基于实测高光谱和电磁感应数据的区域土壤盐渍化遥感监测研究

以新疆渭干河—库车河三角洲绿洲为例，利用实测得到的不同盐渍化程度的盐渍土高光谱数据和电磁感应数据(EM38)协同构建土壤高光谱盐分指数遥感监测模型，将该模型通过尺度效应转换用于校正传统的Landsat-TM多光谱遥感影像的土壤盐分光谱指数，用校正过的TM影像进行区域土壤盐分的反演，并利用实测土壤盐分数据对反演结果进行分析与验证。结果表明：将高光谱和电磁感应数据与多光谱遥感技术相结合进行区域土壤盐渍化信息的提取，其精度和反演效果(R2=0.799 3，p<0.01)明显优于传统多光谱遥感方法中单纯利用土壤盐分指数所建立的监测模型(R2=0.587 4，p<0.01)，为今后更好地实现土壤盐渍化的高精度遥感动态监测研究提供了科学依据。

Research on Remote Sensing Monitoring of Soil Salinization Based on Measured Hyperspectral and EM38 Data

In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different salinization monitoring models. The authors’ conclusion is that with R2=0.799 3 (p<0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2=0.587 4，p<0.01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization.