This work describes the first global scale assessment of a Global Navigation Satellite Systems Reflectometry (GNSS-R) experiment performed on-board the Soil Moisture Active Passive (SMAP) mission for soil moisture and biomass determination. Scattered GPS L2 signals (1227.6 MHz) were collected by the SMAP’s dual-polarization (Horizontal H and Vertical V) radar receiver and then processed on-ground using a known replica of the GPS L2C code. The scattering properties over land are evaluated using the Signal-to-Noise Ratio (SNR), the Polarimetric Ratio (PR), and the width of the waveforms’ trailing and leading edges. These parameters show sensitivity to the effects of the Earth’s topography and Above Ground Biomass (ABG) even over Amazonian and Boreal forests. These effects are shown to be an important factor in precise soil moisture and biomass determination. Additionally, it is found that PR shows sensitivity to soil moisture content over different land cover types. In particular, the following values of the PR are found over: (a) tropical forests ~−1.2 dB; (b) boreal forests ~0.8 dB; (c) Greenland ~2.8 dB; and (d) the Sahara Desert ~3.2 dB.

Information about wetland dynamics remains a major missing gap in characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage. A review of current satellite methods to delineate and monitor wetland change shows some recent advances, but much improved sensing technologies are still needed for wetland mapping, not only to provide more accurate global inventories but also to examine changes spanning multiple decades. Global Navigation Satellite Systems Reflectometry (GNSS‐R) signatures from aircraft over the Ebro River Delta in Spain and satellite measurements over the Mississippi River and adjacent watersheds demonstrate that 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. Advantages as well as constraints of GNSS‐R are presented, and the synergy with various satellite observations are considered to achieve a breakthrough capability for multidecadal wetland dynamics monitoring with frequent global coverage at multiple spatial and temporal scales.

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.

Ocean surface altimetry with GNSS reflectometry (GNSS-R) has the potential to improve the observation and retrieval of mesoscale ocean current flows by increasing the spatial density of ocean surface observations. The leading challenges in current GNSS-R altimetry investigations include precise delay retracking, correction of ionospheric effects, and spacecraft receiver positioning. Here, we present improved methods to account for these challenges. A reflection-model-based approach is presented for delay retracking that uses simulated delay-Doppler maps (DDMs) to retrieve the specular delay from measured DDMs. Global ionosphere maps are used to estimate the group delay effect along the direct and reflection paths. Precise orbit determination techniques are used to improve estimates of the receiving spacecraft position and reduce systematic intersatellite biases. We analyze altimetric height retrievals from two cases, strongly coherent or diffuse reflections. We study the residual systematic error signals with a focus on understanding the errors identified above. The strong coherent observations have narrow, symmetric delay waveforms and provide higher than average signal-to-noise ratio. The DDMs of diffuse observations, originate from relatively rough ocean surfaces and have the characteristic horse-shoe shape. Only measurements from the ocean and seas surrounding Indonesia are considered as this area provides a significant number of strongly coherent reflections. More than 50,000 single-point observations and week-long averaged results are presented to quantify the noise and systematic characteristics of the retrieved surface heights.

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote sensing methods, which exhibit limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS-R have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA CYGNSS satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on VIIRS and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find our method detects 35.4% more surface water than Sentinel-1 while the VIIRS- and MODIS-based products underestimate by 4.8 and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC), and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision makers and emergency managers.

When GNSS signals reflect off of the surfaces of lakes, rivers, wetlands, and other inland water bodies, the surfaces are often sufficiently smooth to produce coherent reflections. The observable produced from coherent reflections made by GNSS Reflectometry (GNSS-R) instruments exhibits particular features with respect to diffusely scattered signals by rough land and wind-driven oceans allowing detection of such smooth bodies. Several different GNSS-R coherence detection approaches have been reported in the literature and developed among the GNSS-R community over the last several years; however, the merits of each approach are difficult to compare because they are often applied to different scenarios and quantified in different ways, independently of each other. This paper provides a unified comparison of a wide variety of different GNSS-R coherence detection approaches, which is the most extensive published to date. The approaches are applied to a common data set from the NASA CYGNSS satellites that includes both the standard Level-1 DDM science product as well as raw baseband signal recordings. Additionally, simulated observables are generated with varying coherent and non-coherent reflection components to exercise algorithms over a wide range of SNRs and relative powers. Objective measures of accuracy are used to quantify the performance of each approach in the context of relative implementation complexity. Conclusions are presented on the pros/cons of the various methods as they relate to various applications such as real-time in-orbit coherence detection or post-processing on the ground.