小波变换耦合CARS算法提高土壤水分含量高光谱反演精度

为实现干旱地区土壤水分含量（soil moisture content,SMC）的快速监测,该文以渭干河-库车河绿洲为靶区,采用小波变换（wavelet transform,WT）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,再通过竞争性自适应重加权（competitive adaptive reweighted sampling,CARS）滤除冗余变量,筛选出与SMC相关性较好的波长变量,并叠加各层特征光谱的优选波长变量作为最优变量集,用偏最小二乘回归（partial least squares regression,PLSR）构建土壤水分含量预测模型并进行分析。结果显示：...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. 16; pp. 144 - 151
Main Author 蔡亮红 丁建丽
Format Journal Article
LanguageChinese
Published 新疆大学资源与环境科学学院,乌鲁木齐,830046%新疆大学绿洲生态教育部重点实验室,乌鲁木齐,830046 2017
Subjects
光谱分析
变量优选
含水率
土壤
小波变换
竞争适应重加权采样算法
CARS
小波变换
竞争适应重加权采样算法
变量优选
含水率
spectrum analysis WT
光谱分析
soil
variable selection
moisture content
土壤
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Summary:为实现干旱地区土壤水分含量（soil moisture content,SMC）的快速监测,该文以渭干河-库车河绿洲为靶区,采用小波变换（wavelet transform,WT）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,再通过竞争性自适应重加权（competitive adaptive reweighted sampling,CARS）滤除冗余变量,筛选出与SMC相关性较好的波长变量,并叠加各层特征光谱的优选波长变量作为最优变量集,用偏最小二乘回归（partial least squares regression,PLSR）构建土壤水分含量预测模型并进行分析。结果显示：1）小波分解过程中,土壤反射率与SMC的相关性不断增强,到小波变换第6层分解（L6）处达到最高,因此小波变换最大分解层数为6层分解;2）通过对土样进行WT-CARS耦合算法筛选出变量,得出的最优变量集包括400-500、1 320-1 461、1 851-1 961、2 125-2 268 nm区域之间共131个波长变量;3）相对于全波段预测模型,各层特征光谱的CARS优选变量预测模型的精度均高,并且基于最优变量集的预测模型的精度最高,该模型的建模集均方根误差0.021、建模集决定系数0.721、预测集均方根误差0.028、预测集决定系数0.924、相对分析误差2.607。说明WT-CARS耦合算法使其在建立模型时尽可能少地损失光谱细节、较为彻底的去除噪声,同时还能对无信息变量进行有效去除,为该研究区SMC的预测提供新的思路。
Bibliography:11-2047/S
soil; moisture content; spectrum analysis WT; CARS; variable selection
Cai Lianghong, Ding Jianli （1. College of Resources ＆ Environmental Science, Xinjiang University, Urumqi 830046, China; 2. Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi 830046, China）
The rapid estimation of soil moisture content（SMC） is of great significance to precision agriculture in arid areas.Hyperspectral remote sensing technology has been widely used in the estimation of SMC due to that it＇s non-destructive and rapid,and has high spectral resolution characteristics.Meanwhile,there are a lot of factors,such as massive spectral data,and surface conditions,which might affect the spectra,increasing the difficulty in extracting the effective information,and reducing the prediction accuracy of SMC.Noise reduction must be considered in developing hyperspectral estimation models,but how to reduce noise while retaining as much useful information as possible needs investigation.As advanced spectral
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.16.019