Abstract: After the process of surface mining, stripping and covering, the vertical distribution of organic carbon (SOC) and total nitrogen (TN) in the reclaimed mine soil undergoes great changes, which directly determines the selection of vegetation species and the effectiveness of ecological restoration. Therefore, under the dual carbon target, monitoring and mapping SOC and TN in reclaimed soil profile (0−100 cm) are the important basis for evaluating ecological restoration and carbon sequestration in mining areas. Compared with traditional chemical methods, hyperspectral imaging is a rapid and non-destructive technique that can be used for the effective estimation of the important indicators of soil properties. Imaging spectroscopy technology has the advantage of spectral integration compared to point spectral measurement. Therefore, the reclaimed soil from arid and semi-arid grassland mining areas in northern China was selected as the research object, and soil profiles (0−100 cm) from different mining areas, modes and years were collected. The vertical distribution patterns and hyperspectral characteristics of SOC and TN in profiles were revealed under different cultivation years and patterns. And then, with the feature bands and spectral indices of SOC and TN, the interpretable machine learning models were constructed to achieve mapping SOC and TN contents in profiles. The results indicated that: ① The spectral reflectance of the soil samples decreased with the increase of SOC and TN contents. By combining stacked feature selection methods (Pearson correlation coefficient–successive projections algorithm, PCC-SPA), hyperspectral data were reduced in dimension. 30 bands of SOC and 18 bands of TN were clearly identified, and three-dimensional (3D) spectral indices were established; ② By comparing different machine learning algorithms (partial least squares regression; PLSR, random forest; RF, and radial basis function model; RBF), the prediction accuracy could be improved by filtering and transforming the selective bands of hyperspectral data. The RF with selective bands and optimal 3D spectral indices demonstrated the best results with R²=0.97, RMSE=7.5 g/kg, LCCC=0.84, bias=3.70 for SOC and R²=0.78, RMSE=0.33 g/kg, LCCC=0.74, bias=0.19 for TN; ③ Using SHAP value to explain the importance ranking of input features in machine learning models, it was found that the contribution of 3D spectral index to model prediction is greater than that of most feature bands; ④ With the optimization model, the SOC and TN content of each pixel in the hyperspectral image could be quickly predicted, and the mapping and visualization of the SOC and TN contents in the mine soil profile could be realized. Mapping profiles of reclaimed soil in open-pit coal mining areas based on imaging hyperspectral technology can help understand and reveal the formation and evolution process of reclaimed soil, clarify the driving forces of reclamation years and vegetation types in the succession process of the profile. It can also provide important data sources and technical support for dynamic monitoring of carbon sequestration potential of reclaimed soil in open-pit coal mining areas and evaluation of the benefits of soil carbon sequestration technology.