Shallow-water bathymetry is essential for coastal dynamics, navigation, engineering, and marine-resource management, especially where in situ observations are sparse. This study proposes a wave-stripe-based method for shallow-water bathymetric retrieval from Sentinel-1A VV-polarized SAR imagery. After texture-preserving denoising, multidirectional Gabor enhancement, K-means segmentation, and skeleton tracking, wave-crest centerlines are extracted and local wavelengths are measured as the normal spacing between adjacent crest lines. The dominant wave period is estimated from deep-water wavelengths, and water depth is retrieved using the linear wave dispersion relation. The method was evaluated using five Sentinel-1 acquisitions, with SAR-derived representative wave periods ranging from 12.45 to 14.96 s. Based on the mean retrieval performance across the five acquisitions, the 0–20 m depth range yielded an RMSE of 3.87 m, an MAE of 3.56 m, and r = 0.95, while the corresponding values for 0–50 m were 6.06 m, 5.02 m, and 0.97, respectively. The most stable performance occurred within the 5–20 m depth range. The method reduces fixed-window spectral averaging and improves the representation of local wavelength variability, supporting rapid bathymetric mapping along open-coast environments with clear swell-induced wave stripes, relatively gentle bathymetric slopes, and limited field observations.

This study evaluates the accuracy of ERA5 10-m wind speed and significant wave height (SWH) using one-year hourly observations from 16 buoys in the Bohai and Yellow Seas. ERA5 agrees closely with the buoys (wind: Bias 0.04 m/s, root-mean-square error (RMSE) 1.40 m/s, scatter index (SI) 0.24, correlation coefficient (r) 0.90; SWH: Bias 0.11 m, RMSE 0.23 m, SI 0.30, r 0.94). Errors decrease offshore, and the control is offshore distance rather than water depth: a 2.5-fold depth change at fixed distance alters wind RMSE by 0.02 m/s, whereas a doubling of distance at fixed depth alters it by 0.22–0.24 m/s. Wind Bias has a marked diurnal cycle whose amplitude decreases offshore and reverses seasonally; SWH Bias stays positive but weakens in summer; both variables overestimate weak and underestimate strong conditions. The absolute errors of the two variables vary out of phase, so their correlation cancels (−0.40 to 0.02), whereas the relative errors remain strongly coupled (0.59–0.80). Above 13 m/s, ERA5 underestimates wind by 11% while overestimating SWH by 6%, an inconsistency that locates the high-sea-state error in the wave model rather than in the forcing. These quantified errors constrain design-wave estimation and Bias correction in coastal engineering.