A Stochastic Approach for Near Real-Time Estimation Using GNSS-Reflectometry

Global Navigation Satellite System Reflectometry (GNSS-R) has established itself as a versatile remote sensing method applicable to various environmental monitoring tasks, including the water level measurements. In this paper, we propose a novel stochastic approach to estimate water levels in near real-time using GNSS-R. By integrating GNSS signal-to-noise ratio (SNR) measurements with a priori tidal constituent data, our methodology enhances the accuracy and efficiency of water level monitoring. The paper begins with an introduction to the evolution of GNSS-R as a remote sensing tool, with a specific focus on its applications in water level monitoring. By analyzing SNR data, GNSS-R enables the measurement of water surfaces surrounding a GNSS receiver, making it a tool for a coastal monitoring. Our study addresses the primary objective of estimating water levels in near real-time, leveraging the GNSS-R technique. To achieve this, we apply a stochastic model to fuse GNSS SNR measurements with tidal constituents. This integration not only enhances the precision of water level estimations but also simplified the monitoring process. The outcomes of this research hold significant promise for a range of hydrological and environmental applications. By advancing the capabilities of GNSS-R in water level estimation, our stochastic approach contributes to more accurate and timely data for researchers and professionals in the GNSS-R field. Ultimately, this research lays the foundation for improved water resource management and informed decision-making in the face of evolving environmental challenges.