While satellite-derived bathymetry (SDB) techniques have been developed to map seafloor depth over large coastal regions, they still face challenges in providing robust estimates very close to shore (less than 400 m). These existing methods struggle in the nearshore zone due to shallow water conditions and wave dynamics. Traditional bathymetric approaches, relying heavily on in situ measurements, are limited in terms of spatiotemporal resolution and coverage, particularly in these challenging coastal areas. This study demonstrates the applicability of a colour-based method for deriving nearshore bathymetry from satellite imagery, entirely independent of ground-based data. The methodology combines wave-breaking detection via the SandBar Index (SBI), empirical relationships derived from wave dynamics, and spectral analysis of optical data from Sentinel-2 images. Breaking wave zones are identified and linked to corresponding wave heights from buoy data. Using the classical breaker height-to-depth ratio, water depths are estimated along cross-shore transects. The spectral ratio \(\frac{\text{ln}(B)}{\text{ln}(G)},\) sensitive to shallow water depths, is calibrated with these depth estimates, enabling the reconstruction of full bathymetric profiles. Results highlight the strength and accuracy of this approach, demonstrating that reliable nearshore bathymetric estimates can be obtained directly from satellite observations, even in areas traditionally considered difficult for SDB. This method provides a cost-effective, scalable solution for nearshore bathymetry, offering significant potential for use in remote or data-scarce regions.