## 2 Dataset and Study Area  
Shaanxi province belongs to the arid/semi-arid region of the Loess Plateau, which is one of the seven major grape-producing regions in China. The study area is located in the central Shaanxi province and spans an area of approximately 1000 km\({}^{2}\). Throughout the entire study area, there are seven planting conditions, in general, that appear in cropland over a full year (Table 1): crop rotations of spring corn to vegetables, winter wheat to corn, vegetables to vegetables, vineyard, peach trees, greenhouse, and other forest. Note that the forest mentioned in this paper is a collection of trees planted on cropland, including sophora japonica, apple trees, persimmon trees, pine trees, and other trees except peach trees because they have a small planting area over the study region. Among the seven planting conditions, the rotation of winter wheat to corn takes up more than 60% of the planting area of the study region; vegetables and fruit trees rank second and third, respectively. Because it is extremely difficult to recognize the types of crops planted in greenhouses without the support of other data, the study only considers vines planted in normal cropland without any materials covering its canopy, and further crop type recognition is not performed for the greenhouse category. A field survey was conducted on June 26, 2018, and 581 geo-tagged samples were collected (shown in the right panel of Figure 1). Geo-tagged samples were used to identify the unique features of each crop type. Training/testing data for each type of crop were acquired manually and independently from the Sentinel-2 image based on the analysis of ground samples and the images (many small polygons were randomly drawn to select sample pixels using ENVI software); the details are listed in Table 1. The training/testing datasets were mainly defined in pixels because the identification process was conducted at the pixel level. As a valuable data resource for vegetation monitoring, the Sentinel-2 satellite constellation has had a revisit cycle of five days since early 2018, which makes it possible to build dense and consistent time series data all over the world to mitigate the problems of cloud and cloud shadow contamination [43]. Despite its excellent revisit ability, Sentinel-2 also performs well in spatial and spectral resolutions, including three narrow bands for cloud screening and atmospheric correction at 60 m, and three red-edge bands and two shortwave infrared bands that provide key information about vegetation at 20 m, in addition to four classical bands (i.e., blue, green, red, and near infrared bands) at 10 m. The wide swath of Sentinel-2, coupled with its five-day revisit cycle, creates the opportunity to address the challenges that remain for precise mapping and monitoring in the agricultural field. Sentinel-2 was the only satellite data source used in the study, and the study area was fully covered by the tile of 49 SBU in the Worldwide Reference System (WRS-2). The Level-1C products of Sentinel-2A/B provided by the European Space Agency were downloaded from Sentinel Hub ([[https://apps.sentinel-hub.com/eo-browser/](https://apps.sentinel-hub.com/eo-browser/)]([https://apps.sentinel-hub.com/eo-browser/](https://apps.sentinel-hub.com/eo-browser/))) for the period of January 1-December 31, 2018. Finally, 33 scenes of Sentinel-2 images that had a cloud percentage lower than 50% or for which the spatial distribution of clouds did not obscure the study area were acquired to build an original image time