A Simplified Model for Simulating Complex Signals in GNSS-Reflectometry over Land
The availability of complex signals in global navigation satellite system-reflectometry (GNSS-R) has gained growing attention across a range of applications, due to its capacity to preserve phase information and provide high along-track resolution. HydroGNSS will employ a high-rate complex signal mo...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/418391 |
| Acceso en línea: | http://hdl.handle.net/10261/418391 https://api.elsevier.com/content/abstract/scopus_id/105025651414 |
| Access Level: | acceso abierto |
| Palabra clave: | Coherent channel Complex signal Global navigation satellite system-reflectometry (GNSS-R) HydroGNSS Simulator |
| Sumario: | The availability of complex signals in global navigation satellite system-reflectometry (GNSS-R) has gained growing attention across a range of applications, due to its capacity to preserve phase information and provide high along-track resolution. HydroGNSS will employ a high-rate complex signal mode, known as the “coherent channel,” to capture both the in-phase and quadrature components of signals reflected at the specular point. This work presents a simulation framework for analyzing the coherent channel in GNSS-R at high temporal resolution. Given the computational resources and limitations of fully detailed simulations, a simplified model is proposed, which incorporates a well established reflectivity model, and new empirical and theoretical functions. The surface reflectivity model estimates it from parameters, such as soil roughness, moisture, vegetation cover, and water fraction, while the innovative modeling block, here called the complex field model, derives the coherent and diffuse field amplitudes from reflectivity, and it assigns electromagnetic phases that behave, statistically, as in actual data. The validation is conducted at different levels, first using actual amplitude and reflectivity measurements as input, then starting from auxiliary surface information, yielding correlations with the coherence coefficient of 0.93, 0.74, and 0.61, respectively. This validation approach facilitates the differentiation of the errors introduced by each of the modules. The results support the feasibility of the proposed framework as a practical and quick tool to investigate complex signals under varying reflected surface conditions. Higher accuracy will require a tighter integration of the surface reflectivity model and the complex field mode. |
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