## 4 Materials and Methods
### Methods  
#### 4.3.1 The AGB Model Based on Random Forest Algorithm  
Random forest algorithm is a supervised machine learning algorithm based on a decision tree proposed by [PERSON] in 2001 [PERSON] (2001). It improves the prediction accuracy of the model by summarizing a large number of decision trees and is a new algorithm characterized by rapid calculation and high accuracy that may replace traditional machine learning methods such as neural networks. Especially for dealing with big data and a large number of variables, the effect of a large number of variables can be predicted very well, and it is known as one of the best algorithms at present [PERSON] (2001). The prediction model based on the random forest algorithm is a branch of the machine learning model, which is a kind of integrated model. It is an algorithm that integrates multiple trees via the idea of integrated learning, and its basic unit is the decision tree. There are two important concepts involved here: decision tree and ensemble learning. A decision tree is a tree-like structure where each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a class. In a word, a decision tree is a series of judgments to classify data based on their characteristics. Ensemble learning \begin{table}
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Table 3: Introduction to data sources. solves a single prediction problem by building a combination of several models. It works by generating multiple models that learn and make predictions independently and form a final prediction that is better than any single model [57]. In general, it builds a forest of many decision trees by randomly generating hundreds to thousands of decision trees and then selects the tree with the highest degree of repetition as the final result (Figure 10a). The following are six steps for constructing a regression prediction model using a random forest algorithm:  
(1) Collect data sets: Collect data sets that will be used for training in the random forest model. (2) Random sampling: Multiple sub-data sets are randomly extracted from the training data set. These sub-data sets are usually the same size as the original data set, but their samples are randomly selected. (3) Construct a decision tree: For each sub-data set, a decision tree is built independently, and all the data run along the tree. All of the data are run down the tree, and proximities are computed for each pair of cases. (4) Integrate decision tree: Using the constructed decision tree to form a random forest. For regression problems, the average method is usually used, that is, the average of the regression results of multiple trees. (5) Predict and evaluate its performance: Use a constructed random forest model to make regression predictions. Validation data sets can be used to evaluate the performance of the model. (6) Adjust the parameters and apply the model: According to the results of the model performance evaluation, the parameters are adjusted to optimize the performance of the model and applied to the new data for regression prediction. NDVI, EVI, GPP, ET, LAI, TMP, and PRE were selected as independent variables to build the model in this paper (Figure 10b). The measured grassland AGB was used as the dependent variable. They belong to the step of collecting the dataset. In addition, the random forest algorithm was built using the Scikit-learn tool in Python3.9, and the importance of each independent variable was evaluated. They belong to the step of constructing and integrating decision trees. Machine learning methods typically require cross-validation to detect overfitting [58]. The optimal number of inputs was determined using a k-fold cross-validation.