This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI-POLARCSIC and PTI-TELEDETECT.-- Special Issue Remote Sensing Techniques for Ocean Dynamics: State of the Art, Present and Future Applications.-- 29 pages, 13 figures, 2 tables, This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three- dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 re- analysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 meters. Due to a lower stratification of the water column, the performance of the SQG approach is better in fall and winter than in the spring and summer. In the second part, we apply the SQG approach validated withthe reanalysis outputs to satellite-derived upper ocean currents and validate the results against in-situ measurements. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct upper ocean dynamics in the Arctic and Subarctic Seas up to 400 meters. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocities observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 meters with higher resolution and lower uncertainties, M. Umbert received funding from H2020 Marie Skłodowska-Curie Actions under Grant Agreement No. 840374. E. De Andres received funding from Margarita Salas fellowship un-´ der Next Generation EU Grant Agreement No. UP2021-035. This work has also been funded by the AEI with the ARCTIC-MON project (PID2021-125324OB-I00). [...] We also received funding from the Spanish government through the ”Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) and from the ESA Arctic+Salinity project (AO/1-9158/18/I-BG), Peer reviewed

13 pages, 7 figures, 2 tables.-- Code availability: Underlying research code can be accessed at https://github.com/martaumbert/Ocean-Science-2024 (De Andrés et al., 2024).-- Data availability: Underlying research data are available at https://doi.org/10.6084/m9.figshare.c.7084813.v1 (Umbert, 2024), The hydrography of the Arctic Ocean has experienced profound changes over the last 2 decades. The sea ice extent has declined by more than 10 % per decade, and its liquid freshwater content has increased mainly due to glaciers and sea ice melting. Further, new satellite retrievals of sea surface salinity (SSS) in the Arctic might contribute to better characterizing the freshwater changes in cold regions. Ocean salinity and freshwater content are intimately related such that an increase (decrease) in one entails a decrease (increase) in the other. In this work, we evaluate the freshwater content in the Beaufort Gyre using surface salinity measurements from the satellite radiometric mission Soil Moisture and Ocean Salinity (SMOS) and TOPAZ4b reanalysis salinity at depth, estimating the freshwater content from 2011 to 2019 and validating the results with in situ measurements. The results highlight the underestimation of the freshwater content using reanalysis data in the Beaufort Sea and a clear improvement in the freshwater content estimation when adding satellite sea surface salinity measurements in the mixed layer. The improvements are significant, with up to a 70 % reduction in bias in areas near the ice melting. Our research demonstrates how remotely sensed salinity can assist us in better monitoring the changes in the Arctic freshwater content and understanding key processes related to salinity variations that cause density differences with potential to influence the global circulation system that regulates Earth's climate, This project was funded by Marie Skłodowska-Curie grant agreement no. 840374. Eva De Andrés is funded by Margarita Salas grant no. UP2021-035 under the NextGenerationEU program and supported by the MCIN/AEI project PID2020-113051RB-C31. We also received funding from the AEI with the ARCTIC-MON project (PID2021-125324OB-I00) and from the ESA Arctic+ Salinity project (AO/1-9158/18/I-BG) and Arctic+ SSS CCN (4000125590/18/I-BG). This research was supported by the European Union's Horizon 2020 research and innovation program under grant agreement no. 101003826 via the project “CRiceS – Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system”, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

14 pages, 14 figures, 1 table, The retrieval of sea ice thickness using L-band passive remote sensing requires robust models for emission from sea ice. In this work, measurements obtained from surface-based radiometers during the MOSAiC expedition are assessed with the Burke, Wilheit and SMRT radiative transfer models. These models encompass distinct methodologies: radiative transfer with/without wave coherence effects, and with/without scattering. Before running these emission models, the sea ice growth is simulated using the Cumulative Freezing Degree Days (CFDD) model to further compute the evolution of the ice structure during each period. Ice coring profiles done near the instruments are used to obtain the initial state of the computation, along with Digital Thermistor Chain (DTC) data to derive the sea ice temperature during the analyzed periods. The results suggest that the coherent approach used in the Wilheit model results in a better agreement with the horizontal polarization of the in situ measured brightness temperature. The Burke and SMRT incoherent models offer a more robust fit for the vertical component. These models are almost equivalent since the scattering considered in SMRT can be safely neglected at this low frequency, but the Burke model misses an important contribution from the snow layer above sea ice. The results also suggest that a more realistic permittivity falls between the spheres and random needles formulations, with potential for refinement, particularly for L-band applications, through future field measurements, This project is funded from the AEI with the ARCTIC-MON project (PID2021-125324OB-I00) and also with the Programación Conjunta Internacional project called ‘MEJORANDO LOS MODELOS DE EMISIVIDAD DEL HIELO MARINO EN LAS MICROONDAS DE BAJA FRECUENCIA’ (ICE-MOD), with reference PCI2019-111844-2. This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI-POLARCSIC and PTI-TELEDETECT and is supported by the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

22 pages, 11 figures, supporting information https://doi.org/10.1029/2023JC020733.-- Data Availability Statement: All data used in this study are freely available through public repositories. SMOS SSS data (Martínez et al., 2020) are freely distributed from the Barcelona Expert Center https://doi.org/10.20350/DIGITALCSIC/12620. SIC data were obtained from OSI-450 (OSI SAF, 2017) and OSI-430b (OSI SAF, 2020) products from EUMETSAT. Data sets of OSTIA SST (Good et al., 2020) and TOPAZ4b SSS (Xie, 2022) are generated by E.U. Copernicus Marine Service Information. ORAS5 (Zuo et al., 2018) reanalysis data on SSS, SIT, wind stress, and sea ice velocity are delivered by the ECMWF, Arctic sea ice is retreating. In the Beaufort Gyre (BG), liquid freshwater content (FWC) has increased by 40% in the last two decades, with sea ice melting being one of the contributors. Combining satellite observations and reanalysis outputs, we study the sea ice meltwaters in the BG from 2011 to 2019. SMOS sea surface salinity (SSS) data were limited to the ice-free period, and reanalysis outputs showed limited accuracy in capturing the postmelt freshening observed in SMOS SSS. Applying criteria of SSS 25 psu and sea ice presence, we identified meltwater lenses (MWLs) across different years with higher frequency in 2011, 2015, and 2016, contrasting with scarcity in 2012, 2017, and 2019. MWLs showed lower SSS and temperature than river-influenced waters. Daily evolution of SSS and sea ice area within the MWLs exhibited similar patterns during both melting and formation stages. Furthermore, events of sea surface salinification following sea ice formation suggest that SMOS SSS might be capturing information on brine rejection. Atmospheric conditions suggested that the anticyclonic wind regime in 2011 contributed to meltwater retention, while the cyclonic wind pattern in 2016 favored outward meltwater dispersion. From our estimations, September's sea ice melting may contribute 10%–30% of the BG's annual FWC increase, though our method likely underestimates meltwater volume due to unaccounted advection processes. This study provides a validated method for MWL detection and underscores the importance of monitoring sea ice meltwaters to better understand regional variability in the BG within a changing climate, E. De Andrés is supported by Margarita Salas Grant UP2021-035 under the Next Generation EU program. M. Umbert is funded by Marie Skłodowska-Curie Grant Agreement No. 840374. This study was also financed by the MCIN/AEI projects DINGLAC (PID2020-113051RB-C31) and ARCTIC-MON (PID2021-125324OB-I00), and from the ESA Arctic+Salinity project (AO/1–9158/18/I-BG). This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI-POLARCSIC, and is supported by the Spanish Government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

Special Issue Machine Learning and Statistical Learning with Applications 2025.-- 18 pages, 5 figures, 5 tables.-- The data supporting the conclusions of this article will be made available by the authors on request. These data were derived from the following resources available in the public domain: The production of SMOS sea ice thickness data was funded by the ESA project SMOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service, and data from 15 October 2010 to 15 April 2021 were obtained from AWI. The BGEP data used in the validation were collected and made available by the Beaufort Gyre Exploration Program based at the Woods Hole Oceanographic Institution (https://www2.whoi.edu/site/beaufortgyre, accessed on 24 July 2025) in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences. Remote sensing data processing was executed at the Barcelona Expert Center on Remote Sensing (BEC-RS, https://bec.icm.csic.es, accessed on 24 July 2025) of the Institut de Ciències del Mar ICM-CSIC. The source codes are available for downloading at the following link: https://github.com/ferranhema/smos-mlsit (accessed on 24 July 2025), Data availability: Hernández-Macià, Ferran; Gabarró, Carolina; 2025; BEC SMOS Sea Ice Thickness [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17749; http://hdl.handle.net/10261/407464, This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR), This project is funded from the AEI with the ARCTIC-MON project (PID2021-125324OB-I00), and from a Doctorat Industrial (AGAUR), with expedient number 2023 DI 0007., This work is supported by the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (Grant CEX2024-001494-S funded by AEI 10.13039/501100011033), Peer reviewed

13 pages, 5 figures, 4 tables, supplementary material https://doi.org/10.1017/S095410202510031X, The Earth’s poles have always aroused great interest and fascination, first for explorers looking for the Northern Pass in the Arctic or for a new continent and natural resources to exploit, and today for scientists due to the significant role that the Arctic and Antarctic oceans play in the dynamics and future of our planet, especially in the current context of global change. To raise awareness of the importance and vulnerability of the polar oceans and to bring scientific advances in marine science to schools and the general public, we have worked from different approaches: 1) the online participation of students in polar oceanographic expeditions (through the ICM Divulga educational website), 2) face-to-face talks and workshops on polar ecosystems involving the observation of samples and/or videos, 3) the coordination and the edition of the book Observando los polos with a global and multidisciplinary vision of the state of scientific knowledge on polar areas, 4) the elaboration of the photographic exhibition ‘Una mirada polar’ and 5) the multi-institutional collaborative project surrounding the XIth Scientific Committee on Antarctic Research (SCAR) International Biology Symposium. These approaches, some of which involved Dr Andrés Barbosa’s collaboration, align with his objective of disseminating the results of research and scientific experience to the public. An evaluation of the strengths and weaknesses of the diverse strategies used to provide education about polar science is presented, This study is part of the POLARCSIC platform activities and had the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (Grant CEX2024-001494-S funded by AEI 10.13039/501100011033) to the ICM CSIC. [...] We sincerely appreciate the opportunities offered by the project leaders of PEGASO (CTM2012-37615, R. Simó), PI-ICE (CTM2017-89117-R, E. Berdalet and M. Dall’Osto), POLAR CHANGE (PID2019-110288RB-I00, M. Dall’Osto and R. Simó), SSSICE-SO (ARICE-H2020-PONANT ANTARCTIC SEASON 2023–2024, M. Umbert) and ARCTIC-MON (PID2021-125324OB-I00, C. Gabarró). ‘A Polar Gaze’ was partially funded by the Fundación General del CSIC Cuenta la Ciencia project. The book Observando los polos was an initiative of PTI (now Connection) POLARCSIC (https://www.polarcsic.es). The ‘Antarctica: Life on the Edge - Scientific Voyages Today’ exhibition was co-funded by the Maritime Museum of Barcelona and the Institut de Ciències del Mar of Barcelona (CSIC), Peer reviewed

27 pages, 10 figures, 3 tables, supplemental material https://doi.org/10.1109/JSTARS.2026.3654597/mm1; https://doi.org/10.1109/JSTARS.2026.3654597/mm2, In the Arctic, sea surface salinity (SSS) serves as a key tracer of freshwater variability and upper-ocean dynamics. While spaceborne SSS observations provide a unique capability for monitoring near-surface variability across undersampled polar regions, systematic quality assessments are essential to improve retrieval accuracy and ensure reliable interpretation. This study evaluates the spatio-temporal accuracy of five widely utilized satellite-derived products from the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active-Passive (SMAP) missions across nine pan-Arctic regions through comparative analysis with in situ and model reanalysis data. While satellite and reanalysis products generally agreed on large-scale September salinity patterns, major discrepancies emerged in river-influenced areas. Arctic-dedicated satellite products exhibited stronger temporal correlations with model reanalysis (r up to 0.95) and greater spectral consistency relative to global datasets. However, salinity overestimation near ice margins and meltwater outputs persisted across most observational records—particularly during summer months—highlighting the impact of meltwater stratification and unresolved contamination on SSS retrievals. Comparative analysis with in situ measurements showed that the CCI dataset demonstrated the closest agreement among the five SSS products in the Nordic Seas, with RMSD (R2) values ranging from 0.41 to 0.54 (0.21 to 0.69). Accurately capturing freshwater masses in the central Arctic remains a challenge (RMSD > 1.12), with BEC exhibiting smaller errors in most marginal seas but less variability in the eastern Arctic. These findings reinforce the added value of regionally tailored satellite products in extending the utility and improving the accuracy of SSS monitoring in the Arctic Ocean, The work of María Sánchez-Urrea was supported by the Spanish Ministry for Science and Innovation through Ph.D. under Grant PRE2021-099346. The work of Marta Umbert was supported by the ERC-2024-StG FRESH-CARE under Grant 101164517. Thework ofEva De Andréswas supported byARCTIC-MON project under Grant PID2021-125324OB-I00. This work was supported in part by the Agencia Estatal de Investigación through the ARCTIC-MON project (Advances in monitoring sea ice thickness and snow depth, ocean dynamics, and freshwater content in the Arctic using remote sensing data) under Grant PID2021-125324OB-I00 and in part by the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation under Grant CEX2019-000928-S, Peer reviewed

24 pages, 10 figures, 1 table, supplementary material https://www.frontiersin.org/articles/10.3389/frsen.2026.1768049/full#supplementary-material.-- Data availability statement: The products are publicly available through the
Digital CSIC server: https://digital.csic.es/handle/10261/340924 (last accessed: 20
December 2023), with the DOI number: https://doi.org/10.20350/digitalCSIC/15721
(S´anchez-Urrea et al., 2023). All code associated with the algorithm presented in this study is openly
accessible under the Creative Commons 4.0 license via Zenodo (https://doi.org/10.5281/
zenodo.16603282; S´anchez-Urrea et al., 2025), In the regime-shifting Arctic, organic carbon export from river watersheds is expected to rise due to changes in hydrological regimes and permafrost thawing, affecting coastal and shelf biogeochemistry. Ocean color remote sensing enables monitoring inaccessible areas like the Beaufort Sea, improving our knowledge of coastal dynamics and land-to-ocean transport of Chromophoric Dissolved Organic Matter (CDOM) and Dissolved Organic Carbon (DOC) during the ice-free season. While multi-sensor datasets provide consistent monitoring over extended periods, the viability of using merged L3 remote sensing reflectance (Rrs) to derive carbon-based regional products remains unexplored. This study uses the Copernicus-GlobColour multi-sensor merged Rrs to generate a long-term dataset of CDOM absorption at 443 nm, aCDOM(443), and DOC concentrations in the Mackenzie River–Beaufort Sea region. We employ a modified GIOP (Generalized Inherent Optical Properties) inversion algorithm to retrieve aCDOM(443); then, derive DOC using a region-specific aCDOM(443)-DOC relationship. Validation against in situ observations shows performance comparable to previous studies, with a MdAPD (r2) of 56% (0.55) for aCDOM(443) and 29% (0.70) for DOC. This novel dataset allows for a detailed analysis of riverine plume dynamics, interannual variability, and trends. Average summer plume extent ranges from 43,068 to 92,388 km2 and is significantly correlated with annual discharge (r = 0.6, p< 0.001). When compared to independent in situ DOC estimates (1999-2017) at the river mouth, our satellite product shows consistent variability patterns. Contrary to initial expectations, a significant decline of −0.017 m−1yr−1 and −3.40 μM yr−1 is observed over a 26-year period for aCDOM(443) and DOC, respectively, at the Mackenzie delta outflow, resulting from a slight increase (1998–2015) followed by a decrease (2016–2023). These results suggest that trends in DOC fluxes need to be assessed using longer time series while considering significant uncertainties in both retrieval accuracy and variable spatiotemporal coverage over multidecadal periods, This work has been carried out as part of the ARCTIC-MON project (Advances in monitoring sea ice thickness and snow depth, ocean dynamics, and freshwater content in the Arctic using remote sensing data), funded through the Agencia Estatal de Investigación (AEI) (grant no. PID 2021-125324OB-I00). María Sánchez-Urrea gratefully acknowledges the financial support from the PhD Grant PRE 2021-099346 from Spanish Ministry for Science and Innovation. MU was funded by Marie Skłodowska-Curie Grant Agreement No. 840374. ED was funded by Margarita Salas Grant No. UP 2021-035 under the Next-Generation EU program and supported by the MCIN/AEI project PID 2020-113051RB-C31. RG-A was supported by the Independent Research Fund Denmark (4251-00058B). This work constitutes a contribution to the CSIC Thematic Interdisciplinary Platform PTI-POLARCSIC and PTI-TELEDETECT and is supported by the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019–000928-S), Peer reviewed

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA's Copernicus Imaging Microwave Radiometer (CIMR).