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土壤光谱特征分析及盐渍化信息提取——以新疆渭干河/库车河绿洲为例
Soil spectrum characteristics and information extraction of salinization: a case study in Weigan-Kuqa Oasis in Xinjiang

查看参考文献23篇

文摘 土壤盐渍化严重制约土地生产力,实时监测土壤盐渍化有利于农业正常生产。选择新疆渭干河—库车河绿洲的光谱反射率数据,研究不同程度盐渍化土壤的光谱特征;并对绿洲所在的库车县的环境与灾害监测预报小卫星的高光谱数据进行盐渍化信息提取。提取步骤为:首先对土壤光谱反射率数据进行14种形式的变换,再与土壤含盐量进行相关分析、逐步回归分析,建立估算不同盐渍化程度的土壤含盐量方程,用均方根误差验证方程的精度;其次,建立植被和土壤波谱库;最后,在波谱库的数据基础上,使用波谱角分类法(SAM)对环境与灾害监测预报小卫星的高光谱数据进行分类。用同步实测数据对分类效果进行精度评价,效果较好,这一结果为今后该区域的高光谱应用奠定了基础,对区域农民耕作方式提出了警示,为区域可持续发展实践提供了参考。
其他语种文摘 Soil salinization is a process of global land degradation, which hazards the environment. It is caused by inefficient irrigation and the excessive use of water. It reduces the productivity of land. Xinjiang is the typical area of arid and semi-arid region. The monitoring of soil salinization timely and effectively is not only beneficial to the production of agriculture, but also in favor of sustainable development of agricultural land. The purpose of this study is how to improve the classification accuracy. In this paper, the author uses Spectral Angle Mapper method extracting the information of soil salinaztion. In order to improve the accuracy of classification, the author determines the appropriate soil and vegetation spectral library. The appropriate soil and vegetation spectral library decide the accuracy. The use of field measurements of soil spectral reflectance is used to study the soil spectral characteristics. It is combined with hyperspectral data of the Chinese environmental and disaster monitoring and forecasting of small satellites and classified soil salinization based on hyperspectral image. In the first part of this paper, according to the definition of degree of soil salinaztion, four classed of soil are classified, namely non-salined soil, slight-salined soil, moderate-salined soil and heavy-salined soil. The data from four classes of soil can be converted to fourteen transforms of soil spectral reflectance. There are fifteen transforms of soil spectral reflectance. They are the original and the converted fourteen transforms of soil spectral reflectance. According to the findings of the correlation analysis of fifteen transforms of soil spectral reflectance with soil salt content, regression analysis are done. The equations are chosen to estimate soil salt content. And root mean square error (RMSE) is employed to verify the accuracy of the equations. The best equations of estimating soil salt content are decided. In the second part of this paper, the author decides the soil spectral library according to the result of spectral characteristics. The vegetation spectral library is based on field measurements and actual survey. In the final part of this paper, the author uses the SAM method to classify the hyperspectral image based on the soil spectral library and vegetation spectral library. Such a classification is proved good, laying the foundation for the region's hyperspectral applications, giving warning to regional farmers for appropriate farming methods, provided the data for the region's sustainable development. This article identifies the soil and vegetation spectral library of the study area. This helps to further study on spectral characteristics in this region. Environmental and disaster monitoring and forecasting of small satellite is designed and developed by our country. China centre for resources satellite data and application provided for our researchers free. HSI hyperspectral data is also China's the only hyperspectral remote sensing images. This study attempts to contribute to our development of hyperspectral remote sensing images. To speed up the satellite for the applications of environmental monitoring. It is better for the country's environmental monitoring services. The development of hyperspectral remote sensing images will bring a new opportunity and challenge to remote sensing technology. Our researchers should strive for the development of hyperspectral remote sensing technology. The development of hyperspectral remote sensing will promise a better future.
来源 地理科学进展 ,2014,33(2):280-288 【核心库】
关键词 光谱特征 ; 盐渍化 ; 高光谱数据 ; 渭干河/库车河绿洲 ; 新疆
地址

新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 830046

语种 中文
文献类型 研究性论文
ISSN 1007-6301
学科 农业基础科学
基金 中国科学院资源与环境信息系统国家重点实验室开放研究基金 ;  国家自然科学基金重点项目 ;  国家教育部长江学者与创新团队发展计划
文献收藏号 CSCD:5062859

参考文献 共 23 共2页

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引证文献 7 篇

1 吾木提·艾山江 基于WorldView-2影像的土壤含盐量反演模型 农业工程学报,2017,33(24):200-206
CSCD被引 7 次

2 姚阔 高光谱遥感土地质量指标信息提取研究进展 地理与地理信息科学,2014,30(6):7-12
CSCD被引 4 次

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