###### Abstract
This may be the reason that few studies have been conducted to identify vineyards using single-/multi-phased satellite images in which the spatial resolution is greater than that of UAV images. Within the existing research of vineyard recognition based on satellite imaging, the features used are basically employed from the literature on similar crop identification/detection studies. Spectrums in the range of visible to short wave infrared are the basic features that are commonly employed by previous studies, which justified broad-band multispectral remote sensing imagery of high spatial resolution shows potential applications for vineyard identification [3; 9]. Vegetation indices derived from the spectral bands are another import feature component, which are designed to characterize different physiological or biochemical features of vineyards' canopy. For example, the normalized difference vegetation index (NDVI) was used to indicate the condition of vine leaf area index (LAI) [8], the perpendicular vegetation Index (PVI) and ratio vegetation index (RVI) were adopted to present the canopy density of the vineyard [24], and band ratios and indices like red-edge/blue and the modified soil-adjusted vegetation index (MSAVI) were used to depict the difference between vineyards and other classes [25]. Texture features are often associated with very high spatial resolution, so they are widely used in UAV imagery. Since vineyard mapping using UAV images is too costly to be applied on a large scale, dense time series RS data is expected to be the solution for large-scale and accurate vineyard mapping if the subtle differences, both in spectrum and phenological phase, between vineyards and other crops over the entire growing season/year are fully considered and exploited. Therefore, a study on identifying vineyards using time series satellite images is necessary and valuable. With the increasing success of RS techniques and the increasing number of satellites in orbit, a huge volume of RS data with a high spatial and temporal resolution is freely accessible to users all over the world, which satisfies the prerequisite for a variety of applications and enables new applications. Abundant data sources benefit the construction of time series RS data, which can fully depict phenological features of vegetation; thus, it is important to distinguish crop type from each other. To date, time series RS data have been successfully applied to many practical applications, such as land cover classification [26; 27], change detection [28; 29], crop type classification [30], crop phenology detection [31; 32], and crop monitoring [33; 34; 35] because they can provide informative features for subsequent classification/regression algorithms. In the context of crop type mapping, many approaches have exploited time series RS data, and can be roughly divided into three categories: First, time series data have directly served as the input of the traditional supervised classification method (e.g., random forest (RF)), where time series data are regarded as independent features. Their temporal information contributes nothing to the classification result in these approaches [36]. Second, deep learning approaches, such as convolutional neural networks (CNNs), have been applied to identify crop types by considering original long time series data as the only input [37; 38; 39; 40; 41]. Within such approaches, time series images are directly stacked, then CNNs automatically construct convolutional filters of three dimensions to extract the features to serve its class prediction layer. Third, the Fourier transform and wavelet transform (WT) have been used to perform frequency analysis and extract phenological features to complete the task of crop type identification because time series RS data contemporaneously contains temporal and amplitude information [32; 42]. That is, detecting sowing and harvesting date, peak point of growth, rising and falling rate of vegetation index, then treating them as the input vector of the classification algorithm.