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快速准确地获取土壤盐分信息是监测和治理土壤盐渍化现象的重要前提。该文以新疆维吾尔自治区典型盐渍化区域——艾比湖流域为研究区,analytical spectral devices(ASD)光谱仪采集的土壤高光谱数据和advanced space borne thermal emission and reflection radiometer(ASTER)影像为数据源,结合实测土壤盐分含量信息,对遥感定量反演土壤盐渍化现象进行研究。再经过光谱反射率数学变换后,结合相关性分析,利用多元回归方法分别建立基于重采样后的高光谱和影像光谱的土壤含盐量估算模型,对遥感影像光谱盐分估算模型进行校正,以提高遥感定量监测盐渍化土壤的精度。结果表明:ASTER影像光谱反射率二阶导数变换和ASD重采样光谱的对数的二阶导数变换所建立的盐分估算模型最佳,决定系数R2分别为0.59和0.82。经ASD重采样光谱模型校正后的ASTER影像光谱的盐分估算模型精度R2为0.91,有效地提高大尺度条件下土壤盐渍化反演精度。研究为大尺度土壤盐分定量遥感监测提供了一种有效方法。
Rapid and accurate access to soil salinity information is an important prerequisite for monitoring and controlling soil salinization. In this paper, the soil hyperspectral data and the advanced space borne thermal emission and reflection radiometer (ASTER) images collected from the analytical spectral devices (ASD) spectrometer were selected as the data sources in the typical salinization area of the Xinjiang Uygur Autonomous Region - Based on the measured soil salinity information, the phenomenon of soil salinization by remote sensing was quantitatively analyzed. After the mathematical transformation of the spectral reflectance, combined with the correlation analysis, using multiple regression method to establish soil salinity estimation model based on resampled hyperspectral and spectral images, the spectral salinity estimation model of remote sensing images to be corrected to improve Remote sensing to quantitatively monitor the accuracy of salinized soils. The results show that the salt estimation model established by the second derivative transformation of the logarithm of ASTER spectral reflectance second order and ASD resampled spectra is the best, with the determination coefficients R2 of 0.59 and 0.82, respectively. The accuracy of the salt estimation model ASR corrected by ASD resampled spectral model is 0.91, which can effectively improve the accuracy of soil salinization in large scale. The study provides an effective method for the quantitative remote sensing monitoring of large-scale soil salinity.