露天煤矿区复垦土壤在经过开采—剥离—覆盖过程后，其有机碳(SOC)和全氮(TN)的垂直分布发生巨大变化，并且直接决定植被恢复物种的选择和生态恢复效果。因此，在双碳目标下，复垦SOC和TN在0～100 cm剖面的监测及制图，是评估矿区生态恢复及复垦工程固碳的重要基础。相比于传统的化学方法，高光谱技术是一种快速而且无损，已经广泛应用于土壤属性估测的技术，成像高光谱技术相比于点位光谱测量具有图谱合一的优势。因此以我国北方干旱半干旱草原大型露天煤矿复垦土壤为研究对象，采集不同复垦工程、复垦年限的土壤剖面样本，揭示不同复垦年限和复垦方式下土壤剖面SOC和TN垂直分布规律和高光谱特征，通过深度挖掘和筛选SOC和TN特征波段和指数，构建可解释性机器学习模型，从而实现土壤剖面SOC和TN的制图，结果表明：① 土壤光谱反射率随SOC和TN质量分数的增加而降低，通过集成皮尔森相关性分析和选择连续投影算法，对点状高光谱数据进行降维和去冗余，最终明确表征SOC质量分数的30个特征波段和表征TN质量分数的18个特征，并基于特征波段建立了SOC和TN三维光谱特征指数；② 通过对比偏最小二乘回归(PLSR)、随机森林模型(RF)和径向基函数(RBF)神经网络预测模型，发现对光谱数据进行特征波段筛选和变换，可以提高预测精度，其中随机森林模型预测SOC和TN的精度最高，对SOC的预测结果为R2=0.97、RMSE=7.5 g/kg、LCCC=0.84、bias=3.70，TN预测结果为 R2=0.78、RMSE=0.33 g/kg、LCCC=0.74、bias=0.19；③ 利用SHAP可解释机器学习模型方法对输入特征进行重要度排序，发现提出的三维光谱指数对模型预测的贡献大于大部分特征波段；④ 使用最佳模型可以快速绘制出成像高光谱图像中每个像素的SOC和TN质量分数，实现矿山土壤剖面中SOC和TN质量分数的制图和可视化。基于成像高光谱技术绘制露天煤矿区复垦土壤剖面可以揭示复垦土壤形成和演化过程，阐明复垦年限、复垦植被类型在剖面演替过程中的驱动力，为露天煤矿区复垦土壤碳汇潜力的动态监测以及土壤固碳技术的效益评价提供重要的数据来源和技术支持。

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.