Authors

Abstract

Efficient monitoring of soil salinity is of significant importance for the utilization and ecological restoration of saline–alkali soils. This study investigates typical saline–alkali soils by employing hyperspectral imaging and machine vision technologies to develop a quantitative prediction model for soil salinity. First, spectral and image data of soil samples were acquired. Subsequently, spectral preprocessing was performed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Moving Average smoothing (MA). Characteristic spectral bands were extracted using the Competitive Adaptive Reweighted Sampling (CARS), Variable Combination Population Analysis (VCPA), Iteratively Retaining Informative Variables (IRIV), and a combined VCPA–IRIV approach. Finally, the selected spectral features were fused with image features to establish a Support Vector Regression (SVR) model. The results demonstrated that, compared with single-source data, the SVR model based on feature-level data fusion significantly improved the prediction accuracy of soil salinity. Among the tested models, the SNV + VCPA–IRIV + SVR combination achieved the best performance (Rc² = 0.9889, RMSEC = 0.4790, Rp² = 0.9569, RMSEP = 1.0484, RPD = 3.4423). The results indicate that feature-level data fusion combining hyperspectral and machine vision data can effectively predict soil salinity in saline–alkali soils, while improving model stability compared to single-data-source approaches.

Abstract in Chinese

有效监测土壤盐分对于盐碱地的利用和生态恢复具有重要意义。本研究针对典型的盐碱地，采用高光谱和机器视觉技术构建土壤盐分的定量预测模型。首先，收集了土壤样本的光谱和图像信息。随后，采用标准归一化变量（SNV）、多散射校正（MSC）及移动平均平滑（MA）进行光谱预处理。通过竞争自适应重加权算法（CARS）、变量组合群体分析（VCPA）、信息变量的迭代保留（IRIV）和变量组合群体分析–信息变量的迭代保留（VCPA–IRIV）提取了光谱特征波段。最后，将这些特征与图像特征结合，建立了支持向量回归（SVR）模型。结果表明，与单一数据相比，基于特征级融合的SVR模型显著提高了土壤盐分的预测精度。其中，MSC+VCPA–IRIV+SVR是最优算法组合（Rc²=0.9889，RMSEC=0.4790，Rp²=0.9569，RMSEP=1.0484，RPD=3.4423）。研究表明，结合高光谱和机器视觉数据的特征级融合方法能够有效预测盐碱地的土壤盐分，从而解决了单一数据源方法在建模稳定性方面的不足。