干旱区生态环境知识资源中心(Arid): 基于高光谱多尺度分解的土壤含水量反演

Arid

	基于高光谱多尺度分解的土壤含水量反演
其他题名	Inversion of Soil Moisture Content Based on Hyperspectral Multi-Scale Decomposition
	蔡亮红; 丁建丽
来源期刊	激光与光电子学进展
ISSN	1006-4125
出版年	2018
卷号	55 期号:1 页码:13001-
中文摘要	土壤水分含量(SMC)的快速估测能促进干旱、半干旱地区精准农业的发展。以渭干河-库车河绿洲为研究区,采用小波变换对反射光谱进行1~8层小波分解,通过相关性分析确定最大分解层数,并对原始反射率至最大分解层数以内的各层特征光谱进行9种常规数学变换处理,然后将土壤反射率与SMC进行相关性分析,并从每层特征光谱的各种变换中筛选出相关系数最大的波段,将其作为敏感波段,通过灰色关联分析(GRA)从中筛选出最佳波段组合,利用偏最小二乘回归建立SMC预测模型并进行分析。结果显示:在小波变换过程中,随着分解层数增加,土壤反射率与SMC的相关性呈先增后减的趋势,L6处通过0.01水平下的显著性波段最多,L6的特征光谱在去噪的同时还能最大限度地保留光谱细节,为本研究中的最大分解层;将小波变换和微分变换相结合可以深度挖掘光谱的潜在信息,提高土壤反射率与SMC之间的关联度;根据所有SMC模型的统计参数综合对比分析可以确定基于L-GRA建立的模型精度最优,其建模集均方根误差为0.026,建模集决定系数为0.710,预测均方根误差为0.030,验证集决定系数为0.965,相对分析误差为2.800;小波变换和灰色关联分析的结合在建立模型时能尽可能少地损失光谱细节,较为彻底地去除噪声,同时还能对无信息变量进行有效去除。
英文摘要	The rapid estimation of soil moisture content(SMC)is of great significance to precision agriculture in arid and semi-arid areas.Using Organ River-Kuqa River delta oasis as research area,we adopt wavelet transform to realize 1-8layer wavelet decomposition for reflectance spectrum.The maximum number of decomposition layers is determined by correlation analysis,nine routine mathematical transformation methods are used for conducting characteristic spectrum of each layer from original reflectance to maximum number of decomposition layers,and the correlation analysis between reflectance of soil and SMC is carried out.Waveband with maximum correlation coefficient is taken as sensitive waveband filtrated from all kinds of transformation of characteristic spectrum of each layer.Optimum waveband combination is filtrated by grey relational analysis (GRA).SMC prediction model is developed and analyzed by partial least squares regression.The results show that,with the increase of the number of decomposed layers,the correlation between soil reflectance and SMC increases and then decreases,and L6is the most significant band at 0.01level.In general,the characteristic spectrum of L6can maximally preserve the spectral details while denoising,so the maximum decomposition order of the wavelet is 6order decomposition;In general,it is shown that the combination of wavelet transform and differential transform can deepen the spectral potential information and improve the correlation between reflectance of soil and SMC.Comparing the predictive effects of SMC estimating models,the model based on L-GRA is much better than others,and it has better performance in predicting SMC in the study area(root mean square error of calibration is 0.026,determination coefficient is 0.710, root mean square error of prediction is 0.030,determination coefficient is 0.965,and residual predictive deviation is 2.800).It is shown that the combination of wavelet transform and GRA makes it possible to lose the spectral details as little as possible and remove the noise more completely when the model is established,at the same time,it can effectively remove the non-information variables.
中文关键词	光谱学 ; 高光谱 ; 土壤水分含量 ; 小波变换 ; 灰色关联分析
英文关键词	spectroscopy hyperspectral soil moisture content wavelet transform grey relational analysis
类型	Article
语种	中文
国家	中国
收录类别	CSCD
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:6177964
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237877
作者单位	蔡亮红, 新疆大学资源与环境科学学院;;新疆大学, 智慧城市与环境建模自治区普通高校重点实验室;;绿洲生态教育部重点实验室, 乌鲁木齐;;乌鲁木齐, 新疆;;新疆 830046;;830046, 中国.; 丁建丽, 新疆大学资源与环境科学学院;;新疆大学, 智慧城市与环境建模自治区普通高校重点实验室;;绿洲生态教育部重点实验室, 乌鲁木齐;;乌鲁木齐, 新疆;;新疆 830046;;830046, 中国.
推荐引用方式 GB/T 7714	蔡亮红,丁建丽. 基于高光谱多尺度分解的土壤含水量反演[J]. 新疆大学,2018,55(1):13001-.
APA	蔡亮红,&丁建丽.(2018).基于高光谱多尺度分解的土壤含水量反演.激光与光电子学进展,55(1),13001-.
MLA	蔡亮红,et al."基于高光谱多尺度分解的土壤含水量反演".激光与光电子学进展 55.1(2018):13001-.