## 3 Results  
### Performance Assessment  
In this study, single bands and the single-band ratio, band combination, and water index of the Landsta8 OLI images in the study area were used to establish the feature library of the GBDT model. The single-sample Kolmogorov-Smirnov test (K-S test) was used in SPSS software to determine whether these variables were in line with the normal distribution. The results showed that the value of progressive significance of the test samples was greater than 0.05, proving that the variables were in line with the normal distribution. In this case, Pearson's correlation analysis was used to test the importance of the features.  
Features with high importance (correlation coefficient higher than 0.6) are shown in Table 3. The features with correlation coefficients higher than 0.7 (B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4, B1 + B4, and B2 \(-\) B4) were selected as the input variables.  
The input features of the GBDT model were added successively, and the accuracy of the inversion results was evaluated and compared (Table 4). The results demonstrated that the inversion accuracy was enhanced as more variables were added, suggesting that additional variables could significantly improve the performance of the GBDT model for the retrieval of the Chl-a concentration, and the GBDT model with all the selected variables performed with a higher accuracy (R\({}^{2}\) = 0.778).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Variables**} & **MAE** & **MAPE** & **RMSE** & \multirow{2}{*}{**R\({}^{2}\)**} \\  & **(\#g/L)** & **(\%)** & **(\#g/L)** & \\ \hline B4 & 2.641 & 51.365 & 3.616 & 0.043 \\ B4, B3 + B4 & 1.416 & 27.539 & 1.970 & 0.685 \\ B4, B3 + B4, B3 & 1.387 & 26.988 & 1.912 & 0.695 \\ B4, B3 + B4, B3, B1 \(-\) B4 & 1.284 & 24.968 & 1.793 & 0.729 \\ B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4 & 1.247 & 24.250 & 1.731 & 0.755 \\ B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4, B1 + B4 & 1.303 & 25.355 & 1.752 & 0.752 \\ B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4, B1 + B4, & 0.998 & 19.414 & 1.626 & 0.778 \\ B2 \(-\) B4 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of assessing the accuracy of multiple feature variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **Correlation** & **Feature** & **Correlation** & \multirow{2}{*}{**Feature**} & **Correlation** \\  & **Coefficient** & & & **Coefficient** & & **Coefficient** \\ \hline B4 & 0.763 ** & B2 \(-\) B3 & \(-\)0.694 ** & & B4 + B7 & 0.674 ** \\ B3 + B4 & 0.751 ** & B4/B1 & 0.691 ** & & B4 + B6 & 0.668 ** \\ B3 & 0.725 ** & B4 + B5 & 0.689 ** & & B3 + B7 & 0.664 ** \\ B1 \(-\) B4 & \(-\)0.724 ** & B1 \(-\) B3 & \(-\)0.686 ** & & B3 + B6 & 0.660 ** \\ B2 \(+\) B4 & 0.717 ** & B2 \(+\) B3 & 0.686 ** & & B4/B2 & 0.647 ** \\ B1 \(+\) B4 & 0.706 ** & B3 \(+\) B5 & 0.680 ** & & F4/B & \(-\)0.614 ** \\ B2 \(-\) B4 & \(-\)0.704 ** & B1 \(+\) B3 & 0.675 ** & & B1/B4 & \(-\)0.609 ** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Importance of modeling features.  
The inversion results and the fitting of the measured values of the GBDT model constructed with B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4, B1 + B4, and B2 \(-\) B4 as the input features are shown in Figure 6. The GBDT model performed well, as indicated (MAE = 0.998 \(\mu\)g/L, MAPE = 19.414%, RMSE = 1.626 \(\mu\)g/L, and R\({}^{2}\) = 0.778).