_Applied_  
_sciences_  
_Article_  
**The Classification Performance and Mechanism of**  
**Machine Learning Algorithms in Winter Wheat**  
**Mapping Using Sentinel-2 10 m Resolution Imagery**  
**[PERSON] 1,2,2, [PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2**  
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River  
Regions (Hennen University), Ministry of Education, Kaifeng 475004, China; [EMAIL_ADDRESS] (P.F. ); [EMAIL_ADDRESS] (P.W. ); [EMAIL_ADDRESS] (G.V. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (F.L. ); [EMAIL_ADDRESS] (J.Z.) College of Environment and Planning, Henan University, Kaifeng 475004, China  
Correspondence: [EMAIL_ADDRESS]  
Received: 25 June 2020; Accepted: 21 July 2020; Published: 23 July 2020  
**Featured Application: Machine learning algorithms are essential to crop identification and land use/cover. Our work indicates that compared to RF and CART algorithms, SVM achieves best performance in identifying winter wheat. Though SVM is sensitive to algorithm parameters, it obtains maximum value of accuracy score and minimum residuals by tuning hyperparameters. Therefore, we recommend that SVM will be a suitable and effective algorithm if researchers employ machine learning algorithms for crop identification, land use/cover and other similar research with small sample data. Meanwhile, no matter which algorithm we choose, we should focus on the importance of the performance and mechanism. The enumeration method, similar to grid search, should be used to fine-tune the hyperparameters when discussing the use of various algorithm parameters. **  
**Abstract: Machine learning algorithms are crucial for crop identification and mapping. However, many works only focus on the identification results of these algorithms, but pay less attention to their classification performance and mechanism. In this paper, based on Google Earth Engine (GEE), Sentinel-2 10 m resolution images during a specific phenological period of winter wheat were obtained. Then, support vector machine (SVM), random forest (RF), and classification and regression tree (CART) machine learning algorithms were employed to identify and map winter wheat in a large-scale area. The hyperparameters of the three machine learning algorithms were tuned by grid search and the 5-fold cross-validation method. The classification performance of the three machine learning algorithms were compared, the results of which demonstrate that SVM achieves best performance in identifying winter wheat, and its overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and kappa coefficient (Kappa) are 0.94, 0.95, 0.95, and 0.92, respectively. Moreover, 50 various combinations of training and validation sets were used to analyze the generalization ability of the algorithms, and the results show that the average OA of SVM, RF, and CART are 0.93, 0.92, and 0.88, respectively, thus indicating that SVM and RF are more robust than CART. To further explore the sensitivity of SVM, RF, and CART to variations of the algorithm parameters--namely, (C and gamma), (tree and split), and (maxD and minSP)--we employed the grid search method to iterate these parameters, respectively, and to analyze the effect of these parameters on the accuracy scores and classification residuals. It was found that with the change of (C and gamma) in (0.01-1000), SVM's maximum variation of accuracy score is up to 0.63, and the maximum variation of residuals is 76,215 km2. We concluded that SVM is sensitive to the parameters (C and gamma) and presents a positive correlation. When the parameters (tree and split) change between (100-600) and (1-6), respectively, the RF's maximum variation of accuracy score is 0.08, and the maximum variation of residuals is 1157 km\({}^{2}\), indicating that RF is low in sensitivity toward the parameters (tree and split). When the parameters (maxD and minSP) are between (10-60), the maximum accuracy change value is 0.06, and the maximum variation of residuals is 6943 km\({}^{2}\).