Water transparency is one of the important parameters to describe the optical properties of seawater. It's a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. Firstly, this paper verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Secondly, the water transparency estimation results retrieved from GOCI (Geostationary Ocean Color Imager) using four atmospheric correction algorithms were compared and analyzed with in-situ data in study area. The four atmospheric correction algorithms are the standard atmospheric correction algorithms of KOSC GOCI GDPS1.3 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA, and MUMM (Management Unit of the North Sea Mathematical Models). At last, GOCI data on Mar 09, 2018 were used to analyze the differences of hourly changes of transparency caused by different atmospheric correction algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R ² >0.8, MAPE = 16.4%). The difference of transparency value caused by atmospheric correction algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency presented based on different atmospheric correction algorithms, while the moment when the minimum value of transparency presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different atmospheric correction algorithms are not completely consistent.

长江冲淡水向外海扩展对于黄、东海水文生物环境以及物质循环有重大影响。通过分析1998－2014年连续17年卫星观测结果，研究夏季长江冲淡水向外海扩展的年际间变化规律，分析径流量和风场对其变化的影响。结果显示，夏季长江冲淡水向外海的扩展路径主要有东北偏北型、东北型和偶见型三类，以东北型为主；冲淡水向东扩展位置主要分布在123－126°E区间，年际间差异显著。扩展过程的年际变化主要体现在扩展路径是否转向、向东扩展的态势呈现7弱8强还是7盛8衰。长江径流量的多寡对冲淡水向外海扩展的远近起到重要影响作用，径流量较大时，冲淡水向东扩展较远，径流量较小时，冲淡水向东扩展的距离会受到明显的限制。风向的变化是导致冲淡水扩展过程呈现多样性的一个重要原因。东南风、东风对于冲淡水向东北方向扩展起到促进作用，对于向东南方向扩展起到一定的阻碍作用，东北风对冲淡水向外海的扩展起到阻碍作用，迫使冲淡水盘踞在长江口近岸海域。

Bohai Sea is China's inland sea, its complex marine and atmospheric optical properties pose a challenge to the application of satellite data to retrieve chlorophyll-a concentration (CHLA) with high accuracy. The high accuracy retrieval results of CHLA require simultaneous consideration of the adaptability of atmospheric correction algorithms and CHLA retrieval models. In this study, four atmospheric correction methods (the standard atmospheric correction algorithms of GDPS1.4.1 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA (Seadas_Default), and Management Unit of the North Sea Mathematical Models (Seadas_MUMM)) and four CHLA retrieval models (OC2, YOC, OC3G, OC2M-HI) were selected in the process of applying GOCI (Geostationary Ocean Color Imager) data to retrieve CHLA in Bohai Sea. Based on the in-situ data, the adaptability of their pairwise combinations in retrieval of CHLA in Bohai Sea was evaluated. The results indicate that the OC2 and OC3G models significantly overestimated the CHLA. The combination of the Seadas_Default atmospheric correction algorithm with the YOC CHLA retrieval model, or the combination of the Seadas_MUMM atmospheric correction algorithm with the YOC CHLA retrieval model, is more suitable for the retrieval of CHLA using GOCI data in Bohai Sea. Additionally, this study shows that the CHLA obtained based on the data from 8-scene GOCI data were different to the data obtained based on single-scene GOCI data (approximating traditional polar-orbiting satellite sensor data) in daily, monthly, and yearly average results. The monthly mean difference between the two is the most significant, ranging from -0.66 to 1.49 ug/l.

Atmospheric correction (AC) uncertainties critically constrain satellite chlorophyll-a (CHLA) retrieval in optically complex coastal waters. Existing AC algorithms perform divergently across water types, and no single algorithm is universally optimal. Although multi-source fusion has been widely explored, current studies predominantly integrate satellite sensors or inversion models while neglecting uncertainties inherent to the preprocessing AC step. In this study, we developed a pixel-wise AC optimization method using the QA score model to evaluate and select spectrally complementary outputs from multiple AC algorithms. Applied to GOCI data over the Shandong Peninsula, four algorithms (GDPS 1.3, GDPS 2.0, Seadas_Default, and Seadas_MUMM) were employed. For each pixel, the optimal remote sensing reflectance (R_rs) was selected based on QA scores, followed by CHLA retrieval via the YOC model. Validation against 96 in situ measurements demonstrated significantly improved accuracy (r = 0.868, RMSE = 0.582 μg/L, ε = 16.9%) compared with any single AC method. This study confirms that pixel-wise AC optimization and selection effectively suppress algorithm-specific uncertainties, providing a robust strategy for enhancing satellite-derived CHLA estimates in complex coastal waters.

The Shandong Peninsula is located between the Yellow Sea and the Bohai Sea. The optical properties of the ocean and atmosphere in the surrounding sea area are complex. This poses a challenge for the application of satellite data to retrieve high-precision chlorophyll-a concentration (CHLA). Currently, the emphasis in CHLA retrieval research, both domestically and internationally, primarily centers on retrieval algorithms. However, there is a scarcity of research on the uncertainty in CHLA retrieval accuracy resulting from atmospheric correction, a crucial pre-processing step in satellite data. Furthermore, limited attention has been given to the necessity of considering the impacts of various atmospheric correction algorithms in the CHLA retrieval process and their successful integration. In view of this, we proposed a new method for the retrieval of chlorophyll-a concentration that incorporates an atmospheric correction effect evaluation mechanism. It can also be considered as a fusion algorithm. This method performs atmospheric correction effect optimization for each pixel based on the QA Score model, aiming to improve the accuracy of chlorophyll-a concentration (CHLA) retrieval when applying satellite data. The method has been applied to GOCI data to retrieve the CHLA in the waters around the Shandong Peninsula. The results obtained are significantly better than those obtained by applying a single atmospheric correction method.