Gap Filling Method and Estimation of Net Ecosystem CO2 Exchange in Alpine Wetland of Qinghai–Tibet Plateau

The net ecosystem CO2 exchange (NEE) and water and energy fluxes at the alpine ecosystem level were obtained through the eddy covariance technique in an alpine wetland of the Longbao Region, Qinghai–Tibet Plateau. Our research used the NEE as the research object combined with meteorological factors. The NEE prediction model was constructed using Reddyproc and machine learning. Moreover, the effects of the data and features on the models and the selection of the model parameters were discussed. The results revealed the following information: (1) After removing the NEE outliers according to the friction wind speed thresholds of the different seasons, the NEE interpolation accuracy (R2) reached 0.65. Additionally, the NEE data dispersion decreased after removing the outliers, and the data quality improved effectively. (2) The decision coefficients (R2) of the eight kinds of combined machine learning algorithm models varied from 0.22 to 0.62, and the root mean square error (RMSE) ranged from 2.10 to 2.99 μmol s−1 m−2. Additionally, the multilayer perceptron (MLP) model had the best stability and the best interpolation effect. (3) There was a seasonal difference between the estimated values of Reddyproc and the estimated values of MLP. The monthly mean values of January, February, March, and October were lower than the monthly mean values of the latter, while the monthly mean values from April to September were higher than the monthly mean values of the latter, indicating that the prediction of the machine learning algorithm tends towards the carbon source in the cold season (nongrowing season) and tends towards the carbon sink in the warm season (growing season). (4) Reddyproc detected the outliers through the relationship between the night NEE and frictional wind speed, which made it possible to accurately estimate the nighttime flux under the condition of determining the threshold of the night frictional wind speed, thus obtaining a better NEE estimate with fewer input parameters. Before the training and prediction of the MLP model, the NEE was detected for the time series outliers, and the prediction accuracy was significantly improved, indicating that the elimination of the time series outliers is essential for NEE model training and further indicating that the understanding of the potential mechanism of the NEE is of great significance for the prediction model.