Moghaddam, MahtaMelebari, Amer2025-05-292025-05https://doi.org/10.25549/usctheses-oUC11399KHQPhttps://hdl.handle.net/20.500.14154/75501Soil is the foundation of life on Earth, supporting plant growth, animals, and microbes that form the base of most food chains. Additionally, soil is essential in controlling the interactions between vegetation and the atmosphere, as well as their dynamics. Soil moisture has a first-order impact in controlling the global water cycle balance and a high impact on determining weather patterns. Healthy soil is essential for sustainable agriculture and food security, as well as mitigating climate change. Soil properties include soil moisture, texture, and carbon content. Additionally, properties such as surface roughness and aboveground biomass are intimately connected to the properties of the soil itself. Monitoring the dynamic properties of soil and what overlies it assists in preserving soil health. Multiple spaceborne and airborne remote sensing missions exist to monitor various soil properties. Furthermore, numerous analytical and computational algorithms have been developed to estimate soil properties using remote sensing systems, including radars, radiometers, and global navigation satellite system (GNSS)-reflectometry (GNSS-R). Although existing methods have shown great success in retrieving soil properties, such as soil moisture, there remains a need for new systems and algorithms to estimate these properties more accurately and with higher spatial and temporal resolutions. This dissertation presents the development of a suite of electromagnetic signal models and their use in the retrieval of soil properties. More specifically, this includes the development of a next-generation GNSS-R delay-Doppler map (DDM) model with the purpose of retrieving soil moisture. The model, called improved geometric optics with topography (IGOT), is applicable to surfaces with topographic relief. Additionally, the model is extended to forested areas by improving accounting for vegetation attenuation and adding the vegetation volume scattering effects. Moreover, the analytical sensitivity of the model to land surface parameters is investigated. The model is validated against National Aeronautics and Space Administration (NASA) Cyclone GNSS (CYGNSS) mission observations over multiple areas with good performance. The effects of volume scattering of vegetation were found by the model to be insignificant and negligible in most cases. Multiple physics-based algorithms are developed in this work to retrieve soil properties from GNSS-R DDMs as well as from multiple monostatic radars. Specifically, an algorithm for retrieving soil moisture and surface roughness from DDMs is developed. This algorithm uses a hybrid local/global optimizer and an electromagnetic forward scattering model that is applicable to vegetated surfaces without topography. The algorithm is validated using retrievals from CYGNSS observations compared with in situ soil moisture measurements. An unbiased root mean square error (ubRMSE) better than 0.1 m3m−3 is achieved. The same optimizer is used with a backscattering version of the forward model to estimate soil moisture from multiple radars with various frequencies and polarization. Retrievals from simulated measurements showed high retrieval accuracy. The last algorithm is for retrieving both soil moisture and vegetation water content (VWC) from multiple GNSS-R observations. It uses a local optimizer with the IGOT model. A surface roughness map, which is derived using multiple years of CYGNSS data, is used in the retrievals. Such a roughness map is needed because the small-scale of roughness (at the electromagnetic wavelength scale) is not captured by the digital elevation model (DEM). Using CYGNSS DDMs over the Jornada Experimental Range (JER) area, NM, USA, the algorithm is validated against the NASA flagship Soil Moisture Active Passive (SMAP) mission products. The validations showed that the algorithm can retrieve soil moisture with an ubRMSE of 0.075 m3m−3 over the validation site. Additionally, the results showed that the observations, and therefore the retrieval algorithm, are not sensitive enough to VWC for retrievals from CYGNSS observations. Two innovative radar observation architectures are explored for next-generation agile Earth observation systems. The first architecture employs a nonuniform sampling (NUS) receiver for signals-of-opportunity (SoOp) beyond GNSS-R. Both simulated and measured data are used in the study. The analysis demonstrates that while a NUS introduces a minor negative impact compared to the conventional uniform signal sampling schemes, its overall performance remains promising for future applications. The second architecture is tasking agile satellite constellations to reduce the uncertainty of retrievals of geophysical parameters. This is achieved by optimizing satellite measurement schedules and configurations. In this investigation, calculating the retrieval performance across ranges of radar frequencies and configurations and vegetation landcover types is performed using Monte Carlo simulations and the hybrid optimizer retrieval method mentioned earlier.269en-USEM modelingGNSS-Rsoil moisturesoil propertiesSignal-of-opportunityThesis