###### Abstract  
Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications. vineyard; identification; CWT; CNN; Sentinel-2; remote sensing 20191126410.3390/rs11222641.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.3390/rs112226410.390/rs112226410.390/rs112226410.390/rs11226410.390/rs112226410. producer, with 9 million tones. The production of wine grapes reached 180,000 tonnes in 2014 and has doubled over the past 15 years (+112% compared with 2000) [2]. Therefore, timely and accurate information about the cultivation area and spatial distribution of vineyards would provide strong support for precisely estimating its production and predicting market performance, in addition to adjusting and optimizing the planting area at a regional or country level. Remote sensing (RS) is the ideal technique for obtaining such information because it is fast, objective, and has a wide observation range. However, previous studies using RS data have mainly focused on mapping grape varieties [3; 4], retrieving physical and biochemical parameters (e.g., leaf area index and chlorophyll) [5; 6; 7; 8; 9; 10], evaluating the growing state (e.g., evaporation, water stress, and soil moisture) [9; 11; 12; 13], and detecting disease [14], in addition to estimating the quality [15] and production [16] of grapes. Research on vineyard identification is quite rare, and most studies have typically been based on the data with very high spatial resolution (e.g., unmanned aerial vehicle images, UAV images) [17; 18; 19; 20; 21; 22]. Among these vineyard identification studies, textural features caused by unique planting approaches are clearly reflected in UAV images, and thus contribute the most to the vineyard identification results, whereas spectral features contribute less because of the similarity, in terms of the phenological phase and canopy reflectance, with other crops [23].