###### Abstract
As early as 1973, NASA began to use Earth resources satellites to monitor land use, vegetation coverage, and water accumulation in the Appalachian mining area [26]. Significant advancements in multispectral and hyperspectral remote sensing technologies [27], using satellite [28], airborne [29; 30], and ground-based platforms [31], have greatly enhanced ecological evaluation in mining regions. Due to the severe surface damage from resource extraction, ground surveys have become crucial for monitoring and assessing these environments [32]. Leveraging quantitative remote sensing techniques, we can precisely monitor ecological components, including vegetation, water, and soil, enabling a comprehensive analysis and assessment of an ecological environment's quality. Vegetation serves as the clearest indicator of ecological and environmental changes in mining areas [33]. Among various indices, the Normalized Difference Vegetation Index (NDVI) is widely recognized as one of the most effective for characterizing vegetation changes [34; 35; 36; 37]. With the rapid development of remote sensing satellite sensors, a series of innovative red-edge vegetation indices (REVIs), represented by the Sentinel-2 satellite, have been proposed to monitor and evaluate the growth status and health condition of vegetation [38; 39; 40]. [PERSON] et al. [41] analyzed the spatiotemporal variation in the vegetation indices in subsidence with the help of the NDVI and five typical REVIs. In mining areas, relying on a single environmental indicator is insufficient to reflect ecological trends due to the complexity and diversity of ecosystems. A single indicator, such as the NDVI, only captures one aspect, potentially leading to misjudgments. It also overlooks the interactions between water, soil, and air quality and lacks the capacity for comprehensive assessment. Therefore, a multi-indicator integrated assessment is necessary for a more accurate and holistic evaluation of an ecological environment. To address this, the Ministry of Environmental Protection of China introduced the Ecological Environment Index (EI), a comprehensive metric that includes six weighted indicators: biological richness, vegetation coverage, water network density, land stress, pollution load, and environmental restrictions [42]. Despite its significant role in ecological assessments, the EI's indicator selection and weight determination processes are subjective and complex. Firstly, the selection of EI indicators often relies on expert judgment, which can lead to inconsistent results across different studies. Secondly, the determination of indicator weights lacksuniform standards and is typically conducted through methods like expert scoring or the Delphi method, both of which are inherently subjective. Additionally, the EI model's applicability across different regions and time scales is debated, as varying ecological characteristics and stressors may necessitate different indicators and weights. To address these limitations, researchers have developed more objective methodologies for weighting individual ecological indicators. For instance, principal component analysis (PCA) can automatically assign indicator weights, reducing the influence of human subjectivity. Furthermore, optimization models that incorporate regional ecological features can better reflect regional ecological changes. Future research should focus on developing more objective and adaptable assessment models to enhance the accuracy and reliability of ecological environmental evaluations. With the help of PCA, [PERSON] et al. proposed the Remote Sensing Ecological Index (RSEI), which encompasses four key indicators: greenness, wetness, dryness, and heat [43]. Many scholars have since enhanced the RSEI model, successfully adapting it for ecological quality assessments and monitoring in diverse regions, including mining areas, cities, river basins, and nature reserves [44].