Ocean colour data are crucial for monitoring and assessing marine ecosystems. In this study, the Data Interpolating Empirical Orthogonal Functions (DINEOF) approach was applied to the Ocean Colour Climate Change Initiative (OC-CCI), chlorophyll-a (Chl-a) and Secchi disk depth (Zsd) to completely reconstruct the missing pixels in the Bohai Sea during 1997–2019. The results of cross-validation demonstrate that the DINEOF reconstructed data have a good agreement with the satellite-measured data. Based on monthly cloud-free satellite data reconstructions, the Zsd series showed high negative correlation with log10 (Chl-a). The Zsd as a function of log10 (Chl-a) can be well fitted by the cubic polynomial in the offshore waters. The Chl-a in the entire Bohai Sea showed a significant decreasing trend (−0.013 mg/m³/year), while the Zsd exhibited a significant increasing trend (0.0065 m/year), and both had regional-seasonal variations. In addition, the ensemble empirical mode decomposition (EEMD) results reveal highly nonlinear trends of Chl-a and Zsd. The linear and nonlinear trends of Chl-a and Zsd suggest the deterioration of water quality in the Bohai Sea was not continued over the past two decades. This study presents the first simultaneous investigation of Chl-a and Zsd using the 23 years of cloud-free reconstructions in the Bohai Sea.

Offshore aquaculture is critical for a marine fishery economy. The spatial distribution of aquaculture that characterizes it plays a vital role in the sustainable development of marine resources and the protection of the marine environment. In recent years, China’s aquaculture has developed rapidly; specifically, the scale of aquaculture has dramatically expanded, and large-scale aquaculture has gradually grown in popularity. Although high-resolution satellite data can accurately extract aquaculture areas, the extraction of a large area of the sea area requires a copious amount of data. In contrast, medium-resolution satellite images allow for the extraction of aquaculture areas from large sea areas with a smaller amount of data, offering significant advantages. Therefore, we used Landsat8 satellite data to extract and count the number of aquaculture rafts based on the Hough transform and Canny edge detection methods. We tested the accuracy of this method by selecting Haizhou Bay as the study area for the experiment and accuracy verification and found that the automatic extraction accuracy for the number of aquaculture rafts was more than 90%. Additionally, we calculated statistics on the number of aquaculture rafts in Haizhou Bay over the past seven years. The findings presented in this paper offer a significant reference value for local marine utilization, marine environment protection, and marine disaster prevention and mitigation.