The instrument used for ocean colour remote-sensing works in the visible wavelengths, and the presence of clouds frequently lead to invalid observations. The formation of clouds is known to be influenced by mesoscale oceanic processes (e.g., eddies and temperature fronts), but these influences are often overlooked in missing data reconstructions. By analysing more than 10 years of chlorophyll-a (chl-a) images from satellite observations, we found an area with persistent data missing in the Eastern China Sea during winter. This area stretches from 123° E and 28° N to 126° E and 34° N, with a width of approximately 100 km. The data gap is closely related to the wintertime sea surface temperature fronts, with higher cloud coverage and thus more data missing on their warm sides. This implies that the chl-a values in the persistent data gap may be consistently lower than those on the cold side, so the spatial-based interpolation has a tendency to overestimate them. We applied three data interpolation methods to fill the data gap. Two methods that count temporal relevancies, i.e., the temporal linear interpolation and the data interpolating empirical orthogonal functions (DINEOF) interpolation, perform better than the Kriging interpolation which only considers spatial autocorrelation.