Long-Term Quantitative Analysis of the Temperature Vegetation Dryness Index to Assess Mining Impacts on Surface Soil Moisture: A Case Study of an Open-Pit Mine in Arid and Semiarid China

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine area of China over the period from 2000 to 2021. Using the temperature vegetation dryness index (TVDI), derived from the Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) feature space, this paper proposes a method—the TVDI of climate factor separation (TVDI-CFS)—to disentangle the influence of climate factors. The approach employs the Geographically and Temporally Weighted Regression (GTWR) model to isolate the influence of temperature and precipitation, allowing for a precise quantification of mining-induced disturbances. Additional techniques, such as buffer analysis and the Dynamic Time Warping (DTW) algorithm, are used to examine spatiotemporal variations and identify disturbance years. The results indicate that mining impacts on surface SM vary spatially, with disturbance distances of 420–660 m and strong distance decay patterns. Mining expansion has increased disturbance ranges and intensified cumulative effects. Inter-annual TVDI trends from 2015 to 2021 reveal clustered disturbances in alignment with mining directions, with the largest affected area in 2016. These findings provide a systematic valuable insights for ecological restoration and sustainable environmental management in mining-affected areas.