Abstract

An accurate determination of the soil organic matter (SOM) levels present in reclaimed mine soil is necessary to evaluate the success of the ecological reclamation of mines. Visible and near-infrared diffuse spectroscopy is a fast and efficient method for collecting data for soil management during the reclamation of soil removed from mines. In this work, we used spectroscopy to characterize and estimate the SOM of lands from different reclamation years after coal mining in semi-arid grasslands of North China. Our goals were: (1) to explore the SOM characteristics and the spectra of reclaimed mine soil with different reclamation ages, and (2) to establish a reliable and accurate SOM prediction model by comparing support vector machine (SVM), partial least-squares regression (PLSR), and random forest (RF) modeling methods by determining the optimal preprocessing method and input spectral region. The results showed that spectral characteristics are useful indicators for understanding progressive SOM changes in the topsoil at different reclamation ages. RF is a more appropriate method for assessing the performance of a selected spectral region and provides more accurate results with a root mean square error (RMSE) of 4.34 g·kg⁻¹ between predicted and observed SOM values. This study provides an alternative method that uses spectroscopy to estimate reclaimed soil conditions for the environmental monitoring of mining sites.

摘要

准确测定回收的矿山土壤中存在的土壤有机质 (SOM) 水平对于评估矿山生态回收的成功与否是必要的。可见光和近红外漫射光谱是一种快速有效的方法，用于在开垦矿山土壤期间收集土壤管理数据。在这项工作中，我们使用光谱学表征和估计了华北半干旱草原采煤后不同开垦年限土地的 SOM。我们的目标是：(1) 探索不同复垦年龄的再生矿土的 SOM 特征和光谱，以及 (2) 通过比较支持向量机 (SVM)、偏最小二乘法建立可靠和准确的 SOM 预测模型回归（PLSR），和随机森林 (RF) 建模方法，通过确定最佳预处理方法和输入光谱区域。结果表明，光谱特征是了解不同开垦年龄表土中 SOM 渐进变化的有用指标。RF 是一种更适合评估所选光谱区域性能的方法，可提供更准确的结果，均方根误差 (RMSE) 为 4.34 g·kg^-1在预测和观察到的 SOM 值之间。这项研究提供了一种替代方法，该方法使用光谱学来估计开垦土壤条件，以进行采矿场的环境监测。