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土壤含盐量与电导率的高光谱反演精度对比研究
引用本文:彭杰,王家强,向红英,滕洪芬,柳维扬,迟春明,牛建龙,郭燕,史舟. 土壤含盐量与电导率的高光谱反演精度对比研究[J]. 光谱学与光谱分析, 2014, 34(2): 510-514. DOI: 10.3964/j.issn.1000-0593(2014)02-0510-05
作者姓名:彭杰  王家强  向红英  滕洪芬  柳维扬  迟春明  牛建龙  郭燕  史舟
作者单位:1. 塔里木大学植物科学学院,新疆 阿拉尔 843300
2. 浙江大学环境与资源学院农业遥感与信息技术应用研究所,浙江 杭州 310058
基金项目:国家自然科学基金项目(41061031, 41261083, 41271234, 41161037), 国家(973计划)前期专项项目(2011CB411909, 2012CB426507)资助
摘    要:探明土壤盐渍化的高光谱遥感监测机理,对改善高光谱遥感监测精度具有重要意义。以南疆地区温宿县、和田县、拜城县的水稻土为研究对象,通过分析土样的高光谱数据和室内测定的盐分与电导率数据,研究了耕作土壤含盐量与电导率的关系,并比较了含盐量和电导率与不同光谱指标的相关性以及二者高光谱反演的精度。结果表明,南疆水稻土的含盐量与电导率的相关性较低,二者之间的关系因地区差异而有较大的变化;含盐量与反射率、一阶微分、连续统去除之间的相关性要优于电导率,特别在一些土壤盐渍化的敏感波段尤为突出;以含盐量建立的多元线性回归、主成分回归、偏最小二乘回归模型的决定系数和相对分析误差均高于电导率。研究表明高光谱信息对土壤含盐量的响应比电导率更敏感,以含盐量为监测指标的高光谱反演精度明显要优于电导率。该结果可为提高土壤盐渍化高光谱遥感监测精度提供理论依据。

关 键 词:电导率  含盐量  高光谱  反演精度  土壤   
收稿时间:2013-05-02

Comparative Study on Hyperspectral Inversion Accuracy of Soil Salt Content and Electrical Conductivity
PENG Jie,WANG Jia-qiang,XIANG Hong-ying,TENG Hong-fen,LIU Wei-yang,CHI Chun-ming,NIU Jian-long,GUO Yan,SHI Zhou. Comparative Study on Hyperspectral Inversion Accuracy of Soil Salt Content and Electrical Conductivity[J]. Spectroscopy and Spectral Analysis, 2014, 34(2): 510-514. DOI: 10.3964/j.issn.1000-0593(2014)02-0510-05
Authors:PENG Jie  WANG Jia-qiang  XIANG Hong-ying  TENG Hong-fen  LIU Wei-yang  CHI Chun-ming  NIU Jian-long  GUO Yan  SHI Zhou
Affiliation:1. College of Plant Science, Tarim University, Alar 843300, China2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Abstract:The objective of the present article is to ascertain the mechanism of hyperspectral remote sensing monitoring for soil salinization, which is of great importance for improving the accuracy of hyperspectral remote sensing monitoring. Paddy soils in Wensu,Hetian and Baicheng counties of the southern Xinjiang were selected. Hyperspectral data of soils were obtained. Soil salt content (St) an electrical conductivity of 1∶5 soil-to-water extracts (EC1∶5) were determined. Relationships between St and EC1∶5 were studied. Correlations between hyperspectral indices and St, and EC1∶5 were analyzed. The inversion accuracy of St using hyperspectral technique was compared with that of EC1∶5. Results showed that: significant (p<0.01) relationships were found between St and EC1∶5 for soils in Wensu and Hetian counties, and correlation coefficients were 0.86 and 0.45, respectively; there was no significant relationship between St and EC1∶5 for soils in Baicheng county. Therefore, the correlations between St and EC1∶5 varied with studied sites. St and EC1∶5 were significantly related with spectral reflectance, first derivative reflectance and continuum-removed reflectance, respectively; but correlation coefficients between St and spectral indices were higher than those between EC1∶5 and spectral indices, which was obvious in some sensitive bands for soil salinization such as 660, 35, 1 229, 1 414, 1 721, 1 738, 1 772, 2 309 nm, and so on. Prediction equations of St and EC1∶5 were established using multivariate linear regression, principal component regression and partial least-squares regression methods, respectively. Coefficients of determination, determination coefficients of prediction, and relative analytical errors of these equations were analyzed. Coefficients of determination and relative analytical errors of equations between St and spectral indices were higher than those of equations between EC1∶5 and spectral indices. Therefore, the responses of high spectral information to St were more sensitive than those of high spectral information to EC1∶5. Accuracy of St predicted from high spectral data was higher than that of EC1∶5 estimated from high spectral data. The results of this study can provide a theoretical basis to improve hyperspectral remote sensing monitoring accuracy of soil salinization.
Keywords:Electrical conductivity  Soil salt content  Hyperspectral  Inversion accuracy  Soil
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