## 4 Results
### Scalograms  
To determine the optimal maximum value of the scale, the maximum scale was varied from 1 to 500 to explore the relationship between the maximum scale and the entropy of the scalogram for different planting conditions. Figure 5 shows how the scalograms' entropy relates to the increase of the maximum scale in different cases. Clearly, either three VIs or seven planting conditions demonstrate a uniform trend; that is, as in maximum scale increased, the entropy of the scalogram increased accordingly. However, an entropy saturation phenomenon appeared gradually in three VIs and seven planting conditions when the maximum scale value was greater than 200, particularly in the NDVI case. Therefore, the scales were set to the range of 1-200 in the study, and the consequent CWTs were performed based on this scale setting.  
Once the mother wavelet and scales were determined, applying the specific CWT to the reconstructed time series yielded the corresponding scalogram that represented the time series data in both the frequency and time domain. As the scalogram examples in Figure 6 show, in the two-dimensional feature space, that is, frequency and time, each planting condition had a different  
Figure 5: Relation between the entropy of the scalogram and maximum scale for (**a**) spring corn-vegetables, (**b**) peach trees, (**c**) forest, (**d**) greenhouse, (**e**) vineyard, (**f**) winter wheat-corn, and (**g**) vegetables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & ** NDVI** & **EVI** & **GCVI** \\ \hline Spring corn-Vegetables & 0.825 & 0.648 & 0.716 \\ Peach trees & 0.812 & 0.523 & 0.526 \\ Forest & 0.665 & 0.634 & 0.343 \\ Green house & 0.726 & 0.216 & 0.652 \\ Vineyard & 0.752 & 0.795 & 0.311 \\ Winter wheat-Corn & 0.875 & 0.559 & 0.660 \\ Vegetables & 0.862 & 0.486 & 0.559 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconstruction accuracy of example time series in Figure 4 (R2).  
signature in the three VIs' cases. For example, the crop rotations of spring corn to vegetables (Figure 6a) and winter wheat to corn (Figure 6f) differed in both the coefficient distribution pattern and its magnitude; particularly in the GCVI case, they had great dissimilarities. Similarly, noticeable differences in all three scalograms easily distinguished the vineyard (Figure 6e) from peach trees (Figure 6b) and other forest (Figure 6c), even though many subtle differences still existed among the latter two classes, such as the magnitude difference in the EVI scalograms and the difference in the distribution shape in the GCVI scalograms. For the greenhouse (Figure 6d) and vegetables (Figure 6g), both the magnitude difference of the coefficient and the difference in the distribution pattern in all three scalograms provided sufficient evidence to distinguish them from each other. From the view of distinguishing the vineyard from the other six classes, although NDVI scalograms could not provide an obvious visual difference to distinguish vineyard from crop rotation of spring corn to vegetables, greenhouse, and vegetables, the distribution shape and magnitude differences in the EVI and GCVI scalograms excluded the rotation of spring corn to vegetables, greenhouse, and vegetables. To summarize, the magnitude differences and distribution patterns in all three types of scalograms ensured that time-related frequency features provided sufficient detail to the CNN classifier to successfully complete the vineyard identification task.  
Figure 6: Examples of scalograms for (**a**) spring corn-vegetables, (**b**) peach trees, (**c**) forest, (**d**) greenhouse, (**e**) vineyard, (**f**) winter wheat-corn, and (**g**) vegetables.