Soil Vis-NIR spectral response had been widely proposed as an alternative to costly and time-consuming laboratory determination of soil physical and chemical properties. However its use for measuring soil quality index directly has not been well explored. This study compares the effectiveness of different machine learning models on a large spectral library using a database collected by the European Union project “Land Use and Coverage Area frame Survey” (LUCAS). Three approaches to predicting mineral soil features by processing their spectral response for the Vis-NIR range were tested. Prediction models of clay content, pH in CaCl₂, organic carbon (SOC), calcium carbonate (CaCO₃), nitrogen (N), and cation exchange capacity (CEC) were analyzed. Three types of models were assessed: a Stacked AutoEncoder, a convolutional neural network, and a stack model composed of a set of multilayer perceptron algorithms with two different regression estimation solutions. Modeling with CNN was identified as the optimal solution. Similar, and in some cases, better results can be obtained from ensembles of machine learning algorithms. The estimates of soil characteristics made with the help of the Stacked AutoEncoder showed the greatest errors. The use of soil feature estimates to support soil and land classification was also analyzed. An indicator describing the state of the topsoil is presented, which assists the objective classification of soils. The research showed that the accuracy of the estimation of the proposed Topsoil Quality Index (TQI) estimated directly based on Vis-NIR spectral response and indirectly based on estimated values of selected soil features is practically identical. The research confirms the suitability of Vis-NIR spectroscopy for topsoil assessment.

土壤 Vis-NIR 光谱响应已被广泛提议作为昂贵且耗时的实验室土壤物理和化学特性测定的替代方案。然而，它在直接测量土壤质量指数方面的应用还没有得到很好的探索。本研究使用欧盟项目“土地使用和覆盖区域框架调查”（LUCAS）收集的数据库，在大型光谱库上比较了不同机器学习模型的有效性。测试了通过处理 Vis-NIR 范围的光谱响应来预测矿质土壤特征的三种方法。粘土含量、CaCl _{2 中的}pH 值、有机碳 (SOC)、碳酸钙 (CaCO ₃ ) 的预测模型)、氮 (N) 和阳离子交换容量 (CEC) 进行了分析。评估了三种类型的模型：堆叠自动编码器、卷积神经网络和由一组具有两种不同回归估计解决方案的多层感知器算法组成的堆叠模型。使用 CNN 建模被确定为最佳解决方案。类似的，在某些情况下，可以从机器学习算法的集合中获得更好的结果。在 Stacked AutoEncoder 的帮助下对土壤特性进行的估计显示出最大的误差。还分析了使用土壤特征估计来支持土壤和土地分类。提供了描述表土状态的指标，有助于对土壤进行客观分类。研究表明，直接基于 Vis-NIR 光谱响应和间接基于所选土壤特征的估计值估计的拟议表土质量指数 (TQI) 的估计精度实际上是相同的。研究证实了 Vis-NIR 光谱在表层土壤评估中的适用性。