Therefore, compared to RF, CART is sensitive to the parameters (maxD and minSP) and has poor robustness. In general, under the conditions of the hyperparameters, SVM and RF exhibit optimal classification performance, while CART has relatively inferior performance. Meanwhile, SVM, RF, and CART have different sensitivities toward the algorithm parameters; that is, SVM and CART are more sensitive to the algorithm parameters, while RF has low sensitivity toward changes in the algorithm parameters. The different parameters cause great changes in the accuracy scores and residuals, so it is necessary to determine the algorithm hyperparameters. Generally, default parameters can be used to achieve crop classification, but we recommend the enumeration method, similar to grid search, as a practical way to improve the classification performance of the algorithm if the best classification effect is expected. machine learning algorithms; classification performance; winter wheat mapping; Sentinel-2; large-scale +
Footnote †: journal: Medical Imaging
processing software (ENVI, ERDAS, ArcGIS, etc.) is used to process images, the processing is time-consuming, which leads to the low efficiency of image processing. With Google Earth Engine (GEE) platform, the above operations can be completed with a few lines of code, which greatly improves the efficiency of data processing. These problems limit the research of high-resolution large-scale crop classification. GEE provides a good solution to the above problems [25]. GEE is the world's most advanced cloud platform for remote sensing big data processing, which not only has efficient computing capabilities, but also assembles a large number of public geospatial data sets, including many free image resources (such as Landsat, MODIS, and Sentinel), meteorological data, land use/cover data, and population distribution data [25]. Using GEE, it is easy to conduct research pertaining to vegetation monitoring [26; 27], land use/cover [28; 29], water change [30], and drought analysis [31]. Moreover, lots of machine learning algorithms are built into GEE, and these have always been an important concern in crop mapping and land use/cover [8; 15; 29; 32]. Recently, many researchers have employed machine learning algorithms to conduct research, and have achieved fruitful results. Many previous works showed that SVM is a popular non-parametric algorithm. Moreover, compared with the traditional classification algorithms maximum likehood (ML), neural networks (NN), k-nearest neighbor (KNN), SVM has better classification performance [19; 33; 34]. [PERSON] [35] applied SVM and artificial neural networks (ANN) to SPOT-5 image classification, and the results showed that SVM classification effect was slightly higher than ANN. Although the effects of different parameters on classification accuracy were compared in detail, the relationship between classification accuracy and final classification residual was not paid attention to. Because SVM is sensitive to the change of algorithm parameters (C and gamma), although the accuracy score is large, it may be the result of overfitting. [PERSON] [19] compared the classification performance of SVM, NN, CART algorithms in land-classification research with MODIS data, and analyzed the accuracy change under the condition of limited training data. The results showed that compared with NN and CART algorithms, SVM has stronger generalization ability when the amount of data is small, and the highest accuracy is 83%. Similarly, although the paper notes that the parameter (C and gamma) has an impact on the classification accuracy of SVM, it does not explore how the algorithm parameters affect the accuracy change of SVM. In addition, the effect of mixed pixel produced by MODIS with medium resolution is also a factor restricting the classification accuracy of SVM. RF is also a popular classification algorithm in crop mapping and land use/cover. Many research works show that its classification accuracy is relatively stable and robust.