绿洲土壤盐分含量高光谱反演建模

Hyperspectral inversion modeling of salt content in oasis soil

以新疆渭干河-库车河三角洲绿洲为研究区,以实测土壤高光谱数据和土壤盐分作为基础数据,分析多种光谱指数与土壤盐分的相关性,筛选特征波段,使用逐步多元线性回归、单变量回归和主成分回归3种方法构建土壤盐度高光谱监测模型。研究表明,基于逐步多元线性回归方法,以对数二阶微分光谱特征波段所构建的盐渍化遥感监测模型最优,模型的稳定性和预测精度最高,可有效估测土壤含盐量。此项研究成果满足了对干旱区盐渍化监测的需求,为干旱区土壤盐分定量反演提供了可靠的参考。

Taking Weigan River-Kuqa River Delta oasis in Xinjiang as research area,with the measured soil hyperspectral data and soil salinity as foundational data,the correlation of various spectral indices and soil salinity was analyzed,feature bands were select-ed,and three methods of stepwise multiple linear regression,univariate regression,and principal component regression were used to construct a hyperspectral monitoring model for soil salinity.The research indicated that based on stepwise multiple linear regression,the salinization remote sensing monitoring model utilizing logarithmic second-order differential spectral feature bands was best,with the highest stability and prediction accuracy,which could effectively estimate the soil salt content.The research results met the de-mand for salinization monitoring in arid regions,and provided a reliable reference for quantitative inversion of soil salinity in arid re-gions.