基于高光谱指数和电磁感应技术的区域土壤盐渍化监测模型

摘要

The present research attempts to establish a soil salinization monitoring model through a combination of remote and near sensing technologies.An electromagnetic induction instrument (EM38) was used to measure the electronic conductivity of soil samples collected from the delta oasis between the Weigan River and the Kuqa River in the north rim of the Tarim basin.Hyperspectral images were obtained via ASD Field Specpro FR and were transformed via 11 different approaches including root mean squares,logarithm,inversion,inversion-logarithm,continuum removal,and first order differentiation,etc.After the transformation,the obtained soil spectra that correlate well with soil electronic conductivity as measured by the EM38 were used to calculate five salinity indexes (salinity index 1,2,and 3,normalized differential salinity index,and brightness index).Our analyses suggest that the salinity index 2 obtained via first order differentiation transformation of the spectra with the wavelengths of 456,686 and 1 373 nm generated the highest correlation with salinity information derived via the EM38.By incorporating near sensing information (soil electronic conductivity information obtained via EM38),the current research provides a potentially more accurate approach to monitoring and predicting soil salinization in the future.%以新疆渭干河-库车河三角洲绿洲不同盐渍化程度的土壤为研究对象,以电磁感应仪(EM38)测得的盐渍土电导率数据和ASD Field Specpro FR光谱仪测得的盐渍土高光谱数据为基础数据源.对利用Savitzky-Golay滤波进行平滑去噪后所得的光谱曲线进行均方根、对数、倒数、倒数的对数、连续统去除、一阶微分等11种光谱变换,并从中选取土壤光谱波长与基于EM38建立的土壤盐分解译模型,所解译出的土壤盐分数据相关性最好的变换形式及响应波段进行SI,BI和NDSI等5种盐分指数的计算,通过精度检验选取最优高光谱指数,以此建立区域土壤盐渍化监测模型.结果表明；以一阶微分变换后的土壤光谱反射率456,686和1 373 nm的波段组合作为最佳敏感波段所构建的SI2高光谱指数与EM38数据相结合建立的土壤盐渍化监测模型为最优模型；与传统的遥感方法中单纯利用盐分指数所建立的监测模型相比,基于高光谱指数和EM38数据所建立的土壤盐渍化监测模型的预测精度更高,该模型的建立为进一步提高土壤盐渍化的监测和预测精度的研究提供参考借鉴.