The problem of heavy metal pollution of soils in China is severe . The traditional spectral methods for soil heavy metal monitoring and assessment cannot meet the needs for large-scale areas . Therefore, in this study, we used HyMap-C airborne hyperspectral imagery to explore the estimation of soil heavy metal concentration . Ninety five soil samples were collected synchronously with airborne image acquisition in the mining area of Yitong County, China . The pre-processed spectrum of airborne images at the sampling point was then selected by the competitive adaptive reweighted sampling (CARS) method . The selected spectral features and the heavy metal data of soil samples were inverted to establish the inversion model . An ensemble learning method based on a stacking strategy is proposed for the inversion modeling of soil samples and image data . The experimental results show that this CARS-Stacking method can better predict the four heavy metals in the study area than other methods . For arsenic (As), chromium (Cr), lead (Pb), and zinc (Zn), the determination coefficients of the test data set (R) are 0.73 , 0.63 , 0.60, and 0.71, respectively . It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.