## 2 Methods
### P. australis Interpretation and AGB Retrieval
#### 2.4.2 Interpretation of _P. australis_  
In this study, as the plant growth model is a generic zero-dimensional plot-scale model, the potential inter-annual variation in the spatial distribution of _P. australis_ in the study area was neglected and _P. australis_ was interpretated using the OHS hyperspectral image of June 2021, as the plant was matured at that time. A total of 344 polygon samples of water body and vegetation were randomly selected from the high-resolution BJ-2 image. As _Nymphaea tetragona_ is the dominant floating plant and _T. orientalis_ is the most common coexistent species of _P. australis_ in the HWNR [48; 49], vegetation samples were further categorized into _P. australis_, _N. tetragona_, _T. orientalis_, and other vegetation (Figure S1), resulting in five classes in total. The polygon samples were divided into two groups. One group consisting of 237 training samples was used for interpretation, and the other group consisting of 107 validation samples was used for evaluating the accuracy of interpretation (Table 3).  
Separability metrics including Jeffries-Matusita distance (JM) and the transformed divergence (TD) were adopted to evaluate the efficiency of the selected training samples [50]. The values of these two metrics generally range from 0 to 2, and the corresponding separability is considered to be good if the metric value is greater than 1.9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Training Samples** & **Validation Samples** & **Total** \\ \hline Water body & 74 & 32 & 106 \\ _P. australis_ & 59 & 33 & 92 \\ _N. tetragona_ & 14 & 10 & 24 \\ _T. orientalis_ & 15 & 11 & 26 \\ Other vegetation & 75 & 21 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and validation samples from the high-resolution BJ-2 image.  
Support vector machine (SVM) was used to extract _P. australis_ in the study area. SVM is an advanced non-parametric machine learning classifier that has been proved to be robust and reliable for _P. australis_ interpretation using hyperspectral images [51]. This method follows the idea of inputting samples into a high-dimensional space and constructing a hyperplane to separate samples into different classes, and a separation margin defined by the hyperplane indicates the difference between two classes of samples, with the optimal hyperplane that can maximize the margin selected to generate the ultimate separation result [52]. With the advantage that fewer training samples are generally required, SVM also demonstrates better adaptability and faster training speed than other machine learning methods [53].  
In the present study, the radial basis function (RBF) kernel was selected for SVM, as it has been proven to be more accurate than other kernels [54]. Following relevant previous studies [55], the two key parameters of SVM--i.e., the value of gamma in RBF kernel and penalty parameter--were set as 0.01 and 100, respectively. Overall accuracy (OA) and Kappa coefficients were calculated to assess the accuracy of interpretation results. Producer's accuracy (PA) and user's accuracy (UA) were also calculated to estimate the degree of misclassification for each class, as PA and UA represent the percentage of correct interpretation results in the validation samples and the training samples pixel by pixel [56]. Based on the feature information revealed by the high-resolution BJ-2 image, artificial visual interpretation was also performed on the results of SVM to correct the misclassified pixels and obtain the final interpretation result. All the steps described in this section were carried out in ENVI 5.3 (L3 Harris Technologies, Broomfield, CO, USA).