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Bathymetric mapping is integral to maintaining marine ecosystems, managing coastal zones, and safeguarding the environment. However, achieving accurate large-scale bathymetric maps remains a challenge in China, particularly in nearshore turbid waters. To address this gap, we leveraged seasonal Gaofen-6 (GF-6) data to conduct bathymetry mapping in the Yellow River Delta-Laizhou Bay area. In our study, we found that longer wavelengths, such as those in the red-edge2 and near-infrared (NIR) bands, exhibited superior performance in determining bathymetry. Moreover, specific band ratios derived from GF-6 data—such as Blue/NIR (BN), Violet/NIR (VN), Blue/Red-edge2 (BE), Violet/Red-edge2 (VE), Green/NIR (GN), and Green/Red-edge2 (GE)—showed promising outcomes, particularly in turbid nearshore waters. When comparing models, the random forest regression (RFR) model outperformed the classification and regression trees (CART) model in turbid nearshore areas, showing higher R2 values and lower RMSE. Notably, both models demonstrated higher accuracy in March compared to May and October. Incorporating the Normalized Difference Turbidity Index (NDTI) notably improved bathymetric results, especially in turbid sea regions. Furthermore, nearshore bathymetry proved highly susceptible to natural processes, seasonal variations, and human activities. The significant discrepancies in bathymetry among coastal areas emphasize the need for tailored management strategies to enhance coastal management and foster sustainable marine economic development. • The GF-6 data with different seasons were used to map nearshore bathymetry. • Longer wavelengths had better bathymetric performance in nearshore turbid waters. • The band ratios of BN, VN, BE, VE, GN and GE had good bathymetry performances. • Better bathymetry results were modelled with NDTI, especially in the turbid sea. • Adaptive strategies were proposed according to water depth and human activities. [ABSTRACT FROM AUTHOR]