 18 d and the L2 product are available. These Arctic SSS products cover the period from 2011 to 2019.-- This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI Teledetect and PolarCSIC. Argo data were collected and made freely available by the International Argo program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, last access: 25 January 2022). The Argo program is part of the Global Ocean Observing System, Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) 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 TB 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, This work has been carried out as part of the ESA Arctic+Salinity project (AO/1-9158/18/I-BG), which permitted the production of the database, and the Ministry of Economy and Competitiveness, Spain, through the National R&D Plan under L-BAND project ESP2017-89463-C3-1-R. [...] With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)