## 4 Discussion  
### Validation of Previous Relevant Research  
Advances in machine learning algorithms have facilitated a more nuanced understanding of the intricate interactions between vegetation and the atmosphere, particularly in the context of water-carbon exchange [22]. [PERSON] et al. [48] utilised the RF algorithm to estimate GPP and compared these estimates with GPP data derived from MODIS. The accuracy of the RF algorithm in estimating GPP was superior to the GPP estimated by MODIS [49]. They found that the model's explanatory power, measured by R\({}^{2}\), exceeded 0.80 in all instances. This study specifically aimed to estimate the vegetation GPP in Xinjiang. Using datasets including GOSIF, NIRv, and LAI, a thorough analysis was conducted to evaluate and compare the applicability of the GPP\({}_{\text{RF}}\) model with those of the GPP\({}_{\text{XGBosst}}\) and GPP\({}_{\text{LUE}}\) models using GOSIF data, which have a strong relationship with GPP [50]. The findings revealed that the RF model offers a considerable advantage in reducing the uncertainty associated with vegetation GPP estimation in arid environments [51].  
Specifically, the GPP\({}_{\text{RF}}\) estimation results indicated that the mean vegetation GPP in Xinjiang was 319.343 gC m\({}^{-2}\)a\({}^{-1}\) in 2009. Compared to the corresponding annual mean value of 329.421 gC m\({}^{-2}\)a\({}^{-1}\) derived from the GPP\({}_{\text{MODIS}}\) product, the two datasets showed good consistency, with an error rate of only 3.156%; this demonstrated the precision and dependability of the methodology employed in this study. To further assess the  
Figure 8: Path analysis of the impact of PRE, TEM, LEI, PAR, and FAPAR on GPP (red and blue indicate positive and negative influences, respectively; arrow thickness represents the degree of influence, where the thicker the arrow, the greater the influence. *** represent \(p<0.001\)).  
reasonableness of the estimation results, we compared the results with those of other studies. [PERSON] et al. [13] found that the GPP of vegetation in the northern Tianshan Mountains in Xinjiang was \(\sim\)500 gC m\({}^{-2}\)a\({}^{-1}\) using the GOSIF and DTEC estimation models. [PERSON] et al. [52] reported a mean GPP value of 518.36 gC m\({}^{-2}\)a\({}^{-1}\) for vegetation in northern China based on the VPM model. Although the results are higher than the estimates here, it is important to note that our focus was on the arid zone of Xinjiang, [PERSON] et al. [13] focused on the northern area of the Tianshan Mountains in Xinjiang, and [PERSON] et al. [52] covered a wider area of northern China. Such geographical variations may have contributed to the observed discrepancies in the GPP estimates among these studies.  
Conversely, [PERSON] and [PERSON] [53] showed annual mean vegetation GPP values in Xinjiang of 187.0-309.4 gC m\({}^{-2}\)a\({}^{-1}\) using GLASS GPP product data. Our findings align well with these previous findings, further validating the reliability and validity of this study. In summary, we found that the RF model offers considerable advantages by comparing the application of traditional models and machine learning algorithms to estimate the vegetation GPP in the arid zone of Xinjiang.