## 2 Materials and Methods
### Methods
#### 2.3.3 Classification Algorithms  
At present, remote sensing classification methods such as the support vector machine (SVM), the classification regression trees (CART), and the random forest (RF) algorithms have been widely used in land cover mapping and crop type identification [4]. In order to analyze the differences in the accuracies of the extraction and classification of the different land cover types when using different classification algorithms, and when using the same feature variable combination and the differences in the accuracies of the extraction and classification of different land cover types when using the same classification algorithm but different combinations of feature variables, three classification algorithms were used in this study. These three classification algorithms are introduced below.
* Support Vector Machine The support vector machine (SVM) was proposed by [PERSON] in 1995 [48]. The SVM has significant advantages in terms of nonlinear problems, small samples, and a high dimensionality. It is widely used due to its small training samples and support for high-dimensional feature spaces. The parameters that need to be adjusted when using the SVMto classify remote sensing images are the kernel function type, the gamma value of the kernel function, and the cost parameter.
* Classification and Regression Tree  
The classification and regression tree (CART) was proposed by [PERSON] et al. in 1984 [48]. It is widely used in land cover extraction and remote sensing image classification research due to its simple structure, fast calculation speed, and the advantage of being easy to understand. The parameters that need to be optimized when using CART to classify remote sensing images are the maximum and minimum numbers of leaf nodes in each tree.
* Random Forest  
The random forest (RF) algorithm was proposed by [PERSON] in 2001 [49]. Studies have shown that the RF has the advantages of stability, rapidity, and high precision in processing remote sensing data; and therefore, it has become the most widely used classifier among current remote sensing classification algorithms. It has important applications in crop extraction, image classification, and agricultural regression models [50,51]. The two main parameters that need to be adjusted and optimized for the RF when classifying remote sensing images are the number of decision trees that need to be created and the minimum number of leaf nodes. Studies have indicated that the values of the RF parameters have little effect on the accuracy [51], so the number of decision trees was set as 300. Additionally, when using RF for classification in GEE, the importance score of the parameters participating in the classifier can be calculated by using the \"explain\" function. The value of the importance score is not absolute, and there is no uniform and fixed value range, but changes with the number of sampling data and feature parameters participating in the classification [52,53]. The importance and contribution of the feature parameters participating in the classification can be determined by the relative value of the importance score in a specific situation.