## 2 Materials and Methods
### Data Used and Preprocessing  
The remote sensing imagery used in this study consists of Landsat TM, Landsat ETM+, and Landsat OLI images acquired over the Shendong mining area (grid row 127, column 33) from 1990 to 2023. The images, with a spatial resolution of 30 m and a temporal resolution of 16 days, were obtained freely from the Geospatial Data Cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))) and the U.S. Geological Survey (USGS) ([[https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/)]([https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/))). The majority of the imagery was captured during the months of June to September, under clear, cloud-free conditions, exhibiting good data quality. The choice of this specific timeframe is because it corresponds to the peak growing season in the study area, when vegetation is most active and ecological conditions best represent annual ecosystem dynamics. During this period, vegetation cover is highest, and the impacts of climatic factors, such as precipitation and temperature, on ecological quality are most apparent. Moreover, the relatively clear weather conditions during these months help to minimize the effects of cloud cover, ensuring higher-quality images for analysis. However, some imagery was affected by cloud cover, cloud shadows, or other factors, leading to lower quality. To ensure the reliability of the data, images with high cloud cover or poor quality were carefully selected and excluded. Preprocessing steps included cloud removal, radiometric calibration, atmospheric correction, and image registration, with the root mean square error (RMSE) of registration controlled to within 0.5 pixels. For cloud removal, we applied the cloud-masking function available on the Google Earth Engine (GEE) platform to reduce the impact of clouds and cloud shadows ([[https://code.earthengine.google.com/](https://code.earthengine.google.com/)]([https://code.earthengine.google.com/](https://code.earthengine.google.com/))). Radiometric calibration and atmospheric correction were performed using the Landsat T1_L2 dataset provided by GEE, which had already undergone these corrections, eliminating the need for additional processing. This ensured that the data were of high quality and suitable for ecological analysis. The kernel Normalized Difference Vegetation Index (k\(NDVI\)), as well as the Normalized Difference Bareness Index (NDBSI), Wetness Index (\(WET\)), and Land Surface Temperature (\(LST\)) indices necessary for constructing the Remote Sensing Ecological Index (\(RSEI\)), were calculated from the aforementioned Landsat data on the Google Earth Engine (GEE) platform. Subsequently, an \(RSEI\) for the period of 1990-2023 was developed based on Principal Component Analysis (PCA). Land-use/land-cover (LUCC) data for China from 1990 to 2023 was derived from the CLCD dataset, which was developed by [PERSON] and [PERSON] [28] from all available Landsat data. This dataset, generated using Landsat satellite data, incorporates a Random Forest classification method coupled with spatial-temporal filtering and logical reasoning techniques, achieving an overall accuracy of 80%. The CLCD classifies land use into the following six categories: grassland; cropland; forestland; built-up land; water body; and unused land. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on precipitation, temperature, humidity, and other data, was employed as an indicator of regional drought severity. The difference between precipitation and evapotranspiration is used to reveal regional drought characteristics and is considered a robust metric for drought monitoring. Therefore, SPEI was selected in this study to represent the drought severity in the study area. Precipitation, temperature, and humidity data from 1990 to 2023 were obtained from the China Meteorological Data Service Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))).