###### Abstract
The Ministry of Ecology and Environment of China used the analytic hierarchy process (AHP) or the entropy weight method to develop an important environmental index (EI) to evaluate the regional ecological environment. Since vegetation plays an active role in the Earth's environmental system [19; 20; 21], the ecological index can also be realized via remote sensing or GIS methods. For example, some studies have proposed remote sensing and the use of net primary productivity (NPP) [22], the normalized difference vegetation index (NDVI) [23], the enhanced vegetation index (EVI) [24] and the re-mote sensing-based ecological index (RSEI) [25] to measure the variations in regional ecosystems. [PERSON] et al. constructed the RSEI through principal component analysis to analyze the spatio-temporal evolution trends of the eco-environmental quality in the Yellow River Basin in China [26]. Quantitative evaluation of ecological indicators can be based on models summarized from different perspectives [27; 28; 29; 30]. Taking quantitative analysis of ecosystem service functions as an example, the InVEST and RUSLE models have been widely used to assess the spatio-temporal dynamics of ecosystem services or habitat quality [31; 32; 33; 34]. According to related studies, the methods used to determine the driving mechanisms of ecological environment changes can be classified into two categories: non-spatial regression models and spatial regression models. The non-spatial regression category includes the stepwise regression model [35], scenario analysis [36], constrained ordination technique [37], curve estimate method [38],and ordinary least squares (OLS) [39]. The spatial regression category mainly includes the geo-detector method (GD) [40], geographically weighted regression (GWR) [41], and spatial lag model [42]. Compared with non-spatial regression models, spatial regression models consider the spatial heterogeneity of the driving factors by constructing local regression equations for each region [43; 44], which is impossible in non-spatial regression models. Therefore, spatial regression models are widely used in ecological environment research. For example, [PERSON] et al. selected evaluation indicators such as soil, topography, and night light data in the arid region of northwest China and used the geo-detector model to analyze ecological vulnerability [45]. Henan Province is located in the middle and lower reaches of the Yellow River in China. It is an economic agglomeration area along the Yellow River and forms part of the important support belt of the ecological barrier of the Yellow River Basin. In 2019, the Chinese government proposed an ecological conservation and high-quality development strategy for the Yellow River Basin, and it holds an important strategic position in China [2; 46]. Henan Province assumes great responsibility for the ecological protection and high-quality development of the Yellow River Basin. However, currently, the research on the mechanismsof ecological environment change in Henan Province are few and how specific measures should be drawn up is still unclear. Thus, clarifying the driving mechanisms is critical to designing policies and coordinating the relationships between economic development and ecological environment protection. Therefore, this study focused on Henan Province to analyze the spatio-temporal changes in ecosystem quality from 2010 to 2020. Based on the spatial regression model, the ecological driving mechanisms in different natural domains were explored by constructing an index system that is suitable for the development of Henan Province. Based on the research, corresponding environmental protection measures were proposed for the rapid healthy development of Henan Province. Our research can provide a reliable reference for regional sustainable development in China.