## 1 Introduction
This guideline introduced the Ecological Index (\(EI\)), primarily based on remote sensing technology, aimed at providing an annual comprehensive evaluation standard for ecological environment at the county level, and above, across China [7]. This index has been widely applied domestically; however, various issues have emerged during its application such as the rationality of the assigned weights, the setting of normalization coefficients, and the accessibility of indicators [17]. Consequently, [PERSON] et al. [18] proposed the Remote Sensing Ecological Index (\(RSEI\)) by coupling various indicators, including wetness (\(WET\)), dryness (\(NDSI\)), greenness (\(NDVI\)), and heat (\(LST\)) indices. Within these components, greenness indicates surface vegetation cover and growth; wetness is more adept at reflecting surface vegetation and soil moisture; dryness characterizes the level of surface dryness; and heat represents surface temperature [18]. _RSEI_ not only facilitates the monitoring and assessment of ecosystems by integrating diverse data sources but also provides insights into ecosystem dynamics, biodiversity distribution, and overall health conditions [7]. Furthermore, _RSEI_ has the capacity to identify potential ecological problems and risks [12]. Therefore, _RSEI_ has been widely adopted and validated in various regions due to its ease of acquisition and robust reliability. Research examining the drivers of changes in ecological environment quality has demonstrated a strong temporal correlation between the Normalized Difference Vegetation Index (_NDVI_) and the years of coal extraction at the mining face within the Shendong central mining area [19]. Interestingly, the impact of coal mining-induced subsidence on vegetation cover is not exclusively detrimental [20]. Soil moisture content is widely recognized as a paramount soil physicochemical factor governing plant growth in arid environments; groundwater table depth influences surface vegetation characteristics through its modulation of soil moisture availability [21]. The Loess Plateau, known for its profound soil erosion and environmental degradation, has undergone extensive ecological restoration efforts since 1999 to address soil loss [22]. However, investigations have shown that the singular pursuit of increased vegetation cover, without adequate consideration of plant type diversity, has led to suboptimal ecological recovery in specific regions [23]. In certain areas, vegetation cover may have approached, or is approaching, the environmental carrying capacity, suggesting a state of near-saturation [19]. In support, [PERSON] et al. [24] documented that climate and human activities account for 94% of the greening trend observed within the Yellow River Basin (YRB) since the beginning of the 21 st century. Consequently, to ensure the enduring effectiveness of YRB ecological restoration initiatives, it is imperative that evidence-based approaches are adopted that explicitly acknowledge the complex interrelationships between ecological processes and resource utilization within coupled human-natural systems [7]. A robust assessment of ecological environment quality is foundational for safeguarding natural ecosystems and promoting ecological civilization. It is important to note that many current studies investigating ecological environmental quality (_EEQ_) in the Loess Plateau are often constrained to small-scale areas and specific time periods due to challenges associated with acquiring high-resolution data, laborious pre-processing, and image cloud removal. Consequently, the majority of researchers continue to utilize MODIS series data for _EEQ_ assessment [25; 26; 27]. However, given that many ecological restoration policies in the Loess Plateau were initiated after 1999 and coal mining occurs at the workforce scale, employing only 1 km-resolution MODIS data may pose difficulties in exploring the impacts of mining activities and ecological restoration projects on _EEQ_[7].