Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y⁻¹, and highest in the SYS at 0.015 m y⁻¹. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (${\mathrm{R}}^2$) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and cause ecological, environmental, and economic problems. To understand MAB dissipation features, we used multiple sensors to analyze the spatiotemporal variation of the MAB dissipation phase in the southern Yellow Sea. The results show the variation in the daily dissipation rate (DR) was inconsistent from year to year. Based on the DR variation, a simple method of estimating MAB dissipation days was proposed for the first time. Verification results of the method, from 2018 to 2020, showed the estimated dissipation days were relatively consistent with the results obtained by remote sensing imagery. From 2007 to 2020, the order in which macroalgae landed in the coastal cities of Shandong Peninsula can be roughly divided into two types. In one type, the macroalgae landed first in Rizhao, followed by Qingdao, Rushan, and Haiyang. In the other type, they landed in the reverse order. The MABs annual distribution density showed significant differences in the southern Yellow Sea. These results provided a basis for evaluating the MABs’ impact on marine ecology and formulating the green-tide prevention and control strategies.

Sea surface temperature (SST) is one of the important dynamic parameters to characterize the physical characteristics of seawater, and it is also an important basic auxiliary parameter for the quantitative observation of other ocean parameters. In view of the lack of remote sensing data mainly caused by cloud cover over the ocean, based on the long-term MODIS Aqua L3 8-day mean SST products from January 2003 to March 2015, the missing data in the East China Sea are reconstructed by Data Interpolating Empirical Orthogonal Function (DINEOF) method, and the reconstruction results are evaluated by error analysis before and after cross-validation data reconstruction. It shows that the image reconstructed by DINEOF method can reflect the temporal and spatial variation characteristics of sea surface temperature in the study area, and the root mean square error of reconstruction is 0.4272. DINEOF method can reconstruct large area missing data without any prior information and has high accuracy.

Secchi disk depth (SDD) is a crucial indicator for assessing changes in the water environment. Coastal water transparency varies due to weather, climate, and human activities. Systematic observations are crucial for assessing water quality and ecosystem health. This study used Landsat 8 imagery to investigate the transparency of Jiaozhou Bay (JZB) from 2013 to 2022 and to explore the primary factors influencing transparency changes. Among natural factors, precipitation plays a dominant role in transparency changes. After 2019, there was an increase in rainfall in JZB, which usually leads to reduced transparency. However, the significant improvement in transparency, attributed to a sharp reduction in human activities due to the coronavirus disease 2019 (COVID-19), highlights the substantial impact of human activities on JZB's transparency.