# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Classifier Algorithms
#### 2.4.2 Random Forests  
A RF classification algorithm is based on the construction of several decision trees, by (randomly) selecting just one subset among all available samples (the Forest). Each tree votes for a class, and the majority vote determines the final class. This means that RF is an improved version of the Decision Tree classification. This technique has also been widely used in the context of remote sensing classifications, where it has been applied to multispectral data, multitemporal images, and SAR and optical data.  
The method adopted in the present study was proposed by [PERSON] [46]. In its most classical form, it uses a randomly built multiple decision tree, with training on different subsets of data. Specifically, each tree of the RF is trained on a random subset of data (called features). The RF algorithm is known to be one of the most efficient classifiers, and has been widely reported in remote sensing literature in recent decades. This algorithm has been used for various applications, including the analysis and classification of soil texture and land use in the agricultural sector.  
The development of a RF classifier involves the construction of Classification And Regression Trees (CART) from the samples. A subset of input features (also called attributes) is randomly selected to form a set of predictor variables. Each tree is built by performing an individual learning algorithm that splits the set of input variables into subsets, based on an attribute value test. For the classification, each tree gives a unit of vote for the most popular class in each instance. A majority vote of all the trees determines the final label.  
The RF classifier has three important parameters affecting its performance: the number of trees (K) (typically, the greater the number of trees, the better the accuracy), the maximum depth of the tree (a tree cannot have a depth exceeding 25), and the minimum number of samples in each node.  
The computational complexity of a RF is reduced by limiting the number of features it has. The split RF process is repeated on each derived subset, until the score contains very similar samples, or the splitting no longer adds value to the predictions. For implementation purposes, it is indeed unlikely that the RF reaches this maximum depth. During the decision phase, each tree classifies the input feature data. Then, the RF outputs the class label that has received the majority vote. In the case of a RF, the parameters have a very weak influence on the results of the classifications. In the present study, the parameters were set as shown in Table 3.  
One of the great strengths of a RF is its ability to determine the information that is useful in each of the features. RFs are also extremely flexible and highly accurate. The RF classifier has the highest efficiency in terms of the accuracy it can achieve with a large volume of data, for a given computer processing time. In practice, RF runtimes are quite fast, when compared to SVM classifiers. The main weakness of RFs is their complexity. They require a large number of features and training samples, and data over-fitting can easily occur [44; 45].