## 4 Discussion
### Mechanisms of the Spatio-Temporal Variations in the \(K_{\rm d}\)(490) Distribution  
Our study shows that in the absence of algal blooms, the changes in \(K_{\rm d}\)(490) for the waters of Lake Taihu are mainly caused by changes in SPM, especially the changes in SPIM. An increase in the SPM concentration results in an increase in the light attenuation in the water, which results in a decrease in the transparency and eutrophic depth of the water and thereby affects the spatial and seasonal distributions of aquatic vegetation [65; 66]. As a shallow lake, the high dynamic ratio index (the square root of the surface area divided by the average depth, 25.6 km/m) of Lake Taihu indicates that winds are the main driving force for sediment resuspension. Wind- and wave-induced sediment resuspension will increase the SPM concentration in the water and thereby increase light attenuation in the water [52; 67; 68]. Several studies have demonstrated that the wind-induced resuspension is important in sediment related turbulence, the main driving factor for the spatial and temporal variations in \(K_{\rm d}\) for Lake Taihu [27; 61; 69]. To further examine the effects of wind on the spatial distribution of \(K_{\rm d}\)(490), we analyzed the relationships between the OLCI-derived \(K_{\rm d}\)(490) values and the mean wind speeds pixel by pixel at different times before the acquisition of the remote sensing image (Figure 14). Taking the four virtual stations as an example (Figure 14a), the Pearson correlation coefficient (\(r\)) between the OLCI-derived \(K_{\rm d}\)(490) and the mean wind speed for different hours (h = 1, 2, 3, \(\ldots\), 96 h) before the satellite image acquisition time was calculated, and the maximum Pearson correlation coefficient (\(r\)) and corresponding hours were then determined and recorded. The maximum Pearson correlation coefficient (\(r\)) can indicate the influence of the wind speed on \(K_{\rm d}\)(490), and the corresponding hours can indicate the duration of the wind speed effect on \(K_{\rm d}\)(490). The maximum correlation coefficients at stations S1-S4 are 0.56, 0.39, 0.43, and 0.46 (\(p<0.05\)), which correspond to 50, 14, 11, and 15 h, respectively. This indicates that the wind speed has the longest lasting effect and a significant impact on \(K_{\rm d}\)(490) at station S1 and insignificant and short impacts on \(K_{\rm d}\)(490) at stations S2-S4. This is because station S1 (located in central Lake Taihu) is more prone to wind effects, which can lead to sediment resuspension, whereas stations S2-S4 (located in the bay areas) are relatively closed, and the wind speed has an insignificant impact on sediment resuspension at these stations. In addition, the time delay for waves resulting from a change in the wind field varies between different waters, and the bay areas tend to stabilize before central Lake Taihu [68]. Figure 14b shows the spatial distribution of the maximum correlation coefficients. The correlation coefficient is the highest (\(r\approx 0.6\), \(p<0.05\)) in central Lake Taihu, followed by Gonghu Bay and Meiliang Bay (\(r\approx 0.4\), \(p<0.05\)), which indicates that the wind speed has a greater impact on \(K_{\rm d}\)(490) in central Lake Taihu than in the bay areas. No significant statistical correlations are found in southwestern and northwestern Lake Taihu and in Zhushan Bay (\(r\approx 0.2\), \(p<0.05\)), which indicates that wind speed-induced sediment resuspension is not the only factor that affects the SPM concentration in these areas; the sediment distribution, lake currents, precipitation, and runoff can all affect the SPM concentration [70] and thereby affect \(K_{\rm d}\)(490). Figure 14c shows the spatial distribution of the numbers of hours corresponding to the highest correlation. Southwestern Lake Taihu corresponds to the largest number of hours (>80 h), central Lake Taihu corresponds to a relatively large number of hours (>48 h), and the bay areas correspond to a few hours (<15 h).