# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Classification of Hierarchical Random Forest  
The decision tree is one of the most effective tools for estimating target variables or classifying patterns. A decision tree divides the input space into sections and assigns a response value to each section [25]. In simple terms, the average of the target values related to the patterns in each area can be used to determine the answer in regression problems, or, in other words, the responsibilities assigned to each area based on the average of the target values corresponds to the learning patterns in each area. RF is a new development method for decision trees that uses grounded rules to combine the predictions of several single algorithms. To create each tree, a different set of existing patterns is chosen, with each fixed design being replaced. The total number of available ways [26] will be used to determine the size of this chosen category. Because it performs better in research with extensive input data and various features, and estimates the purposes required for mapping, the RF algorithm is much more efficient than other classification models, such as neural networks. We use a unique process called bootstrapping in the random forest method. Each tree in this method represents one of the training samples that is chosen at random, and sub-branches in each of these trees are treated as a random set of input features. As can be seen in Figure 4, the classification method was broken down into two stages. The first stage involved creating classes, determining water and forest crop classes, and the second stage involved classifying the crops studied in this study. The appropriate network parameters are determined by the search in the random forest method. The minimum sample size required for a leaf node, the minimum sample size required to divide a node, the impurity criterion, and the number of trees are examples of these parameters.  
The RF algorithm falls under the ensemble learning methods, in which multiple decision trees (forming a random forest) are built during training, after which the mode of the predicted classes of the individual trees forms the output class of the forest. The RF classifier usually outperforms simple decision trees due to less over-fitting. The random forest is constructed using a bootstrapping technique in which each tree is fitted based on a random subset of training samples with replacement, while at each split in this tree, a random subset of the input features is also selected. The classification method was divided into two different stages, the first stage including classes made, and water and forest with crop classes were determined, and in the second stage, the classification of crops studied in this research was done. In the random forest method, the appropriate parameters of the network are determined by the search. These parameters can be defined as the minimum  
Figure 4: Schematic overview of two-step hierarchical classification procedure.  
sample size needed for a leaf node, the minimum sample size required to divide a node, the impurity criterion, and the number of trees [27].