Satellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area, but the sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel Regularized Least-Square approach is developed to relax the limitation to the range of satellite operating conditions. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes and phases of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason-3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea-level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.

翻译：卫星测高技术以全球覆盖和高分辨率测量水位，为广泛而精细地理解沿海地区潮汐变化提供了前所未有的机遇，但采样频率过低无法满足奈奎斯特频率要求，且每年数据点数量不足以识别足够多的潮汐分量来捕捉潮汐变化的年度趋势。为解决这些问题，本研究开发了一种新的正则化最小二乘方法，以放松对卫星运行条件的范围限制。该方法利用区域潮汐振幅的先验信息支持最小二乘分析，从而获取不同长度和时间间隔的水位时间序列中潮汐分量的振幅和相位。在特拉华湾和加尔维斯顿湾进行的合成数据实验表明，所提方法能够高精度地确定潮汐振幅，且采样间隔可扩展到主要测高卫星的应用水平。进一步利用Jason-3测高任务的数据验证了该算法，证明了其对不规则和含噪声数据的适用性。这种新方法有助于识别沿海地区由海平面上升和人类活动引起的潮汐变化，为沿海洪水风险评估和生态系统健康分析提供依据。