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

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