摘要
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 373nm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。
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 v/a ASD Field Specpro FR and were transformed v/a 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 v/a first order differentiation transformation of the spectra with the wave- lengths of 456, 686 and 1 373 nm generated the highest correlation with salinity information derived v/a the EM38. By incorporating near sensing information （soil electronic conductivity information obtained v/a EM38）, the current research provides a potentially more accurate approach to monitoring and predicting soil salinization in the future.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2013年第6期1658-1664,共7页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(41261090
41130531
41161063
41261049)
教育部新世纪优秀人才支持计划
霍英东教育基金项目(121018)资助
