## 5 Discussion
### Applicability in Other Practical Scenarios  
The cost of building a time series of RS data has decreased because an increasing number of satellites have been launched into space, and the volume of usable data has largely increased. The time series observation is more capable of characterizing the features of the changes of land surface over a long time, which provides researchers with the opportunity to conduct numerous studies, such as phenological phase detection, crop growing state monitoring, land use/landcover change detection, and crop type identification. In this study, the time series of three VIs derived from Sentinel-2 data were used to identify vineyards based on the combination of the CWT and a CNN. For the CWT, we observed that the scalogram, which was the result of applying the CWT to a reconstructed time series, contained more abundant information than the original and reconstructed time series because it simultaneously reserved the frequency and time information. More importantly, the CWT was not sensitive to the mother wavelet and ground classes when it was applied to NDVI, EVI, and GCVI time series, which implies that the CWT is stable and can be used in other frequency analysis scenarios.  
Figure 10: (a) Vineyard identification result of the LSTM model, (b) difference between CNN and LSTM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** &
\begin{tabular}{c} **Input/Output** \\ **Shape** \\ \end{tabular} & **Param \#** \\ \hline Input layer & (365,3) & 0 \\ Lstm\_1 (LSTM) & (None,100) & 41,600 \\ Dense\_1 (Dense) & (None,1) & 101 \\ \hline \hline \end{tabular} Total params: 41,701; Trainable params: 41,701; Non-trainable params: 0  
\end{table}
Table 5: LSTM structure.  
In addition to the CWT, the CNN was another important part of the proposed approach. As the CNN has achieved tremendous success in the fields of object detection, image classification, and handwriting recognition, it has become a common, effective solution to the image classification task because of its unique properties. In this study, the CNN also achieved a better identification result than RF. All the previous studies conducted to classify images using CNN proves that the CNN is suitable for image classification. Therefore, the proposed approach is considered to be applicable to other applications based on time series RS data (e.g., crop type classification, mapping land use/landcover). One drawback of the proposed approach is that the computational cost is higher than that of using the original time series data and a traditional supervised method (e.g., RF) to perform the classification task. The two classification processes were conducted on a Think Station P710 with 120 GB of memory and 2 Intel(R) Xeon(R) CPU E5-2640 (2.4 GHz, 40 cores); RF consumed 1.2 min, and our approach consumed 5.6 min (only including generating scalograms and classification with CNN). The time series construction of the whole study region consumed 87 min. However, with the overall improvement of computational power and the decline of computational cost, it appears that the relatively higher computational cost of the complex solution will not be a problem in the future.