<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>

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.

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