Thesis (M.S.)--George Mason University, 2008. / Vita: p. 65. Thesis director: John Qu. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Earth System Science. Title from PDF t.p. (viewed Jan. 11, 2009). Includes bibliographical references (p. 59-64). Also issued in print.

Thesis (M. Phil.)--University of Hong Kong, 1981.

Thesis (M.S. - Hydrology and Water Resources)--University of Arizona. / Includes bibliographical references (leaves 96-99).

Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 151. Thesis director: John J. Qu. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Earth Systems and GeoInformation Sciences. Title from PDF t.p. (viewed June 30, 2008). Includes bibliographical references (p. 135-150). Also issued in print.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 127-137).

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.