## 4 Discussion  
### Analysis of the Applicability of Multisource SIF Data in Estimating GPP  
The overall applicability of using multisource SIF to comprehensively evaluate GPP data in arid regions shows that the overall ranking of the applicability of the four satellites for GPP estimation is as follows: RTSIF > CSIF > SIF_OCO2_005 > GOSIF (based on spatial characteristics, responsiveness to GPP influencing factors, GPP/SIF values under different weather conditions, and other standards). The significance of RTSIF is greater than 0.5, with a confidence interval of 95%. This is because the sensors and generation principles of the four SIF satellite products are different. Among them, CSIF, GOSIF, and SIF_OCO2_005  
Figure 10: Changes in spatial characteristics of quarterly average values before and after the improvement of SIF satellite product data. (**a1–d1**) The spatial variation characteristics of the mean values of each season before improvement, (**a1**) for spring, and so on. (**a2–d2**) The spatial variation characteristics of the mean values of each season after improvement, (**a2**) for spring, and so on. all come from OCO_2 sensors. This sensor has an early launch time and a narrow spectral band (757-775 nm) [62], leading to a lack of certain physiological and physical significance in the generated satellite data. However, RTSIF is derived from the TROPOMI sensor on Sentinel-5P, which was launched later and has a more comprehensive spectral band (735-785 nm) [21; 22], leading to a more refined understanding of various mechanisms in the obtained SIF data set. Furthermore, CSIF, GOSIF, and SIF_OCO2_005 all utilize the MODIS data set, but MODIS exhibits a noticeable lag effect [63], resulting in a significant deviation from GPP, thus reducing its inversion accuracy to some extent. In contrast, on the basis of the complete sensor band, RTSIF comprehensively considers different weather conditions to generate [21; 22], which to some extent improves the inversion accuracy. Additionally, the applicability of RTSIF in GPP estimation varied across the three ecosystems in the study area. The pasture grassland area exhibits the highest R\({}^{2}\) value of 0.85, with a significance greater than 0.5 and a confidence interval of 95%, indicating the best suitability for GPP estimation. This is attributed to the relatively stable growth cycle of the grassland in Xinjiang, where the green-up period occurs between days 110 and 150 each year, the growing season falls between days 140 and 160, and senescence appears between days 270 and 290. The overall growth cycle of the grassland is stable, with no significant interannual fluctuations in SIF contribution and relatively stable attribute characteristics. Consequently, the suitability of SIF data for GPP estimation in this area was slightly higher than that of the cropland area with a larger leaf area index. This finding is consistent with the conclusions of [PERSON]'s [64] research, who utilized machine learning and model construction methods to study the spatiotemporal and phenological characteristics of grasslands in Xinjiang over the last 20 years. In the cultivated and farmland area, such as those cultivating maize and cotton, GPP and SIF are much higher than those of forests and grasslands, with an overall high measurability. However, because of their shorter growth cycle and significant reduction in SIF contribution during the nongrowing season, their suitability for GPP estimation using SIF data is relatively lower than that of the pasture grassland area with an R\({}^{2}\) fitting value of 0.84, but with a significance also greater than 0.5 and a confidence interval of 95%. This is consistent with the conclusion of [PERSON] [65], who used multiple crops for global farmland GPP estimations. The carbon sink in the desert vegetation area is primarily generated through non-photosynthetic processes, with complex controlling factors and trends. This conclusion is drawn from research by [PERSON] et al.