## 2 Study Area and Data Sources
### Methodology
#### 2.3.1 Research Process
This paper used the Coal Dust Index (ICDI) to describe coal dust's impact on the ecological environment of resource cities. The NDBSI describes surface aridity caused by urbanization, built-up land expansion, and increased rock or soil exposure, while the ICDI quantifies coal dust pollution from mining. Both indices assess ecological pressure from human activities [55]. Large-scale urban expansion, the establishment of over 100 coal mines, and extensive road construction have encroached on vegetated areas, leading to reduced biodiversity, altered water cycles, soil erosion, and decreased carbon storage. The Normalized Difference Vegetation Index (NDVI) is an effective vegetation index known for its sensitivity, simplicity, wide application, and ease of interpretation, making it widely used in ecological monitoring and assessment. However, the NDVI is limited in quantifying vegetation health. In coal resource cities, large-scale mining and severe coal dust pollution have damaged vegetation health. Therefore, we propose the Enhanced Vegetation Index (EVI) to quantitatively assess vegetation health. This study used the NDVI and EVI to reflect the environmental conditions of resource-based cities [56; 57], As urban expansion accelerates and mining areas increase, soil degradation and surface temperature around cities and mines rise, causing ecological damage. The Moisture Index (WET) accurately reflects soil moisture content and assesses uncovered vegetation and bare soil areas. Land surface temperature (LST) accurately reflects urban surface temperature changes, crucial for assessing the urban heat island effect and climate change impact. Resource-based cities are prone to heat island effects due to industrial activities and urbanization. The LST helps identify high-temperature areas, which exert pressure on ecosystems, affecting plant and animal survival. The LST helps identify these areas and formulate mitigation measures, such as increasing green spaces and water bodies to reduce surface temperature. The WET and LST can be integrated with the NDVI and EVI for a comprehensive ecological environmental quality assessment [58; 59]. These six components were carefully integrated to formulate the RCEI, as shown in Equation (1):  
\[RCEI=f(WET,NDVI,EVI,NDBSI,LST,ICDI) \tag{1}\]  
\[RSEI=f(NDVI,WET,LST,NDBSI) \tag{2}\]  
Before the calculation of the RSEI and RCEI, the water body was masked using the Modified Normalized Water Body Index (mNDWI) to ensure that the WET index accurately represented the humidity of the study area [60]. The mathematical formulations for the aforementioned remote sensing indicators are detailed in Table 2. These six-component indicators were then integrated through principal component analysis (PCA), and the first principal component (PC1) was selected to formulate the RCEI and RSEI. Given the diverse units and value ranges of each indicator, normalization of the six indicators' values within the range of [0, 1] was essential prior to PCA analysis to ensure consistency and comparability [61]. This normalization was achieved using GEE feature analysis to compute PC1. Subsequently, based on PC1, the initial RCEI (RCEI\({}_{0}\)) and RSEI (RSEI\({}_{0}\)) was derived and expressed as outlined in Equations (3) and (4). Finally, using the normalization method to derive the RCEI from the RCEI\({}_{0}\) and the RSEI from the RSEI\({}_{0}\) yielded values in the range [0, 1]. The resulting RCEI values were then graded in accordance with the \"Specification for Ecological Condition Evaluation Indices\", with the grading criteria detailed in Table 3.  
\[RCEI_{0}=1-PC1[f(WET,NDVI,EVI,NDBSI,LST,ICDI)] \tag{3}\]  
\[RSEI_{0}=1-PC1[f(NDVI,WET,LST,NDBSI)] \tag{4}\]