## 5 Conclusions  
Crop mapping is crucial for agricultural production management and food security, and machine-learning algorithms such as SVM, RF, and CART provide important support for this purpose. Based on the GEE platform, we utilized Sentinel-2 10 m resolution multi-spectral images, and combined them with the SVM, RF, and CART algorithms to identify and map winter wheat in large-scale areas. By analyzing the classification results of the three machine-learning algorithms, the value of the OA for SVM is 0.95, and it achieves the best classification performance. In addition, the sensitivity of SVM, RF, and CART were discussed in detail. This research demonstrates that, in general, SVM is more sensitive to parameters (C and gamma); the classification accuracy is optimal when these hyperparameters are obtained. RF is less sensitive to (tree and split), and it is more robust. Compared to RF, CART is more sensitive to parameters (maxD and minSP), with unstable classification performance and inferior robustness. In this research, considering the algorithms' complexity, SVM, RF, and CART, three simple but efficient algorithms were employed for winter wheat identification and mapping. In future research, more machine and deep learning algorithms should be applied to crop identification. However, no matter which algorithms we choose, we should pay more attention to the algorithms' internal mechanism, which is more conducive to improving the accuracy of classifications. Meanwhile, the enumeration method, similar to grid search, should be employed to fine-tune the hyperparameters when discussing the use of various algorithm parameters.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/15/5075/s1](http://www.mdpi.com/2076-3417/10/15/5075/s1)]([http://www.mdpi.com/2076-3417/10/15/5075/s1](http://www.mdpi.com/2076-3417/10/15/5075/s1)), Code link: The code link of wheat classification based on GEE; Table S1: Various assessment indicators under the 50 groups of different training sets and validation sets; Table S2: The basic indicators for box-plots.  
Conceptualization, P.F. and X.Z.; methodology, P.F. and X.Z.; writing--original draft preparation, P.F., P.W., H.Z., F.L. and J.Z.; writing--review and editing, P.F., X.Z., P.W., Y.W., H.Z., F.L. and J.Z.; supervision, X.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Global Environment Facility (GEF), the integrated management mainstreaming project of water resources and water environment (MWR-C-3-9), the Major Research Projects of the Ministry of Education (16 JJD770019), and the cooperation base open fund of the Key Laboratory of Geospatial Technology for the middle and lower Yellow River regions and the CPGIS (JOF 201602).  
The authors acknowledge the financial support provided by Global Environment Facility (GEF), the integrated management mainstreaming project of water resources and water environment, the Major Research Projects of the Ministry of Education, and the cooperation base open fund of the Key Laboratory of Geospatial Technology for the middle and lower Yellow River regions and the CPGIS. The authors especially thank the anonymous reviewers for their constructive comments and insightful suggestions that greatly improved the quality of this manuscript.  
The authors declare no conflict of interest.