## 3 Results
### Temporal and Spatial Patterns from the OLCI-Derived \(K_{\rm d}\)(490) in Lake Taihu  
The OLCI-derived \(K_{\rm d}\)(490) data (24 scenes during November 2016-April 2017, Figure 6) were averaged to calculate the \(K_{\rm d}\)(490) distribution for Lake Taihu (Figure 7a). The \(K_{\rm d}\)(490) values for all of Lake Taihu ranged from 1.26 to 14.87 m\({}^{-1}\) with a mean value of 7.11 \(\pm\) 2.08 m\({}^{-1}\). The \(K_{\rm d}\)(490) value for southwestern Lake Taihu was highest, followed by \(K_{\rm d}\)(490) for central Lake Taihu, whereas the \(K_{\rm d}\)(490) values for the bays were low. The spatial distribution of the coefficients of variation (CV) was calculated from the OLCI-derived \(K_{\rm d}\)(490) (Figure 7b). The largest \(K_{\rm d}\)(490) CVs were in Meiliang Bay, followed by Gonghu Bay and Zhushan Bay, and the smallest \(K_{\rm d}\)(490) CVs were in open areas. Evidently, the distribution of the CVs of \(K_{\rm d}\)(490) is opposite to that of the values of \(K_{\rm d}\)(490); i.e., high-\(K_{\rm d}\)(490) areas generally have low CV values. In general, the \(K_{\rm d}\)(490) values for southwestern and central Lake Taihu were high and varied less significantly than those in other areas. Virtual stations were set up in central Lake Taihu and three typical bay areas, including Zhushan Bay, Meiliang Bay, and Gonghu Bay (S1-S4, Figure 1), to observe and analyze the temporal and spatial differences in \(K_{\rm d}\)(490) between different areas of the lake. As illustrated in Figure 8, which shows the OLCI-derived \(K_{\rm d}\)(490) at the four virtual stations, \(K_{\rm d}\)(490) at station S1 (located in an open area) (\(8.37\pm 2.31\) m\({}^{-1}\)) is significantly higher than those at the other virtual stations (\(t\)-test, \(p\)! 0.05); the \(K_{\rm d}\)(490) values at stations S2 (located in Zhushan Bay) (4.39 \(\pm\) 1.81 m\({}^{-1}\)) and S4 (located in Gonghu Bay) (3.86 \(\pm\) 1.17 m\({}^{-1}\)) are the next highest, whereas \(K_{\rm d}\)(490) at station S3 (located in Meiliang Bay) (3.19 \(\pm\) 3.86 m\({}^{-1}\)) is the lowest. The \(K_{\rm d}\)(490) values at stations S1 and S2 are significantly higher in March than in any other month (\(p\)! 0.05), which is mainly because the wind speeds are relatively high in March (2.47 \(\pm\) 0.68 m/s). There is no significant difference in the \(K_{\rm d}\)(490) values at the other stations between different months. In general, the distribution of \(K_{\rm d}\)(490) for Lake Taihu exhibited significant spatial and temporal variations between November 2016 and April 2017 (Figure 6). By further observing and analyzing OLCI red-green-blue (RGB) images of 24 scenes of Lake Taihu and the corresponding \(K_{\rm d}\)(490) values, two distribution patterns for \(K_{\rm d}\)(490) are identified. Type I is the distribution of \(K_{\rm d}\)(490) under significant bloom coverage (?25% coverage) (e.g., 3 and 6 November and 4 December 2016). Overall, \(K_{\rm d}\)(490) was low (5.01 \(\pm\) 1.95 m\({}^{-1}\)) under these conditions, which is mainly because the wind speeds were low, and almost no wind-induced sediment resuspension occurred during large-scale  
Figure 5: Development and validation of the algorithm for estimating \(K_{\rm d}\)(490) in Lake Taihu from the OLCI-derived \(R_{\rm rs}\). outbreaks of algal blooms. In areas with outbreaks of algal blooms, sunlight was blocked by the algae that accumulated on the surface and was unable to penetrate to deeper water. In comparison, in areas without algal blooms, high-\(K_{\mathrm{d}}\)(490) areas were mainly distributed near areas with outbreaks of algal blooms, and low-\(K_{\mathrm{d}}\)(490) areas were distributed in regions outside the areas with algal bloom outbreaks. Type II is the distribution of \(K_{\mathrm{d}}\)(490) under low bloom coverage (<25% coverage) (e.g., on 15, 16, and 30 December 2016). In general, the \(K_{\mathrm{d}}\)(490) values were high (7.60 \(\pm\) 2.14 m\({}^{-1}\)) under these conditions.