## 5 Conclusions  
Using Sentinel-3A-OLCI data, satellite monitoring of \(K_{\text{d}}\)(490) for the waters of Lake Taihu was achieved for the first time since this satellite was put into operation in October 2016. The patterns of and driving factors for the spatial and temporal variations results show that \(K_{\text{d}}\)(490) is the highest in southwestern Lake Taihu, followed by central Lake Taihu, and that \(K_{\text{d}}\)(490) is relatively low in the bay areas. Wind- and wave-induced sediment resuspension is the main factor that affects the variations in \(K_{\text{d}}\)(490). Moreover, winds have a longer impact on \(K_{\text{d}}\)(490) in central Lake Taihu than in the bay areas because winds and waves tend to stabilize in the bay areas before central Lake Taihu.  
This study also showed that SPIM controls the underwater light attenuation and distribution in the waters of Lake Taihu through absorption and scattering, which affect the transparency and eutrophic depth of the water and consequently the variations in the seasonal and spatial distributions of aquatic vegetation. Based on synchronous satellite and ground datasets, a novel dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was found to accurately estimate \(K_{\text{d}}\)(490) for turbid inland waters. The development of new algorithms that take advantage of the improved radiometric and spatial resolution of OLCI data will increase in the coming years. Compared to available algorithms, the algorithm developed in this study has a higher accuracy and can reduce the effects of atmospheric and environmental factors.  
Satellite imagery improves our understanding of the characteristics of the photoecological environments in water and thereby supports the evaluation and management of lake water quality. In future work, data acquired by multiple satellite sensors, such as MERIS, MODIS, and OLCI, can be used to determine the historical evolution of \(K_{\text{d}}\)(490) for inland turbid waters. Considering the highly dynamic variations in the waters of Lake Taihu, high temporal-resolution Geostationary Ocean Color Imager (South Korea) and GaoFen-4 (China) satellite data and meteorological and hydrological data can be used to further understand the evolution of spatial patterns inthe photoecological environment over short time scales. In summary, we believe that the addition of OLCI data will provide more possibilities for monitoring, evaluating, and managing lake water quality.  
The authors would like to thank all of the study participants and volunteer contributors ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences: NIGLAS)]. We also thank ESA and NASA for providing the satellite data and processing software. Financial support was provided by the Provincial Natural Science Foundation of Jiangsu,  
Figure 14: (**a**) Pearson correlation coefficients (_r_) between the OLCI-derived \(K_{\text{d}}\)(490) and the mean wind speed for different hours (h = 1, 2, 3,..., 96 h) before the satellite image acquisition time at 4 virtual stations; (**b**) distribution of the maximum Pearson correlation coefficients (_r_); (**c**) distribution of hours corresponding to the maximum Pearson correlation coefficients.  
China (BK20160049), the National Natural Science Foundation of China (41671358, 41431176), the Youth Innovation Promotion Association of CAS (2012238) and Dragon 4.  
[PERSON] principally conceived of the idea for the study. [PERSON] was responsible for setting up the experiments, completing the experiments, and retrieving the data. [PERSON], [PERSON], [PERSON], and [PERSON] contributed to the analysis and discussion. [PERSON] and [PERSON] finalized the manuscript. All of the authors participated in some form in the concept, experimentation, writing, and/or editing of this manuscript.  
The authors declare no conflict of interest.