Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

Abstract

AbstractMany coal mining areas overlapped with agricultural land in the world. However, surface subsidence and waterlogging brought on by coal mining inexorably harm the agricultural land. Soil moisture can reflect the variation of mining impact significantly according to the geographic features of the high underground water table, which shows significant spatial gradient variation within the subsidence basin. Previous studies have used site survey and unmanned aerial vehicle (UAV), which are not applicable to large‐scale assessments of the ecological impacts and defining the scale of impacts. Here, we used the Google Earth Engine (GEE) platform and an optical trapezoid model (OPTRAM) to estimate the soil moisture distribution around 50 mining subsidence waterbody samples in eastern China. Based on the spatial variation in soil moisture, the impact boundary of coal mining was determined using buffer analysis and trend fitting. The results showed that: (1) The OPTRAM can well reflect the soil moisture distribution which shows a good correlation between estimated and measured values, with R = 0.83 and RMSE = 0.054. (2) The spatial trend fitting of soil moisture is suitable for evaluating mining impact, and the impact distances are in the range of 96–762 m, 80% of which are located at 128–436 m. We also find that 28% of subsidence waterbody buffer basins were heavily influenced by human activities, and the coal mining impact boundaries cannot be effectively identified. The study provides a new idea to quantify the ecological and agricultural impact boundary of coal mining, which could be applied to other similar regions across the globe.