## 3 Results
### Performance and Test of Methods Applied for Hypotheses I and II  
Scatterplots for SFs for the two regions of interest with different types from the image acquired on 11 July and 16 August under hypothesis I (Figure 6A,C,E) show little support for a linear correlation for either emergent or submerged vegetation types, but moderate support for floating-leaved vegetation. Scatterplots for the ranked SF values for the two regions under hypothesis II (Figure 6B,D,F) show strong support for a linear relation for all types, though a linear model seems less appropriate for emergent and submerged vegetation types in the tails of their SF distributions. Ranked SF values for all three vegetation types had overall \(R^{2}>0.90\). Equations for calculating modified threshold for SFs under the hypotheses I and II are shown in Tables 6 and 7. Figure 4: _Cont._  
Figure 5: Areal extent of floating-leaved and submerged vegetation mapped from traditional classification tree models for 11 July, 16 August and 26 September 2013, for the four study sections in Taihu Lake. Figure 6: Correlations between Spectral features (SFs) of ROIs for image T\({}_{1}\) (11 July 2013) and image T\({}_{2}\) (16 August 2013) under hypothesis I and hypothesis II. Correlation between SF1 of ROI 1 for image T\({}_{1}\) and image T\({}_{2}\) under hypothesis I (A) and hypothesis II (B); Correlation between SF\({}_{2}\) of ROI 2 for image T\({}_{1}\) and image T\({}_{2}\) under hypothesis I (C) and hypothesis II (D); Correlation between SF\({}_{3}\) of ROI 3 for image T\({}_{1}\) and image T\({}_{2}\) under hypothesis I (E) and hypothesis II (F). See Table 3 for descriptions of SF\({}_{1}\), SF\({}_{2}\) and SF\({}_{3}\); See Table 4 for descriptions of ROI 1, ROI 2 and ROI 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region of Interest** & \(\mathbf{R^{2}}\) & **Equation** & \multicolumn{2}{c}{**Thresholds of**} \\
**(ROI)** & \(\mathbf{R^{2}}\) & **Equation** & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{i}}\) \\ \hline ROI 2 & 0.4350 & \(\mathbf{aMCT_{1\to 2}^{2}\!-\!SF_{2}}=\!-\!0.6938\!\times\!\mathbf{TCT_{1}\!-\!SF_{2}}\!-\!0.0851\) & \(\mathbf{aMCT_{1\to 2}^{2}\!-\!SF_{2}}\) & \(-\!0.0608\) \\ ROI 3 & 0.2337 & \(\mathbf{aMCT_{1\to 2}^{2}\!-\!SF_{3}}\!=\!0.3112\!\times\!\mathbf{TCT_{1}\!-\!SF_{3}}\!+ \!0.0331\) & \(\mathbf{aMCT_{1\to 2}^{2}\!-\!SF_{3}}\) & \(0.7044\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Equations and resulting modified thresholds for spectral features (SFs) for models developed under hypothesis I. Note we did not include an equation for emergent vegetation, as there was no correlation between image dates (see Figure 6A) and therefore no support for generating modified CT model thresholds with this approach. See Equation (1) for definitions of mathematical terms. See Table 4 for descriptions of ROIs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region of** & \(\mathbf{R^{2}}\) & **Equation** & \multicolumn{2}{c}{**Threshold of**} \\
**Interest (ROI)** & \(\mathbf{R^{2}}\) & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{i}}\) \\ \hline ROI 1 & 0.9290 & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{1}}\!=\!0.4703\!\times\!\mathbf{TCT_{1}\!-\!SF_{1}}\!+\!0.2232\) & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{1}}\) & 0.2467 \\ ROI 2 & 0.9937 & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{2}}\!=\!-\!1.0485\!\times\!\mathbf{TCT_{1}\!-\!SF_{2}}\!-\!0. 0378\) & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{2}}\) & \(-\!0.0745\) \\ ROI 3 & 0.9797 & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{3}}\!=\!0.6372\!\times\!\mathbf{TCT_{1}\!-\!SF_{3}}\!+ \!0.0067\) & \(\mathbf{bMCT_{1\to 2}^{2}\!-\!SF_{3}}\) & 0.1290 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Equations and resulting modified thresholds for spectral feature (SFs) for models developed under hypothesis II. See Equation (2) for definitions of mathematical terms. See Table 4 for descriptions of ROIs.