Wang Haifeng, Chen Yinwen, Zhang Zhitao, Chen Haorui, Li Xianwen, Wang Mingxiu, Chai Hongyang
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, Shaanxi, China.
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, China.
PeerJ. 2019 Jan 22;7:e6310. doi: 10.7717/peerj.6310. eCollection 2019.
Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca, Na, Cl, Mg and SO was very high, that of CO was high and K was relatively lower, but HCO failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.
土壤盐渍化是干旱地区农业和生态环境可持续发展的主要障碍。利用可见-近红外（VIS-NIR）光谱技术准确反演土壤中主要水溶性盐离子，可提高盐碱土治理的有效性。然而，土壤盐离子光谱模型的准确性受光谱数据高维性和噪声信息的影响。本研究旨在通过探索不同盐离子的敏感光谱区间来优化光谱模型，从而提高模型精度。为此，从中国内蒙古沙壕渠灌区采集了120个土壤样本。在实验室测定原始反射光谱和盐离子含量后，采用标准正态变量变换（SNV）对光谱数据进行预处理。随后，利用灰色关联度（GC）、逐步回归（SR）和投影变量重要性（VIP）方法选择各离子的敏感光谱区间。最后，基于敏感光谱区间研究了偏最小二乘回归（PLSR）和支持向量回归（SVR）两种模型的性能。结果表明，基于不同分析方法选择的敏感光谱区间建立的模型精度不同：VIP最高，SR次之，GC最低。不同离子的最优反演模型不同。总体而言，PLSR和SVR均取得了满意的模型精度，但PLSR在预测效果上优于SVR。不同离子的最优反演精度差异较大：Ca、Na、Cl、Mg和SO 的预测精度非常高，CO 的预测精度较高，K的预测精度相对较低，而HCO 则没有任何预测能力。这些研究结果为优化水溶性盐离子光谱模型及其预测精度提供了一种新方法。
