Purpose

Comparing to conventional laboratory methods, visible–near-infrared reflectance (vis–NIR) spectroscopy is a more practical and cost-effective approach for estimating soil physical and chemical properties.

Materials and methods

This paper aims to build statistical machine learning models to investigate the efficiency of spectral data for comprehensive evaluation of the soil quality indicators. Seventeen physical and chemical properties were measured using standard methods as indicators of soil quality. Soil samples were scanned in the laboratory in the vis–NIR range (350–2500 nm), the calibration set of 31 samples and the validation set of 13 samples for cross-validation and independent validation; twenty-four preprocessing methods were tested to improve predictions, and a partial least squares regression (PLSR) was used to predict soil quality indicators.

Results and discussion

Comparing model indices, the model constructed based on the PLSR machine learning method has a good predictive power (R² > 0.9, ratio of performance to deviation (RPD) > 3.0). For physical and chemical properties, the bulk density (BD, R² = 0.97, RPD = 5.90), soil organic matter (SOM, R² = 0.98, RPD = 8.56), pH (R² = 0.95, RPD = 4.40), and TN (R² = 0.98, RPD = 6.67) concentration were predicted. This indicates that the method is suitable for the prediction of these soil elements in this study area. For the heavy properties, except for Mn, Zn, Cd, and As, the other five heavy metal concentrations were well predicted. It can be seen that the prediction ability of the construction model is Hg, Cr, Pb, Ni, and Cu in order of superiority to inferiority. The results show that a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost, and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.

中文翻译：

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可见光和近红外光谱与化学计量学能够预测土壤的物理和化学性质

目的

与传统的实验室方法相比，可见-近红外反射（vis-NIR）光谱法是一种更实用，更具成本效益的估算土壤物理和化学性质的方法。

材料和方法

本文旨在建立统计机器学习模型，以研究光谱数据对土壤质量指标进行综合评估的效率。使用标准方法测量了十七种物理和化学性质，作为土壤质量的指标。实验室在可见-近红外范围（350-2500 nm）中对土壤样品进行了扫描，校准集为31个样品，验证集为13个样品，以进行交叉验证和独立验证。测试了二十四种预处理方法以改善预测，并使用偏最小二乘回归（PLSR）来预测土壤质量指标。

结果和讨论

比较模型指标，基于PLSR机器学习方法构建的模型具有良好的预测能力（R ²  > 0.9，性能与偏差之比（RPD）> 3.0）。对于物理和化学性质，堆密度（BD，R ²  = 0.97，RPD = 5.90），土壤有机质（SOM，R ²  = 0.98，RPD = 8.56），pH（R ²  = 0.95，RPD = 4.40），和TN（R ² = 0.98，RPD = 6.67）浓度被预测。这表明该方法适用于该研究区域中这些土壤元素的预测。对于重金属，除Mn，Zn，Cd和As外，其他五个重金属的浓度均得到了很好的预测。可以看出，构建模型的预测能力是从劣到优依次为Hg，Cr，Pb，Ni和Cu。结果表明，光谱和化学计量学方法的结合可以作为一种实用，快速，低成本和定量的方法来评估陕西省土壤的理化性质。