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This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.

The National Aeronautics and Space Administration’s (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission has grown substantial attention within the land remote sensing community for estimating soil moisture (SM), wind speed, flood extent, and precipitation by using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique. CYGNSS constellation generates important earth surface information called Delay-Doppler Maps (DDMs) from GNSS reflection measurements. Many previous findings considered only designed features from CYGNSS DDMs, such as the peak value of DDMs, whereas the whole DDMs are affected by SM, topography, inundation, and overlying vegetation. This dissertation explores a deep learning approach for estimating SM by leveraging spaceborne GNSS-RDDM observations provided by the CYGNSS constellation along with other remotely sensed geophysical data products. A data-driven approach utilizing convolutional neural networks (CNNs) that is trained jointly with three types of processed DDMs of Analog Power, Effective scattering area, and Bistatic Radar Cross-section (BRCS) with other auxiliary geophysical information such as normalized difference vegetation index (NDVI), elevation, soil properties, and vegetation water content (VWC). The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a 9km × 9km resolution. The model is also evaluated using in-situ measurements from International Soil Moisture Network (ISMN). The proposed approach is first explored in the Continental United States (CONUS) and then extended for global SM retrieval. The most challenging validation efforts show potential improvement for future spaceborne SM products with high spatial and temporal resolution. In addition, several SM fusion algorithms have been explored in order to combine several CYGNSS-based SM products. The fusion algorithm can help to achieve better estimation performance compared to individual products and keep the properties of individual products.

La réflectométrie GNSS (GNSS-R) est une technique d'observation de la Terre reposant sur un système radar bi-statique passif qui utilise, comme signaux d'opportunité, les signaux GNSS en bande L. Les travaux présentés dans ce manuscrit de thèse ont pour but de développer des méthodes de traitement du signal dédiées à l'altimétrie au sol par GNSS-R. L'altitude entre le récepteur GNSS-R et la surface de réflexion est déduite de la différence de chemin entre les signaux direct et réfléchi. On propose trois méthodes d'estimation dédiées à l'altimétrie par GNSS-R, pour un récepteur mono-fréquence, utilisant respectivement les observables de code, de puissance (carrier-to-noise ratio C/N₀) et de phase des signaux GNSS observés. Nous proposons un estimateur de la pseudo-distance qui utilise la mesure de délais de code sous-échantilloné aidée par la mesure de phase. On montre que l'estimateur sub-résolution proposé permet d'obtenir une précision qui est inférieure à la résolution en délai de code. Le deuxième estimateur s'appuie sur une méthode de calibration qui normalise la puissance de la somme des signaux direct et réfléchi (Interférence Pattern Technique). On montre par l'étude des bornes de Cramèr-Rao que l'estimateur proposé permet de réduire le temps de mesure et de conserver une précision centimétrique. La mesure de phase est une grandeur circulaire qui évolue linéairement avec l'élévation du satellite. Dans ce contexte, nous proposons deux estimateurs qui s'appuient sur un modèle de régression circulaire et la distribution circulaire de von Mises. Des expérimentations sur données réelles viennent conclure ce manuscrit de thèse et montrent la faisabilité des trois méthodes d'estimation proposées. La précision centimétrique est atteinte. / The Global Navigation Satellite Systems Reflectometry (GNSS-R) is an Earth observation technique. It is based on a passive bi-static radar system using the L-band signal coming directly from a GNSS satellite and this same signal reflected by the Earth surface. The aim of the presented work is to develop signal processing methods for altimetry measurements using ground based GNSS-R. The altitude is derived from the difference of path between the direct and reflected signals. We propose three estimators for GNSS-R altimetry measurement using respectively the code observations, the carrier-to-noise ratio C/N₀ observations, and the phase observations obtained by a mono-frequency receiver. Firstly, we define a pseudo-range estimator using under-sampling code delay observations aided by phase measurements. We show that the proposed estimator allows avoiding accuracy limitations due to the receiver resolution. Secondly, a calibration method has been developed for the Interference Pattern Technique, for normalizing the C/N₀ of the combination of the direct and reflected signals. The Cramèr-Rao Lower Bound of this estimation technique is studied. We show that the proposed estimator allows reducing the observation duration while keeping the centimeter accuracy. Thirdly, a last method is proposed in order to evaluate the difference of path between the direct and reflected signals using phase measurement.The phase measurement is an angular data evolving linearly with the satellite elevation. In this context, we propose two estimators based on a circular-linear regression and the von Mises distribution. Experimentation on real data conclude this manuscript and show the feasibility of these methods. The centimeter accuracy is reached.

Lorsqu'une grandeur est observée par une série temporelle de mesures périodiques, telles que des mesures d'angles, les outils statistiques du domaine linéaire ne sont plus adaptés à l'estimation des paramètres statistiques de cette série. Nous proposons des filtres et un opérateur de fusion adaptés aux grandeurs aléatoires circulaires. Ils sont développés dans un cadre bayésien avec la distribution circulaire normale de von Mises. De plus, on propose un estimateur circulaire GLR (Global Likehood Ratio) de ruptures dans un signal angulaire s'appuyant sur un filtre particulaire défini dans le domaine circulaire. Le traitement des signaux GNSS repose traditionnellement sur l'estimation récursive, en boucles fermées, de trois paramètres : le délai de code, la phase et la fréquence porteuse du signal reçu. On montre dans cette thèse que l'estimation de la phase et de la fréquence en boucle fermée est à l'origine d'imprécisions et de perturbations additionnelles. On propose une nouvelle approche pour l'estimation de ces paramètres en boule ouverte. Dans ce cas, les mesures de phase sont des angles, définis entre ‒π et π. Statistiquement, elles peuvent être modélisées par la distribution circulaire de von Mises et l'on utilise donc les outils d'estimation circulaire développés dans cette thèse pour les traiter. On montre l'intérêt de l'approche proposée pour le positionnement et pour l'altimétrie par réflectométrie des signaux GNSS. La précision obtenue est de l'ordre du centimètre, pour des durées d'intégrationd'une milliseconde. On propose également une technique de résolution de l'ambiguïté de phase, utilisable dans une approche mono-récepteur et mono-fréquence. / When a value is observed by a temporal series of periodic measurements, such as angle measurements, the statistic tools of the linear domain are no longer adapted to the estimation of the statistical parameters of this series. We propose several filters and a fusion operator adapted to angular random variables estimation. They are developed in a Bayesian framework with the von Mises circular normal distribution. Moreover, we propose a Global Likelihood Ratio circular change estimator which relies on a particle filter defined in the circular domain. GNSS signal procedding traditionnaly relies on the recursive estimation of three parameters in lock loops : the code delay, the phase and the frequency of the received signal carrier. We show in this thesis that the phase and frequency estimations, when realized in a lock loop, cause imprecisions and additional perturbations. We proposea new approach for the estimation of these parameters in an open loop. in this context, the phase measurements are angles defined between ‒π et π. They can be modeled using the von Mises distribution, thus we use the developed circular estimation tools in order to process them. We show the benefit of the proposed approach for positioning and for height estimation using the GNSS-Reflectometry technique. The obtained precision is of the centimeter order, for integration times of one millisecond. We also propose a phase ambiguity resolution technique which can be used in a mono-receiver , mono-frequency approach.

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The potential for high spatio-temporal resolution microwave measurements has urged the adoption of the signals of opportunity (SoOp) passive radar technique for use in remote sensing. Recent trends in particular target highly complex remote sensing problems such as root-zone soil moisture and snow water equivalent. This dissertation explores the continued open-sourcing of the SoOp coherent bistatic scattering model (SCoBi) and its use in soil moisture sensing applications. Starting from ground-based applications, the feasibility of root-zone soil moisture remote sensing is assessed using available SoOp resources below L-band. A modularized, spaceborne model is then developed to simulate land-surface scattering and delay-Doppler maps over the available spectrum of SoOp resources. The simulation tools are intended to provide insights for future spaceborne modeling pursuits.