基于表观电导率与实测光谱的干旱区湿地土壤盐分监测

以新疆艾比湖滨盐渍化土壤为对象，利用磁感应电导仪和光谱仪测得的盐渍土表观电导率和可见光/近红外光谱数据，选取与EM38解译的土壤盐分相关性最好的光谱变换形式和特征波长，分别建立多元逐步回归、偏最小二乘回归和支持向量回归的土壤盐分监测模型。结果表明：（1）表观电导率两种模式相结合建立的盐分含量解译模型的拟合优度达到0.91，即在该区域内电磁感应技术可用于土壤盐分含量的间接监测。（2）一阶微分处理优于二阶微分，经一阶微分变换后的光谱可以较好地预测土壤盐分含量。（3）3种建模方法中，支持向量回归的建模精度最高，偏最小二乘回归和多元逐步回归次之。干旱区湖滨湿地土壤盐分含量的估测模型宜选取基于平滑后的原始一阶微分光谱数据建立的支持向量回归模型。

Apparent Electronic Conductivity and Measured Spectral for Monitoring Soil salt Content in Arid Lakeside Wetland

This paper selected the Ebinur Lake as a study area and established multivariate stepwise regression, partial least square regression and support vector machine regression model based on the site measured data of apparent electronic conductivity and spectral with EM38 and ASD Fieldspec HH. In the three models, the apparent electronic conductivity measured with EM38 selected as independent variable and the measured soil soluble salt content as a dependent variable, and the selected spectral transformation form and characteristic bands have best correlation with the interpreted soil salinity data from EM38 used for model establishment. Results show that, firstly, the correlation coefficient of multivariate regression model reached up to 0.91, and indicated electromagnetic induction technology can be used for the indirect monitoring of soil salinity in this area; secondly, first-order differential transformation is better than the second-order differential transformation, the spectrum transformed with first-order differential can better predict soil salinity; thirdly, the spectrum transformed with first-order differential has good performance in all the three established models, and the model with support vector machine has higher accuracy than the partial least square and multivariate stepwise regression models. Accordingly, the support vector machine regression model established with first order differentiation transformation can be used for as a desirable monitoring model for arid lakeside wetland soil salinization.