## 3 Results
### Characteristics of Spatial and Temporal Evolution of Vegetation GPP on the Qinghai-Tibet Plateau
#### 3.2.2 Model Parameter Optimization Process  
As shown in Figure 5a, based on the MOD17A2H product, the interannual spatial distribution of GPP on the Qinghai-Tibet Plateau from 2001 to 2022 reveals a decreasing trend from the southeast to the northwest. The eastern and southeastern regions of the Qinghai-Tibet Plateau exhibit higher GPP values, although areas with high values are relatively sparse. The transition zone is located in the central part of the plateau, where vegetation primarily consists of grasslands and shrubs. This region experiences a relatively arid climate, with precipitation and temperature conditions gradually worsening, leading to significantly lower GPP values compared to the southeast. In contrast, the northwestern part of the plateau displays the lowest GPP values, characterized by scarce precipitation, high elevation, and an arid climate that limits plant growth. In comparison, the southeastern part of the Qinghai-Tibet Plateau, with abundant precipitation, lower topography, and a humid climate conducive to plant growth, shows significantly higher GPP values than the high-altitude and arid northwestern region. Figure 5b illustrates the spatial distribution of the multi-year average GPP values on the Qinghai-Tibet Plateau from 2001 to 2022, as simulated by the MODIS-PSN model. The model estimates show a gradual decrease in GPP from southeast to northwest. Areas with GPP greater than 600 gC/m\({}^{2}\)/year account for 32.2% of the total study area (excluding regions shown in white in the figure as non-study areas). The northwestern portion of  
Figure 4: Annual average GPP variation trends of simulated GPP on the Qinghai-Tibet Plateau from 2001 to 2022.  
the plateau, including parts of Xinjiang and northern Tibet at higher elevations, exhibits relatively low GPP values, indicating lower ecological productivity in these regions. These areas are primarily characterized by cold, arid climatic conditions that limit vegetation growth, resulting in lower GPP. The high-altitude regions, such as the central and eastern mountainous areas of the Qinghai-Tibet Plateau, also show relatively low GPP, likely due to the combined effects of high elevation, low temperatures, and limited precipitation. In contrast, the southeastern portion of the plateau, including regions like Sichuan and Yunnan, demonstrates significantly higher GPP values. These areas, being lower in elevation, experience relatively warmer and more humid climatic conditions that support diverse vegetation and vigorous photosynthetic activity, leading to higher GPP values. The GPP in these lowland areas is significantly higher than in the high-altitude regions, showing a clear spatial gradient. The southeastern part of the plateau consistently exhibits high GPP values, indicating a strong GPP of the ecosystem in these regions. The central regions show a gradual decline in GPP, with vegetation primarily composed of grasslands and shrubs, closely linked to increasing elevation and decreasing precipitation. The northwestern regions of the Qinghai-Tibet Plateau, with sparse vegetation and dominated by bare land and deserts, exhibit the lowest GPP values. The spatial variation in GPP is largely attributed to differences in the plateau's topography, precipitation, temperature, solar radiation, saturated water vapor pressure, evapotranspiration, and leaf area index, which result in an imbalanced distribution of GPP across the region. Figure 6 displays the difference between the model-simulated interannual GemP and the annual mean GPP from the MODIS product, calculated using ArcGIS 10.8.2. This comparison highlights the regional characteristics of the differences and potential ecological and environmental impact mechanisms. In general, the southeastern region is characterized by positive differences, indicating that the model-estimated GPP exceeds the MODIS GPP. In contrast, the central and northwestern regions are dominated by negative differences, suggesting that the model underestimates GPP compared to MODIS.