Abstract
The excavation of subterranean coal has led to a plethora of ecological and environmental issues, which seriously restrict the sustainable development of society. As one of the important physical indicators of soil, soil moisture content needs to be scientific, real-time, and comprehensively monitored. Due to the low efficiency of manual measurement, methods based on remote sensing data inversion have received widespread attention and in-depth research in recent years. In this study, a new ReMPDI index (Red edge Modified Perpendicular Drought Index) is constructed, and six retrieval models of soil moisture content based on machine learning algorithms are compared and analyzed, and the accuracy is verified by measured sampling data. The following conclusions were obtained: (1) Using the red edge band as the horizontal axis, and the near infrared band NIR as the vertical axis is the optimal spatial band combination of spectral characteristics for constructing soil lines; (2) The determination coefficient (R2) of ReMPDI index based on REdge-NIR spectral feature space and adding vegetation cover factor is the highest, which is-0. 798, and there is a significant correlation, which is better than MPDI and PDI index; (3) The model inversion accuracy of the RF is significantly higher than SVM, BPNN, PLSR, CNN, and RBFNN, with an error of only 9.52% compared to the measured results. The results of this study can provide a theoretical basis and technical support for the fine monitoring of surface soil moisture content on a large scale in mining areas.