## 1 Introduction  
The ecosystem carbon cycle refers to the process of CO\({}_{2}\)-based carbon exchange between ecosystems and the outside world through biological action [1]. The primary components of the ecosystem carbon cycle are terrestrial ecosystems [2; 3; 4], which serve vital purposes such as storing carbon, preserving soil and water, and biodiversity [5]. Ways to achieve the sustainable development of terrestrial ecosystems and the stable growth of terrestrial carbon stocks have become important issues in achieving carbon balance. During the carbon cycle, vegetation effectively reduces greenhouse gasses through photosynthesis [6], which become the primary productivity of terrestrial ecosystems [7; 8; 9] and have a profound effect on regulating the carbon equilibrium. Net primary productivity (NPP) refers to the quantity of organic carbon that vegetation captures, minus what is expended by autotrophic respiration per unit of time and space [10]. NPP not only indicates the ability for vegetation growth and net carbon sequestration but also determines the material and energy available to human beings and animals [11]. Moreover, it is an important ecological indicator reflecting the functional structure and sustainability of terrestrial ecosystems in the region [12; 13; 14; 15]. With the development of remote sensing technology, more and more scholars have conducted systematic research on the spatiotemporal variation characteristics and influencing factors of regional NPP based on MODIS data. MOD17A3 is an NPP product dataset obtained using the light energy utilization model and BIOME-BGC model. Recently, researchers have widely applied and tested this product in biomass estimation, environmental monitoring, carbon cycling, and global change, demonstrating its ability to accurately reflect the spatiotemporal changes in global ecosystems [16]. Vegetation NPPresearch in Northeast China has also benefited greatly from it [17]. With a resolution of 500 m, the MOD17A3 HGF data product offers a higher resolution than the MOD17A3 data, and its accuracy has undergone thorough verification. [PERSON] used the Biome-BGC model to figure out the vegetation NPP in the Three Gorges Reservoir area [18]. She then compared the MOD17A3 HGF data with the model's estimated and measured values to make sure the data were correct. Due to the changing characteristics of NPP, many scholars have used Theil-Sen median trend analysis combined with the Mann-Kendall test method to explore the long-term changing trends in NPP. The combination of these two methods can effectively determine the trend in long-term data series changes, and there is no specific requirement for data distribution types, making it widely used in vegetation carbon sequestration analysis. Researchers have used this method to analyze global NPP changes, which generally show an increasing trend. However, in tropical regions such as the Amazon rainforest and the Congo Basin, NPP shows a decreasing trend [19]. The trend in vegetation growth dynamics in Nepal suggests an overall increase in NPP, but the changes are not significant [20]. Within the arid regions of China, NPP shows an increasing trend, mainly distributed in the semi-arid areas of northern Shaanxi, the border between Shanxi and Inner Mongolia, the northern part of Shandong, the western part of Northeast China, and the Tianshan Mountains in arid regions of Xinjiang. NPP tends to decrease mainly in arid and semi-humid areas, such as the junction of Xizang and Sichuan, central and southern Hebei, and northeast Inner Mongolia [21]. For local areas, there are significant differences in NPP between different districts and counties due to varying water and heat conditions. This directly reflects the changing trend of vegetation growth status in these areas and plays an important role in exploring the differences in carbon sequestration levels between regions. Many studies have shown that the increase in NPP from vegetation is the result of both natural and anthropogenic factors [22; 23; 24; 25].