## 1 Introduction
Therefore, based on the parameter differences in different regions, comprehensively comparing and improving different SIF satellite products, combined with the MLR model and comprehensively improving SIF satellite products for GPP inversion, in order to characterize the spatial characteristics and influencing factors of GPP, is a topic that is both worthy of exploration and full of challenges. The implementation of this study can provide a new exploration path for the analysis of carbon cycle mechanisms, especially in the arid northwest region of China (with a fragile ecology) [18]. Xinjiang is a typical arid region in northwest China. Due to its location far inland, surrounded by mountains and basins, it has low rainfall, relatively low vegetation coverage, relatively high sun radiation, and an extremely sensitive climate, meaning its GPP's spatiotemporal characteristics are different from those of other regions. Its overall performance is characterized by strong seasonal fluctuations (a sharp increase from March to June and a sharp decrease from July to October), relatively low GPP attribute values (due to multiple limitations such as the water content, vegetation coverage, and sun radiation), and unique spatiotemporal patterns (affected by multiple factors such as high mountains, basins, and agricultural irrigation) [19; 20]. Exploring different GPP estimation methods and inversion models in the region, as well as revealing the spatiotemporal distribution patterns and influencing factors of GPP, is of great significance for measuring the carbon sequestration capacity of ecosystems in different regions of Xinjiang, maintaining the dynamic balance of ecosystems, evaluating the agricultural production potential, judging comprehensive changes in the climate, and formulating targeted energy-saving and emission-reduction policies. Given the uniqueness of GPP in this region and the significance of research, many scholars have conducted different studies on it. However, most of the research focuses on the direct application of one or several SIF satellite products [21], the simple application of certain models or methods [22], and the direct integration of certain data [23]. There is a lack of systematic evaluations, parameter localization, and comprehensive integration research. In light of this, in a previous study, we selected a local region of Xinjiang as the study area and utilized station flux observed data from three different ecosystems in the area (farmland, grassland, and desert vegetation). By coupling different indicators and methods, we comprehensively evaluated the effectiveness of multiple SIF satellite products for GPP estimation and the feasibility of precision improvement. The results indicated that TROPOMI SIF (RTSIF) should be prioritized for the comprehensive representation of GPP in arid regions, as it had smaller errors and a better spatiotemporal pattern. Additionally, based on the comprehensive classification of the study area and the combination of two improvement methods, a higher-precision RTSIF dataset was ultimately generated [24]. Based on the high-precision RTSIF dataset generated in previous research, this study classified the weather types in the study area, optimized the parameters of the MLR model in the C4 vegetation area, and coupled the station data of environmental factors (photosynthetically active radiation (PAR), soil temperature (Tsoil), vapor pressure deficit (VPD), air temperature (Tair), and soil water content (SWC)) and remote sensing data. Ultimately, the feasibility of using the parameter-optimized MLR model and the SIF with improved accuracy to invert GPP in arid regions was determined. The spatio-temporal characteristics of the comprehensive impact of environmental factors on GPP under different ecosystems and weather conditions in arid regions were revealed and it was analyzed whether this impact shows a linear or non-linear expression pattern.