## 6 Conclusions  
Grape, either table grape or wine grape, is widely cultivated in China because of its high economic value. In this study, an approach was designed to identify a vineyard using time series RS data, including original time series reconstruction, the CWT of the reconstructed time series, and training a CNN classifier. First, three VIs (i.e., NDVI, EVI, and GCVI) were derived from the original Sentinel-2 image collection to form the original time series; second, an iterated S-G method that focused on removing outliers and filling missing values was applied to the original time series to yield a daily time series over a full year; third, the CWT was applied to each previous reconstructed time series to generate the corresponding scalogram; and finally, three scalograms were stacked together to serve as the input of the CNN classifier to complete the identification task. The results demonstrated the following: (i) the CWT had stable performance on the reconstructed time series, which provided the CNN classifier with more time-related frequency information, and was not sensitive to the mother wavelet and ground classes; (ii) the CNN classifier with scalograms as input achieved a better vineyard identification result than RF, with an overall accuracy of 89.66% (overall accuracy was 79.78%), thereby making great progress by improving the overall accuracy by 9.87%; and (iii) the CNN captured the unique and subtle features that hide in the scalogram to distinguish the vineyard from other classes, and the combination of the CWT and CNN has more accurate performance than RF in vineyard identification using time series satellite images.  
To the best of our knowledge, a few studies have been conducted to identify vineyards using the time series of satellite image data; thus, this is the first time that the proposed approach has been applied to vineyard identification. The proposed approach fully explored the frequency information of time series data by applying the CWT, and then used the advantages of the CNN in image recognition to capture those distinguishing features in scalograms. Since the result has shown that applying CWT to a time series of vegetation indices, in terms of the effectiveness in extracting information of time series, is insensitive to the type of vegetation index, surface category, and type of mother wavelet, the information entropy is stably increased after applying CWT to time series. Furthermore, just as the CNN has achieved good results in classifying scalogram images in our research, many previous studies on the classification of images using CNN have also proved that CNN has unique advantages for image recognition. Therefore, the approach is considered to be able to be applied to many other practical scenarios, and further researches on testing the performance of the proposed approach in applications like classifying other crops (e.g., corn, soybean, and rice) and mapping land use/landcover based on time series images are recommended and appreciated.  
**Author Contributions:** Conceptualization, L.Z. and Q.L.; methodology, L.Z., Y.Z., and H.W.; software, L.Z.; validation, L.Z. and H.W.; formal analysis, L.Z.; investigation, L.Z. and H.W.; writing--original draft preparation, L.Z. and Y.Z.; writing--review and editing, L.Z.; visualization, L.Z.; funding acquisition, Q.L. and X.D.  
**Funding:** This research was funded by the National Key R&D Program on Monitoring, Early warning and Prevention of Major National Disaster grant number 2017 YFC1502802, the National Science Foundation of China grant number 41701486, and Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education grant number GTYR201808.  
**Conflicts of Interest:** The authors declare no conflict of interest.