Comparison of GNSS-R Coherent Reflection Detection Algorithms Using Simulated and Measured CYGNSS Data

When global navigation satellite system (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 article provides a unified comparison of a wide variety of different GNSS-R coherence detection approaches, which is the most extensively published to date. The approaches are applied to a common dataset from the NASA Cyclone Global Navigation Satellite System (CYGNSS) satellites that includes both the standard Level-1 delay–Doppler map (DDM) science product and raw baseband signal recordings. In addition, simulated observables are generated with varying coherent and noncoherent reflection components to exercise algorithms over a wide range of signal-to-noise ratios (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 postprocessing on the ground.