## 3 Methods  
### Rsei  
Using MODIS remote sensing data to describe the EE level [22], the RSEI takes a value ranging from 0 to 1, where a low value indicates a poor ecological condition, a high value represents a good ecological condition, and a value of 1 signifies a perfect ecological condition. MODIS remote sensing data collected between June and October in 2002, 2007, 2012, 2017, and 2022 were used to calculate the RSEI. The four indicators utilized in the RSEI are greenness (NDVI for vegetation), wetness (WET for soil wetness), heat (LST for temperature), and dryness (NDBSI for floor area). By coupling and integrating the four indicators and using the PCA, the weights of each indicator are automatically determined, avoiding any interference from human-determined weights. Then, the first principal component (PC1) is obtained, and the RSEI assessment model is constructed. The RSEI is expressed as:  
\[RSEI=f(NDVI,Wet,LST,NDBSI) \tag{1}\]  
1. Greenness can be calculated by: \[\text{NDVI}=(\rho_{NIR}-\rho_{red})/(\rho_{NIR}+\rho_{red}),\] (2) where \(NIR\) and \(red\) are the near-infrared (NIR1) and red bands of the MODIS 8-day reflectance image (MOD09A1), respectively. 2. Wetness is calculated using the following equation [45]: \[Wet=0.1147B1+0.2489B2+0.2408B3+0.3132B4-0.3122B5-0.6416B6-0.5087B7\] (3) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & **Product** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Scenes** \\ \end{tabular} \\ \hline NDVI & \multirow{3}{*}{MOD09A1} & \multirow{3}{*}{500} & \multirow{3}{*}{8-Days} & \multirow{3}{*}{16} \\ WET & & & & \\ NDBSI & & & & \\ \hline LST & MOD11A1 & 1000 & 1-Days & 122 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GEE data description. Here, B1 to B7 represent bands 1 to 7, namely, red, NIR1, blue, green, NIR2, shortwave infrared 1 (SWIR1), and SWIR2 bands of the MODIS images, respectively. 3. Heat is expressed as: \[\text{LST}=0.02\times\text{DN}-273.15\] (4) where DN represents the grayscale value of the surface temperature. 4. Dryness is expressed as follows: \[\text{NDBSI}=\frac{\text{IBI}+\text{SI}}{2}\] (5) The Building Index (IBI) and Bare Soil Index (SI) can be calculated as follows: \[\text{SI}=\frac{\left(\rho_{\text{{visir1}}}+\rho_{red}\right)-\left(\rho_{ \text{{nir}}}+\rho_{blue}\right)}{\left(\rho_{\text{{visir1}}}+\rho_{red} \right)+\left(\rho_{\text{{nir}}}+\rho_{blue}\right)}\] (6) \[\text{IBI}=\frac{\frac{2\rho_{\text{{pair1}}}}{\rho_{\text{{ext1}}}+\rho_{ \text{{nir}}}}-\left[\frac{\rho_{\text{{nir}}}}{\rho_{\text{{nir}}}+\rho_{red} }+\frac{\rho_{green}}{\rho_{green}+\rho_{\text{{nir1}}}}\right]}{\frac{2\rho_{ \text{{nir1}}}}{\rho_{\text{{swir1}}}+\rho_{\text{{nir}}}}+\left[\frac{\rho_{ \text{{nir}}}}{\rho_{\text{{nir}}}+\rho_{red}}+\frac{\rho_{green}}{\rho_{ \text{{nir1}}}}\right]}\] (7) where \(\rho_{\text{{blue}}}\), \(\rho_{\text{{green}}}\), \(\rho_{\text{{red}}}\), \(\rho_{\text{{nIR}}}\), \(\rho_{\text{{swir1}}}\) represent the reflectance in the blue, green, red, the near-infrared and short-infrared bands1, respectively. 5. PCA: Since the four indicators have non-uniform magnitudes, calculating the PCA directly would result in unbalanced weights for each indicator. Therefore, before conducting PCA, these indicators should be normalized first, and their magnitudes should be unified to the range \([0,1]\) using the following equation: \[NI_{i}=\left(I_{i}-I_{\text{min}}\right)/\left(I_{\text{max}}-I_{\text{min}}\right)\] (8) where \(NI_{i}\) is the normalized indicator value, \(I_{i}\) is the indicator value in quadrant \(i\), \(I_{\text{max}}\) is the maximum indicator value, and \(I_{\text{min}}\) is the minimum indicator value. After normalization, PC1 can be calculated using the four indicators. In order to ensure that a larger PC1 value represents a better condition, PC1 is subtracted by 1 to obtain the initial ecological indicator \(RSEI_{0}\).