The aim of this thesis is to estimate parameters of the moving target using different radar configurations. First the Doppler effect and the time delay of the received signal is examined. The investigation of these phenomenons is made for radial and non-radial movement of simple monostatic radar configuration and after that for the bistatic and multistatic MIMO configuration. The ambiguity function is presented for all these configurations. The ambiguity function is then used to estimate target parameters from the received signal. The received signal model and ambiguity function for the monostatic radar configuration is developed using Matlab software. Finally the practical use of these implementations is presented and compared.

<div>Surface soil moisture is one of the few direct hydrological variables which can be measured. It plays a crucial part in the water cycle, agriculture, drought development, runoff generation, and many other phenomena. Satellite observations from active and passive microwave radiometers are best suited for the retrieval of soil moisture. The relationship between soil dielectric constant and water content is direct and is used to determine the surface soil moisture levels. Active microwave remote sensing techniques measure the energy reflected from target surfaces (ocean, soil, biomass) after transmitting a pulse of microwave energy, whereas passive microwave sensors measure the self-emissions of the target surfaces. The passive missions by ESA's SMOS and NASA's SMAP have demonstrated this technology for remote sensing on a global scale. Global Navigation Satellite System-Reflectometry (GNSS-R) is an alternative approach to the remote sensing of soil moisture, as demonstrated through several airborne and ground-based experiments. The new technique of Signals of Opportunity (SoOp) uses a bistatic radar configuration in which the non-cooperative transmitter already transmits signals designed for communication or navigation. The receiver reuses the reflected energy from the target surface (ocean, soil, biomass), thereby making the digital communication and navigation signal spectrum useful to the remote sensing science community. Several airborne and ground-based experiments have been conducted on the use of digital communication signals, a range of frequencies from P-band to Ku-band, for measurement of ocean surface roughness, wind speed, and soil moisture. </div><div> </div><div>This thesis presents the preliminary results obtained for reflectivity retrievals for the 2017 and 2018 S-band tower-based SoOp field experiment conducted at Purdue's Agronomy Center for Research and Education (ACRE). XM signals were recorded by a sky-facing antenna and an Earth-facing antenna mounted atop a tower. The line-of-sight (direct) signal is captured by the sky-facing antenna and reflected signal from the soil captured by the Earth-facing antenna was used for the ambiguity function of XM transmission. A link budget was used to determine the received signal to noise ratio (SNR). The cross-correlation between the direct and the reflected XM signals was used to estimate reflectivity values. The reflectivity retrievals were compared with the in-situ soil moisture content at 5 cm depth obtained by the HydraProbes. The reflectivity values were also verified by a Signals of Opportunity (SoOp) Coherent Bistatic (SCoBi) simulated model.</div>

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The agricultural sector is the backbone which supports the livelihoods and the economic development of nations across the globe. In consequence, the need for robust and continuous monitoring of agricultural crops is primordial to face the interlinked challenges of growth rate population, food security and climate change. Synthetic Aperture Radar (SAR) sensors have the powerful imaging capability of operating at almost all weather conditions, independent of day and night illumination. By penetrating through clouds and into the vegetation canopy, the incident radar signal interacts with the structural and dielectric properties of the vegetation and soil, thus providing critical information of the crop state, such as height, biomass, crop yield or leaf structure, which can help devise sustainable agricultural management practices. This is achieved by means of the Polarimetric SAR Interferometry (PolInSAR) technique, which by coherently combining interferometric SAR acquisitions at different polarization states allows for the retrieval of biophysical parameters of the vegetation. In this framework, this thesis focuses on the development of crop monitoring techniques that properly exploit satellite-based PolInSAR data. All the known InSAR and PolInSAR methodologies for this purpose have been analysed. The sensitivity of these data provided by the TanDEM-X bistatic system to both the physical parameters of the scene (height and structure of the plants, moisture and roughness of the soil) and the sensor configuration (polarization modes and observation geometry) is evaluated. The effect of different simplifications made in the physical model of the scene on the crop estimates is assessed. The interferometric sensitivity requirements to monitor a crop scenario are more demanding than others, such as forests. Steep incidences associated with the largest spatial baselines provided by the available data set lead to the most accurate estimates under all the different model assumptions. Shallower incidences, on the other hand, generally yield important errors due to their characteristic shorter spatial baselines. Through the methodologies proposed in this thesis, PolInSAR data have shown potential to refine current methods for the quantitative estimation of crop parameters. Results encourage to continue further research towards the objective of achieving operational crop monitoring applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P. Noelia Romero-Puig received a grant from the Generalitat Valenciana and the European Social Fund (ESF) [ACIF/2018/204].

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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.

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A study is undertaken of the numerical reconstruction of acoustic and electromagnetic (EM) localized waves (LWs). The latter are carrier-free ultra-wideband pulses characterized by large focusing depths and extended ranges of localization. Special emphasis is placed on finite energy LWs that can be generated by dynamic aperture antennas with independently addressable elements. The reconstruction techniques are based on Huygens and Rayleigh-Sommerfeld integral I and II representations, both in the time and frequency domains. In contradistinction to the Weyl representation,they lend themselves to the physical realization of space-time aperture sources capable of generating localized wave solutions propagating away from the aperture plane. A detailed comparison of the three reconstruction techniques has been carried out in connection with LW solutions to the scalar wave equation, especially with respect to their handling of acausal components incorporated in the aperture excitation fields. In addition, a study is presented of the characteristic properties of LWs propagating through dispersive media modeled by the Klein-Gordon equation. It is demonstrated that contrary to expectation, the depletion of the spectral components of the LW Klein-Gordon field may be slower than that associated with the free space scalar field. Previous work by Power et al. [73] is extended by studying the acoustic bistatic scattering of a modified power spectrum (MPS) pulse from rigid and compressible spheres. The analysis allows the extraction of the radius of a sphere from the backscattered data. Finally, a special class of electromagnetic (EM) LWs, referred to as azimuthally polarized X waves (APXWs), is derived and their reconstruction is addressed, both in the time and frequency domains. / Ph. D.