## 1 Introduction
The index is widely used in the field of ecological evaluation [20; 21], and it includes remote sensing data (vegetation cover index, water network density index, biological abundance index), land surface monitoring data (land degradation index), and annual statistics (environmental quality index). However, statistical data are generally obtained through annual statistical data at the county level and above. Therefore, it is difficult to reflect the spatial differentiation of ecological quality by the ecological quality index based on the county level. In addition, the accuracy of information extraction forland use is low in areas with complex topography. In general, the application of the EI has great limitations. [PERSON] et al. [22] extracted the vegetation coverage, biological abundance, and land degradation index based on MODIS data and built an evaluation model to analyze the ecological environment changes in Shaanxi Province. [PERSON] [23] constructed the remote sensing ecological index (RSEI) model through the vegetation index, humidity component, land surface temperature, and dryness index, which was used for monitoring the changes in ecological environmental quality and has been widely used [24; 25; 26]. The RSEI is an ecological index based on remote sensing information, which is the most intuitive reflection of natural factors and the ecological environment by integrating multiple indicators as the leading indicator. The index is composed entirely of remote sensing data, so it has the characteristics of timeliness and rapidity. The index realizes the objective and quantitative evaluation of the regional ecological environment and can invert the ecological environment quality in different regions well [27; 28; 29; 30]. At present, most studies have focused on the eastern coastal areas or the central areas where human activities are more frequent, with few reports on the ecologically fragile areas in the arid area of NW China. Therefore, the studies on evaluating ecological quality based on satellite products in the arid region of NW China promote the interdisciplinary integration of remote sensing and ecology, and enrich the theory and method system of ecological evaluation. The fragile ecological environment in NW China is highly sensitive to global climate change [31; 32]. Fuyun County is located in the northernmost part of the Xinjiang Uygur Autonomous Region, which is a typical MBS area, and a crucial fragile ecological protection area of the country. In recent years, grassland desertification, biodiversity reduction, carbon sink loss, and other phenomena have appeared in this region [33], which not only endangered the production, life, and development of local farmers and herders, but also threatened regional ecological security [34]. Currently, studies on the MBS in the arid region of NW China mainly focus on grassland productivity change [35], land use change [36], ecosystem services [37], and household livelihood change [38]. However, few studies have focused on monitoring and evaluating the ecological quality of the MBS in the arid region. As an essential part of the ecosystem, vegetation plays various roles, such as climate regulation, water conservation, and habitat provision. Especially in the arid region of NW China, vegetation has a more significant and vital impact on the ecological environment, while data of animal and microbial factors are lacking and difficult to quantify. Therefore, this study adopted the vegetation as the evaluation object, with the help of remote sensing products, selected the vegetation growth and the main non-biological factors that affected its growth as the evaluation indicators, and established the evaluation model to comprehensively evaluate the ecological environment of the vegetation in the MBS in NW China. The improved RSEI [39] is introduced in this paper.