A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near-infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO₃, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R² values and lower RMSE than the other investigated models. The estimation accuracy for CaCO₃, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

中文翻译：

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结合 Vis-NIR 光谱和高级统计分析，估算与林道建设相关的土壤化学性质

土壤化学特性的彻底量化对于评估森林土壤的工程特性对于道路设计、施工和维护至关重要。在这里，我们研究了可见-近红外 (Vis-NIR) 光谱与先进的统计分析相结合，用于估计土壤化学性质的适用性。收集了 60 个森林土壤样品并分析了 pH、电导率 (EC)、CaCO ₃、有机物 (OM) 和阳离子交换容量 (CEC) 以及已建立的实验室方法。光谱测量是使用 Vis-NIR 光谱仪在 350-2,500 nm 范围内进行的。为了从反射光谱估计上述土壤特性，先进的统计技术包括偏最小二乘回归 (PLSR)、混合偏最小二乘和人工神经网络 (PLS-DI-ANN) 模型、混合偏最小二乘和自适应神经模糊推理系统 (PLS) –DI–ANFIS) 模型，以及窄带光谱指数。所得结果表明PLS-DI-ANFIS模型在从反射光谱及其一阶导数估计pH、EC、OM和CEC方面显示出巨大的潜力，表现出较高的R ²值和低于其他研究模型的 RMSE。然而，对于所有应用的方法，CaCO ₃的估计精度都很低。结果证实，Vis-NIR 光谱可用作标准化学土壤分析技术的快速且经济高效的替代方案，有助于森林道路的设计、施工和维护。