## 4 Discussion
### Analysis of GPP Estimation Accuracy Based on Improving SIF  
The results of precision improvement of SIF data for arid regions indicate that the linear methods can enhance SIF data accuracy for different underlying surfaces (R\({}^{2}\) increased values for the cultivated and farmland area, the pasture grassland area, and the desert vegetation area were 0.01, 0.02, and 0.13, respectively). However, canopy methods are only applicable to regions with a higher leaf area (R\({}^{2}\) increased values for the cultivated and farmland area and pasture grassland area were 0.06 and 0.05, respectively), and cannot be improved for areas with low vegetation cover. The adaptability of canopy methods was validated in a study by [PERSON] [68], who considered the canopy as a factor for global SIF data precision improvement, ultimately reconstructing six sets of SIF data at different resolutions. The adaptability of linear methods was confirmed in a study by [PERSON] et al. [69], where linear methods were used to improve the precision of five solar radiation reanalysis data sets in the eastern Gobi Desert in Xinjiang, eliminating the impact of errors in the reanalysis data on radiation assessments. In addition, in areas of desert vegetation, the error reduction values of MB, RMSE, and SD based on the linear improvement method were 0.0001, 0.0007, and 0.0126, respectively. However, using the linear method to improve the areas of the pasture grassland and cultivated and farmland, except for a decrease in the R\({}^{2}\) fitting value, other fitting parameters, on the contrary, increased overall, with the error increase ranges of the MB, RMSE, and SD between 0.0006-0.0011, 0.0003-0.0146 and 0.0323-0.0799, respectively. This further indicates that the linear method has a wider applicability than the canopy method in arid regions, but the improvement effect is relatively weak in areas with high vegetation coverage. Therefore, the canopy and linear improvement methods can be alternately used to improve the accuracy of SIF satellite products, then the two methods can be integrated to ultimately achieve a precision improvement of SIF data in arid regions. Furthermore, in conjunction with land use data, SIF data improvement was conducted using the canopy and linear methods, with an overall improvement rate of 0.11% (0.037% for cultivated and farmland area, 0.028% for pasture grassland area, 0.016% for desert vegetation area, 0.025% for other area). After improvement, areas with higher vegetation cover, such as cropland on the northern and southern slopes of the Tianshan Mountains and pasture grassland in the Tianshan and Altai Mountains, exhibited the highest attribute values. Conversely, lower values were mainly distributed in areas with smaller proportions of the leaf area index, such as the Gurbantunggut Desert (desert vegetation area) and the eastern Gobi Desert. Additionally, the postimprovement annual average SIF (GPP) values for various underlying surfaces in the northern region of Xinjiang are 0.13 mw/m\({}^{2}\)/nm/sr. Serving as effective proxies for GPP, the spatial distribution of improved SIF better reflects the GPP distribution characteristics in arid regions and further reveals the strong coupling relationship between the GPP and SIF in these areas. The rationality of the attribute values after improvement aligns with the conclusions of numerous scholars studies in arid regions. For example, [PERSON] et al. [70] conducted remote sensing monitoring of grassland GPP on the Mongolian Plateau based on SIF data, showing that the annual average values for various grasslands range from 0.11 to 3.48 gC/m\({}^{2}\) (0.14 mw/m\({}^{2}\)/nm/sr in this study); [PERSON] et al.