土壤盐渍化是干旱区、半干旱区常见的一种土地退化现象，有关盐渍土地理分布及盐渍程度等方面的实时、精准的信息，对治理盐渍土、防止其进一步退化和进行可持续发展至关重要。目前常用的遥感数据有Landsat MSS和TM、SPOT、IRS、Aster等多光谱数据。高光谱遥感技术作为国际遥感科学的研究前沿和热点，除具备常规遥感对地物监测的大面积、适时、非破坏性等优点，还克服了常规遥感的不足，通过其精细的光谱优势能够评价土壤性质细微差异，描述土壤表面状况的光谱信息及其特征的空间信息，具有定量反演地物特性的能力，与多光谱技术相比在盐渍土识别和探测方面具有更大优势。本文以焉耆盆地为研究区，利用盐渍土高光谱数据构建盐渍化土壤盐分遥感反演模型。采用电导法测得土壤含盐量，利用ASD Fr光谱仪采集地面高光谱数据，结合Hyperion遥感数据分析不同盐分含量的土壤光谱曲线特征，提取敏感波段，建立预测土壤盐分的反演模型。结果表明，由反射率对数关系建立的回归方程预测结果较好。论文共分5章，第1章主要阐述了土壤盐渍化的研究背景、国内外研究进展，以及高光谱遥感的基本概念和在土壤方面的研究概况。介绍了本文的研究目标、意义及工作路线。第2章介绍了研究区—焉耆盆地概况（包括地理位置、气候特征、水文特征和土壤盐渍化状况），实验数据的获取情况。第3章介绍了高光谱图像的处理过程；对实验采集到的盐渍土高光谱数据结合盐分含量进行光谱特征分析。利用单相关分析和逐步选入波段方法确定表征盐渍化土壤盐分信息的最佳波段组合。第4章基于高光谱遥感数据，构建盐渍土盐分反演模型，并进行模型精度检验。第5章结论与展望。Salinization is the major land degradation processes in arid and semi-arid regions. In order to prevent further deterioration of salt-affected soils and make development in a sustainable way, up-to-data and reliable information on the spatial distribution, categories of these soils is of paramount importance. The multi-spectral data such as MSS and TM Landsat, SPOT, IRS are used currently. As hot point and frontier in remote sensing, hyperspectral remote sensing technique not only has the advantages of traditional remote sensing that can timely and undisturbedly be used to detect large area，but also has special advantages. It has very high spectral resolution. More delicate spectral difference of soil salinity can help us to precisely forecast salt content. Compared with multi-spectral techniques, hyperspectral has greater advantages in saline soil identification and detection. Based Yanqi Basin for the study, the remote sensing quantitative retrieval model was founded by using hyperspectral data about saline soil. Measured soil salinity, using ASD spectrometer got ground hyperspectral data, analysis spectral curves of different salt content integrating remote sensing data. Extracted sensitive band, founded retrieval model. The results showed that regression equation from reflectivity is good. The first chapter mainly introduced the background of salinization, research progress about hyperspectral remote sensing, and the basic concepts of soil salinity. Introduced the objectives and significance of study. In the second chapter mainly about study area profile, how got the experimental data. The third chapter introduced the hyperspectral image processing, analysis spectral features. Using single correlation analysis elected band characterization, in order to get the best band combinations. In the fourth part, construct salinity inversion model based on hyperspectral remote sensing data. The last chapter is about conclusions and outlook
