Topography Emerges as the Leading Land-Surface Control on CYGNSS GNSS-R Coherence Across the Tropics
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Summary
Understanding how land surfaces shape the coherence of reflected navigation-satellite signals is critical for judging where GNSS-R (Global Navigation Satellite System Reflectometry) land applications can work and where they may fail. This study quantifies pan-tropical modulation of CYGNSS (Cyclone Global Navigation Satellite System) delay-Doppler map (DDM) coherence by topography, surface water, soil moisture and vegetation. Using an explainable machine-learning framework built on a pixel number (PN) coherence proxy, the authors show that smooth, wet and moderately vegetated surfaces strengthen coherence, whereas rough, steep and densely vegetated terrain weakens it. The analysis also identifies where each process dominates and approximate empirical transition ranges, providing an interpretable observational basis for GNSS-R land applications.
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