55 research outputs found

    HISTORICAL AND FORECASTED KENTUCKY SPECIFIC SLOPE STABILITY ANALYSES USING REMOTELY RETRIEVED HYDROLOGIC AND GEOMORPHOLOGIC DATA

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    Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses. This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil moisture across areas of Eastern Kentucky for use in hazard analyses. These techniques include: A novel approach for determination of satellite-based soil moisture retrieval correction factors for use in acquisition of low orbit-based soil moisture retrievals in site-specific analyses, unique spatiotemporal modeling of soil moisture at various depths within the soil layer through assimilation of satellite-based and land surface modeled soil moisture estimates, and the development of a novel workflow to effectively provide 7-day forecasts of soil moisture for use in subsequent forecasting of landslide hazards. Soil moisture retrieved through the previous approaches was implemented within landslide hazard and susceptibility analyses across known rainfall-induced landslides within Eastern Kentucky. Investigated analyses were conducted through a coupling of spatial soil moisture retrievals with that of site-specific geomorphologic data. These analyses proved capable in the detection of incipient failure conditions indicative of landslide occurrence over these known investigated slides. These soil moisture-based analyses show that inclusion of soil moisture, as hydromechanical variable, yields a more capable hazard analysis approach. Additionally, these analyses serve as a means to gain a better understanding of the coupled hydro-mechanical behavior associated with the initiation of rainfall-induced landslides

    The Detection and Classification of Radio Frequency Interference in the Soil Moisture Active Passive Dataset

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    Soil moisture data is used in weather forecasting, drought detection, flood anticipation, and crop monitoring. NASA's Soil Moisture Active Passive (SMAP) mission orbits the Earth and operates a L-Band radiometer measuring the natural emission from the Earth to retrieve soil moisture. Despite the L-Band being a protected part of the spectrum to allow for passive observation of the Earth, radio frequency interferences (RFI) are observed in the SMAP measurements. This RFI is generated by illegal emissions within the protected band or by transmitters in adjacent frequency bands. Detecting RFI is critical, as it corrupts the radiometer measurements and can potentially bias the soil moisture retrievals, if the RFI is undetected. The detection is possible thanks to nine detection algorithms that are implemented in ground processing. Despite the overall good performance of the detection algorithms, some undetected RFI, also defined as residual RFI, are still noticeable in the SMAP measurements. This research is divided into two parts. The first part of the work focuses on classifying RFI sources using deep learning to provide a better understanding of the RFI environment. In this part of this study, the brightness temperatures for each SMAP radiometer measurement, or footprint, are treated as an image which will be the input to a deep learning neural network to classify the types of RFI into three groups: no RFI, wideband RFI, and narrowband RFI. The first results confirmed that using a neural network to classify different RFI types is possible and the SMAP footprints were successfully classified with and accuracy of 98%. The second part of the project investigates the implementation of image processing techniques to detect residual RFI by using mean filtering to detect and remove residual RFI in SMAP data. The filter was used on weekly max hold filtered brightness temperature maps, i.e., after RFI detection was performed and convolves over the global image, identifying regions with large spatial variations. Initial results for the mean filtering algorithm demonstrated the potential of this technique to detect spatial areas contaminated with residual RFI sources.No embargoAcademic Major: Computer Science and Engineerin

    Advanced methods for earth observation data synergy for geophysical parameter retrieval

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    The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

    Ocean remote sensing techniques and applications: a review (Part II)

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    As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

    Air Traffic Management Abbreviation Compendium

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    As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages. In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation

    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

    Estimation de l'humidité du sol à haute résolution spatio-temporelle : une nouvelle approche basée sur la synergie des observations micro-ondes actives/passives et optiques/thermiques

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    Les capteurs micro-ondes passifs SMOS et SMAP fournissent des données d'humidité du sol (SM) à une résolution d'environ 40 km avec un intervalle de 2 à 3 jours à l' échelle mondiale et une profondeur de détection de 0 à 5 cm. Ces données sont très pertinentes pour les applications cli- matiques et météorologiques. Cependant, pour les applications à échelle régionales (l'hydrologie) ou locales (l'agriculture), des données de SM à une haute résolution spatiale (typiquement 100 m ou plus fine) seraient nécessaires. Les données collectées par les capteurs optiques/thermiques et les radars peuvent fournir des indicateurs de SM à haute résolution spatiale, mais ces deux approches alternatives ont des limites. En particulier, les données optiques/thermiques ne sont pas disponibles sous les nuages et sous les couverts végétaux. Quant aux données radar, elles sont sensibles à la rugosité du sol et à la structure de la végétation, qui sont tous deux difficiles à caractériser depuis l'espace. De plus, la résolution temporelle de ces données est d'environ 6 jours. Dans ce contexte, la ligne directrice de la thèse est de proposer une nouvelle approche qui combine pour la première fois des capteurs passifs micro-ondes, optiques/thermiques et actifs micro-ondes (radar) pour estimer SM sur de grandes étendues à une résolution de 100 m chaque jour. Notre hypothèse est d'abord de nous appuyer sur une méthode de désagrégation existante (DISPATCH) des données SMOS/SMAP pour atteindre la résolution cible obtenue par les radars. A l'origine, DISPATCH est basé sur l'efficacité d' évaporation du sol (SEE) estimée sur des pixels partiellement végétalisés à partir de données optiques/thermiques (généralement MODIS) de température de surface et de couverture végétale à résolution de 1 km. Les données désagrégées de SM sont ensuite combinées avec une méthode d'inversion de SM basée sur les données radar afin d'exploiter les capacités de détection des radars Sentinel-1. Enfin, les capacités de l'assimilation des donnés satellitaires de SM dans un modèle de bilan hydrique du sol sont évaluées en termes de prédiction de SM à une résolution de 100 m et à une échelle temporelle quotidienne.Dans une première étape, l'algorithme DISPATCH est amélioré par rapport à sa version actuelle, principalement 1) en étendant son applicabilité aux pixels optiques entièrement végétalisés en utilisant l'indice de sécheresse de la végétation basé sur la température et un produit de couverture végétale amélioré, et 2) en augmentant la résolution de désagrégation de 1 km à 100 m en utilisant les données optiques/thermiques de Landsat (en plus de MODIS). Le produit de SM désagrégé à la résolution de 100 m est validé avec des mesures in situ collectées sur des zones irriguées au Maroc, indiquant une corrélation spatiale quotidienne variant de 0,5 à 0,9. Dans un deuxième étape, un nouvel algorithme est construit en développant une synergie entre les données DISPATCH et radar à 100 m de résolution. En pratique, le produit SM issu de DISPATCH les jours de ciel clair est d'abord utilisé pour calibrer un modèle de transfert radiatif radar en mode direct. Ensuite, le modèle de transfert radiatif radar ainsi calibré est utilisé en mode inverse pour estimer SM à la résolution spatio-temporelle de Sentinel-1. Sur les sites de validation, les résultats indiquent une corrélation entre les mesures satellitaires et in situ, de l'ordre de 0,66 à 0,81 pour un indice de végétation inférieur à 0,6. Dans une troisième et dernière étape, une méthode d'assimilation optimale est utilisée pour interpoler dans le temps les données de SM à la résolution de 100 m. La dynamique du produit SM dérivé de l'assimilation de SM DISPATCH à 100 m de résolution est cohérente avec les événements d'irrigation. Cette approche peut être facilement appliquée sur de grandes zones, en considérant que toutes les données (télédétection et météorologique) requises en entrée sont disponibles à l' échelle globale.SMOS and SMAP passive microwave sensors provide soil moisture (SM) data at 40 km resolution every 2-3 days globally, with a 0-5 cm sensing depth relevant for climatic and meteorological applications. However, SM data would be required at a higher (typically 100 m or finer) spatial resolution for many other regional (hydrology) or local (agriculture) applications. Optical/thermal and radar sensors can be used for retrieving SM proxies at such high spatial resolution, but both techniques have limitations. In particular, optical/thermal data are not available under clouds and under plant canopies. Moreover, radar data are sensitive to soil roughness and vegetation structure, which are challenging to characterize from outer space, and have a repeat cycle of at least six days, limiting the observations' temporal frequency. In this context, the leading principle of the thesis is to propose a new approach that combines passive microwave, optical/thermal, and active microwave (radar) sensors for the first time to retrieve SM data at 100 m resolution on a daily temporal scale. Our assumption is first to rely on an existing disaggregation method (DISPATCH) of SMOS/SMAP SM data to meet the target resolution achieved by radars. DISPATCH is originally based on the soil evaporative efficiency (SEE) retrieved over partially vegetated pixels from 1 km resolution optical/thermal (typically MODIS) surface temperature and vegetation cover data. The disaggregated SM data is then combined with a radar-based SM retrieval method to exploit the sensing capabilities of the Sentinel-1 radars. Finally, the efficacy of the assimilation of satellite-based SM data in a soil water balance model is assessed in terms of SM predictions at the 100 m resolution and daily temporal scale. As a first step, the DISPATCH algorithm is improved from its current version by mainly 1) extending its applicability to fully vegetated optical pixels using the temperature vegetation dryness index and an enhanced vegetation cover product, and 2) increasing the targeted downscaling resolution from 1 km to 100 m using Landsat (in addition to MODIS) optical/thermal data. The 100 m resolution disaggregated SM product is validated with in situ measurements collected over irrigated areas in Morocco, showing a daily spatial correlation in the range of 0.5-0.9. As a second step, a new algorithm is built on a synergy between DISPATCH and radar 100 m resolution data. In practice, the DISPATCH SM product available on clear sky days is first used to calibrate a radar radiative transfer model in the direct mode. Then the calibrated radar radia- tive transfer model is used in the inverse mode to estimate SM at the spatio-temporal resolution of Sentinel-1. Results indicate a positive correlation between satellite and in situ measurements in the range of 0.66 to 0.81 for a vegetation index lower than 0.6. As a third and final step, an optimal assimilation method is used to interpolate 100 m resolution SM data in time. The assimilation exercise is undertaken over irrigated crop fields in Spain. The analyzed SM product derived from the assimilation of 100 m resolution DISPATCH SM is consistent with irrigation events. This approach can be readily applied over large areas, given that all the required input (remote sensing and meteorological) data are available globally

    Inundações em múltiplas escalas na América do Sul : de áreas úmidas a áreas de risco

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    South America hosts some of the major river systems on Earth, often associated with large floodplains that are inundated every year, such as the Pantanal and many Amazon wetlands. Interfluvial wetland complexes are also found across the continent, with particular geomorphic settings and unique savanna or grassland vegetation. South American wetlands can provide distinctive ecosystem services such as biodiversity supporting, food provision and flood attenuation. On the other hand, humans have settled around wetlands for millennia, benefiting from all resources they provide, and have adapted to its flood regime as well adapted its landscape, defining what has been called human-water systems. Yet, an increasing number of South American people have been negatively affected by extreme floods. Moving from continental to local scales, this thesis invites the readers to a journey across major South American wetland systems and their unique hydrological dynamics, under the light of the satellite era and the breakthrough advances on hydrologic-hydrodynamic modeling in the last decades. This work is founded on the proposition of a continental wetland research agenda, and based on a comparative hydrology approach. Floods are studied through both natural wetland processes and hazard dimensions. The first part presents a set of studies on the Amazon basin wetlands, from the development of 1D and 2D models to simulate hydrological processes in contrasting wetland types in the Negro river basin to the basin-wide intercomparison of 29 inundation products and assessment of long-term inundation trends. While most wetland studies have been conducted over the central Amazon floodplains, major knowledge gaps remain for understanding the hydrological dynamics of interfluvial areas such as the Llanos de Moxos and Negro savannas, where the inundation is less predictable and shallower. The second part of the thesis leverages satellite-based datasets of multiple hydrological variables (water levels, total water storage, inundation extent, precipitation and evapotranspiration) to address the hydrology of 12 large wetland systems in the continent. It shows the major differences among river floodplains and interfluvial wetlands on the water level annual amplitude, time lag between precipitation and inundation, and evapotranspiration dynamics. Finally, the third part addresses the flood hazard component of human-wetland interactions through large-scale assessments of flood hazard dynamics and effects of built infrastructure (dams) on flood attenuation. The dynamics of the great 1983 floods, one of the most extreme years ever recorded in the continent, is assessed with a continental hydrological model. Then, the capabilities of continental models to simulate the river-floodplain-reservoir continuum that exists across large river basins are assessed with case studies for major river basins affected by human intervention (Itajaí-Açu and upper Paraná river basins in Brazil). While this thesis enlightens some relevant hydrological processes regarding South American floods and their positive and negative effects to human societies and ecosystems in general, major knowledge gaps persist and provide great research opportunities for the near future. The launching of many hydrology-oriented satellite missions, and an ever-growing computational capacity, make the continental hydrology agenda related to wetlands and floods a great research topic for the upcoming years.A América do Sul abriga alguns dos maiores sistemas hídricos do planeta, frequentemente associados a grandes planícies de inundação, como o Pantanal e várias áreas da Amazônia. Áreas úmidas (AU’s) interfluviais são também encontrados no continente, com características geomorfológicas particulares, e vegetações de savana e gramíneas únicas. As AU’s da América do Sul provêm diversos serviços ecossistêmicos, como suporte à biodiversidade, provisão de alimento e atenuação de cheias. Humanos têm se estabelecido ao redor de AU’s por milênios, se beneficiando dos recursos providos por elas. Eles se adaptaram ao seu regime de inundação, e adaptaram sua paisagem, definindo o que tem sido chamado de sistemas sociedade-água. Por outro lado, um número crescente de pessoas têm sido negativamente afetado por cheias extremas. Da escala continental à local, esta tese convida o leitor a uma jornada através de importantes AU’s da América do Sul e suas particulares dinâmicas de inundação, sob a luz da era dos satélites e dos grandes avanços em modelagem hidrológica-hidrodinâmica das últimas décadas. Este trabalho é baseado na proposta de uma escala continental de pesquisa sobre AU’s, e é baseado em uma abordagem de hidrologia comparativa. Inundações são estudadas em múltiplas dimensões, de processos de AU’s naturais à questão do perigo para humanos. A primeira parte apresenta uma série de estudos sobre as AU’s da bacia amazônica, desde o desenvolvimento de modelos 1D e 2D para simular processos hidrológicos em tipos contrastantes de AU’s na bacia do Rio Negro, até a intercomparação de 29 produtos de inundação e avaliação de tendências de inundações de longo prazo para a escala da bacia amazônica. Enquanto a maioria dos estudos de AU’s foi conduzida nas várzeas do rio Amazonas, importantes lacunas do conhecimento permanecem para a compreensão da dinâmica hidrológica de áreas interfluviais como Llanos de Moxos e as savanas do rio Negro, onde a inundação é menos previsível e mais rasa. A segunda parte da tese utiliza dados oriundos de satélites relacionados a múltiplas variáveis hidrológicas (níveis d’água, armazenamento total de água, extensão de áreas inundadas, precipitação e evapotranspiração) para estudar a hidrologia de 12 grandes sistemas de AU’s do continente. São destacadas as grandes diferenças entre planícies de inundação e AU’s interfluviais em termos de amplitude anual de níveis d’água, defasagem entre precipitação e inundação, e dinâmica de evapotranspiração. Por fim, a última parte da tese aborda o componente de perigo de inundação das interações sociedade-água através de avaliações em grande escala da dinâmica de inundação e dos efeitos de infraestruturas construídas (como barragens) na atenuação de cheias. A dinâmica das grandes cheias de 1983, um dos anos mais extremos já registrados no continente, é avaliada com um modelo hidrológico continental. Depois, a capacidade de modelos continentais para simular o continuum entre rios, planícies de inundação e reservatórios que existe em grandes bacias hidrográficas é avaliada com estudos de casos para importantes bacias afetadas pela intervenção humana (bacia dos rios Paraná e Itajaí-Açu). Enquanto esta tese avança a compreensão de relevantes processos hidrológicos relacionados a inundações na América do Sul em múltiplas escalas, bem como seus efeitos positivos e negativos nas sociedades humanas e ecossistemas em geral, importantes lacunas do conhecimento persistem e fomentam importantes oportunidades de pesquisa futuras. O lançamento de várias missões satelitais orientadas a hidrologia, e uma cada vez mais crescente capacidade computacional, faz da agenda continental de hidrologia relacionada a AU’s e inundações um grande tópico de pesquisa para os próximos anos

    L-Band Multi-Polarization Radar Scatterometry over Global Forests: Modelling, Analysis, and Applications

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    Spaceborne L-band radars have the ability to penetrate vegetation canopies over forested areas, suggesting a potential for regular and frequent global monitoring of both the vegetation state and the subcanopy soil moisture. However, L-band radar’s sensitivity to both vegetation and ground also complicates the relationship between the radar observations and the ecological and geophysical parameters. Accurate yet parsimonious forward models of the radar backscatter are valuable to building an understanding of these relationships. In the first part of this thesis, a model of L-band multi-polarization radar backscatter from forests, intended for use at regional to global spatial scales, is presented. Novel developments in the model include the consideration of multiple scattering within the dense vegetation canopy, and the application of a general model of plant allometry to mitigate the need for much intensive field data for training or over-tuning towards specific sites and tree species. Aided by our model, in the remainder and majority of the thesis, a detailed analysis and interpretation of L-band backscatter over global forests is performed, using data from the Aquarius and SMAP missions. Quantitative differences in backscatter predicted by our model due to freeze/thaw states, branch orientation, and flooding are partially verified against the data, and fitted values of aboveground-biomass and microwave vegetation optical depths are comparable to independent estimates in the literature. Polarization information is used to help distinguish vegetation and ground effects on spatial and temporal variations. We show that neither vegetation nor ground effects alone can explain spatial variations within the same land cover class. For temporal variations during unfrozen periods, soil moisture is found to often be an important factor at timescales of a week to several months, although vegetation changes remain a non-negligible factor. We report the observation of significant differences in backscatter depending on beam azimuthal angle, possibly due to plant phototropism. We also investigated diurnal variations, which have the potential to reveal signals related to plant transpiration. SMAP data from May-July 2015 showed that globally, co-polarized backscatter was generally higher at 6PM compared to 6AM over boreal forests, which is not what one might expect based on previous studies. Based on our modelling, increased canopy extinction at 6AM is a possible cause, but this is unproven and its true underlying physical cause undetermined. Finally, by making simplifying approximations on our forward model, we propose and explore algorithms for soil moisture retrieval under forest canopies using L-band scatterometry, with preliminary evaluations suggesting improved performance over existing algorithms.</p
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