## 4 Discussion  
### Feasibility and Rationality of Evaluation Methods  
#### 4.1.1 Comparison with Remote Sensing Ecological Index (RSEI)  
RSEI serves as an effective indicator for assessing ecological quality and has been widely utilized in ecological assessment tasks across various scales. The Google Earth Engine (GEE) platform was employed to calculate the RSEI of three urban agglomerations in 2001 and 2020 using the MOD09A1, MOD11A2, and MOD13A1 datasets. We conducted an evaluation of the similarities and differences between EQI and RSEI concerning statistical distribution and spatiotemporal changes.  
Figure 7: Scatter chart of EQI change intensity, urban expansion intensity, and synergistic index of all cities in three urban agglomerations. The red balls represent two supercities, Beijing and Shanghai.  
As depicted in Figure 8, the term \"Level Change\" in the third image in Figure 8a-c indicates the subtraction of ecological quality levels from 2020 to 2001 after quantifying EQI levels from the lowest (\"Bad\") to the highest (\"Excellent\") using a scale of 1-5. A smaller value signifies more severe ecological damage. The overall spatiotemporal distribution of EQI closely resembles that of REEI, particularly in the YRD and PRD regions (Figure 8b,c). Both EQI and REEI reveal an increasing polarization trend in ecological quality changes between CYRD and PRD (Figure 8d). However, in comparison to the significant differences in REEI, EQI not only distinctly highlights variations in ecological quality and temporal changes between urban agglomerations but also exhibits a more stable effect, especially in the BTH (Figure 8a).  
Moreover, there are notable distinctions in the ability of different indicators to identify low- value and high-value areas of ecological quality. In contrast to the spatial distribution map of REEI in BTH, EQI excels in identifying ecological quality decline due to urban expansion and improvement resulting from environmental protection and afforestation (Figure 8a). We conducted correlations among REEI, the first three principal components, and the four key indicators of building REEI, including NDVI. Our aim was to clarify the reasons for REEI's less favorable performance in BTH. Figure A1 demonstrates that almost all correlations passed the significance tests. Unlike CYRD and PRD, land surface temperature (LST) exhibits the highest correlation with REEI in BTH, surpassing NDVI. Previous studies have questioned the model applicability and exponential suitability in environments influenced by various factors [93; 94]. Some scholars have confirmed that REEI may lose key changes when depicting long time-series ecological environment status, even resulting in opposite results [95; 96]. The uncertainty of feature vectors and their directions may explain this issue [97]. Some research also inferred that REEI can effectively  
Figure 8: Comparison of spatial distribution and statistics between EQI and REEI. (**a–c**) Spatial distribution and level changes of ecological quality in BTH, CYRD, and PRD respectively. (**d**) Statistical differences in EQI and REEI among the three urban agglomerations in 2001 and 2020. The pink triangle represents the mean value.  
characterize ecological quality only when the direction of NDVI and Wet is positive in the first Principal component [97].