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.

A novel metric for detecting coherence in global navigation satellite system reflectometry (GNSS-R) signals is presented and evaluated. It applies the Von Neumann information entropy metric for density matrices, a powerful indicator of the degree of mixing between states, coherent and incoherent, of the scene under investigation. The metric is applied to a set of raw IF data acquired by the cyclone global navigation satellite system (CYGNSS) observatories over Lake Okeechobee FL, in order to test the sensitivity of the entropy to different land cover types, including wetlands and open water. Visual comparison of results with Sentinel-1 images provides a first step in the validation of the effectiveness of entropy in detecting the presence of water covered by emergent vegetation. In addition, the entropy-based metric could be implemented on future space-based GNSS-R receivers to adapt the incoherent integration times to the observed scene, thus achieving an improvement in along-track resolution.

The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS “Standards for Earth Observation Technical Committee” to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title “Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content.” At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA.

Many sensors are suitable for accurate delineation of open water extent, but in vegetated environments, the vegetation canopy can obscure the presence of standing water from detection. Detecting inundation extent in these vegetated environments is especially critical for identifying flooding extent where surface water may exceed flood boundaries and extend into forests surrounding nearby lakes and streams. Regular and timely observations of water surfaces by optical sensors can be impeded by both cloud cover and by vegetation. Here, two microwave techniques for identifying inundation extent will be investigated and compared: L -band global navigation satellite systems reflectometry (GNSS-R) and L - and C -band synthetic aperture radar (SAR); and will confirm that there are correspondences between metrics derived from GNSS reflected signals and L -band SAR to inundated area, including wetlands covered by vegetation.

In this work we carry out a simulation analysis of ship detection via Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM). The GNSS-R DDM simulator used here is a modified version of an algorithm conceived for the generation of GNSS-R DDMs of the sea surface. The new algorithm is based on an analytical model for the radar cross section of ships and is able to properly account for the presence of ship targets within the scene. The proposed GNSS-R DDM simulator is then exploited for assessing the viability of GNSS-R in ship detection applications at low incidence angles, where the adopted scattering models provide accurate results. The aim of the implemented simulation setup is to analyse what are the preferable conditions for ship detection using standard GNSS-R signal processing chain receiver and compare typical forward left-hand circularly polarized (LHCP) GNSS-R systems with non-standard backward right-hand circularly polarized (RHCP) GNSS-R. The simulation study is two-fold: first, detection performance is evaluated at spaceborne and airborne altitudes for both polarization channels under favorable detection conditions. Then, visibility of ship targets is assessed in terms of their location within the DDM. Simulation results show that ship detection is problematic when using satellite data, whereas interesting results are achieved at airborne altitudes, provided that the aircraft is approximately between the GNSS satellite and the target, and that appropriate RHCP polarization is probed. In such configurations, signal-to-noise-ratios larger than 10 dB are obtained with airborne receivers collecting the RHCP signal.

Measuring ocean mesoscale variability is one of the main objectives of next generation satellite altimeters. Current radar altimeters make observations only at the nadir sub-satellite ground track, which is not sufficient to sample the ocean surface with the required spatial and temporal sampling. The GNSS-R concept has been proposed as an alternative observation system in order to overcome this limitation, since it allows performing altimetry along several points simultaneously over a very wide swath. Latest proposed GNSS-R altimeter configurations allow measuring sea height with an accuracy of few decimeters over spatial scales of 50-100 km, by means of a single-pass. This paper proposes an innovative processing and retracking concept for GNSS-R altimeters based on the acquisition of the full delay-Doppler map (DDM), which allows to acquire multiple waveforms at different Doppler frequencies, whose footprints are located outside the typical pulse-limited region. The proposed processing adapts the Synthetic Aperture Radar (SAR) delay-Doppler concept of spaceborne radar altimeters for use in a GNSS-R system. This processing yields additional multi-look with respect to conventional GNSS-R concepts and translates into an improvement of the altimetry performance estimated to be at least 25%-30%, and even higher, depending on the wanted along-track spatial resolution. The proposed processing can also provide measurements with high spatial resolution at best possible performance, and more generally, offers various possibilities for optimal trade-off between spatial-resolution and height estimation accuracy.