<div>Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that uses reflected satellite navigation signals from the Earth surface in a bistatic radar configuration. GNSS-R observations have been collected using receivers on stationary, airborne and spaceborne platforms. The delay-Doppler map (DDM) is the fundamental GNSS-R measurement from which ocean surface wind speed can be retrieved. GNSS-R observations can be assimilated into numerical weather prediction models to improve weather analyses and forecasts. The direct assimilation of DDM observations shows potential superiority over the assimilation of wind retrievals.</div><div><br></div><div>This dissertation demonstrates the direct assimilation of GNSS-R DDMs using a two-dimensional variational analysis method (VAM). First, the observation forward model and its Jacobian are developed. Then, the observation's bias correction, quality control, and error characterization are presented. The DDM assimilation was applied to a global and a regional case. </div><div><br></div><div>In the global case, DDM observations from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission are assimilated into global ocean surface wind analyses using the European Centre for Medium-Range Weather Forecasts (ECMWF) 10-meter winds as the background. The wind analyses are improved as a result of the DDM assimilation. VAM can also be used to derive a new type of wind vector observation from DDMs (VAM-DDM).</div><div><br></div><div>In the regional case, an observing system experiment (OSE) is used to quantify the impact of VAM-DDM wind vectors from CYGNSS on hurricane forecasts, in the case of Hurricane Michael (2018). It is found that the assimilation of VAM-DDM wind vectors at the early stage of the hurricane improves the forecasted track and intensity.</div><div><br></div><div>The research of this dissertation implies potential benefits of DDM assimilation for future research and operational applications.</div>

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Accurate measurement of soil moisture (SM) has a significant impact on agricultural production, hydrological modeling, forestry, horticulture, waste management, and other environmental fields. Particularly in precision agriculture (PA), high spatiotemporal resolution information about surface SM is crucial. However, the use of invasive SM probes and other sensors is expensive and requires extensive manpower. Moreover, these intrusive techniques provide point measurements and are unsuitable for large agricultural fields. As an alternative, this dissertation explores the remote sensing of surface SM by utilizing the surface reflectivity estimated from global navigation satellite systems reflectometry (GNSS-R) data acquired through smartphones and off-the-shelf, cost-effective U-blox global navigation satellite systems (GNSS) receivers. To estimate surface reflectivity, the GNSS receivers are attached underneath a small unmanned aircraft system (UAS), which flies over agricultural fields. Additionally, this dissertation investigates a fully custom UAS-based dual-polarized L-band microwave radiometric measurement technique over agricultural areas to estimate surface brightness temperature (����). The radiometer measures surface emissivity as ����, allowing for the estimation of surface SM while considering the detection and removal of radio frequency interference (RFI) from the radiometric measurements. This radiometer processes the data in near real-time onboard the UAS, collecting raw in-phase and quadratic (I&Q) signals across the study field. This feature mitigates the RFI onboard and significantly reduces post-processing time. In summary, this study highlights the utilization of smartphones and semi-custom GNSS receivers in conjunction with UAS-based GNSS-R techniques and UAS-based L-band microwave radiometry for the estimation of surface reflectivity and ����. The radiometric measurement of surface emissivity is related to surface reflectivity through the relationship (Emissivity = 1 -Reflectivity).

La réflectométrie GNSS (ou GNSS-R) est une technique de télédétection originale et pportuniste qui consiste à analyser les ondes électromagnétiques émises en continu par la soixantaine de satellites des systèmes de positionnement GNSS (GPS, GLONASS, etc.), qui sont captées par une antenne après réflexion sur la surface terrestre. Ces signaux interagissent avec la surface réfléchissante et contiennent donc des informations sur ses propriétés. Au niveau de l'antenne, les ondes réfléchies interfèrent avec celles arrivant directement des satellites. Ces interférences sont particulièrement visibles dans le rapport signal-sur-bruit (SNR, i.e., Signal-to-Noise Ratio), paramètre enregistré par une station GNSS classique. Il est ainsi possible d'inverser les séries temporelles du SNR pour estimer des caractéristiques du milieu réfléchissant. Si la faisabilité et l'intérêt de cette méthode ne sont plus à démontrer, la mise en oeuvre de cette technique pose un certain nombre de problèmes, à savoir quelles précisions et résolutions spatio-temporelles peuvent être atteintes, et par conséquent, quels sont les observables géophysiques accessibles. Mon travail de thèse a pour objectif d'apporter des éléments de réponse sur ce point, et est axé sur le développement méthodologique et l'exploitation géophysique des mesures de SNR réalisées par des stations GNSS classiques. Je me suis focalisé sur l'estimation des variations de hauteur de l'antenne par rapport à la surface réfléchissante (altimétrie) et de l'humidité du sol en domaine continental. La méthode d'inversion des mesures SNR que je propose a été appliquée avec succès pour déterminer les variations locales de : (1) la hauteur de la mer au voisinage du phare de Cordouan du 3 mars au 31 mai 2013 où les ondes de marées et la houle ont pu être parfaitement identifiées ; et (2) l'humidité du sol dans un champ agricole à proximité de Toulouse, du 5 février au 15 mars 2014. Ma méthode permet de s'affranchir de certaines restrictions imposées jusqu'à présent dans les travaux antérieurs, où la vitesse de variation verticale de la surface de réflexion était supposée négligeable. De plus, j'ai développé un simulateur qui m'a permis de tester l'influence de nombreux paramètres (troposphère, angle d'élévation du satellite, hauteur d'antenne, relief local, etc.) sur la trajectoire des ondes réfléchies et donc sur la position des points de réflexion. Mon travail de thèse montre que le GNSS-R est une alternative performante et un complément non négligeable aux techniques de mesure actuelles, en faisant le lien entre les différentes résolutions temporelles et spatiales actuellement atteintes par les outils classiques (sondes, radar, diffusiomètres, etc.). Cette technique offre l'avantage majeur d'être basé sur un réseau de satellites déjà en place et pérenne, et est applicable à n'importe quelle station GNSS géodésique, notamment celles des réseaux permanents (e.g., le RGP français). Ainsi, en installant une chaîne de traitement de ces acquisitions de SNR en domaine côtier, il serait possible d'utiliser les mesures continues des centaines de stations pré-existantes, et d'envisager de réaliser des mesures altimétriques à l'échelle locale, ou de mesurer l'humidité du sol pour les antennes situées à l'intérieur des terres. / GNSS reflectometry (or GNSS-R) is an original and opportunistic remote sensing technique based on the analysis of the electromagnetic waves continuously emitted by GNSS positioning systems satellites (GPS, GLONASS, etc.) that are captured by an antenna after reflection on the Earth's surface. These signals interact with the reflective surface and hence contain information about its properties. When they reach the antenna, the reflected waves interfere with those coming directly from the satellites. This interference is particularly visible in the signal-to-noise ratio (SNR) parameter recorded by conventional GNSS stations. It is thus possible to reverse the SNR time series to estimate the reflective surface characteristics. If the feasibility and usefulness of thismethod are well established, the implementation of this technique poses a number of issues. Namely the spatio-temporal accuracies and resolutions that can be achieved and thus what geophysical observables are accessible.The aim of my PhD research work is to provide some answers on this point, focusing on the methodological development and geophysical exploitation of the SNR measurements performed by conventional GNSS stations. I focused on the estimation of variations in the antenna height relative to the reflecting surface (altimetry) and on the soil moisture in continental areas. The SNR data inversion method that I propose has been successfully applied to determine local variations of : (1) the sea level near the Cordouan lighthouse (not far from Bordeaux, France) from March 3 to May 31, 2013, where the main tidal periods and waves have been clearly identified ; and (2) the soil moisture in an agricultural plot near Toulouse, France, from February 5 to March 15, 2014. My method eliminates some restrictions imposed in earlier work, where the velocity of the vertical variation of the reflective surface was assumed to be negligible. Furthermore, I developed a simulator that allowed me to assess the influence of several parameters (troposphere, satellite elevation angle, antenna height, local relief, etc.) on the path of the reflected waves and hence on the position of the reflection points. My work shows that GNSS-R is a powerful alternative and a significant complement to the current measurement techniques, establishing a link between the different temporal and spatial resolutions currently achieved by conventional tools (sensors, radar, scatterometer, etc.). This technique offers the major advantage of being based on already-developed and sustainable satellites networks, and can be applied to any GNSS geodetic station, including permanent networks (e.g., the French RGP). Therefore, by installing a processing chain of these SNR acquisitions, data from hundreds of pre-existing stations could be used to make local altimetry measurements in coastal areas or to estimate soil moisture for inland antennas.

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<div> <div> <p>Global Navigation Satellite System Reflectometry (GNSS-R) has presented a great potential as an important approach for ocean remote sensing. Numerous studies have demonstrated that the shape of a code-correlation waveform of forward-scattered Global Positioning System (GPS) signals may be used to measure ocean surface roughness and related geophysical parameters such as wind speed. Recent experiments have extended the reflectometry technique to transmissions from communication satellites. Due to the high power and frequencies of these signals, they are more sensitive to smaller scale ocean surface features, which makes communication satellites a promising signal of opportunity (SoOp) for ocean remote sensing. Recent advancements in fundamental physics are represented by the new scattering model and bistatic radar function developed by Voronovich and Zavorotny based on the SSA (Small Slope Approximation). This new model allows the partially coherent scattering in low wind conditions to be correctly described, which overcomes the limitations of diffuse scattering inherited in the conventional KA-GO (Kirchhoff Approximation-Geometric Optics) model. Furthermore, exploration and practice using spaceborne platforms have become a primary research focus, which is highlighted by the launch of CYGNSS (Cyclone Global Navigation Satellite System) in 2016. CYGNSS is a NASA (National Aeronautics and Space Administration) Earth Venture Mission consisting of an 8 micro-satellite constellation of GNSS-R instruments designed to observe tropical cyclones.</p><p>However, in spite of the significant achievements made in the past 10 years, there are still a variety of challenges to be addressed currently in the ocean reflectometry field. To begin with, the airborne demonstration experiments conducted previously for S-band reflectometry provided neither sufficient amount of data nor the desired scenarios to assess high wind retrieval performance of S-band signals. The current L-band empirical model function theoretically does not also apply to S-band reflectometry. With respect to scattering models, there have been no results of actual data processing so far to verify the performance of the SSA model, especially on low wind retrievals. Lastly, the conventional model fitting methods for ocean wind retrievals were proposed for airborne missions, and new approaches will need to be developed to satisfy the requirement of spaceborne systems.<br></p><p>The research described in this thesis is mainly focused on the development, application and evaluation of new models and algorithms for ocean wind remote sensing. The first part of the thesis studies the extension of reflectometry methods to the general class of SoOps. The airborne reception of commercial satellite S-band transmissions is demonstrated under both low and high wind speed conditions. As part of this effort, a new S-band geophysical model function (GMF) is developed for ocean wind remote sensing using S-band data collected in the 2014 NOAA (National Oceanic and Atmospheric Administration) hurricane campaign. The second part introduces a dual polarization L- and S-band reflectometry experiment, performed in collaboration with Naval Research Lab (NRL), to retrieve and analyze surface winds and compare the results with CYGNSS satellite retrievals and NOAA data buoy measurements. The problems associated with low wind speed retrieval arising from near specular surface reflections are studied. Results have shown improved wind speed retrieval accuracy using bistatic radar cross section (BRCS) modeled by the SSA when compared with KA-GO, in the cases of low to medium diffuse scattering. The last part focuses on the contributions to the NASA-funded spaceborne CYGNSS project. It shows that the accuracy of CYGNSS ocean wind retrieval is improved by an Extended Kalman Filter (EKF) algorithm. Compared with the baseline observable methods, preliminary results showed promising accuracy improvement when the EKF was applied to actual CYGNSS data.<br><br></p></div></div>

L'eau fait partie intégrante de la vie sur notre planète et joue un rôle important dans les études pour évaluer l'impact du changement climatique. La recherche des ressources en eau est donc très importante pour la communauté scientifique du "climat" non seulement en surveillant de près le budget régional et mondial des ressources en eau, mais aussi pour comprendre les changements dans la fréquence et l'intensité des événements météorologiques ponctuels. Cela est particulièrement vrai pour les phénomènes météorologiques extrêmes, qui ont de grands impacts socio-économiques. L'impact des tempêtes tropicales plus ou plus intenses, des méga-neiges ou des coupes de poussières est l'un des principaux domaines de la recherche climatique. Le but de mon travail de recherche est de fournir des moyens d'évaluer l'impact des changements climatiques sur les ressources en eau et de trouver des outils flexibles permettant une gestion durable de l'eau. Des études récentes ont montré que l'on peut tirer parti des ondes émises en continu par les constellations du système mondial de navigation par satellite (GNSS) pour mesurer l'humidité du sol. Cette technique de télédétection opportuniste, connue sous le nom de réflectométrie GNSS (GNSS-R), consiste à comparer l'interférence des ondes réfléchies par le sol et celles provenant directement des satellites. Dans ma thèse, je me suis concentré sur la base GNSS-R sur le rapport signal-sur-bruit (SNR) enregistré par un récepteur GNSS conventionnel avec une unique antenne pour récupérer les variations d'humidité du sol. Beaucoup d'études ont montré l'efficacité de la méthode sur les sols argileux, et j'ai démontré dans ma thèse qu'elle était tout aussi efficace sur les sols sableux à condition d'appliquer la méthode du déroulement de phase. Cette méthode que je propose a été appliquée avec succès pour déterminer les variations locales d'humidité du sol: (1) 100% du sable dans le terrain de jeu de volleyball (Toulouse, France); et (2) >85% de sable dans la zone critique sahélienne de Dahra (Sénégal). En outre, la mesure précise et continue des niveaux d'eau des rivières est un élément important de la gestion des ressources en eau, afin d'obtenir une estimation continue du débit de la rivière dans le monde. La précision de la technique GNSS-R pour l'altimétrie fluviale est son pas d'échantillonnage élevé permet de suivre les événements hydrologiques extrêmes. Deux méthodes, les moindres carrés et la technique "Larson", ont été appliquées avec succès pour déterminer les variations des hauteurs au Vietnam: (1) sur la fleuve Rouge (21 ° 2'44.04 "N, 105 ° 51'48.86" E) où les événements et changements morphologiques associés aux événements hydrologiques (tempête tropicale) en 2016; et (2) sur le delta du Mékong (9 ° 31'38.63 "N, 106 ° 12'2.01" E) où les eaux continentales interagissent avec les eaux océanique. Mon travail montre que le GNSS-R est une alternative puissante et un complément significatif aux techniques actuelles de mesure de la gestion des ressources en eau en établissant un lien entre les différentes résolutions temporelles et spatiales actuellement obtenues par les outils conventionnels (in-situ capteurs, télédétection radar, etc.). Cette technique présente le grand avantage d'être basic sur le réseau pérenne du constatation GNSS et peut donc être utilisé sur n'importe quelle station GNSS. Par conséquent, en implantant une chaîne de traitement SNR, on peut automatiquement suivre les variations des environnementaux fondamentaux, i.e. la hauteur de la rivière, la pente locale de la surface de l'eau, les zones inondées, les variations d'humidité du sol et même la hauteur de végétation. / Water is an integral part of life on our planet and it plays an important role in climatic changes. Water resources research is, therefore, very important for the climate communities to not only closely monitor the regional and global water supply budget, but to also understand changes in frequency of occurrence and strength of individual weather events. This is especially true for extreme weather events, which have great societal and economic impacts. Whether we will have more or more intense tropical storms, mega-snow events, or dust-bowls in the near or far future climate. This is one of the key focus areas of climate research. The aim of my PhD work is to provide some answers to assess the impact of future climate change threats on water resources. And we are trying to find the adaptive tools needed for sustainable water resources management. In an effort to optimize water resource management, it is crucial to improve soil moisture situation awareness. With the advent of remote sensing, soil moisture is systematically monitored at the global scale but at the expense of the temporal and/or spatial resolution. Recent studies suggested to take advantage of continuously emitted waves by the Global Navigation Satellite System (GNSS) constellations, to retrieve soil moisture. This opportunistic remote sensing technique, known as GNSS Reflectometry (GNSS-R), consists in comparing the interference of reflected waves by the ground and those which come directly from satellites. In my thesis, I focused on GNSS-R technique base on the signal-to-noise ratio (SNR) recorded by conventional GNSS receiver with single antenna to retrieve soil moisture variations. Previous studies show the efficiency of this methodology for clay soil and I demonstrate for the first time, it's efficiency for sandy soil using Unwrapping phase method. This method that I propose has been successfully applied to determine local soil moisture variations of : (1) 100% of sand in Volleyball playground (Toulouse, France); and (2) >85% of sand in the Sahelian critical zone of Dahra (Senegal). In addition, accurate and continuous measurement of river water levels is an important element in water resource management, to obtain an ongoing estimate of the river's flow around the world. The accuracy of GNSS-R technique for river altimetry is useful for detection of extreme hydrological events and to show the competition between continental and oceanic water near coastal area. The two methods, Least Square and "Larson" methods, has been successfully applied to determine local variations in Vietnam of: (1) the Red river (21°02'44.04"N, 105°51'48.86"E) to identify flood events and morphological changes associated to the hydrological events (tropical storm) in 2016; and (2) the Mekong river delta (9°31'38.63"N, 106°12'2.01"E) where continental water interacts with oceanic water. My work shows that GNSS-R is a powerful alternative and a significant complement to the current measurement techniques for managing water resource by establishing a link between the different temporal and spatial resolutions currently achieved by conventional tools (in-situ sensors, remote sensing radar, etc.). This technique has a great advantage based on already-developed and sustainable GNSS satellites networks and can be applied to any GNSS geodetic station. Therefore, by installing a processing chain of the SNR acquisitions, we are able to monitor various environmental parameters i.e. height river, local slope of water surface, flooded areas, soil moisture variations and even vegetation/plant height.

<p>Multiple Signals of Opportunity (multi-SoOp) across different frequencies and polarizations</p> <p>offer a potential breakthrough for remote sensing of root-zone soil moisture (RZSM). Deeper penetration depths of existing communication transmissions in the frequency ranges of 137–138, 240–270, and 360–380 MHz enable the estimation of RZSM by complementing global navigation satellite system reflectometry (GNSS-R) in L-band. The small form factor of the multi-SoOp observatory allows for high spatiotemporal coverage of RSZM by a satellite constellation in a cost-effective manner. This study aims to develop models and tools to define mission requirements for various system parameters that affect observation accuracy and coverage, for the advancement of spaceborne multi-SoOp remote sensing. These parameters include frequency and polarization combinations, observation error, inter-frequency temporal coincidence, and configuration of the satellite constellation. We present the development of a retrieval algorithm and the sensitivity analysis of retrieval accuracy. The retrieval algorithm was evaluated using synthetic observations generated from multiyear time series of in-situ soil moisture (SM) and satellite-based vegetation data. The combined use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. A bivariate model, derived from the sensitivity analysis, facilitates error prediction for future science missions. We introduce a framework for tradespace exploration of the multi-SoOp satellite constellation. A constellation design study indicates that a Walker constellation comprising 24 satellites with 3 orbital planes at 500 km and 50° inclination optimizes the coverage and mission cost under mission requirements. A tower-based field experiment validated the performance of a prototype antenna for multi-SoOp using the interference pattern technique. More field experiments with improved instruments are required to further advance the multi-SoOp technique.</p>

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