Special Issue Remote Sensing of Ocean-Atmosphere Interactions.-- 25 pages, 14 figures, 7 tables, 1 appendix.-- Data Availability Statement: Data supporting reported results can be found at: Meteosat Second Generation rain rates https://msgcpp.knmi.nl/ (accessed on 15 January 2022); wind divergence (as an L3 swath gridded (interpolated) product) at https://doi.org/10.48670/moi-00182 (accessed on 15 January 2022) (EU Copernicus Marine Service). The L3 product is produced by KNMI from L2 swath divergence and curl, which is available from KNMI on request (scat@knmi.nl), Air–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface. Downbursts of heavy rain in MCSs produce strong gusts and large divergence and vorticity in surface winds. In this paper, 12.5 km wind fields from the ASCAT-A and ASCAT-B tandem mission, collocated with short time series of Meteosat Second Generation 3 km rain fields, are used to quantify correlations between wind divergence and rain in the Inter-Tropical Convergence Zone (ITCZ) of the Atlantic Ocean. We show that when there is extreme rain, there is extreme convergence/divergence in the vicinity. Probability distributions for wind divergence and rain rates were found to be heavy-tailed: exponential tails for wind divergence (P∼e−αδ with slopes that flatten with increasing rain rate), and power-law tails for rain rates (P∼(R∗)−β with a slower and approximately equal decay for the extremes of convergence and divergence). Co-occurring points are tabulated in two-by-two contingency tables from which cross-correlations are calculated in terms of the odds and odds ratio for each time lag in the collocation. The odds ratio for extreme convergence and extreme divergence both have a well-defined peak. The divergence time lag is close to zero, while it is 30 min for the convergence peak, implying that extreme rain generally appears after (lags) extreme convergence. The temporal scale of moist convection is thus determined by the slower updraft process, as expected. A structural analysis was carried out that demonstrates consistency with the known structure of MCSs. This work demonstrates that (tandem) ASCAT winds are well suited for air–sea exchange studies in moist convection, This work was supported in part by the European Organization for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Visiting Scientist Program under reference OSI-SAF-AVS-15-02 and in part by the Spanish R&D projects L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). The publication fee was partially supported by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

This work is a contribution to CSIC PTI Teledetect.-- 23 pages, 11 figures.-- Data Availability Statement: HYCOM reanalysis outputs (GLBu0.08/expt_19.1) analyzed in this study can be found at hycom.org/data/glbu0pt08/expt-19pt1, Satellite altimeters provide quasi-global measurements of sea surface height, and from those the vertically integrated geostrophic velocity can be directly estimated, but not its vertical structure. This study discusses whether the mesoscale (30–400 km) dynamics of three regions in the South Atlantic can be described by the surface quasi-geostrophic (SQG) theory, both at the surface and in depth, using outputs from an ocean general circulation model. At these scales, the model surface eddy kinetic energy (EKE) spectra show slopes close to k−5/3 (k−3) in winter (summer), characterizing the SQG and quasi-geostrophic (QG) turbulence regimes. We use surface density and temperature to (a) reconstruct the stream function under the SQG theory, (b) assess its capability of reproducing mesoscale motions, and (c) identify the main parameters that improve such reconstruction. For mixed layers shallower than 100 m, the changes in the mixed-layer depth contributes nine times more to the surface SQG reconstruction than the EKE, indicating the strong connection between the quality of the reconstruction and the seasonality of the mixed layer. To further explore the reconstruction vertical extension, we add the barotropic and first baroclinic QG modes to the surface solution. The SQG solutions reproduce the model density and geostrophic velocities in winter, whereas in summer, the interior QG modes prevail. Together, these solutions can improve surface correlations (>0.98) and can depict spatial patterns of mesoscale structures in both the horizontal and vertical domains. Improved spatial resolution from upcoming altimeter missions poses a motivating scenario to extend our findings into future observational studies, We would like to acknowledge the financial support of Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (130215/2018-0) and Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP (2019/02968-9, 2019/13830-8, and 2017/09659-6). This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe,” and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge the financial support from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). [...] This work supports the SWOT-Brésil project, in preparation for the future SWOT wide-swath altimetry mission. SWOT-Brésil project is financed by CNES/INSU via the French TOSCA programme, Peer reviewed

8 pages, 3 figures.-- Data Availability Statement: The details of the model configuration, as well as, the simulated Sea Surface Temperatures generated for this study, and the singularity analysis described in Section 3 are available in https://doi.org/10.20350/digitalCSIC/14487 (Isern-Fontanet et al., 2022), The contribution of ocean fronts to the properties and temporal evolution of Sea Surface Temperature (SST) structure functions have been investigated using a numerical model of the California Current system. First, the intensity of fronts have been quantified by using singularity exponents. Then, leaning on the multifractal theory of turbulence, we show that the departure of the scaling of the structure functions from a straight line, known as anomalous scaling, depends on the intensity of the strongest fronts. These fronts, at their turn, are closely related to the seasonal change of intensity of the coastal upwelling characteristics of this area. Our study points to the need to correctly reproduce the intensity of the strongest fronts and, consequently, properly model processes such as coastal upwelling in order to reproduce SST statistics in ocean models, This work was supported by the Ministry of Economy and Competitiveness, Spain, and FEDER EU through the EXPLORA Ciencia National R + D Plan under TURBOMIX project (CGL2015-73100-EXP) and through the projects PROMISES (ESP2015-67549-C3-1-R) and COSMO (CTM2016-79474-R). This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and ERDF. A way of making Europe, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge support from Fundación General CSIC (Programa ComFuturo). This work acknowledges the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect, Peer reviewed

9 pages, 4 figures, supplementary information https://doi.org/10.1038/s41598-022-10265-1.-- This work is a contribution to CSIC PTI Teledetect, Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable reflects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensification of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ near-surface salinity measurements. The largest positive differences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratification of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fluxes, This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. , and in part by the European Space Agency by means of the Contract SMOS ESL L2OS. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

26 pages, 24 figures, 4 tables.-- Data availability: Access to the data is provided by the Barcelona Expert Center, through its FTP service. The DOI of the L3 product is https://doi.org/10.20350/digitalCSIC/13859 (González-Gambau et al., 2021a). The DOI of the L4 product is https://doi.org/10.20350/digitalCSIC/13860 (González-Gambau et al., 2021b). Seasonal averaged L4 SSS products are also available in the HELCOM catalogue (https://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9d979033-1136-4dd1-a09b-7ee9e512ad14, BEC team, 2021b), and they can be visualized in the HELCOM Map and Data service (https://maps.helcom.fi/website/mapservice/?datasetID=9d979033-1136-4dd1-a09b-7ee9e512ad14, last access: 9 November 2021).-- This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect, This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightness temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land–sea and ice–sea contamination, the high contamination by radio-frequency interference (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters, and the poor characterization of dielectric constant models for the low SSS range in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics.

Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9 d maps in a 0.25∘ grid; https://doi.org/10.20350/digitalCSIC/13859, González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05∘ grid; https://doi.org/10.20350/digitalCSIC/13860, González-Gambau et al., 2021b)​​​​​​​, which are computed by applying multifractal fusion to L3 SSS with SST maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatiotemporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga.
The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region, and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse, This work has been carried out as part of the Baltic+ Salinity Dynamics project (4000126102/18/I-BG), funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity).
Filenames: BEC_SM____SMOS__GLO_L3__X_YYYYMMDDTHHMMSS_025km_TT_____v4.0.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file,
- TT: indicates the temporal coverage of the data file (1d, 3d, 9d, 1m and 1y for daily, 3 days, 9 days, 1 month and 1 year, respectively).
Sensor: Satellite SMOS / MIRAS.
Spatial resolution: 25 km x 25 km.
Spatial grid: WGS_84 / EASE2_M25km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), · Surface soil moisture (SM) · Data quality index of surface soil moisture (SM_DQX) · Variance of surface soil moisture (SM_VARIANCE) · Number of L2 soil moisture measures (N_SM) · Vegetation optical depth at nadir (VOD) · Data quality index of vegetation optical depth at nadir (VOD_DQX) · Variance of vegetation optical depth at nadir (VOD_VARIANCE) · Number of L2 vegetation optical depth measures (N_VOD) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and 16-dayTerra MODIS NDVI collection 6. Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_TT_REP_v6.0.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file,
- TT: indicates the temporal coverage of the data file (1d for daily and 3d for 3 days).
Sensor: Satellite SMOS / MIRAS.
Spatial resolution: 1 km x 1 km.
Spatial grid: WGS_84 / EASE2_M01km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Number of L4 measures (N) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

8 pages, 7 figures, The airborne stepped frequency microwave radiometer (SFMR) provides the measurements of 10-m ocean surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modeling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-m layer between 10 and 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise, and the use of actual 10-m dropsonde measurements should be further investigated, This work was supported in part by the MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe through the Spanish Research and Development Project L-BAND under Grant ESP2017-89463-C3-1-R, in part by the MCIN/AEI/10.13039/501100011033 through the Project INTERACT under Grant PID2020-114623RB-C31, in part by the Spanish Government through the Severo Ochoa Center of Excellence Accreditation under Grant CEX2019-000928-S, in part by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) through the Tender 16_166-STC under C-band High and Extreme- Force Speeds (CHEFS) Project EUM/CO/16/4600001953, and in part by the Jet Propulsion Laboratory, California Institute of Technology, through the National Aeronautics and Space Administration (NASA) Postdoctoral Program (NPP), initially administered by Universities Space Research Association and now administered by Oak Ridge Associated Universities, under a contract with NASA, Peer reviewed

Special Issue Remote Sensing of the Sea Surface and the Upper Ocean.-- 16 pages, 11 figures, 2 tables.-- Data Availability Statement: The data used in the paper are available from the PODAAC website with url: https://podaac-opendap.jpl.nasa.gov/opendap/, accessed on 30 September 2022, The normalized standard deviation (Kp) of the noise that affects scatterometer Normalized Radar Cross-Sections (σ0s) plays a key role in the ocean and more in particular coastal wind retrieval procedures and the a posteriori quality control. This paper presents a method based on SeaWinds measurements to estimate Kps. The method computes the standard deviation of the differences between the full-resolution (slice) σ0s and the footprint (egg) σ0. The results are compared to the median of Kps provided with SeaWinds σ0s, showing some non-negligible differences. Kps estimated on non-homogeneous surfaces are larger than those estimated on sea, whereas no differences are appreciated in the provided Kps, which is likely due to the ability of this methodology to account for the effect of the scene variability in the estimates. The presence of inter-slice biases is demonstrated with a trend with the antenna azimuth angle. A multi-collocation slice cross-calibration procedure is suggested for the retrieval stage. Finally, a theoretical model of the distribution of σ0s is proposed and used to validate Kps. The results prove the efficacy of this model and that the provided Kps seem to be largely underestimated at low-wind regimes, This work was supported in part by the European Organization for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility (EUMETSAT OSI-SAF) projects under reference VS20_01, VS20_03, and VS21_03, and in part by project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July 2022, Kuala Lumpur, Malaysia.-- © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-- 4 pages, 3 figures, The ERA* stress-equivalent wind (UI0S) is a correction of the ECMWF Fifth Reanalysis (ERA5) output by means of geo-Iocated scatterometer-ERA5 differences over a 3-day temporal window, in which the combined sampling of the Advanced Scatterometers on board the Metop satellite series (ASCAT-A, -B, and -C) and the SCATSat-l scatterometer (OSCAT2) have been used, for the year 2019. ERA * can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as western boundary current systems, highly stationary), wind effects associated with the ocean mesoscales (sea surface temperature), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, e.g., at the inter-tropical convergence zone, This work was supported in part by the Spanish R&D project LBAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033, Peer reviewed

16 pages, 8 figures, 1 table.-- Data Availability Statement: Data for this paper are available at the following data centers we gratefully thank: the Physical Oceanography Distributed Active Archive Center (PO.DAAC) for the Aquarius and SMAP L2 data (https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_V5?ids=Keywords:Platforms:Projects&values=Oceans:Salinity/Density:Salinity::Aquarius_SAC-D::AQUARIUS%20SAC-D (accessed on 1 March 2020) and https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L2_SSS_V4?ids=Keywords:Platforms&values=Oceans:Salinity/Density:Salinity::SMAP (accessed on 1 March 2020)); the ESA SMOS Online Dissemination Service for SMOS L2 data (https://smos-diss.eo.esa.int/oads/access/ (accessed on 1 March 2020)); 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 (accessed on 1 March 2020), https://www.ocean-ops.org (accessed on 1 March 2020)). The Argo Program is part of the Global Ocean Observing System [39], Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions, S.F., F.M.B. and A.H. were funded under the NASA contract WBS 718296.02.02.01.59 thanks to the NASA Ocean Salinity Science Team. Funding for the work of F.M.B., K.M.U.C. and S.K.B. was also provided by the NASA Ocean Salinity Science Team under grant #80NSSC18K1322. C.G.-H. and V.G.-G. want to acknowledge the INTERACT project (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033 and contributes to CSIC PTI Teledetect. They also acknowledge the financial support from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). M.K. acknowledges support from NOAA (Award NA21OAR4310258), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and 16-dayTerra MODIS NDVI version 6.1
Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_1d_REP_v6.1.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file.
Time resolution: Daily. Maps frequency generation: Daily. Sensor: Satellite SMOS / MIRAS. Spatial resolution: 1 km x 1 km. Spatial grid: WGS_84 / EASE2_M01km, Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land., INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

This work is a contribution to CSIC PTI Teledetect.-- VII Encuentro de Oceanografía Física (EOF) - Expanding Ocean Frontiers Conference, VIII International Symposium on Marine Sciences, 6-8 July 2022, Las Palmas de Gran Canaria, España, In the framework of the European Space Agency (ESA) World Ocean Circulation project (WOC), the enhanced ocean wind forcing product (ERA*) has been developed to address the growing demand for accurate forcing from the ocean modelling community. The ERA* is a corrected reanalysis product that optimally combines the capacities of scatterometer observations and model reanalysis stress equivalent wind (U10S) output, by means of a geolocated scatterometer-based correction (SC) applied to the European Centre forMediumrange Weather Forecasts (ECMWF) Fifth reanalysis U10S output (ERA5). The SC consists of accumulated ERA5-scatterometer U10S component (zonal and meridional) differences over time windows of typically a few days to a month, at each ocean grid point (Trindade et al. (2020)). ERA* is able to introduce true smaller scale signal into ERA5 that corresponds to the physical processes absent or misrepresented by the latter, e.g., strong current effects (such as WBCs, highly stationary), wind effects associated with the ocean mesoscales (SST), coastal effects (land see breezes, katabatic winds), parameterization errors, and large-scale circulation effects, e.g., at the ITCZ. This is verified by comparing both products against independent scatterometer observations (e.g., HSCAT on HY-2A and HY-2B) and buoys winds.
The ERA* product is generated for the period 2010-2020. An optimized configuration of ERA* in terms of the varying scatterometer constellation over the period of interest is sought. In particular, the optimal SC time window size will depend on the scatterometer sampling, i.e., the larger the number of scatterometers in orbit is, the smaller is the time window required. The ERA* reduces the vector root-mean-square difference by 10% (5%) against independent scatterometers (buoys), with respect to that of ERA5. The new ERA* product, which thus significantly outperforms ERA5, will be freely available from theWOC webpage (https://www.worldoceancirculation.org/), This work was supported in part by the ESA study entitled "World Ocean Circulation" (4000130730/20/I-NB) and in part by the Spanish R&D projects LBAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe", and INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000928-S), Peer reviewed

21 pages, 13 figures, 2 tables, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2022.998970/full#supplementary-material.-- Data availability statement: The datasets generated for this study can be found on the web interface (http://www.eo4sibs.uliege.be/) and on Zenodo under data doi: 10.5281/zenodo.6397223 with a full documentation that include Products User Manuals (PUM) and Algorithm Theoretical Basis Document (ATBD). All these products are distributed in netCDF files Grégoire et al. (2022). SMOS SSS and CDM products are also available at https://bec.icm.csic.es/bec-ftp-service/, In this paper, satellite products developed during the Earth Observation for Science and Innovation in the Black Sea (EO4SIBS) ESA project are presented. Ocean colour, sea level anomaly and sea surface salinity datasets are produced for the last decade and validated with regional in-situ observations. New data processing is tested to appropriately tackle the Black Sea’s particular configuration and geophysical characteristics. For altimetry, the full rate (20Hz) altimeter measurements from Cryosat-2 and Sentinel-3A are processed to deliver a 5Hz along-track product. This product is combined with existing 1Hz product to produce gridded datasets for the sea level anomaly, mean dynamic topography, geostrophic currents. This new set of altimetry gridded products offers a better definition of the main Black Sea current, a more accurate reconstruction and characterization of eddies structure, in particular, in coastal areas, and improves the observable wavelength by a factor of 1.6. The EO4SIBS sea surface salinity from SMOS is the first satellite product for salinity in the Black Sea. Specific data treatments are applied to remedy the issue of land-sea and radio frequency interference contamination and to adapt the dielectric constant model to the low salinity and cold waters of the Black Sea. The quality of the SMOS products is assessed and shows a significant improvement from Level-2 to Level -3 and Level-4 products. Level-4 products accuracy is 0.4-0.6 psu, a comparable value to that in the Mediterranean Sea. On average SMOS sea surface salinity is lower than salinity measured by Argo floats, with a larger error in the eastern basin. The adequacy of SMOS SSS to reproduce the spatial characteristics of the Black Sea surface salinity and, in particular, plume patterns is analyzed. For ocean colour, chlorophyll-a, turbidity and suspended particulate materials are proposed using regional calibrated algorithms and satellite data provided by OLCI sensor onboard Sentinel-3 mission. The seasonal cycle of ocean colour products is described and a water classification scheme is proposed. The development of these three types of products has suffered from important in-situ data gaps that hinder a sound calibration of the algorithms and a proper assessment of the datasets quality. We propose recommendations for improving the in-situ observing system that will support the development of satellite products, This work has been carried out as part of the European Space Agency contract Earth Observation data For Science and Innovations in the Black Sea (EO4SIBS, ESA contract n° 4000127237/19/I-EF). MG received fundings from the Copernicus Marine Service (CMEMS), the European Union’s Horizon 2020 BRIDGE-BS project under grant agreement No. 101000240 and by the Project CE2COAST funded by ANR(FR), BELSPO (BE), FCT (PT), IZM (LV), MI (IE), MIUR (IT), Rannis (IS), and RCN (NO) through the 2019 “Joint Transnational Call on Next Generation Climate Science in Europe for Oceans” initiated by JPI Climate and JPI Oceans. The research on SMOS SSS has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033, funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) and the CSIC Thematic Interdisciplinary Platform Teledetect, Peer reviewed

13 pages, 7 figures.-- Data availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, For the first time, an accurate and complete picture of Mixed Layer (ML) water mass dynamics can be inferred at high spatio-temporal resolution via the material derivative derived from Sea Surface Salinity/Temperature (SSS/T) and Currents (SSC). The product between this satellite derived material derivative and in-situ derived Mixed Layer Depth (MLD) provides a satellite based kinematic approach to the water mass (trans)formation framework (WMT/F) above ML. We compare this approach to the standard thermodynamic approach based on air-sea fluxes provided by satellites, an ocean state estimate and in-situ observations. Southern Hemisphere surface density flux and water mass (trans)formation framework (WMT/F) were analysed in geographic and potential density space for the year 2014. Surface density flux differences between the satellite derived thermodynamic and kinematic approaches and ECCO (an ocean state estimate) underline: 1) air-sea heat fluxes dominate variability in the thermodynamic approach; and 2) fine scale structures from the satellite derived kinematic approach are most likely geophysical and not artefacts from noise in SSS/T or SSC—as suggested by a series of smoothing experiments. Additionally, ECCO revealed surface density flux integrated over ML are positively biased as compared to similar estimates assuming that surface conditions are homogeneous over ML—in part owing to the e-folding nature of shortwave solar radiation. Major differences between the satellite derived kinematic and thermodynamic approaches are associated to: 1) lateral mixing and mesoscale dynamics in the kinematic framework; 2) vertical excursions of, and vertical velocities through the ML base; and 3) interactions between ML horizontal velocities and ML base spatial gradients, This work was supported in part by the Spanish RD project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” under Grant FPI PRE2018-086106, and in part by the project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). This work has also been carried out as part of the SOFRESH project (A0/1-10461/20/I-NB), funded by the European Space Agency. This work is moreover a contribution to CSIC PTI Teledetect. The publication fee was partially supported by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

Oceans form Space V Symposium, 24-28 october 2022, Venice, Italy.-- 2 pages, 2 figures, Accurate high and extreme sea surface wind observations are essential for meteorological, ocean, and climate applications. A method to inter-calibrate spaceborne scatterometer and radiometer derived high and extreme winds using NOAA hurricane “hunter” data is presented. The proposed method is effective, providing highly consistent satellite-derived extreme wind datasets in the period 2009-2020. Further work is needed though to consolidate an in-situ reference for extreme wind calibration purposes, This work was supported in part by the MCIN/AEI/10.13039/501100011033 through the Project INTERACT under Grant PID2020-114623RB-C31, and in part by the European Space Agency (ESA) through the OCEAN+EXTREMES Marine Atmosphere eXtreme Satellite Synergy (MAXSS) Project under Contract 4000132954/20/I-NB, Peer reviewed

Living Planet Symposium, 23-27 May 2022, Bonn, Germany, The Baltic Sea is a strongly stratified semi-enclosed sea with a large freshwater supply from rivers, net precipitation and high-saline water exchange from the North Sea. In the Danish Straits the water exchange is hampered by bathymetric and hydrodynamic restrictions. The shallow depth yields to highly variable ocean dynamics. The water exchange with the North Atlantic Ocean is restricted by the narrows and sills of the Danish Straits and river outflows. The bottom water in the deep sub-basins is ventilated by major Baltic saltwater inflows. These complex oceanographic conditions are not well described in current model simulations. Moreover, the available in situ data in the region is temporally
and spatially very sparse.

Earth Observation salinity measurements have a great potential to help in the understanding of the dynamics in the Baltic Sea. However, this basin is one of the most challenging regions for the satellite sea surface salinity (SSS) retrieval. The available EO-based global SSS products are quite limited in terms of spatio-temporal coverage and quality. This is mainly due to technical limitations that strongly affect the brightness temperatures (TB), such as the high contamination by interferences and the contamination close to land and ice edges. Moreover, the sensitivity of TB to SSS changes is very low in cold waters and dielectric models present limitations in this low salinity regime.

In the ESA Baltic+ Salinity Dynamics project, new regional SSS products derived from the European Soil Moisture and Ocean Salinity (SMOS) measurements were developed. In this work, we describe the enhanced algorithms used in the generation of SMOS SSS fields. A complete quality assessment with respect to in situ measurements is also presented. Finally, we compare the satellite SSS measurements with a Baltic reanalysis and in situ measurements time series, focusing on the SSS dynamics captured by Baltic+ SSS products and the added-value that can provide for the understanding of the salinity dynamics. Scientific applications identified from the interaction with users working in the Baltic Sea will also be presented, This work has been carried out as part of the Baltic+ Salinity Dynamics project (4000126102/18/I-BG), funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

Living Planet Symposium, 23-27 May 2022, Bonn, Germany, The amount of data that must be processed in satellite missions is increasing over time, directly affecting the hardware resources and time required to carry out this processing. With more than 11 years in orbit, the SMOS mission has a lot of over-sampled data, which implies a more intensive use of the CPU and greater use of disk space if the processing is done without any type of data management. For this reason, it is increasingly necessary to optimize the resources involved in the processing of large volumes of data. Such optimizations include minimizing the processing time, achieving maximum efficiency of computational resources, and doing a good management of the generated data, both to make it more accessible and to optimize the disk space it demands.
This work presents different techniques that can be applied when designing software architectures for the particular case of the SMOS Sea Surface Salinity data processing. A study is made on how the data can be aggregated and ordered in the first stages of processing to reduce the processing time of the following stages and the disk usage of intermediate products.
The SMOS measurements can be easily divided into smaller independent processing units (such as a half-orbit or a snapshot, which is even smaller and still independent of the other snapshots). The granularity of the data allows the processing to be divided into very small pieces that can be executed in parallel, making an optimal use of CPU resources and reducing the total processing time. Disk operations, such as reading and writing files are also a big part of the processing time. Data has been arranged in a way in which disk operations are minimized (avoiding multiple reads of the same file) .
Preliminary results show an improvement of the 20% of computational time and a reduction of the 40% of the required disk space with respect to the current implementation of the Barcelona Expert Center internal data processing chain, This work has been carried out as part of the SO-FRESH project (AO/1-10461/20/I-NB)
and by means of the contract SMOS ESL L2OS both funded by the European Space Agency. It has also been
supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by
MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the
“Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

23rd International Group for High Resolution Sea Surface Temperature (GHRSST) Science Team Meeting, 27 June -1 July 2022, Barcelona.-- This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect, Sea Surface Temperature (SST) plays an important role in the production of satellite based Sea Surface Salinity (SSS) observations. On the one hand, it is used as an auxiliary parameter for SSS retrieval from brightness temperature to produce L2 and L3 datasets. On the other hand, it is used as a template to increase spatial resolution using multifractal fusion techniques (L4 product). Traditionally, Soil Moisture and Ocean Salinity (SMOS) SSS datasets produced at the Barcelona Expert Center were based on OSTIA SST product. In this work, we revisit this election and assess different sources of satellite-derived SST products. The assessment will consist of:
- comparison with in-situ data (ARGO floats);
- performing a correlated triple collocation analysis [González- Gambau et al., 2020] between the different products to decide which one presents the lower uncertainty;
- spectral and singularity analysis to assess the spatial resolution of each SST product [Hoareau et al. 2018]., This work has been carried out as part of the SO-FRESH project (AO/1-10461/20/I-NB), funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT
(PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation
(CEX2019-000928-S), Peer reviewed

23rd International Group for High Resolution Sea Surface Temperature (GHRSST) Science Team Meeting, 27 June -1 July 2022, Barcelona.-- This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect.-- This work has been carried out as part of the Universitat Autònoma de Barcelona Mathematics bachelor’s thesis of Lluïsa Puig-Moner, Exponents in remote sensed images of the ocean. Horizontal transport and mixing processes are key to properly understand changes in the global ocean. Finite Size Lyapunov Exponents characterise the rate of separation of infinitesimally close trajectories and therefore provide information of the dispersion processes and the Lagrangian Coherent Structures (i.e. transport barriers and fronts). In order to estimate the Finite Size Lyapunov Exponents (and also Finite Time Lyapunov Exponents), a sequence long enough of the velocity field is required. The Singularity Exponents are a dimensionless measure of the degree of regularity or irregularity of a function at each point of its domain. The estimation of the Singularity Exponents is related to the multifractal properties of any ocean scalar satellite image and then no velocity field is required. Numerical estimations of Singularity Exponents show evidence that different ocean scalars present the same Singularity Exponents and that they coincide with the streamlines of the fow. This in particular means that we can estimate the streamlines from the Singularity Exponents derived from Sea Surface Temperature maps. Here we explore the possibility of computing the Finite Size Lyapunov Exponents without the need of any velocity field. For this we numerically analyse one year of satellite ocean images of Absolute Dynamic Topography and Sea Surface Temperature. Numerical estimations show a linear relationship between the Singularity Exponents and the Finite Size Lyapunov Exponents which is more robust in the case of the Singularity Exponent from Sea Surface Temperature than in the case of the Absolute Dynamic Topography. In the talk, we plan to discuss the causes of these differences and the steps forward to address this equivalence from a theoretical point of view, It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

23rd International Group for High Resolution Sea Surface Temperature (GHRSST) Science Team Meeting, 27 June -1 July 2022, Barcelona.-- This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect, In the context of the ESA Baltic+ Salinity Dynamics project, the first SMOS (Soil Moisture and Ocean Salinity) sea surface salinity (SSS) dedicated products over the Baltic Sea have been developed. A first prototype of these SSS products evidenced the sea surface temperature (SST) as one important driver of the SSS errors. Considering the low sensitivity of brightness temperatures to SSS changes in cold waters (with a mean value below 3 degrees C in winter in the Baltic Sea), it is crucial to use the most accurate SST product for the salinity retrieval. Besides, SST is used as a template when using multifractal fusion techniques to increase the spatial resolution of these SSS products.
In order to identify the most suitable SST product over the Baltic Sea, we have analysed the state-ofthe- art EO-based SST products (OSTIA, CMC, REMSS, CCI) [Woo and Park, 2020], by comparing them with the collocated in-situ temperature measurements provided by SeaDataNet. This analysis has revealed that the best quality salinity product over the Baltic Sea is the CCI SST, with a standard deviation of the difference with in-situ of 0.62 degrees C, , which is still large compared to open ocean regions. Therefore, EO SSS products would benefit from improvements in SST products over the Baltic Sea, This work has been carried out as part of the Baltic+ Salinity Dynamics project (4000126102/18/I-BG), funded
by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT
(PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding
from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation
(CEX2019-000928-S), Peer reviewed

Ocean Salinity Conference, 6-9 June 2022, New York, USA, This work has been carried out as part of the Baltic+ Salinity Dynamics project (4000126102/18/I-BG), funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

Ocean Salinity Conference, 6-9 June 2022, New York, USA, This work has been carried out as part of the SO-FRESH project (AO/1-10461/20/I-NB) and by means of the contract SMOS ESL L2OS both funded by the European Space Agency. It has also been supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect.-- Ocean Salinity Conference, 6-9 June 2022, New York, USA, This work has been carried out as part of the regional initiative An Earth Observation Data for Science and Innovation in the Black Sea project funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

International Geoscience and Remote Sensing Symposium (IGARSS), 16-21 July 2023, Pasadena, CA, USA.-- 4 pages, 2 figures.-- © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy, This work was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RB-C31), ERDF funds, the Institute of Space Studies of Catalonia (IECC), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019-000928-S), and the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT, Peer reviewed

XX Congreso de la Asociación Española de Teledetección : Teledetección y Cambio Global: Retos y Oportunidades para un Crecimiento Azul, 5-7 June 2024, Cádiz.-- Este trabajo es una contribución a la PTI Teledetect, coordinada por el CSIC y el INTA.-- 4 pages, 4 figures, [EN] The characterization of remote sensing of ocean variables by means of the Microcanonical Multifactal Formalism is known since 20 years ago. Owing to ocean turbulence, using singularity analysis information about marine hydrography can be retrieved starting from surface temperature or other remote sensing variables. This has been mainly used to develop data fusion techniques, and also for the characterization of ocean currents. In this work, we will show that under the conditions of ocean turbulence there is a precise mathematical relation between singularity exponents (which are a measure of local regularity of the studies variable at each point, what generalizes the concept of differentiability) and the local information content of the variable. This relation enables some powerful conclusions. From the theoretical point of view, this confirms the hypothesis that the set of most singular points contain the majority of the information about the image (to the point that it can be reconstructed from the values of the gradient of the variable over those points). From a more practical point of view, by means of this approximation we can construct supertemplates of the sea state combining the remote sensing information coming from satellite observations of very different variables, and even reconstruct the sea state for the more than 40 years of satellite observations. On the other hand, thanks to the analysis of the temporal evolution of the total information content of the maps of sea surface temperature we can see that by 2016 the planet experience a phase shift regarding ocean dynamics, change tha is currently showing clear signs of acceleration; our analysis shows hat this change is likely a climate tipping point. This framework can also be applied to land variables, [ES] La caracterización de mapas de teledetección marina usando el Formalismo Multifractal Microcanónico es
conocida desde hace 20 años. Debido a la turbulencia oceánica, usando la técnica del análisis de singularidades se puede recuperar información de la hidrografía marina a partir de la temperatura superficial u otras variables de teledetección. Eso ha sido usado principalmente en el desarrollo de técnicas de fusión de datos, y también para la caracterización de corrientes oceánicas. En este trabajo, mostraremos que bajo las condiciones de la turbulencia oceánica, hay una relación matemática y precisa entre los exponentes de singularidad (que son una medida de la regularidad local de la variable estudiada en cada punto, generalizando el concepto de diferenciabilidad) y el contenido de información local de la variable. Esta relación permite una serie de conclusiones muy potentes. Desde el punto de vista teórico, confirma la intuición de que el conjunto de puntos más singulares son los que contienen más información sobre la imagen (al extremo de que ésta puede ser reconstruida a partir de los valores del gradiente de la variable sobre esos puntos). Desde el punto de vista más práctico, mediante esta aproximación se puede construir superplantillas del estado del mar combinando la información de teledetección proveniente de observaciones de variables completamente diferentes, e incluso reconstruir este estado durante los más de 40 años de observaciones satelitales. Por otro lado, gracias al análisis de la evolución temporal del contenido de información total de los mapas de temperatura superficial muestran que hacia el año 2016 el planeta experimentó un cambio de fase en lo que a la dinámica oceánica se refiere, cambio que ahora mismo está mostrando claros síntomas de aceleración; nuestro análisis muestra que ese cambio es probablemente un punto de inflexión climático. Este esquema puede también ser aplicado a variables terrestres, Este trabajo ha sido en parte financiado por el proyecto INTERACT (PID2020-114623RB-C31), financiado por el MCIN/AEI/10.13039/501100011033. El ICM está financiado a través de la acreditación ‘Severo Ochoa Centro of Excelencia’ (CEX2019-000928-S), Peer reviewed

XX Congreso de la Asociación Española de Teledetección : Teledetección y Cambio Global: Retos y Oportunidades para un Crecimiento Azul, 5-7 June 2024, Cádiz.-- Este trabajo es una contribución a la PTI Teledetect, coordinada por el CSIC y el INTA.-- 4 pages, 3 figures, [EN] Gap-filled sea surface temperature (SST) gridded products are generated by combining satellite and, in some cases, in situ observations. Previous studies intercomparing the different SST products focused on assessing the differences between the SST products and in situ observations. However, what is relevant for ocean dynamics studies is the local spatial differences of the SST fields. In this work, we propose a new diagnostic that allows us to assess the dynamical quality of SST products. This new diagnostic is based on the multifractal theory of turbulence and enables the identification of the regions where the SST fields do not properly describe ocean dynamics. This diagnostic is a valuable tool that can help the data satellite SST producers to improve the way the different observations are blended in order to build SST fields that are more dynamically coherent, [ES] Los productos de temperatura superficial del mar (SST acrónimo inglés) sin huecos se generan combinando observaciones satelitales y, en algunos casos, observaciones in situ. Estudios previos de intercomparación entre los diferentes productos de SST se centraron en evaluar las diferencias entre los productos de SST satelitales y las observaciones in situ. Sin embargo, lo relevante para los estudios de dinámica oceánica son las diferencias espaciales locales en los campos de SST. En este trabajo, proponemos un nuevo diagnóstico que nos permite evaluar la calidad dinámica de los productos de SST. Este nuevo diagnóstico se basa en la teoría multifractal de la turbulencia y permite identificar las regiones donde los campos de SST no describen adecuadamente la dinámica oceánica. Este diagnóstico es una herramienta valiosa que puede ayudar a los productores de datos satelitales de SST a mejorar la forma en que se combinan las diferentes observaciones para construir campos de SST que sean más dinámicamente coherentes, Este trabajo ha sido en parte financiado por el proyecto INTERACT (PID2020-114623RB-C31), financiado por el MCIN/AEI/10.13039/501100011033. El ICM está financiado a través de la acreditación ‘Severo Ochoa Centro of Excelencia’ (CEX2019-000928-S), Peer reviewed

XX Congreso de la Asociación Española de Teledetección : Teledetección y Cambio Global: Retos y Oportunidades para un Crecimiento Azul, 5-7 June 2024, Cádiz.-- 4 pages, 2 figures, [EN] The validation of a remote sensing product allows the estimation of the error of the geophysical variable it
represents. Classical validation methods are simple, but they often use a dataset that acts as a reference. More complex methods assume the availability of three or more completely independent datasets. Correlated Triple Collocation (CTC) is a validation method that eliminates these limitations since it can be applied to three collocated datasets, two of which have correlated errors. Furthermore, CTC allows the evaluation of the spatial distribution of errors. In this work, the CTC has been used to estimate the errors over land of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions. In both missions, data from radiometric measurements (brightness temperature, TB) and the geophysical variable retrieved over land (soil moisture, SM) have been analyzed. In the TB analysis, two different data versions of the SMOS processor (v620 and v724) have been evaluated along with the SMAP TB. For the SM analysis, in addition to SMOS and SMAP, a third SM dataset has been used, provided by the European Center for Medium-Range Weather Forecasts (ECMWF) model. The results show that the errors of SMAP are smaller than those of SMOS, both at the TB and SM levels. The two TB versions of SMOS have errors with similar spatial patterns and are significantly affected by radio frequency interferences. In the case of SM, the largest errors of both SMOS and SMAP are located in regions with dense vegetation, such as forests. However, the ECMWF SM shows the largest errors in extremely arid areas, [ES] La validación de un producto de teledetección permite la estimación del error de la variable geofísica que representa. Los métodos de validación clásicos son sencillos, pero frecuentemente utilizan un conjunto de datos que actúa como referencia. Los métodos más complejos asumen la disponibilidad de tres o más conjuntos de datos completamente independientes. La Colocación Triple Correlada (CTC) es un método de validación que elimina estas limitaciones, ya que se puede aplicar a tres conjuntos de datos colocalizados, de los cuales dos de ellos tienen errores correlados. Además, la CTC permite la evaluación de la distribución espacial de los errores. En este trabajo, la CTC se ha utilizado para estimar los errores sobre tierra de las misiones SMOS (Soil Moisture and Ocean Salinity) y SMAP (Soil Moisture Active Passive). En ambas misiones, se han analizado los datos de las medidas radiométricas (temperatura de brillo, TB) y de la variable geofísica recuperada sobre tierra (humedad del suelo, SM). En el análisis de la TB, se han evaluado dos versiones de datos diferentes del procesador de SMOS (v620 y v724) junto con la TB de SMAP. Para el análisis de la SM, además de SMOS y SMAP, se ha utilizado un tercer conjunto de datos de SM, proporcionado por el modelo del Centro Europeo de Previsiones Meteorológicas a Plazo Medio (ECMWF). Los resultados muestran que los errores de SMAP son menores que los de SMOS, tanto a nivel de TB como de SM. Las dos versiones de TB de SMOS tienen errores con patrones espaciales similares y están significativamente afectadas por interferencias de radiofrecuencia. En el caso de la SM, los mayores errores tanto de SMOS como SMAP están localizados en las regiones con vegetación densa, como los bosques. Sin embargo, la SM de ECMWF muestra los errores mayores en zonas extremadamente áridas, Este estudio ha sido apoyado por el Ministerio de Ciencia, Innovación y Universidades (MCIN/AEI/10. 13039/501100011033) a través de los proyectos INTERACT (PID2020-114623RB-C32 and PID2020- 114623RB-C31), el premio CEX2019-000928-S Severo Ochoa a la Excelencia de ICM-CSIC y la Plataforma Temática Interdisciplinar (PTI) TELEDETECT, Peer reviewed

IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 1 figure.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum, This work was supported by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RB-C31), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019-000928-S), the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT, and ERDF funds., Peer reviewed

IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 6 figures.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Satellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining reasonable computational time and costs can be challenging. It is thus necessary to include big data algorithms and dedicated management techniques in the processing of such datasets.In this regard, the optimization of the number of interpolations and projections is one of the key aspects, as the native errors of the measurements propagate at each processing step. Besides each interpolation and projection implies a non-negligible increase in total computational time.This work is based on the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission, but it could be easily extended to any other satellite mission where individual values for each measurement are retrieved. We propose a redefinition of the complete processor chain so it can work with the measurements within the instrument coordinate system. This allows us to avoid projection-related errors during the generation of the final product.Additionally, we introduce a novel algorithm to project those measurements taking into account the actual spatial extent of the acquisitions instead of taking them as points, so measures are averaged weighted by the area they cover on the Earth-based grid. This method is optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. Our algorithm has demonstrated its potential when incorporated into the SMOS SSS processor at the Barcelona Expert Center (BEC), allowing us to keep a final resolution very close to the one attained at the antenna coordinate system, The work has been funded by the European Space Agency through the SMOS Expert Support Laboratory (ESL) for SMOS Level 1 and Level 2 over Land, Ocean, and Ice (4000130567/20/I-BG) and by MCIN/AEI/10.13039/501100- 011033 through the project INTERACT (PID2020-114623RB-C31). This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI Teledetect, with the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2024), Acting for Sustainability and Resilience, 7-12 July 2024, Athens, Greece.-- 4 pages, 4 figures.-- © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, In the framework of the ESA regional initiative Arctic+ Salinity, a new Sea Surface Salinity (SSS) product (v4) has been developed from the Soil Moisture and Ocean Salinity (SMOS) measurements. The data processing starts from the SMOS Level 0 (L0) data and applies specific algorithms for reducing the contamination close to the ice edge at Level 1 (L1) (in the Brightness Temperature reconstruction) and at Level 2 (L2) (in the correction of biases in the salinity retrieval). In this work we explain these algorithms and present the new 13-year temporal series of SMOS SSS maps as well as the main results of the quality assessment, The work has been funded by the ESA Arctic+ Salinity project (AO/1-9158/18/I-BG) and Arctic+ SSS CCN (4000125590/18/I-BG) and by MCIN/AEI/10.13039/501100-011033 through the project INTERACT (PID2020-114623RB-C31). This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI Teledetect, with the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

6 pages, 4 figures, supplementary materials https://doi.org/10.1016/j.aosl.2024.100521, The variability of the sea surface salinity measurements provided by the Soil Moisture and Ocean Salinity mission reveals the presence of a local salinity maximum (a salty tongue) in the northwestern tropical Atlantic. The sea surface salinity positive anomaly starts increasing in July, reaches its maximum in January, and then disappears. The local maximum of salinity appears in the late boreal autumn/winter, when the Amazon discharge is minimal and the salty water from the South Atlantic is transported by the North Brazil Current along the shelf break across the equator. The authors validate the satellite observations in this area and assess the vertical structure of the salinity maximum by using in situ measurements and an in situ-interpolated product, This publication was financed by the Fundação para a Ciência e a Tecnologia (FCT) through the project UIDP/04292/2020 (https://doi.org/10.54499/UIDP/04292/2020), UIDB/04292/2020 (https://doi.org/10.54499/UIDB/04292/2020), awarded to MARE and through project LA/P/0069/2020 (https://doi.org/10.54499/LA/P/0069/2020) granted to the Associate Laboratory ARNET. Estrella Olmedo has been funded by the European Space Agency through the SMOS Expert Support Laboratory (ESL) for SMOS Level 2 over land, ocean, and ice [4000130567/20/I-BG] and by MCIN/AEI/10.13039/501,100,011,033 through the project INTERACT [PID2020-114623RB-C31]. Her contribution represents a contribution to CSIC Thematic Interdisciplinary Platform PTI Teledetect and she has the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation [CEX2019–000928-S], Peer reviewed

13 pages, 15 figures, 1 table, The soil moisture and ocean salinity (SMOS) satellite mission, launched in 2009, provides global measurements of sea surface salinity (SSS) using L-band radiometry. In this article, we revisit the algorithms to empirically correct the residual latitudinal and seasonal biases seen in the debiased non-Bayesian (DNB) retrieval algorithm. We characterize these biases that affect the retrieved SSS and derive empirical corrections to mitigate them. We also revisit the filtering criteria for the new release of brightness temperature (v724). We compare the SSS retrieved by using different DNB filtering configurations with in-situ Argo measurements and discuss the importance of these corrections and the optimal configuration parameters for the generation of the Level 3 SSS maps. We observe significant improvements in the retrieved SSS when compared to the ones retrieved in the Barcelona Expert Center global SSS product v2, This work was supported by the MCIN/AEI/10.13039/501,100,011,033 through the projects EO4TIP and INTERACT under Grant PID2023-149659OB-C21 and Grant PID2020-114623RB-C31, With funding form the Spanish government through the "Severa Ochoa Centre of Excellence" accreditation (CEX2019-000928-S), Peer reviewed

23 pages, 19 figures, 3 tables.-- Data availability: The DOI of the BEC SO SSS product is: https://doi.org/10.20350/digitalCSIC/15493 (González-Gambau et al., 2023). Access to the data is provided by the Barcelona Expert Center, through its FTP service (https://bec.icm.csic.es/data-access-ftp/, BEC, 2025), in the directory /becftp/OCEAN/SSS/SMOS/SouthernOcean/v1.0/L3/9day (https://doi.org/10.20350/digitalCSIC/17709). The product is also available in the ESA Open Science Catalog (https://opensciencedata.esa.int/products/sofresh-sea-surface-salinity/collection, ESA, 2025). The Argo profilers dataset from which the temporal correction is derived are available through the SEANOE webpage (https://doi.org/10.17882/42182, Argo, 2025) or all the options to the access to data provided there, This paper presents a newly developed Sea Surface Salinity (SSS) product for the Southern Ocean (SO), derived from SMOS (Soil Moisture and Ocean Salinity) measurements by the Barcelona Expert Center (BEC). The primary challenges in retrieving SSS from L-band brightness temperature (TB) measurements in the Southern Ocean include degraded sensitivity in cold waters, radiometric signal contamination near sea ice edges and low variability in SSS across the region. To address these challenges, significant improvements were made to the retrieval algorithms. The BEC SO SSS product v1.0 delivers 9 d SSS maps on a 25 km EASE-SL grid, generated daily. The time series spans from 1 February 2011, to 31 March 2023, with spatial coverage below 30° S (https://doi.org/10.20350/digitalCSIC/15493, González-Gambau et al., 2023).

The product shows high accuracy farther than 150 km from sea ice edges, with nearly zero bias and a standard deviation of 0.22 (compared to marine mammal data) and 0.25 (compared to TSG data from research vessels). Larger errors are observed within 150 km from the ice edges, due to residual sea-ice contamination and sampling-related errors in these dynamic areas. The product effectively captures seasonal and interannual variability, in line with the SOSE regional model. Although differences between satellite-derived and in situ salinity are more pronounced in these regions, the satellite product successfully reproduces the dynamics near ice edges.
This product will significantly contribute to the understanding of processes influenced by upper-ocean salinity, including sea ice dynamics, particularly, the reduction of Antarctic sea ice extent and the opening of offshore polynyas, This research has been carried out as part of the Southern Ocean Freshwater (SO-FRESH) project (grant no. AO/1-10461/20/I-NB), funded by the European Space Agency. It has been also supported by the EU H2020 CRices project (Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system)(grant no. 101003826) and by the Spanish R&D projects INTERACT (grant no. PID2020-114623RB-C31) and EO4TIP (grant no. PID2023-149659OB-C21), which are funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S).
The article processing charges for this open-access publication were covered by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

IEEE 2024 International Geoscience and Remote Sensing Symposium (IGARSS 2024).-- 19 pages, 20 figures, 2 tables, Satellite datasets are growing larger due to extended mission durations and improved instrument resolutions, creating challenges in efficiently projecting measurements onto geographical grids. This requires the implementation of Big Data algorithms and specialized data management techniques, with a particular focus on optimizing interpolations and projections. These processing steps are critical as they propagate measurement errors and significantly increase computational time. This work presents a new interpolation algorithm for satellite missions where individual values for each measurement are retrieved. We conduct this study using the sea surface salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission. However, it can easily be extended to other multiangular acquisition missions. We suggest keeping the measurements within the instrument coordinate system (antenna coordinates) until the final product is generated. This allows us to avoid multiple projection-related errors during the intermediate interpolations. Additionally, we introduce a novel algorithm to project those measurements, taking into account the actual area of the acquisitions instead of considering them as points. Therefore, measurements are weighted based on the area they cover over the Earth. This method is numerically optimized to transform 2-D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. The methodology was successfully tested using the SMOS mission’s SSS processor at the Barcelona Expert Center (BEC). Final level 3 SSS maps maintain a high resolution close to the one native on the instrument, enabling the characterization of ocean dynamics at finer scales, This workw as supported in part by the European Space Agency through the SMOS Expert Support Laboratory (ESL) for SMOS Level 1 and Level 2 over Land, Ocean, and Ice under Grant 4000130567/20/I-BG, in part by MCIN/AEI/10.13039/501100-011033 through the projects EO4TIP and INTERACT under Grant PID2023-149659OB-C21 and Grant PID2020-114623RB-C31, and in part by the CSIC Thematic Interdisciplinary Platform PTI Teledetect, through the “Severo Ochoa Centre of Excellence” accreditation underGrant CEX2019-000928-S, Peer reviewed

The ERA* stress-equivalent wind (UI0S) is a correction of the ECMWF Fifth Reanalysis (ERA5) output by means of geo-Iocated scatterometer-ERA5 differences over a 3-day temporal window, in which the combined sampling of the Advanced Scatterometers on board the Metop satellite series (ASCAT-A, -B, and -C) and the SCATSat-l scatterometer (OSCAT2) have been used, for the year 2019. ERA * can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as western boundary current systems, highly stationary), wind effects associated with the ocean mesoscales (sea surface temperature), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, e.g., at the inter-tropical convergence zone., This work was supported in part by the Spanish R&D project LBAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033., Peer Reviewed

Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable refects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensifcation of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ nearsurface salinity measurements. The largest positive diferences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratifcation of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fuxes., This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. , and in part by the Euro-pean Space Agency by means of the Contract SMOS ESL L2OS. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect., Peer Reviewed

The ERA* stress-equivalent wind (UI0S) is a correction of the ECMWF Fifth Reanalysis (ERA5) output by means of geo-Iocated scatterometer-ERA5 differences over a 3-day temporal window, in which the combined sampling of the Advanced Scatterometers on board the Metop satellite series (ASCAT-A, -B, and -C) and the SCATSat-l scatterometer (OSCAT2) have been used, for the year 2019. ERA * can correct for local, persistent NWP model output errors associated with physical processes that are absent or misrepresented by the model, e.g., strong current effects (such as western boundary current systems, highly stationary), wind effects associated with the ocean mesoscales (sea surface temperature), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, e.g., at the inter-tropical convergence zone., This work was supported in part by the Spanish R&D project LBAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033., Peer Reviewed, Postprint (published version)

Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable refects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensifcation of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ nearsurface salinity measurements. The largest positive diferences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratifcation of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fuxes., This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. , and in part by the Euro-pean Space Agency by means of the Contract SMOS ESL L2OS. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect., Peer Reviewed, Postprint (published version)