Monitoring oceanographic parameters is essential for marine engineering construction, coastal safety, marine analysis, and climatic analysis. Traditionally, oceanographic parameters have been monitored using different sensors. With the development of global navigation satellite systems (GNSS), signal-to-noise ratio (SNR) observations have been found to oscillate due to the interference between direct and reflected signals. GNSS-interferometry reflectometry (GNSS-IR) utilizes the characteristics of these oscillations to retrieve the parameters of a reflecting surface. For GNSS marine application, GNSS-IR sea-level retrieval technology has considerably improved and is undergoing rapid development; however, only a few studies have estimated significant wave height (SWH) and wind direction using GNSS-IR. The cut-off elevation, an SNR parameter, is the elevation angle at which the dominant power of one SNR arc switches between coherent and incoherent energy and is related to SWH and wind direction. Methods to estimate SWH and wind direction were presented based on the cut-off elevation. Moreover, we introduced the Generalized Shallow Water Wave Growth (GSWWG) model to estimate the wave peak period based on retrieved SWH. The sea level, wind direction, SWH, and wave peak period were estimated using a geodetic GNSS receiver. Data were collected before and after Hurricane Delta using the GNSS site CALC, Louisiana, USA. First, the classical GNSS-IR method was used to retrieve sea levels. Second, the wavelet analysis method was processed to SNR arcs to form wavelet spectra showing the nonstationary power and estimate their cut-off elevations. Third, the lower cut-off elevation was used to estimate the wind direction, and the upper cut-off elevation was used to estimate the SWH. Finally, the wave peak period was estimated from the retrieved SWH based on the GSWWG model. The results showed a root mean square error of 4.72 cm between sea-level measurements and sea-level retrievals, and a correlation coefficient > 80% between wind speed measurements and SWH retrievals estimated from GPS L5 and GLONASS R2. The points of SWH retrievals vs. wind speeds of GPS L5 and GLONASS R2 corresponded well to the wind–SWH relationship deduced from the GSWWG model. These results indicated that GNSS-IR technology could estimate the wave peak period in addition to its previously known applications for retrieving sea level, wind direction, and SWH, providing additional oceanographic services.

监测海洋参数对于海洋工程建设、海岸安全、海洋分析和气候分析至关重要。传统上，海洋参数已使用不同的传感器进行监测。随着全球导航卫星系统 (GNSS) 的发展，已发现由于直接信号和反射信号之间的干扰，信噪比 (SNR) 观测值会发生振荡。GNSS 干涉反射仪 (GNSS-IR) 利用这些振荡的特性来检索反射面的参数。在GNSS海洋应用方面，GNSS-IR海平面反演技术有了长足进步，正在快速发展；然而，只有少数研究使用 GNSS-IR 估计了有效波高 (SWH) 和风向。截止高度，SNR 参数，是一个 SNR 弧的主导功率在相干和非相干能量之间切换的仰角，与 SWH 和风向有关。提出了基于截止高程估计 SWH 和风向的方法。此外，我们引入了广义浅水波浪增长（GWWG）模型，以根据检索到的 SWH 估计波浪高峰期。使用大地 GNSS 接收器估计海平面、风向、SWH 和波峰周期。使用美国路易斯安那州的 GNSS 站点 CALC 在飓风三角洲之前和之后收集数据。首先，使用经典的 GNSS-IR 方法来检索海平面。其次，小波分析方法对信噪比弧进行处理，形成显示非平稳功率的小波谱并估计其截止高程。第三，下界标高用于估算风向，上界标高用于估算 SWH。最后，基于 GSWWG 模型从检索到的 SWH 中估计波峰期。结果表明，海平面测量和海平面反演之间的均方根误差为 4.72 厘米，风速测量和从 GPS L5 和 GLONASS R2 估计的 SWH 反演之间的相关系数 > 80%。GPS L5 和 GLONASS R2 的 SWH 反演点与风速的关系很好地对应于 GSWWG 模型推导出的风-SWH 关系。这些结果表明，GNSS-IR 技术除了以前已知的用于检索海平面、风向和 SWH 的应用外，还可以估计海浪高峰期，从而提供额外的海洋学服务。