## 4 Discussion
### The Spatiotemporal Variation Characteristics of GPP in Arid Regions
Second, for linear regression fitting, the SIF changes captured by RTSIF satellite products in farmland, grassland, and desert vegetation areas are 85%, 84%, and 62%, respectively. Third, regarding sensitivity to light energy allocation, the ratio is close to 1:1. This is because RTSIF satellite products were constructed using machine learning methods trained on SIF data obtained from Sentinel-5P satellite sensors under clear weather conditions [31; 32]. The Sentinel-5 Psatellite sensor started operating relatively late, has a relatively comprehensive range of parameters and functions, and obtains a longer SIF signal band. Therefore, the SIF dataset generated based on Sentinel-5P satellite sensors has good applicability. For example, [PERSON] et al. [63] constructed HCSIF using the weighted stacking algorithm trained on SIF data obtained from Sentinel-5P satellite sensors. HCSIF also has good applicability, as HCSIF satellite products can capture 87% of SIF changes. The inversion of GPP using the improved SIF and MLR models confirms the applicability of the MLR model in arid regions [2]. Specifically, the inverted GPP can capture 86% of the changes in GPP in farmland areas, 82% in grassland areas, and 89% in desert vegetation areas. Moreover, the RMSE ranges from 0.0029 to 0.0916, the SD ranges from 0.0052 to 0.2712, and the MB ranges from \(-\)0.0002 to 0.0003. The GPP generated by multifactor coupling synthesis shows peak values in the TM, the north and south slopes of the TM, and the northernmost Altai Mountains. In contrast, it shows a lower value in areas where tree growth is not strong, such as the Gurbantunggut Desert. The GPP attribute values are ranked by season as summer > spring > autumn > winter, showing a high correlation with vegetation coverage and sun radiation. This indicates that the attribute values of GPP in arid areas are relatively high in areas with high vegetation coverage and during periods of intense sun radiation (summer and spring), while in other cases, the attribute values of GPP are not significant. This conclusion is consistent with findings from many scholars. For example, [PERSON] et al. [64] compared and elaborated on the influencing factors of comprehensive changes in GPP in the Yellow River region and indicated that the leaf area index is the main driving factor of GPP. [PERSON] et al. [58] discussed the comprehensive impact of different drought scales on GPP and found that the GPP of the 400 mm contour line (indirectly affecting sun radiation) shows significant drought stress fluctuations (high GPP attribute values with significant drought stress fluctuations). Grassland and shrubland areas also exhibited significant drought stress fluctuations (areas with vigorous vegetation growth have high GPP attribute values, resulting in significant drought stress fluctuations). This study confirms the feasibility of using the MLR model for GPP inversion in arid regions and reveals the comprehensive changes in and patterns of GPP in different seasons in arid regions. These findings are of great significance and are a reference value for constructing GPP in arid regions based on SIF and MLR models and analyzing their spatial carbon functions.