## 2 Materials and Methods
### Studying Methods
#### 2.2.2 Machine Learning Algorithm  
The interpolation effect of machine learning algorithm was studied using the effective meteorological factors of 30 min and NEE observations. The shortwave downward radiation, saturated water pressure, air temperature, relative humidity, soil temperature, soil heat flux, soil water content, and wind speed were measured with the micrometeorological observation system for 30 min from January to October 2019. Using them as input variables, the NEE observations of the corresponding time periods were taken as output variables. Additionally, 80% of the observations were taken as training sets, and 20% were taken as test sets for training. Machine learning regression algorithm was used to interpolate the missing or discarded data to obtain the complete flux time series, and multiple linear regression (MLR), classification regression tree (CART), random forest (RF), multilayer perceptron (MLP), K-nearest neighbor (KNN), adaptive boosting (ADABOOST), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) were used in this study. The purpose of MLR is to construct a linear model on the existing data set through two or more independent variables to fit the relationship between the components of the feature vector of the data set and to find a straight line or a hyperplane to minimize the error between the predicted value and the value [25; 26]. CART is a tree structure where each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. The decision tree adopts a top-down recursive method. The basic idea is to construct a tree with the fastest entropy decrease based on information entropy. The entropy at the leaf node is zero. At this time, the instances in each leaf node  
Figure 2: Reddyproc interpolation method process. belong to the same class [25; 26; 27]. The essence of random forest (RF) is to randomly select k new self-help sample sets from the samples and to construct k classification regression trees. The samples that are not extracted each time form K out-of-bag data. Each tree grows to maximum without any cutting. The generated trees are composed of random forests, and the new data are classified by random forests. The classification results depend on the number of tree classifier votes [25; 26; 27]. Multilayer perceptron (MLP), also known as artificial neural network, first randomly initializes all parameters and then iteratively trains and continuously calculates the gradient and updates the parameters until a certain condition is satisfied (such as the error being small enough and the number of iterations being large enough); that is, it optimizes them [28; 29; 30; 31]. K-nearest neighbor (KNN), given a training data set, for a new input instance, finds k instances nearest to the instance in the training set. The majority of these k instances belong to which class the instance belongs to. KNN includes three important elements: distance measurement, k-value selection, and classification decision rules. KNN distance measure generally uses the Euclidean distance measure [28; 29; 30; 31]. Adaboost is a boosting algorithm that learns multiple weak classifiers by changing the weight of training samples and that linearly combines them into a strong learner. Firstly, the weight of the wrong samples classified by the weak classifier in the previous round is increased, and the weight of the correct samples is reduced. Secondly, the linear combination of multiple weak classifiers is carried out to improve the weight of weak classifiers with good classification effect and to reduce the weight of weak classifiers with high classification error rate [25; 26; 27]. The gradient boosting decision tree (GBRT) first gives an objective loss function whose domain is the set of all feasible weak functions (basis functions). Then, through iteration, a basis function in the negative gradient direction is selected to gradually approach the local minimum.