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Hydrocarbon micro-leakage of oil and gas resources (including coalbed methane) may induce spectral changes of surface soil and vegetation. To detect surface hydrocarbon micro-leakage by using remote sensing technology is a new method for early exploration of coalbed methane with wide range and low cost. At present, the studies of this kind of method mainly focus on bare soil minerals and seldom on widespread vegetated areas. The important reason is that the biophysical process of hydrocarbon microleakage toxicity to vegetation roots is complex and the spectral characteristics that can be used to extract vegetation anomalies are vague. Moreover, the spectral features selected according to a small number of sampled spectra are accidental, which leads to the low accuracy of the extraction results. Therefore, this work first discussed the mechanism of hydrocarbon micro-leakage poisoning to vegetation roots. The vegetation spectral features that can effectively reflect the effect of hydrocarbon micro-leakage were selected based on PROSAIL model afterwards, and a red-edge position index based on Sentinel-2/MSI band configuration was proposed. Then, we marked the mine sites across our study area-Qinshui basin on Google Earth for long-term vegetation spectral characteristics statistics, and compared it with that of control area to determine how these spectral features are actually affected by hydrocarbon microleakage. Finally, marked samples were divided into training set and test set, which were used to find the optimal spectral feature threshold combination by threshold space method and verified it. The statistical results show that, compared with the control area, an obvious blue shift was revealed by the red-edge position index of the mine samples in the experimental area, and the near-infrared reflectance decreased and the red valley reflectance increased, which was in consistent with the mechanism of hydrocarbon micro-leakage poisoning vegetation and the results of spectral simulation. In background mountain forest area, the 80% recall rate of vegetation samples in mine buffer zone can be balanced with the 5% misclassification rate of vegetation samples, which showed the rationality of this method. In this paper, we analyzed and optimized the spectral characteristics of hydrocarbon micro-leakage affecting vegetation, and used multi-spectral data to construct spectral index to extract hydrocarbon micro-leakage vegetation anomaly according to the spectral statistics of mines buffer. This method combines theoretical simulation with large sample statistics, which can provide reference for the research of extracting hydrocarbon micro-leakage vegetation anomaly by remote sensing.