Dissertation Defense Announcement for M M Nabi on 3/20/2024 at 12:30 PM

March 11, 2024

Dissertation Title: Deep Learning based Soil Moisture Retrieval using GNSS-R Observations from CYGNSS

When: 20 March 2024, 12:30 PM

Where: Simrall 228

Candidate: M M Nabi

Degree: Doctor of Philosophy in Electrical and Computer Engineering

Committee Members: Dr. Ali C. Gurbuz, Dr. John E. Ball, Dr. Vuk Marojevic, Dr. Mehmet Kurum


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, 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 soil moisture by leveraging spaceborne GNSS-R DDM 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 soil moisture products at a 9km × 9km resolution. The model is also evaluated using in-situ measurements from the International Soil Moisture Network (ISMN). The proposed approach is first explored in the Continental United States (CONUS) and then extended for global soil moisture retrieval. The most challenging validation efforts show potential improvement for future spaceborne soil moisture products with high spatial and temporal resolution. In addition, several soil moisture fusion algorithms have been explored to combine several CYGNSS-based soil moisture products. The fusion algorithm can help to achieve better estimation performance compared to individual products and keep the properties of individual products.