## 1 Introduction  
Food security a major challenge to the sustainable development of human beings [1, 2], and the eradication of hunger is the second target of the United Nation (UN)'s Sustainable Development Goals (SDGs) [3]. Corn serves as livestock feed, industrial raw material, and human food, and is one of the cardinal crops in the world. Corn cultivated areas account for 13% of the total croplands on Earth and rank second among the major crop categories [2]. In the Hai basin, serious overexploitation of groundwater is occurring [4]; irrigation accounts for 70-80% of the total water use, and is the chief reason for groundwater depletion [5]. Identifying corn-cropped areas accurately and efficiently is essential for ensuring food security, providing fundamental information to estimate crop yields, make decisions, and adjust crop structures [6], and managing water resources, because corn is a crucial C4 crop with the highest water-use efficiency, which is important for global primary production [7]. Traditional approaches to obtaining crop information, such as ground surveys and sampling, are time consuming, labor intensive, and costly, and cannot obtain continuous spatial distribution data for crops [1; 6]. Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8; 9]. In recent decades, many studies have focused on crop monitoring with optical [10; 11; 12; 13; 14] and synthetic aperture radar (SAR) images [1; 15; 16; 17; 18]. Multitemporal and time series data are powerful tools for identifying different crops and handling the problem of spectral similarity in heterogeneous underlying surfaces in single-period imagery [19; 20; 21; 22; 23]. The use of coarse spatial resolution images as original data at scales of hundreds of meters, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR), is predominant in previous studies because of the fine temporal resolution of the Aqua and Terra satellites. These two satellites revisit the same location at least four times per day, and provide the composition products for free [24; 25; 26; 27; 28]. To distinguish cropland and grassland, [PERSON] et al. used MODIS normalized differenced vegetation index (NDVI) time series and random forest (RF) classifiers and achieved an overall accuracy of 90.1% [20]. Using growing degree days information and a Gaussian mixture model, [PERSON] et al. mapped large areas of winter crops with MODIS NDVI data and obtained good consistency with the official statistical data [25]. However, the spatial resolutions of MODIS and AVHRR were so coarse that each pixel could contain mixtures of different crops or vegetation and crops, especially in China [21]. Since 2008, 30-m resolution Landsat imagery has been freely available, and it has also been widely used in crop classification tasks [29; 30; 31; 32; 33]. [PERSON] et al. mapped the extent of the paddy rice crop in northeastern Asia based on Landsat 8 images, and achieved excellent results with a user accuracy of 92% [10]. [PERSON] et al. recognized a soybean cultivated area in United States with medium-resolution satellite Landsat data and obtained an overall accuracy of 84% [8]. However, the revisiting period of Landsat was half a month, and few images were available during the crop-growing season [9; 21]. Although contradictions are observed between the spatial and temporal resolution for satellite remote sensing data, these contradictions have been partially alleviated since the Sentinel-2 constellation was launched in June 2015 and March 2017, which shortened the revisiting cycle to 10 days for single satellites and five days for satellite constellations. To mine agricultural information from Sentinel-2 data, the European Space Agency launched the 'Sentinel-2 for Agriculture' project in 2014 for cropland and crop-type mapping [34; 35; 36; 37].