# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
## 4 Discussion  
The high spatial heterogeneity of GPP is an obstacle to accurately obtain regional GPP. We found that the carbon flux obtained using the eddy covariance method varied greatly among the different vegetation types. The artificial oasis dominated by croplands showed the highest GPP and carbon sequestration capacity. This indicates that both natural factors and human activity have a great impact on carbon flux and cannot be neglected. Eddy covariance is one of the commonly used techniques to measure ecosystem-scale carbon flux, but still it involves many uncertainties that should be noted when analyzing the data. The uncertainties primarily result from three aspects. First, the eddy covariance data quality is affected by sensor configurations and meteorological conditions [19]. Hence, strict quality control is required for the raw data before its use. Second, due to instrument malfunction and spikes, data gaps are inevitable in the raw data, and gap-filling is a necessary step to obtain continuous carbon flux [20]. This process also leads to uncertainties in the results. Third, the eddy covariance method only directly measures the NEE, and the GPP and  
Figure 4: The weights convergence trajectory of the LUE models during the BMA method training. Figure 3: Residual plots of different models at the five sites (a: VPM; b: EC-LUE; c: C-fix; d: MODIS-PSN; e: GLO-PEM; f: BMA). The dashed line is the 1*SD and 2*SD (SD = 25 gC/m2/8 day for the GPP measurement at Arou site). ER are partitioned from the NEE with some hypothesis, a fact that can also result in uncertainties in the GPP and ER [21; 22]. The LUE model performance changed greatly with vegetation types. The LUE model is a widely used method to estimate the GPP, especially at the regional scale. However, GPP simulated using the LUE model contains uncertainties that result from the model parameters, meteorological input data, and remote sensing input data [23; 24]. Model parameters vary with species and vegetation community compositions, and the LUE models usually only provide default parameters for the major vegetation types. When applying the LUE models in a small region, the default parameters may result in great uncertainties. The uncertainties that result from meteorological input data are primarily caused by gap filling and the interpolation of measured data. Remote sensing input data (such as FPAR, NDVI, EVI, LSWI and others) in the LUE model also contain uncertainties that result from bad data quality [25]. Some studies reported that model parameters have a larger impact on the simulation accuracy than the meteorological input data and remote sensing input data [26]. BMA is an effective method to improve modeling accuracy. Every model has some limitations caused by its hypothesis and parameterization scheme [27]. The average of multiple model outputs can overcome this problem and be thought of as more reliable than a single model output. In many studies, the classical average of a model ensemble has been used to conduct the research [28; 29]. BMA provides a way to obtain the model ensemble weighted average [30]. By training the BMA method with measurement data, different weights are assigned to different models, and the weights also vary with spatial and vegetation type changes. This is more reasonable than the classical average because the performances of the LUE models are different at different locations and with different vegetation types [31; 32]. Many studies have reported that BMA can improve the simulation accuracy than the classical average in stream-flow forecasting models [33] and evapotranspiration models [11]. This is consistent with the finding of the current study, where the RMSE of the BMA method was 5.8 gC/m\({}^{2}\)/8 day during the training period and 8.1 gC/m\({}^{2}\)/8 day during the validation period, while the classical averages were 12.6 gC/m\({}^{2}\)/8 day and 13.98 gC/m\({}^{2}\)/8 day, respectively. This demonstrates that BMA has the potential to improve GPP estimation by integrating multiple LUE models.