## 3 Results and Analysis
### Estimation Model of Chlorophyll Content in Plant Leaves Based on Spectral Parameters  
From Table 2, it can be seen that the chlorophyll content of plant leaves has a good correlation with 10 spectral parameters (RES, BES, YES, YEP, RGP, RWSB, REA, BEA, YEA and LCI), which indicated that it was feasible to estimate the chlorophyll content of _Quercus aquifolioides_ in Sejila Mountain by using these spectral characteristic variables. As shown in Table 3, this study takes 9 typical plant spectral characteristic parameters as independent variables and chlorophyll content as dependent variables, and conducts univariate regression analysis to establish an index, linear and quadratic polynomial chlorophyll content estimation model. Among them, the estimation model \(R^{2}\) of RGP was the highest (y = 206.98e\({}^{-10.85\text{x}}\), \(R^{2}\) = 0.8523), which showed that this model has high accuracy (Figure 8). After the establishment of the estimation model, in order to verify the reliability and applicability of the estimation model, the estimation model was evaluated with three general indexes of root mean square error (RMSE), relative error (RE) and determination coefficient (\(R^{2}\)) (Table 4), and the relationship diagram between the predicted value and the measured value was drawn (Figure 9) to visually show the fitting degree and reliability of the estimation model. The test results showed that the chlorophyll content estimation model based on the spectral characteristic parameters of RGP has achieved good test results. The determination coefficient (\(R^{2}\)) of the test fitting equation reaches above 0.8, and the root mean square error (RMSE) and relative error (RE) were both small, with higher fitting accuracy and smaller relative error. The estimation model of vegetation chlorophyll content has the largest determining coefficient (\(R^{2}\) = 0.8024), the smallest root mean square error (RMSE = 3.0512), the smallest relative entropy (MRE = 9.1721) and the smallest relative error (RE = 6.13%), and the prediction accuracy reaches 95.85%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Spectral Parameter** & **Correlation Coefficient** \\ \hline REP & 0.162 \\ RES & \(-\)0.292 * \\ BEP & \(-\)0.031 \\ BES & \(-\)0.484 ** \\ YEP & \(-\)0.098 \\ YES & \(-\)0.270 * \\ YEP & \(-\)0.421 * \\ RGP & \(-\)0.892 ** \\ GPP & \(-\)0.023 \\ RWSB & \(-\)0.623 ** \\ REA & \(-\)0.318 * \\ BEA & 0.461 * \\ YEA & \(-\)0.264 * \\ LCI & \(-\)0.335 * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationships between chlorophyll content index and spectral feature parameters. * indicates that the correlation reaches a significant level at the level of 0.05, ** indicates that it is extremely significant at the level of 0.01. The number of samples n = 60. Figure 8: Regression model between chlorophyll content and best spectral parameter. (**a**) RES (red edge slope), (**b**) YES (yellow edge slope), (**c**) BES (blue edge slope), (**d**) YEP (yellow edge position), (**e**) RGP (reflectance of green peak), (**f**) RWSB (reflectance of water stress band), (**g**) REA (red edge area), (**h**) YEA (yellow edge area), (**i**) BEA (blue edge area), (**j**) LCI (leaf chlorophyll index). CCI (chlorophyll content index). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression Equation** & \(R^{2}\) **(Determination Coefficient)** & **RMSE (Root Mean Square Error)** & **MRE\% (Min Relative Entropy)** & **RE\%** **(Relative Error)** \\ \hline y = 0.7812x + 9.1464 & 0.8024 & 3.0512 & 9.1721 & 6.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fit evaluation indicators of the measured values (x) and predicted values (y).