The detection of water transparency (Zsd) changes over multiple timescales is an important task that has wide implications from water quality to climate change research. However, to date, the understanding of Zsd variations and their driving factors in the Eastern China Seas is fairly limited. Using the Data Interpolating Empirical Orthogonal Functions (DINEOF) method and a semi‐analytic algorithm, monthly cloud‐free Zsd products over the period 1997–2019 are retrieved from the European Space Agency Ocean Color Climate Change Initiative datasets. Significant Zsd variations on seasonal, interannual, and long‐term timescales, as well as their spatial discrepancies, are quantified in the Eastern China Seas. The first EOF mode of Zsd accounts for 57.46% of the total variance, which is dominated by a seasonal cycle in response to seasonal changes in oceanic and atmospheric factors (e.g., surface winds, sea surface temperature and ocean stratification). The second and third EOF modes are likely associated with seasonal phytoplankton blooms and ocean circulations, respectively. Factors that affect the interannual variation in Zsd, including sea surface temperature, wind speed, river discharge, and the Oceanic Niño Index, are quantitatively evaluated. The long‐term trends suggest different potential driving forces for the Zsd patterns at a regional scale, such as climate‐driven increased sea surface temperatures, weakened surface winds, reduced sediment discharge, and eutrophication of coastal waters. Overall, this study presents the first comprehensive investigation of Zsd variations in the Eastern China Seas at multiple timescales and an analysis of their underlying physical and environmental drivers.

Chlorophyll-a (Chl-a) is an important indicator of phytoplankton bloom and water eutrophication, as well as a basic parameter to assess coastal ecosystem health. This study utilizes a significant amount of observational data regarding Chl-a concentration. The linear Radial Basis Function (RBF-Linear) is a fitting method used to reconstruct the in-situ observation data of Chl-a concentration in the Bohai Sea in August 2017, with two advantages of small comprehensive error and reasonable spatial distribution. This method is selected through 10-fold cross-validation, correlation analysis and spatial distribution comparison. This will provide a potential application prospect for evaluating satellite products and improving the accuracy of ecological models. Then we use this method to obtain the complete three-dimensional spatial field of Chl-a concentration in the Bohai Sea during March, May, August and October from 2016 to 2018. The spatial and temporal distribution of Chl-a concentration in the Bohai Sea shows obvious spatial and temporal distribution characteristics: spatially, the high Chl-a concentration is mainly distributed in coastal waters, especially near estuaries and mariculture area; Temporally, there are two obvious peaks in March and August. Additionally, two indicators of total Chl-a and area with high Chl-a concentration are given in order to assess the marine ecological environment in four sub-regions of the Bohai Sea (Bohai Bay, Liaodong Bay, Laizhou Bay, and Central Bohai Sea), with a focus on the impacts of special weather events and bottom water quality. Along with restoring the marine natural environment, attention should be given to balancing urban development.

The water mass in the East China Sea (ECS) shelf has a complicated three-dimensional (3D) hydrologic structure. However, previous studies mostly concentrated on the sea surface based on the sparse in situ and incomplete satellite-derived observations. Therefore, the 3D interpolation technology was introduced for the reconstruction of hydrologic structure in the ECS shelf using in situ temperature and salinity observations in the summer and autumn of 2010 to 2011. Considering the high accuracy and good fitness of the radial basis function (RBF) methods, we applied the RBF methods to the in situ observations to completely reconstruct the 3D hydrologic fields. Other 3D interpolation methods and 2D methods were also tested for a comparison. The cubic and thin plate spline RBFs were recommended because their mean absolute error (MAE) in the 10-fold cross-validation experiments maintained the order of ~10−2. The 3D RBF reconstructions showed a reasonable 3D hydrologic structure and extra details of the water masses in the ECS shelf. It also helps evaluate regional satellite-derived sea surface temperature (SST). Comparisons between the interpolated and satellite-derived SST indicates that the large bias of satellite-derived SST in the daytime corresponds to weak mixing during low-speed wind and shows seasonal variation.