•We retrieved the SDD products of hourly observations using the Geostationary Ocean Color Imager (GOCI) from 2011 to 2020 in the eastern China seas.•Superpixel image segmentation retrieved by a simple linear iterative clustering algorithm (SLIC) was applied to classify the transparency product.•The relationship between the diurnal variation of water transparency and environmental factors (Wind speed and SST) was been studied. Polar-orbiting ocean color satellites can monitor daily to interannual variations in water transparency (or Secchi disk depth, SDD) from regional to global oceans. However, diurnal variations in SDD of coastal oceans remain poorly understood. Based on the bio-optical SDD algorithm, we retrieved the SDD products of hourly observations using the Geostationary Ocean Color Imager (GOCI) from 2011 to 2020 in the eastern China seas. The determination coefficient (R2) between the SDD product and the in situ dataset is 0.93, with a root mean squared error (RMSE) of 0.86 m. Based on the pixel-level tempo-spatial analysis, superpixel image segmentation retrieved by a simple linear iterative clustering algorithm (SLIC) was applied to classify the SDD product. The reconstruction SDD superpixel products not only match the spatial distribution well but can also more clearly express the spatial gradient. The percentage of the diurnal change in transparency was high in the nearshore (∼10 %), medium in transitional waters (∼5%), and low in offshore waters (∼3%). Finally, we found a significant negative correlation between SDD and wind speed (R2 = 0.65) and a significantly positive correlation between diurnal change range (DCR) and wind speed (R2 = 0.62). In contrast, sea surface temperature (SST) was positively correlated with SDD (R2 = 0.72) but significantly negatively correlated with DCR (R2 = 0.80). These results provide a basis for studying diurnal SDD changes in highly dynamic waters.