Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

NASA's Cyclone Global Navigation Satellite System (CYGNSS) constellation of 8 small satellites was launched into low Earth orbit in 2016. The objectives of its initial two year mission were to study how well GPS signals that are reflected from the ocean surface can measure the winds in hurricanes and how well those measurements can improve our ability to forecast them. In the 5+ years it has been in orbit, CYGNSS has accomplished those objectives. It has also significantly expanded the scope of its scientific investigations. GPS signals reflected from the storm-fres parts of the ocean as well as the signals reflected from land have also been found to contain valuable information about surface conditions. An overview of the scientific impacts of CYGNSS observations, for hurricane prediction studies and many other applications, are presented.

GNSS reflection measurements can be calibrated with data from SMAP to yield estimates of soil moisture with enhanced spatiotemporal resolution useful to certain hydro-logical/meteorological studies. Current approaches use simple models of the relation between the DDM (delay-Doppler map) and soil moisture and can fail in certain regions of the planet. Complex information contained in the complete 2D DDM could help in these areas, and can be extracted through the application of deep learning based techniques. Our work explores the data-driven approach of convolutional neural networks to determine complex relationships between the reflection measurement and surface parameters. We developed a neural network trained using CYGNSS DDMs and ancillary datasets aligned with SMAP soil moisture values; the results of which are analyzed and compared to existing global soil moisture products.

Observations made by the NASA Cyclone Global Navigation Satellite System (CYGNSS) of inland waterways can be used to resolve changes in water boundaries on time scales from days to weeks to months. An example of change on a daily time scale is the response of river width to rapid changes in stream flow rate. An example of change on a weekly time scale is the flood inundation caused by a land falling hurricane. And an example of change on a monthly time scale is the expansion and contraction of a major river delta due to seasonal monsoon rains. CYGNSS measurements of each of these phenomena are presented, and related considerations and implications of its measurement capability are discussed.

This work demonstrates the creation of dynamic inland water body masks at spatial resolutions ranging from 1 to 3 km through the use of a recently developed coherence detector for the delay-Doppler maps produced by the cyclone global navigation satellite system (CYGNSS) constellation. The use of the coherence of the observed measurements reduces many of the uncertainties associated with previous signal-to-noise ratio-based water body detection approaches for CYGNSS. Using data from January 2018 to February 2020 and producing maps representing time intervals ranging from 3 months to 2 years, the water body masks created are found to be associated with a probability of detection that exceeds 80% as compared to the Pekel water mask developed from Landsat observations. The analysis presented in this work highlights the potential of using spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) systems for dynamic inland water body mapping.

Spaceborne GNSS Reflectometry (GNSS-R) measurements have shown strong coherent scattering over inland waters. It has been recognized that GNSS-R could be utilized for monitoring the global surface water distribution by making dynamic maps of wetlands as well as rapid response to flood events. Using the strength of the reflected signals, one can make maps that reveal the presence of water over land. In this paper, we used simulations to analyze the accuracy of these maps. The CYGNSS End-to-end Simulator (E2ES) was extended to include coherent scattering in the heterogeneous scenes where the region around the specular point is composed of both land and water in complex geometries. The simulation is then used to evaluate the accuracy of a simple fractional water in footprint approach to mapping wetland extent. We find that scattering from outside the first Fresnel zone and CYGNSS measurement processing effects significantly impact the accuracy of this approach. However, the accuracy can be improved by combining multiple measurements into a gridded map.

The Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight small satellites, each of which carries specialized GNSS receivers to record surface-reflected GNSS signals. The L-band signals recorded by CYGNSS have been shown to be sensitive to land surface hydrologic variables, including near-surface soil moisture and inundation extent. However, given certain conditions-such as vegetation cover and surface roughness, CYGNSS observations may be more or less sensitive to these variables. The pseudo-random sampling strategy used by CYGNSS presents new opportunities but also new challenges to the monitoring of land surface hydrology. This work, using South America as a case study, quantifies the ability of CYGNSS to monitor soil moisture and inundation extent for different vegetation and roughness conditions and describes how these measurements could be used to sense changes in surface hydrology over time.

The objective of this paper is to model and study the sensitivity of bistatic microwave scattering versus changes in wetland characteristics as observed by a GNSS-R satellite system such as CYGNSS. We develop a simplified scattering model starting from the Water Cloud Model traditionally used in monostatic radar problems. Vegetation is idealized as a cloud of randomly oriented scattering elements over a rough surface representing either soil or water. The bistatic scattering coefficient is modeled as the incoherent sum of soil, water and vegetation scattering weighted by the fraction of each contribution within the CYGNSS footprint. The model is tested against CYGNSS observations across the Everglades National Park for which high-resolution land-cover and water depth maps are available. We show that our simplified model is able to capture to first order the variability of bistatic scattering versus changes in water depth and water fraction. This effort is a step forward towards the development of an effective algorithm to map the dynamic state of tropical wetlands and other regions subject to flooding using CYGNSS measurements.

Characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage require information about wetland dynamics, which remains a major gap in existing knowledge. Despite recent advances in satellite monitoring techniques, dynamic wetland mapping needs to be improved for more accurate global inventories and also to monitor variabilities at sub-km scale over multiple decades. It has been shown that Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) signatures off inundated wetlands can be identified under different vegetation conditions including a dense rice canopy and a thick forest with tall trees, where optical sensors and monostatic radars provide limited capabilities. In this study, we will further investigate the capabilities of the GNSS-R technique for wetland monitoring from aircraft platforms.

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earth's land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

It is now recognized that the enormous challenge of scientifically understanding the Earth system requires careful strategic decisions on what missions are deployed. In a recent report, the National Research Council developed a “value framework” for Earth observing systems with a focus on prioritizing NASA observations that merit long-term continuity. In this paper, we refer to this framework to discuss how high value observations arise from opportunistic use of signals generated by Global Navigation Satellite Systems (GNSS) such as GPS. The increasing number of GNSS constellations internationally, likely to be permanently deployed, suggests that the geosciences community will benefit by adopting these signals for a variety of remote sensing needs. We describe recent progress in using these observations scientifically and developing technology to exploit them. We conclude that dedicated constellations of GNSS science instruments in low Earth orbit capable of receiving both direct and reflected GNSS signals will provide excellent science return in a broad range of areas, and constitute a high value Earth observing system.

It is important to closely monitor the state of the world's wetlands, as climate change and human encroachment in a rapid global urbanization trend threaten to cause large-scale wetland collapse. Because wetlands are often difficult to observe in situ, remote sensing is the only viable way to map wetland extent globally. However, current remote sensing methods suffer limitations in capturing wetland extent, and more importantly, wetland dynamics at appropriate spatial and temporal scales. GNSS-Reflectometry could help fill the current observation gap, as experimental data show that ground-reflected GNSS signals are very sensitive to changes in inundated areas. Furthermore, because this technique only requires a custom developed receiver and antenna system, a constellation of such instruments can potentially be launched at relatively low cost, providing global observations at sub-daily intervals. One challenge remains, however, which is quantitatively formulating the geophysical product of reflections over the land surface in various states of inundation. Here, we use a novel reflection dataset, derived from the SMAP radar receiver, to elucidate the sensitivity of reflections to small land surface features and their seasonal variations. Additionally, we quantify the dynamic range of reflections over both open and closed wetlands, and suggest an algorithm for wetland type classification.

Global Navigation Satellite System (GNSS) signals can be used as a kind of bistatic radar, with receivers opportunistically recording ground-reflected signals transmitted by the GNSS satellites themselves. The ground-reflected signals are sensitive to changes in surface permittivity, which for L-band is primarily a function of the moisture content of the surface. Here, we investigate the ability of GNSS signals, as recorded by a GPS receiver flown on a satellite, to measure changes in wetland extent and flood inundated area. We find that the ground-reflected signals give similar results as flood-inundation maps derived from other sources. Reflected power increases of over 10 dB in the vicinity of wetlands indicates that these signals could successfully map changes in wetlands around the globe.

The potential to use GPS signal-to-noise ratio (SNR) data to estimate changes in vegetation surrounding a ground-based antenna is evaluated. A 1-D plane-stratified model that simulates the response of GPS SNR data to changes in both soil moisture and vegetation is presented. The model is validated against observations of SNR data from four field sites with varying vegetation cover. Validation shows that the average correlation between modeled and observed SNR data is higher than the average correlation between concurrent SNR observations from different satellite tracks at a site. The model also reproduces variations in the SNR metrics amplitude, phase, and effective reflector height over a range of vegetation wet weights from 0 to 4 kg · m-2, with r2values of 0.79, 0.84, and 0.62, respectively. Model simulations indicate that the amplitude of SNR oscillations may be used to estimate vegetation amount when vegetation wet weight is below 1.5 kg · m-2. When vegetation wet weight exceeds 1.5 kg · m-2, the sensitivity of amplitude to changes in vegetation amount decreases. Phase of SNR oscillations also varies consistently with vegetation up to 1.5 kg · m-2. However, phase is also very sensitive to soil moisture variations, thus limiting its utility for estimating vegetation. Effective reflector height is not a consistent indicator of vegetation change. Beyond 1.5 kg · m-2, the constant frequency assumption used to characterize SNR fluctuations does not adequately describe observed data. A more complex approach than the standard SNR metrics used here is required to extend GPS-Interferometric Reflectometry sensing to thicker canopies.

Global Positioning System (GPS) multipath signals can be used to infer volumetric soil moisture around a GPS antenna. While most GPS users concentrate on the signal that travels directly from the satellite to the antenna, the signal that is reflected by nearby surfaces contains information about the environment surrounding the antenna. The interference between the direct and reflected signals produces a modulation that can be observed in temporal variations of the signal-to-noise ratio (SNR) data recorded by the GPS receiver. Changes in the dielectric constant of the soil, which are associated with fluctuations in soil moisture, affect the effective reflector height, amplitude, and phase of the multipath modulation. Empirical studies have shown that these changes in SNR data are correlated with near-surface volumetric soil moisture. This study uses an electrodynamic single-scattering forward model to test the empirical relationships observed in field data. All three GPS interferogram metrics (effective reflector height, phase, and amplitude) are affected by soil moisture in the top 5 cm of the soil; surface soil moisture ($< 1\hbox{-}\hbox{cm}$depth) exerts the strongest control. Soil type exerts a negligible impact on the relationships between GPS interferogram metrics and soil moisture. Phase is linearly correlated with surface soil moisture. The slope of the relationship is similar to that observed in field data. Amplitude and effective reflector height are also affected by soil moisture, although the relationship is nonlinear. Phase is the best metric derived from GPS data to use as a proxy for soil moisture variations.