Accurate chlorophyll a concentration (Chla) retrieval in coastal waters from ocean color remote sensing faces challenges due to the significant optical complexity compared to clear oceanic waters. In this paper, a novel technique for Chla retrieval in turbid coastal waters was proposed and tested in the Bohai Sea based on a global optical water classification system. Firstly, the in situ measured spectral remote sensing reflectance spectra (R_rs(λ)) (n = 559) were classified into different optical water types. Secondly, optimal algorithms were identified with newly tuned model parameters for each water type to achieve accurate Chla retrieval. We found that (1) among all the 23 optical water types (1–23), the water types of 9–22 are present in the Bohai Sea, indicative of moderate or high turbidity, while other water types corresponding to clear oceanic waters are generally absent from our data. (2) Clear spatio-temporal patterns of the water types are revealed. Three basins of the Bohai Sea (the Bohai Bay, Laizhou Bay and Liaodong Bay) receiving tremendous terrestrial inputs and with high turbidity are dominated by the optical water types of 15–22. Water types 9–14 that are less turbid, are mainly distributed in the Bohai Strait and the central Bohai Sea, which are far from the coast (and thus with less terrestrial influences) and relatively deep (and thus with less bottom suspension influences), compared to the 3 basins. (3) Through the identification of the optimal retrieval algorithm and parameter tuning for each water type, the uncertainty of chlorophyll a retrievals has been reduced from 54% (root mean square error of 2.76 mg/m³) to about 36% (1.84 mg/m³). Independent validation with the in situ-satellite match-ups further demonstrates the algorithm’s validity (uncertainty of about 35%).The global optical classification system together with the optimal retrieval algorithm for each optical class, is proved to be a feasible way for ocean color retrieval in high accuracy over optically complex waters.

从海洋彩色遥感技术在沿海水域中准确提取叶绿素a浓度（Chla）面临挑战，这是因为与清澈的海水相比，光学的复杂性很高。本文提出了一种在浑浊的沿海水域中提取Chla的新技术，并基于全球光学水分类系统在渤海进行了测试。首先，就地测量光谱遥感反射光谱（R _rs（λ））（n = 559）分为不同的光学水类型。其次，使用新调整的模型参数针对每种水类型确定最佳算法，以实现准确的Chla检索。我们发现（1）在所有23种光学水类型（1–23）中，渤海中存在9–22的水类型，表明中度或高浊度，而其他与清澈海水相对应的水类型为我们的数据通常不存在。（2）揭示了清晰的时空格局。渤海的三个盆地（渤海湾，莱州湾和辽东湾）接收大量的陆源且浊度很高，其光学水类型为15-22。浊度较小的9-14种水主要分布在渤海海峡和渤海中部，与3个盆地相比，它们远离海岸（因此对陆地的影响较小）并且相对较深（因此对底部悬浮物的影响较小）。（3）通过对每种水类型的最佳检索算法的识别和参数调整，确定叶绿素的不确定性一个检索已经从54％减少（2.76毫克/米的根均方误差³）至约36％（1.84毫克/米³）。通过现场卫星对位的独立验证进一步证明了该算法的有效性（不确定性约为35％）。全球光学分类系统以及针对每个光学类别的最佳检索算法被证明是一种可行的海洋颜色检测方法光学复杂水域上的高精度检索。