Spaceborne GNSS reflectometry for land remote sensing studies

Understanding, quantifying and monitoring soil moisture is important for many applications, e.g., agriculture, weather forecasting, occurrence of heatwaves, droughts and floods, and human health. At a large scale, satellite microwave remote sensing has been used to retrieve soil moisture information. Surface water has also been detected and monitored through remote sensing orbital platforms equipped with passive microwave, radar, and optical sensors. The use of reflected L-band Global Navigation Satellite System (GNSS) signals represents an emerging remote sensing concept to retrieve geophysical parameters. In GNSS Reflectometry (GNSS-R) these signals are repurposed to infer properties of the surface from which they reflect as they are sensitive to variations in biogeophysical parameters. NASA's Cyclone GNSS (CYGNSS) is the first mission fully dedicated to spaceborne GNSS-R. The eight-satellite constellation measures Global Positioning System (GPS) reflected L1 (1575.42 MHz) signals. Spire Global, Inc. has also started developing their GNSS-R mission, with four satellites currently in orbit. In this thesis we propose and validate a method to retrieve large-scale near-surface soil moisture and a method to map and monitor inundations using spaceborne GNSS-R. Our soil moisture model is based on the assumption that variations in surface reflectivity are linearly related to variations in soil moisture and uses a new method to normalize the observations with respect to the angle of incidence. The normalization method accounts for the spatially varying effects of coherent and incoherent scattering. We found a median unbiased root-mean-square error (ubRMSE) of 0.042 cm3 cm-3 when comparing our method to two years of Soil Moisture Active Passive (SMAP) data and a median ubRMSE of 0.059 cm3 cm-3 compared to the observations of 207 in-situ stations. Our results also showed an improved temporal resolution compared to sensors traditionally used for this purpose. Assessing Spire and CYGNSS data over a region in south east Australia, we observed a similar behavior in terms of surface reflectivity and sensitivity to soil moisture. As Spire satellites collect data from multiple GNSS constellations, we found that it is important to differentiate the observations when calibrating a soil moisture model. The inundation mapping method that we propose is based on a track-wise approach. When classifying the reflections track by track the influence of the angle of incidence and the GNSS transmitted power are minimized or eliminated. With CYGNSS data we produced more than four years of monthly surface water maps over the Amazon River basin and the Pantanal wetlands complex with a spatial resolution of 4.5 km. With GNSS-R we could overcome some of the limitations of optical and microwave remote sensing methods for inundation mapping. We used a set of metrics commonly used to evaluate classification performance to assess our product and discussed the differences and similarities with other products.