16 research outputs found

    2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font

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    Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission.Peer ReviewedPostprint (published version

    Deriving vertical total electron content maps from SMOS full polarimetric data to compensate the Faraday rotation effect

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    The Faraday rotation is a geophysical effect that causes a rotation of the electromagnetic field components emitted by the Earth when it propagates through the ionosphere. It depends on the vertical total electron content (VTEC) of the ionosphere, the geomagnetic field, and the frequency. For satellite measurements at the L band, this effect is not negligible and must be compensated for. This is the case of the Soil Moisture and Ocean Salinity (SMOS) mission, where the measured polarimetric brightness temperature must be corrected from the Faraday rotation effect before the retrieval of the geophysical parameters. The Faraday rotation angle (FRA) can be estimated using a theoretical formulation that makes use of external sources for the VTEC and the geomagnetic field. Alternatively, it can be continuously retrieved from the SMOS full-polarimetric data. However, this is not straightforward due to the relatively poor radiometric sensitivity (thermal noise) and accuracy (spatial bias) of its payload MIRAS (Microwave Interferometer Radiometer by Aperture Synthesis). In this thesis, a methodology for estimating the total electron content of the ionosphere by using an inversion procedure from the measured rotation angle has been developed. These SMOS VTEC maps are derived from SMOS measurements in the Extended Alias-Free Field of View (EAF-FoV) by applying spatio-temporal filtering techniques to mitigate the radiometric errors present in the full-polarimetric measured brightness temperatures. Systematic error patterns found in the Faraday rotation angle retrieval have been characterized along the mission and corrected. The methodology is independent, not only of external databases and forward models, but also of the target that is being measured. Eventually, these SMOS-derived VTEC maps can then be used in the SMOS level 2 processors to improve the geophysical retrievals. The impact of using these SMOS VTEC maps to correct the FRA in the SMOS mission instead of the commonly used VTEC data from GPS has also been assessed, particularly over ocean, where the ionospheric effect is stronger. This assessment has demonstrated improvements in the spatial biases, in the stability of the brightness temperatures (especially in the third Stokes parameter), and in the reduction of the latitudinal gradient present in the third Stokes parameters. All these quality indicators point to a better quality of the geophysical retrievals.La rotación de Faraday es un efecto geofísico que causa un giro en las componentes del campo electromagnético emitido por la Tierra cuando éste se propaga a través de la ionosfera. Ésta depende del contenido vertical total de electrones (VTEC) en la ionosfera, el campo geomagnético y la frecuencia. En las medidas de los satélites que operan en banda L, este efecto no es despreciable y se debe compensar. Este es el caso de la misión SMOS (Soil Moisture and Ocean Salinity), por lo que el efecto de Faraday se tiene que corregir en las medidas polarimétricas captadas por el instrumento antes de obtener parámetros geofísicos. El ángulo de rotación de Faraday (FRA) se puede estimar con una fórmula teórica que usa bases de datos externas para el VTEC y el campo geomagnético. Alternativamente, se puede obtener de una manera continua a partir de los datos polarimétricos de SMOS. Sin embargo, esto no se logra con un cálculo directo debido a la pobre sensibilidad radiométrica (ruido térmico) y a la baja precisión (sesgos espaciales) que presenta el instrumento MIRAS (Microwave Interferometer Radiometer by apertura Synthesis), que se encuentra a bordo del satélite. En esta tesis, se desarrolla una metodología para estimar el VTEC de la ionosfera usando un proceso inverso a partir del ángulo de rotación medido. Estos mapas de VTEC se derivan de medidas en todo el campo de visión extendido en donde no hay aliasing. Para mitigar los errores radiométricos en las temperaturas de brillo polarimétricas, se aplican técnicas de filtrados temporales y espaciales. En el ángulo de rotación de Faraday recuperado se detectaron errores sistemáticos. Estos se caracterizaron a lo largo de la misión y se corrigieron. La metodología es independiente, no solo de bases de datos externas y modelos de océano, sino también de la superficie medida. Estos mapas de VTEC derivados de los datos SMOS se pueden usar en el procesador de nivel 2 para mejorar las recuperaciones geofísicas. Se ha evaluado el impacto de usar estos mapas para corregir el FRA en la misión, en vez de los datos de VTEC que comúnmente se emplean (mapas provenientes de datos de GPS), particularmente sobre océano, en donde los efectos de la ionosfera son más críticos. Esta verificación ha demostrado mejoras en el sesgo espacial, en la estabilidad de las temperaturas de brillo (especialmente en el tercer parámetro de Stokes) y en la reducción del gradiente latitudinal presente en el tercer parámetro de Stokes. Todos estos indicadores de calidad apuntan a la obtención de parámetros geofísicos de mejor calidad.Postprint (published version

    Revisiting the global patterns of seasonal cycle in sea surface salinity

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    Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 126(4), (2021): e2020JC016789, https://doi.org/10.1029/2020JC016789.Argo profiling floats and L-band passive microwave remote sensing have significantly improved the global sampling of sea surface salinity (SSS) in the past 15 years, allowing the study of the range of SSS seasonal variability using concurrent satellite and in situ platforms. Here, harmonic analysis was applied to four 0.25° satellite products and two 1° in situ products between 2016 and 2018 to determine seasonal harmonic patterns. The 0.25° World Ocean Atlas (WOA) version 2018 was referenced to help assess the harmonic patterns from a long-term perspective based on the 3-year period. The results show that annual harmonic is the most characteristic signal of the seasonal cycle, and semiannual harmonic is important in regions influenced by monsoon and major rivers. The percentage of the observed variance that can be explained by harmonic modes varies with products, with values ranging between 50% and 72% for annual harmonic and between 15% and 19% for semiannual harmonic. The large spread in the explained variance by the annual harmonic reflects the large disparity in nonseasonal variance (or noise) in the different products. Satellite products are capable of capturing sharp SSS features on meso- and frontal scales and the patterns agree well with the WOA 2018. These products are, however, subject to the impacts of radiometric noises and are algorithm dependent. The coarser-resolution in situ products may underrepresent the full range of high-frequency small scale SSS variability when data record is short, which may have enlarged the explained SSS variance by the annual harmonic.L. Yu was funded by NASA Ocean Salinity Science Team (OSST) activities through Grant 80NSSC18K1335. FMB was funded by the NASA OSST through Grant 80NSSC18K1322. E. P. Dinnat was funded by NASA through Grant 80NSSC18K1443. This research is carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA.2021-09-1

    Exploiting the multiscale synergy among ocean variables : application to the improvement of remote sensing salinity maps

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    Les imatges de teledetecció de la superfície oceànica proporcionen una vista sinòptica de la complexa geometria de la circulació oceànica, dominada per la variabilitat de mesoescala. Estructures com filaments i vòrtex són presents en els diferents escalars advectats pel flux oceànic. L’origen més probable d’aquestes estructures és el caràcter turbulent dels corrents, aquestes estructures són persistents amb el temps i compatibles amb la dinàmica mesoscalar oceànica. A escales espacials de quilòmetres o més, la turbulència és principalment 2D, i una complexa geometria, plena de filaments i remolins de mides diferents, emergeix en les imatges superficials de teledetecció de concentració de clorofil·la-a, salinitat superficial, així com en altres escalars més coneguts com són la temperatura superficial i la topografia dinàmica. L’objectiu d’aquesta tesi és explorar i aplicar metodologies de mapatge que permeten millorar la qualitat de mapes de teledetecció oceànica en general, i en particular de la salinitat superficial del mar (SSS). Les diferents metodologies emprades en aquesta tesi han estat aplicades amb l’objectiu específic de millorar els mapes de teledetecció de salinitat superficial del mar proveïts per la missió SMOS de l’Agència Espaial Europea. SMOS és el primer satèl·lit capaç de mesurar la humitat del sol i salinitat oceànica des de l’espai a escala global. La primera part d’aquesta tesi se centra a analitzar les característiques dels productes de nivell 2 (L2) de salinitat de SMOS i produir mapes de nivell 3 (L3) de salinitat utilitzant aproximacions clàssiques: millora del filtratge, mitjana ponderada i Interpolació Òptima. En el curs de la nostra recerca obtenim un conjunt de recomanacions de com processar les dades de SMOS començant des del nivell L2. Aquesta tesi també presenta una nova tècnica de fusió de dades que permet explotar les estructures turbulentes comunes entre diferents variables oceàniques, representant un pas endavant en la cadena de processat per generar mapes de nivell 4 (L4). Aquesta tècnica de fusió es basa teòricament en les propietats geomètriques dels traçadors advectats per la dinàmica oceànica (Turiel et al., 2005a). Degut a l’efecte de forta cissalla als fluits turbulents, l’estructura espacial d’un traçador oceànic hereta algunes propietats del flux subjacent, i en particular el seu arranjament geomètric. Com a conseqüència, les diferents variables oceàniques mostren propietats d’escala similars a la cascada d’energia turbulenta (Seuront and Schmitt, 2005; Nieves et al., 2007; Nieves and Turiel, 2009; Isern-Fontanet et al., 2007). El mètode de fusió agafa un senyal de menor qualitat (afectat per soroll, forats de dades i/o de resolució més baixa) i en millora la seva qualitat. A més d’això, el mètode de fusió és capaç d’extrapolar les dades de forma geofísicament coherent. Aquesta millora del senyal s’aconsegueix utilitzant una altra variable oceànica adquirida amb major qualitat, cobertura espacial més gran i/o millor resolució. Un punt clau d’aquesta aproximació és la suposició de l’existència d’una estructura multifractal de les imatges de teledetecció oceànica (Lovejoy et al., 2001b), i que les línies de singularitat de les diferents variables de l’oceà coincideixen. Sota aquestes premises, els gradients de les dues variables a fusionar estan relacionats per una matriu suau. Com a primera i simple aproximació, s’assumeix que aquesta matriu és proporcional a la identitat; això porta a un esquema de regressió lineal local. Aquesta tesi mostra que aquesta aproximació senzilla permet reduir l’error i millorar la cobertura del producte de nivell 4 resultant. D’altra banda, s’obté informació sobre la relació estadística entre les dues variables fusionades, ja que la dependència funcional entre elles es determina per cada punt de la imatge.Remote sensing imagery of the ocean surface provides a synoptic view of the complex geometry of ocean circulation, which is dominated by mesoscale variability. The signature of filaments and vortices is present in different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of ocean currents, and those signatures are persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers or more, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration and surface salinity, as well as in other scalars acquired with higher quality such as surface temperature and absolute dynamic topography. The aim of this thesis is to explore and apply mapping methodologies to improve the quality of remote sensing maps in general, but focusing in the case of remotely sensed sea surface salinity (SSS) data. The different methodologies studied in this thesis have been applied with the specific goal of improving surface salinity maps generated from data acquired by the European Space Agency's mission SMOS, the first satellite able to measure soil moisture and ocean salinity from space at a global scale. The first part of this thesis will introduce the characteristics of the operational SMOS Level 2 (L2) SSS products and the classical approaches to produce the best possible SSS maps at Level 3 (L3), namely data filtering, weighted average and Optimal Interpolation. In the course of our research we will obtain a set of recommendations about how to process SMOS data starting from L2 data. A fusion technique designed to exploit the common turbulent signatures between different ocean variables is also explored in this thesis, in what represents a step forward from L3 to Level 4 (L4). This fusion technique is theoretically based on the geometrical properties of advected tracers. Due to the effect of the strong shear in turbulent flows, the spatial structure of tracers inherit some properties of the underlying flow and, in particular, its geometrical arrangement. As a consequence, different ocean variables exhibit scaling properties, similar to the turbulent energy cascade. The fusion method takes a signal affected by noise, data gaps and/or low resolution, and improves it in a geophysically meaningful way. This signal improvement is achieved by using an appropriate data, which is another ocean variable acquired with higher quality, greater spatial coverage and/or finer resolution. A key point in this approach is the assumption of the existence of a multifractal structure in ocean images, and that singularity lines of the different ocean variables coincide. Under these assumptions, the horizontal gradients of both variables, signal and template, can be related by a smooth matrix. The first, simplest approach to exploit such an hypothesis assumes that the relating matrix is proportional to the identity, leading to a local regression scheme. As shown in the thesis, this simple approach allows reducing the error and improving the coverage of the resulting Level 4 product; Moreover, information about the statistical relationship between the two fields is obtained since the functional dependence between signal and template is determined at each point

    The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

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    The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns

    Synergistic optical and microwave remote sensing approaches for soil moisture mapping at high resolution

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    Aplicat embargament des de la data de defensa fins al dia 1 d'octubre de 2022Soil moisture is an essential climate variable that plays a crucial role linking the Earth’s water, energy, and carbon cycles. It is responsible for the water exchange between the Earth’s surface and the atmosphere, and provides key information about soil evaporation, plant transpiration, and the allocation of precipitation into runoff, surface flow and infiltration. Therefore, an accurate estimation of soil moisture is needed to enhance our current climate and meteorological forecasting skills, and to improve our current understanding of the hydrological cycle and its extremes (e.g., droughts and floods). L-band Microwave passive and active sensors have been used during the last decades to estimate soil moisture, since there is a strong relationship between this variable and the soil dielectric properties. Currently, there are two operational L-band missions specifically devoted to globally measure soil moisture: the ESA’s Soil Moisture and the Ocean Salinity (SMOS), launched in November 2009; and the NASA’s Soil Moisture Active Passive (SMAP), launched in January 2015. The spatial resolution of the SMOS and SMAP radiometers, in the order of tens of kilometers (~40 km), is adequate for global applications. However, to fulfill the needs of a growing number of applications at local or regional scale, higher spatial detail (< 1 km) is required. To bridge this gap and improve the spatial resolution of the soil moisture maps, a variety of spatial enhancement or spatial (sub-pixel) disaggregation approaches have been proposed. This Ph.D. Thesis focuses on the study of the Earth’s surface soil moisture from remotely sensed observations. This work includes the implementation of several soil moisture retrieval techniques and the development, implementation, validation and comparison of different spatial enhancement or downscaling techniques, applied at local, regional, and continental scale. To meet these objectives, synergies between several active/passive microwave sensors (SMOS, SMAP and Sentinel-1) and optical/thermal sensors (MODIS) have been explored. The results are presented as follows: - Spatially consistent downscaling approach for SMOS using an adaptive moving window A passive microwave/optical downscaling algorithm for SMOS is proposed to obtain fine-scale soil moisture maps (1 km) from the native resolution (~40 km) of the instrument. This algorithm introduces the concept of a shape-adaptive window as a central improvement of the disaggregation technique presented by Piles et al. (2014), allowing its application at continental scales. - Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula The temporal and spatial characteristics of SMOS and SMAP soil moisture products at coarse- and fine-scales are assessed in order to learn about their distinct features and the rationale behind them, tracing back to the physical assumptions they are based upon. - Impact of incidence angle diversity on soil moisture retrievals at coarse and fine scales An incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of radiative transfer effective parameters single scattering albedo and soil roughness has been carried out, highlighting the importance of such parameterization to accurately estimate soil moisture at coarse-resolution. Then, these parameterizations are used to examine the potential application of a physically-based active-passive downscaling approach to upcoming microwave missions, namely CIMR, ROSE-L and Sentinel-1 Next Generation. Soil moisture maps obtained for the Iberian Peninsula at the three different angles, and at coarse and fine scales are inter-compared using in situ measurements and model data as benchmarks.La humedad del suelo es una variable climática esencial que juega un papel crucial en la relación de los ciclos del agua, la energía y el carbono de la Tierra. Es responsable del intercambio de agua entre la superficie de la Tierra y la atmósfera, y proporciona información crucial sobre la evaporación del suelo, la transpiración de las plantas y la distribución de la precipitación en escorrentía, flujo superficial e infiltración. Por lo tanto, es necesaria una estimación precisa de la humedad del suelo para mejorar las predicciones climáticas y meteorológicas, y comprender mejor el ciclo hidrológico y sus extremos (v.g., sequías e inundaciones). Los sensores pasivos y activos en banda L se han usado durante las últimas décadas para estimar la humedad del suelo debido a la relación directa que existe entre esta variable y las propiedades dieléctricas del suelo. Actualmente, hay dos misiones operativas en banda L específicamente dedicadas a medir la humedad del suelo a escala global: la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, lanzada en noviembre de 2009; y la misión Soil Moisture Active Passive (SMAP) de la NASA, lanzada en enero de 2015. La resolución espacial de los radiómetros SMOS y SMAP, del orden de unas decenas de kilómetros (~40 km), es adecuada para aplicaciones a escala global. Sin embargo, para satisfacer las necesidades de un número creciente de aplicaciones a escala local o regional, se requiere más detalle espacial (<1 km). Para solventar esta limitación y mejorar la resolución espacial de los mapas de humedad, se han propuesto diferentes técnicas de mejora o desagregación espacial. Esta Tesis se centra en el estudio de la humedad de la superficie terrestre a partir de datos obtenidos a través de teledetección. Este trabajo incluye la implementación de distintos algoritmos de recuperación de la humedad del suelo y el desarrollo, implementación, validación y comparación de distintas técnicas de desagregación, aplicadas a escala local, regional y continental. Para cumplir estos objetivos, se han explorado sinergias entre diferentes sensores de microondas activos/pasivos (SMOS, SMAP y Sentinel-1) y sensores ópticos/térmicos. Los resultados se presentan de la siguiente manera: - Técnica de desagregación espacialmente consistente, basada en una ventana móvil adaptativa, aplicada a los datos SMOS Se propone un algoritmo de desagregación del píxel basado en datos obtenidos de medidas radiométricas de microondas en banda L y datos ópticos, para mejorar la resolución espacial de los mapas de humedad del suelo desde la resolución nativa del instrumento (~40 km) hasta resoluciones de 1 km. El algoritmo introduce el concepto de una ventana de contorno adaptativo, como mejora principal sobre la técnica de desagregación presentada en Piles et al. (2014), permitiendo su implementación a escala continental. - Análisis multiescalar de productos de humedad del suelo SMAP y SMOS sobre la Península Ibérica Se han evaluado las características temporales y espaciales de distintos productos de humedad del suelo SMOS y SMAP, a baja y a alta resolución, para conocer sus características distintivas y comprender las razones de sus diferencias. Para ello, ha sido necesario rastrear los supuestos físicos en los que se basan. - Impacto del ángulo de incidencia en la recuperación de la humedad del suelo a baja y a alta resolución Se ha llevado a cabo una calibración adaptada al ángulo de incidencia (32.5°, 42.5° y 52.5°) de los parámetros efectivos, albedo de dispersión simple y rugosidad del suelo, descritos en el modelo de transferencia radiativa � − �, incidiendo en la importancia de esta parametrización para estimar la humedad del suelo de forma precisa a baja resolución. El resultado de las mismas se ha utilizado para estudiar la potencial aplicación de un algoritmo activo/pasivo de desagregación basado en la física para las próximas misiones de microondas, llamadas CIMR, ROSE-L y Sentinel-1 Next Generation. Los mapas de humedad recuperados a los tres ángulos de incidencia, tanto a baja como a alta resolución, se han obtenido para la Península Ibérica y se han comparado entre ellos usando como referencia mediciones de humedad in situ.Postprint (published version

    Earth remote sensing with SMOS, Aquarius and SMAP missions

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    The first three of a series of new generation satellites operating at L-band microwave frequencies have been launch in the last decade. L-band is particularly sensitive to the presence of water content in the scene under observation, being considered the optimal bandwidth for measuring the Earth's global surface soil moisture (SM) over land and sea surface salinity (SSS) over oceans. Monitoring these two essential climate variables is needed to further improve our understanding of the Earth's water and energy cycles. Additionally, remote sensing at L-band has been proved useful for monitoring the stability in ice sheets and measuring sea ice thickness. The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission specifically launched to monitor SM and SSS. It carries on-board a novel synthetic aperture radiometer with multi-angular and full-polarization capabilities. NASA's Aquarius (2011-2015) was the second mission, devoted to SSS monitoring with a combined real aperture radiometer/scatterometer system that allows correcting for sea surface roughness. NASA's Soil Moisture Active Passive (SMAP, 2015-2018) is the second mission dedicated to measure SM. It carries on-board a real aperture full-polarimetric radiometer and a synthetic aperture radar (SAR) for enhanced spatial resolution and freeze/thaw detection. This Ph.D. Thesis is focused on analyzing the geophysical information that can be obtained from L-band SMOS, Aquarius and SMAP observations. The research activities are structured as follows: -Inter-comparison of radiometer brightness temperatures at selected targets. A novel methodology to measure the consistency between SMOS and Aquarius radiometric data over the entire dynamic range of observations (land, ice and ocean) is proposed. It allows detecting spatial/temporal differences or biases without latitudinal limitations neither cross-overs. This is a necessary step to combine observations from different instruments in a long term dataset for environmental, meteorological, hydrological or climatological studies. -Ice thickness effects on passive remote sensing of Antarctic continental ice. The relationship between Antarctic ice thickness spatial variations and changes detected by SMOS and Aquarius measurements is explored. The emissivity of Antarctica is analyzed to disentangle the role of the geophysical contributions (snow layers at different depths and subglacial lakes) to the observed signal. The stability of the L-band signal in the East Antarctic Plateau, calibration/validation site for microwave satellite missions, is assessed. -Microwave/optical synergy for multi-scale soil moisture sensing. The relationship of SM and land surface temperature (LST) dynamics is evaluated to better understand the fundamental SM-LST link through evapotranspiration and thermal inertia physical processes. A new approach to measure the critical soil moisture from time-series of spaceborne SM and LST is proposed. The synergistic use of SMOS SM and remotely sensed LST for refining SM disaggregation algorithms is also analyzed. -Comparison of passive and active microwave vegetation parameters. Recent research has shown that microwave vegetation opacity, sensitive to biomass and water content, and albedo, related to canopy structure, can be retrieved from passive L-band observations. The relationships between these two parameters and radar-derived vegetation descriptors have been explored using airborne observations from the SMAP Validation Experiment 2012 (SMAPVEX12). The obtained relations could allow for improved SM retrievals in active-passive systems, and also to estimate the vegetation properties at high resolution using SAR observations. The Ph.D. Thesis has been developed within the activities of the Barcelona Expert Centre (BEC). The presented results contribute to the use of L-band remote sensing in different scientific disciplines such as climate, cryosphere, hydrology and ecology.Els primers tres d'una sèrie de satèl·lits de nova generació funcionant a la banda L han sigut llançats a l'última dècada. La banda L es molt sensible a la presència d'aigua a l'escena observada, sent considerada òptima per mesurar la humitat del sòl (SM) i la salinitat del mar (SSS) de manera global a la superfície de la Terra. Monitoritzar aquestes dues variables climàtiques essencials es necessari per millorar el nostre coneixement dels cicles de l'aigua i l'energia. La teledetecció a banda L també ha sigut útil per monitoritzar l'estabilitat de les capes de gel i mesurar el gruix de gel marí. La missió Soil Moisture and Ocean Salinity (SMOS, 2009-2017) de l'ESA és la primera específicament llançada per monitoritzar SM i SSS. Porta un nou radiòmetre d'apertura sintètica amb capacitat multiangular i polarització completa. La missió Aquarius (2011-2015) de la NASA va ser la segona, dedicada a monitoritzar SSS amb un sistema de radiòmetre/escateròmetre d’apertura real que permet corregir la rugositat de la superfície del mar. La missió Soil Moisture Active Passive (SMAP, 2015-2018) de la NASA és la segona dedicada a mesurar SM. Porta un radiòmetre d'apertura real i polarització completa i un radar d'apertura sintètica (SAR) per una millor resolució espaial i detecció de congelació/descongelació. Aquesta tesi està enfocada en analitzar la informació geofísica que pot obtenir-se de les observacions a banda L d'SMOS, Aquarius i SMAP. La seva investigació està estructurada com: -Intercomparació de temperatures de brillantor en zones seleccionades. Es proposa un nou mètode per mesurar la consistència entre les dades radiomètriques d'SMOS i Aquarius sobre el rang dinàmic complet d'observacions (terra, gel, oceà). Això permet detectar diferències espaials/temporals o biaixos sense limitacions latitudinals ni creuaments. Aquest pas es necessari per combinar observacions de diferents instruments en un llarg conjunt de dades per estudis mediambientals, hidrològics o climatològics. -Efecte de gruix de gel en teledetecció de gel continental a l'Antàrtida. S'explora la relació entre les variacions espaials del gruix de gel antàrtic i els canvis detectats a les mesures d'SMOS i Aquarius. L'emissivitat de l'Antàrtida es analitzada per discernir el rol de les contribucions geofísiques (capes de gel a diferents profunditats i llacs subglacials) al senyal observat. S'avalua l'estabilitat del senyal a banda L sobre la zona est de l'altiplà antàrtic, lloc per calibratge/validació de satèl·lits de microones. -Sinèrgia de microones/òptic per teledetecció de SM multiescala. S'avalua la correlació entre la SM i la temperatura de la superfície del sòl (LST) per entendre millor la relació SM-LST a través de processos físics d'evapotranspiració i inèrcia tèrmica. Es proposa un nou mètode per mesurar la humitat crítica utilitzant sèries temporals de SM i LST de satèl·lit. S'analitza l'ús de la SM de SMOS amb la LST de teledetecció per refinar algorismes de desagregació de SM. -Comparació de paràmetres passius i actius de microones relatius a la vegetació. Recent investigació ha mostrat que l'opacitat, sensible a la biomassa i el contingut d'aigua, i l'albedo, relacionat amb l'estructura, poden ser recuperats d'observacions passives a banda L. S'exploren les relacions entre aquests dos paràmetres i estimadors de vegetació derivats de radar utilitzant les observacions d'avió de l'experiment de validació d'SMAP 2012 (SMAPVEX12). Les relacions obtingudes podrien permetre millors recuperacions de SM en sistemes actius/passius i estimar les propietats de la vegetació a alta resolució utilitzant mesures de SAR. La tesi s'ha desenvolupat dins les activitats del Barcelona Expert Centre (BEC). Els resultats presentats contribueixen a l'ús de la banda L a diferents disciplines científiques com la climatologia, la criosfera, la hidrologia i l'ecologia

    Interrelationships between soil moisture and precipitation large scales, inferred from satellite observations

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    Soil moisture influences the water and energy cycles of terrestrial environments, and thus plays an important climatic role. However, the behavior of soil moisture at large scales, including its impact on atmospheric processes such as precipitation, is not well characterized. Satellite remote sensing allows for indirect observation of large-scale soil moisture, but validation of these data is complicated by the difference in scales between remote sensing footprints and direct ground-based measurements. To address this problem, a method, based on information theory (specifically, mutual information), was developed to determine the useful information content of satellite soil moisture records using precipitation observations. This method was applied to three soil moisture datasets derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements over the contiguous U.S., allowing for spatial identification of the algorithm with the least inferred error. Ancillary measures of biomass and topography revealed a strong dependence between algorithm performance and confounding surface properties. Next, statistical causal identification methods (i.e. Granger causality) were used to examine the link between AMSR-E soil moisture and the occurrence of next day precipitation, accounting for long term variability and autocorrelation in precipitation. The probability of precipitation occurrence was modeled using a probit regression framework, and soil moisture was added to the model in order to test for statistical significance and sign. A contrasting pattern of positive feedback in the western U.S. and negative feedback in the east was found, implying a possible amplification of drought and flood conditions in the west and damping in the east. Finally, observations and simulations were used to demonstrate the pitfalls of determining causality between soil moisture and precipitation. It is shown that ignoring long term variability and precipitation autocorrelation can result in artificial positive correlation between soil moisture and precipitation, unless explicitly accounted for in the analysis. In total, this dissertation evaluates large-scale soil moisture measurements, outlines important factors that can cloud the determination of land surface-atmosphere hydrologic feedback, and examines the causal linkage between soil moisture and precipitation at large scales

    Ocean Modelling in Support of Operational Ocean and Coastal Services

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    Operational oceanography is maturing rapidly. Its capabilities are being noticeably enhanced in response to a growing demand for regularly updated ocean information. Today, several core forecasting and monitoring services, such as the Copernicus Marine ones focused on global and regional scales, are well-stablished. The sustained availability of oceanography products has favored the proliferation of specific downstream services devoted to coastal monitoring and forecasting. Ocean models are a key component of these operational oceanographic systems (especially in a context marked by the extensive application of dynamical downscaling approaches), and progress in ocean modeling is certainly a driver for the evolution of these services. The goal of this Special Issue is to publish research papers on ocean modeling that benefit model applications that support existing operational oceanographic services. This Special Issue is addressed to an audience with interests in physical oceanography and especially on its operational applications. There is a focus on the numerical modeling needed for a better forecasts in marine environments and using seamless modeling approaches to simulate global to coastal processes

    Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

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    The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences
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