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中文摘要：
土壤盐渍化是干旱半干旱区农业发展的重要制约因素，同时也是干旱区所面临的最主要的生态环境问题之一，因而准确获取土壤的盐渍化信息对于实时掌握其分布范围以及合理地开展盐渍化治理工作具有重要意义。选取渭干河-库车河流域绿洲典型盐渍地作为研究区，以Landsat-TM多光谱遥感影像为基础数据源，首先对影像进行最小噪声分离处理（MNF变换）并计算其像元纯度指数（Pixel Purity Index, PPI），选取能表征区域特征信息的前三个波段构建MNF三维光谱特征空间，然后在向量空间和单行体理论的指导下，结合实地调查，提出“植被亮点区”概念，并定义“盐渍化距离指数（Soil Salinization Distance Index, SDI）”为多维向量空间中包含于单行体中的盐渍化像元到“植被亮点区”的归一化距离。最后利用不同盐分环境下的实测数据对SDI进行精度验证。结果显示：在低植被覆盖区，即中度、重度盐渍化区，SDI与0-10cm内土壤盐分含量相关性要高于0-20cm，R2＞0.83。在相对高植被覆盖区，即农田和轻度盐渍化区，SDI与0-20cm内平均盐分含量相关性要高于0-10cm，R2＞0.81。0-10cm层二者总体精度R2=0.81，0-20cm层二者相关性总体精度R2=0.72。研究表明，SDI指数模型简单、易于构建，精度较高，具有一定的应用价值，有利于干旱区区域大尺度盐渍化的定量分析和监测工作。
英文摘要：
Soil salinization is a critical constraint for agriculture development in arid and semiarid areas, and also one of the most important environmental problems. Therefore, obtaining accurate soil salinization information is crucial to salinization management in those areas. The current study is attempting to derive a relatively straight forward soil salinity index from Landsat TM remote sensing images. First, perform minimum noise fraction （MNF） of the images and calculate their pixel purity index （PPI）; select the first three bands that are good to characterize the feature information of a region to construct a MNF spectral eigenspace; then put forward the concept of “vegetation highlight area” by combining field investigations and following the vector space and single line theories and define soil salinization distance index （SDI）, so as to enable the multi-dimensional vector space to include the normalized distance from a salinization pixel within the single line to the vegetation highlight area; and in the end verify SDI for precision using the data obtained in field investigations of regions different in salinization level. Results show that in areas low in vegetation coverage, that is, areas moderate and severe in salinization, SDI is more closely related to the average soil salt content in the 0-10 cm soil layer, （R2〉 0.83） than in the 0-20 cm soil layer, while in areas high in vegetation coverage, that is, farmland of areas low in soil salinization, it is just the reverse （R2〉 0.81）, indicating that the overall precision of SDI in prediction of soil salinity in the 0-10 cm soil layer is R2= 0.81, and in the 0-20 cm soil layer R2= 0.72. The findings suggest that the SDI index model is simple and easy- to- construct and yet quite high in precision, so it is of high practical value and can be used to help quantitatively analyze and monitor soil salinization on a large scale in the arid and semiarid areas.
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