基于地表光谱建模的区域土壤盐渍化遥感监测研究

摘要/Abstract

摘要： 土壤盐渍化是土地荒漠化和土地退化的主要类型之一,也是世界性资源问题和生态问题,盐渍化土壤主要分布在干旱半干旱地区,而土壤盐渍化的动态监测和预报是目前研究的重要手段.新疆渭干河－库车河三角洲绿洲是我国土壤盐渍化比较严重也比较典型区域之一,以实测获取的绿洲区域范围内不同恶化程度的盐渍化土壤的高光谱反射率及其土壤含盐量为基础数据源,从中优选出对不同盐渍化程度土壤最为敏感的光谱波段,结合Landsat-TM 多光谱遥感影像构建于地表光谱建模的最佳土壤盐渍化监测模型,并将此模型实现大尺度范围内的高精度土壤盐分遥感定量反演.结果表明:(1)反射率一阶微分光谱变换方式最好,与土壤含盐量相关性最佳,最大相关系数可达0.987.(2)利用抛物线模型与最佳土壤盐分指数SI3,构建的地表光谱模型效果最为理想(R²=0.885 2).(3)与单纯以传统多光谱遥感技术构建的土壤盐分监测模型(R²=0.592 5)相比,经尺度转换后的模型,其精度与效果(R²=0.708 4)则更优.为今后可实现高精度干旱区区域大尺度卫星遥感土壤盐渍化监测提供一种科学、有效的途径.

关键词: 地表光谱建模, 尺度转换, 土壤盐渍化

Abstract: As a global environmental problem,soil salinization has induced different types and degrees of land desertification and degradation,and affected the ecological environment stability and regional ecological security together with soil pollution,soil degradation,soil erosion and soil desertification. China was affected by soil salinization seriously in wide distribution,especially in the arid and semi-arid areas in the northwest China. Besides, the trend of the area of soil salinization is continuous to expand and becomes more and more severe. Therefore it is very important to monitor the soil salinization in the arid and semi-arid areas scientifically,accurately and rapidly. Multi-spectral remote sensing technology has become a new method to detect and monitor the soil salinization due to the advantage that it can obtain the feature information of large area repeatedly. However,the remote sensing images have a large number of data,bands and redundant information,which brought difficulties for the interpretation of remote sensing images. It is difficult to accurately distinguish the soil salinization for mild and moderate salinization soil. However,hyperspectral technology has broader spectral response range and high spectral resolution at nanometer level,which can help show the difference of structure and composition of the material, and reflect the subtle spectral characteristics of surface features more accurately. In this study,the delta oasis between the Weigan River and the Kuqa River,Xinjiang,China was selected as the study area,which was influenced by soil salinization seriously. Based on the different soil hyperspectral data and its soil salt content,and using fifteen spectral transformation methods including logarithm,RMS,continuum removal,first-order,second-order RMS and so on,this paper processed the hyperspectral data to find the most sensitive wavelengths,and established a soil salinization monitoring model by combining with the different spectral index models. Then apply the model to the Landsat-TM remote sensing images to achieve the high-precision quantitative inversion of soil salinity at large scale. The results show as follows:(1)The first order differential spectral transformation is the best method for extracting the soil spectral information,with a higher correlation with soil salinity;(2)The surface spectral model based on parabola model and optimal soil salinity index SI 3 has the best effect,R² = 0.871 2;(3) Compared with soil salinity monitoring model(R² = 0.592 5)built by traditional multi-spectral remote sensing technology,the model(R² = 0.700 2)converted by scale transformation showed better effect. The study provided a scientific and effective way to achieve high-precision,large scale remote sensing inversion of soil salinity in arid area.

Key words: the surface spectral modeling, scale transformation, soil salinization

中图分类号:

TP70

王爽, 丁建丽, 王璐, 牛增懿. 基于地表光谱建模的区域土壤盐渍化遥感监测研究[J]. 干旱区地理, 2016, 39(1): 190-198.

WANG Shuang, DING Jian-li, WANG lu, NIU Zeng-yi. Remote sensing monitoring of soil salinization based on surface spectral modeling[J]. , 2016, 39(1): 190-198.

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