Earth’s inland water monitoring is probably the main promising application of Global Navigation Satellite Systems Reflectometry (GNSS-R) techniques. The ultimate spatial resolution under the coherent scattering regime deserves further investigation. The new Cyclone Global Navigation Satellite System (CYGNSS) raw Intermediate Frequency (IF) data product with a temporal resolution down to 2 ms could help to further understand this. In the framework of climate change the "water" is the "new gold". In-space water monitoring could help final users to make decisions with impact in several topics including geopolitics. The use of GNSS-R techniques by future constellations of SmallSats could overcome several limitations of more classical remote sensing techniques. In this work, a novel real-time inland water detector by a future GNSS-R receiver is presented. This detector, the so-called fast entropy Efast, shows the capability to detect small water bodies under thick biomass ~ 450 ton/ha in the Congo basin.

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 sensitivity of remote sensing instruments for measuring inundation extent can vary widely. 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 excess surface water extends into the forests surrounding lakes and streams. In addition, cloud cover can impede timely acquisition of imagery by optical sensors. Here, we examine sensitivity of L-band Global Navigation Satellite Systems Reflectometry (GNSS-R) to flooded conditions relative to the well-known signatures of inundation by L-band SAR, and confirm that there is noticeable sensitivity of GNSS reflected signal to inundated areas, including wetlands covered by vegetation, captured by the strong response of the specular reflection by the underlying water surface.

GNSS Reflectometry (GNSS-R) measurements are very sensitive to the presence of inland waters such as wetlands, floods, rivers and lakes. This paper reviews the basic characteristics of a GNSS-R ‘water detection’ research product, including resolution and temporal sampling of wetlands, and discusses the main known sources of errors. Additionally, a summary of GNSS-R applicability to the study of lakes is provided.

The enduring activity of CYGNSS and TDS-1 observatories has provided clear evidence that ground reflected GNSS signal can be exploited for mapping of geophysical quantities in climate and global monitoring applications. One main challenge is to find out useful variables for determining the level of coherence of the scattered wave in the presence of complex natural landscapes such as water basins, river floods, mixed ice and water. In this study, we approach the concept of wave coherence by analyzing the persistence of signal energy across principal directions, determined via generalized eigenvalue decomposition of the DDM delay waveform correlation matrix. An example shows that the dimensional spread of the eigenvalues is finely sensitive to coherence of the ground reflected wave.