## 2 Materials and Methods
### Studying Methods  
#### 2.2.1 Reddyproc Algorithm  
The Reddyproc algorithm first performed quality checks and filters based on the relationship between measured flux and friction velocity to discard biased data [2]. Second, we filled in data gaps based on information from environmental conditions [18]. Finally, the net flux of carbon dioxide was divided into its total flux into and out of the ecosystem through night-based and day-based methods [19]. The core algorithm included four points: First, median absolute deviation (MAD) was used to detect and eliminate the NEE outliers. Second, the night friction wind speed threshold was calculated. The frictional wind speed decreased, and the NEE measured with eddy correlation system was underestimated when the atmospheric turbulence was weak at night [20; 21]. To avoid systematic bias in nighttime NEE data [2], it is usually necessary to determine the frictional wind velocity threshold, thereby excluding NEE below the frictional wind velocity threshold [22]. Third, NEE was interpolated. Filtered flux data with time series gaps and missing data need to be interpolated using existing flux data and meteorological measurements after filtering NEE data according to different seasonal friction wind speed thresholds. The interpolation step used three kinds of meteorological data, e.g., RG, Tair, and VPD. (1) If all three meteorological data were missing, lookup table (Lut) was used. (2) If Tair or VPD were missing, only RG was used. (3) If all three meteorological data were missing, mean diurnal course (MDC) was used. Finally, the flux was divided according to the relationship between total primary productivity (GPP), ecosystem respiration (Reco), and NEE (NEE = Reco \(-\) GPP) [18]. The nighttime NEE data splitting method assumes that Reco is only related to temperature change and that nighttime vegetation is only respirable. Therefore, the Reco change of daytime vegetation can be inferred from the response curve of nighttime NEE to temperature, and the GPP can be calculated according to the above relationship. The daytime NEE data splitting approach assumes the relationship between daytime NEE and total radiation to be a composite of the effect of RG and VPD on GPP and the effect of temperature on Reco [23; 24]. These total fluxes are essential for understanding land-air interactions.  
Lookup tables (LUT) were created for each site so that missing values of NEE could be looked up based on the environmental conditions associated with the missing data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Name** & **Percentage of** & **Percentage of** & **Percentage of** & **Missing Rate** \\  & **Level 0** & **Level 1** & **Level 2** & \\ \hline Longbao & 47.22 & 24.26 & 10.60 & 17.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical table showing the absence of observed NEE in the study area.  
Tables were created to represent changing environmental conditions based on either six bimonthly periods or four seasonal periods. In mean diurnal course (MDC), a missing observation was replaced by the mean for that time period (half hour) based on adjacent days. The methods for derivation of mean diurnal pattern of bin-averaged (half-) hourly measurements differed mainly in the length of the time interval of averaging (window size of usually 4-15 days) (Figure 2). R and Rstudio from [[https://cloud.r-project.org/](https://cloud.r-project.org/)]([https://cloud.r-project.org/](https://cloud.r-project.org/)) and [[https://www.rstudio.com/](https://www.rstudio.com/)]([https://www.rstudio.com/](https://www.rstudio.com/)) (April 2020).