干旱区生态环境知识资源中心(Arid): 小波变换耦合CARS算法提高土壤水分含量高光谱反演精度

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	小波变换耦合CARS算法提高土壤水分含量高光谱反演精度
其他题名	Wavelet transformation coupled with CARS algorithm improving prediction accuracy of soil moisture content based on hyperspectral reflectance
	蔡亮红; 丁建丽
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2017
卷号	33 期号:16 页码:144-151
中文摘要	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。
英文摘要	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 mining methods, competitive adaptive reweighted sampling (CARS) was used to solve this problem in this study. In the present study, a total of 39 soil samples at 0-20 cm depth were collected from the delta oasis in Xinjiang. The samples were brought back to the laboratory to be dried naturally, ground and passed through a screen with 2 mm hole, and then filled into the black boxes with 12 cm diameter and 1.8 cm depth, which were leveled at the rim with a spatula. Reflectance of soil samples was measured using ASD (analytical spectral devices) Fieldspec 3 Spectrometer in a dark room. We used the following steps to process soil reflectance: First, discrete wavelet transformation (DWT) was used to decompose the original spectra in 8 levels using db4 wavelet basis with MATLAB programming language. In order to select the maximum level of DWT, correlation coefficients between the SMC and the spectra of each level were computed. Secondly, the CARS was used to filter the redundant variables, the wavelength variables with better correlation with SMC were screened out and the characteristic wavelength variables of each decomposition level were superimposed as the optimal variable set. Thirdly, partial least squares regression (PLSR) was employed to build the hyperspectral estimation models of SMC. And then, root mean square error of calibration set (RMSEC), determination coefficient of calibration set (R_c~2 ), root mean square error of prediction set (RMSEP), determination coefficient of predicting set (R_p~2 ) and relative prediction deviation (RPD) were used for accuracy assessment. The results showed that: 1) With the increase of the number of decomposed layers, the correlation between soil reflectance and SMC showed a trend of increasing first and then decreasing, and L6 was the most significant band at 0.01 level. In general, the characteristic spectrum of L6 was denoised, and at the same time, the spectral detail was preserved to the maximum extent, so the maximum decomposition order of the wavelet was 6-order decomposition. 2) The characteristic wavelength variable of the characteristic spectrum was selected by coupling wavelet transform and CARS algorithm. However, if only the CARS selection result of the feature spectrum was taken into account, it was easy to ignore the water features of other characteristic spectra. Therefore, in this study, by adding the characteristic wavelength variables of each layer as the optimal set of variables, it contained 131 wavelength variables near the absorption band (450, 1 400, 1 900, 2 200 nm). 3) Compared with the full-band PLSR model, the accuracy of PLSR model of CARS preferred variables for each decomposition level was high, and the PLSR model of the optimal variable set had the highest accuracy and a better performance in predicting SMC in the study area (RMSEC=0.021, R_c~2 =0.721, RMSEP=0.028, R_p~2=0.924, RPD=2.607).
中文关键词	土壤 ; 含水率 ; 光谱分析 ; 小波变换 ; 竞争适应重加权采样算法 ; 变量优选
英文关键词	soil moisture content spectrum analysis WT CARS variable selection
类型	Article
语种	中文
国家	中国
收录类别	CSCD
WOS类目	Agriculture
WOS研究方向	Agriculture
CSCD记录号	CSCD:6075735
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/2XILL650/236323
作者单位	蔡亮红, 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, 新疆 830046, 中国.; 丁建丽, 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, 新疆 830046, 中国.
推荐引用方式 GB/T 7714	蔡亮红,丁建丽. 小波变换耦合CARS算法提高土壤水分含量高光谱反演精度[J]. 新疆大学,2017,33(16):144-151.
APA	蔡亮红,&丁建丽.(2017).小波变换耦合CARS算法提高土壤水分含量高光谱反演精度.农业工程学报,33(16),144-151.
MLA	蔡亮红,et al."小波变换耦合CARS算法提高土壤水分含量高光谱反演精度".农业工程学报 33.16(2017):144-151.