## 1 Introduction
The satellite platforms mainly include Orbiting Carbon Observatory-2 (OCO_2), Sentinel-5P, MODIS, and GOME-2, and the products mainly include CSIF [19; 20], RTSIF [21; 22], GOSIF [23], SIF_OCO2_005 [24], and others. With the generation of various SIF satellite products, the comparative analysis of the applicability of different products for GPP estimation in different ecosystems has become a new research topic. However, existing studies mostly focus on single ecosystems. For instance, [PERSON] et al. [25] assessed the consistency of five SIF satellite products for rubber plantation ecosystems on Hainan Island. [PERSON] et al. [26] verified the feasibility of using SIF instead of GPP to explore the mechanisms affecting humid, arid, and semi-arid regions. Furthermore, because SIF itself only accounts for 2-3% of solar radiation [27], it is necessary to reduce spatial resolution to eliminate certain noise during measurement, resulting in a relatively low spatial resolution of existing SIF satellite products. For instance, MetOp-A/B sensors mounted on the GOME-2 satellite can only obtain monthly scale SIF data with a resolution of 0.5\({}^{\circ}\)[28]. Therefore, although SIF can serve as an effective substitute for vegetation photosynthesis and be used for GPP estimation, the overall applicability of multisource SIF satellite products in GPP estimation across different regions and ecosystems requires further comparative validation. The feasibility of improving the accuracy of SIF satellite products (considering various influencing factors, such as different underlying surfaces) and achieving effective GPP estimation requires further comparative research, especially in typical ecosystems in arid regions of China. The Xinjiang Uygur Autonomous Region is located in the inland northwest of China, far from the ocean, with extremely low precipitation, making it part of China's arid region. It encompasses typical ecosystems such as pasture grasslands, cultivated and farmlands, and coniferous forests. As well as fragile ecosystems like desert vegetation, natural sand dunes, and desert-oasis transition zones, making it exceptionally sensitive to climate change on both a national and global scale. As a typical arid region, which SIF satellite product is most suitable for estimating GPP in this area? Which improvement method can effectively enhance the progress of SIF satellite products in this region? Do the improved SIF satellite products conform to the comprehensive variation characteristics of GPP in arid regions? The resolution of this series of questions is crucial for achieving the \"carbon neutrality\" goal in arid regions. Unfortunately, these questions have not yet received clear answers. Given this, this study takes the northern region of Xinjiang as the research area and utilizes indirectly observed GPP data from three typical ecosystems in this region (cultivated and farmlands, pasture grasslands, and desert vegetation). Spatial characteristics, linear regression parameters, GPP sensitivity to influencing factors, and GPP/SIF values under different weather conditions are selected as evaluation criteria. These criteria are used to comprehensively assess the overall applicability of four continuously updated SIF satellite products (CSIF, RTSIF, GOSIF, SIF_OCO2_005) for GPP estimation in typical arid ecosystems. Subsequently, the feasibility of employing canopy and linear accuracy improvement methods to enhance the accuracy of the most suitable SIF satellite product for GPP estimation is verified. Finally, the changes in spatial characteristics of SIF data before and after improvement are revealed, indirectly reflecting the spatial distribution patterns of GPP in arid regions. These studies will provide reliable empirical evidence for the applicability and accuracy improvement of SIF satellite products for GPP estimation in arid regions.