干旱区生态环境知识资源中心(Arid): MLR和PLSR的沙壤土盐分含量光谱检测对比研究

Arid

	MLR和PLSR的沙壤土盐分含量光谱检测对比研究
其他题名	A comparison of the salt content in sandy soil between the MLR model and PLSR model
	王涛1 ; 喻彩丽 2; 姚娜 2; 张楠楠 2; 白铁成 3
来源期刊	干旱区地理
ISSN	1000-6060
出版年	2018
卷号	41 期号:6 页码:1295-1302
中文摘要	为了快速有效检测南疆地区典型土壤(沙壤土)的盐分含量变化,利用光谱仪和电导仪测得南疆阿拉尔市红枣种植区盐渍土近红外高光谱和电导率数据,基于7种不同光谱预处理方法和2种特征波长选择算法,分别建立多元线性回归(MLR)和偏最小二乘回归(PLSR)的土壤盐分监测模型.结果表明:7种预处理方法中,归一化,多元散射,变量标准化和一阶导数能够有效提高土壤盐分的预测模型精度.基于多元逐步回归(SMR)波长选择方法的多元线性回归(SMLR)模型的R_(val)~2 >0.948 9,RPD>6.294 9,RMSEP <0.435 6;基于连续投影算法(SPA)的多元线性回归(SPA-MLR)模型的R_(val)~2 >0.956 8,RPD>6.922 1,RMSEP<0.361 6,预测结果要优于偏最小二乘回归(PLSR)模型,其中基于归一化处理后的SMLR和SPA-MLR的预测精度最为理想,分别为R_(val)~2= 0.979 2,RPD =9.907 8, RMSEP = 0.287 6和Rval2= 0.980 5,RPD = 10.50,RMSEP = 0.278 3,而且筛选的特征波长较少.说明归一化是更有效的光谱预处理方法,多元线性回归(MLR)更适合建立南疆典型沙壤土盐分含量的预测模型.
英文摘要	In order to monitor typical soil salt content (sandy loam soil) in South Xinjiang,China quickly and effectively, and to improve the precision of the soil salt content estimation model by removing the noise of soil hyper-spectral absorbance,this paper focused on the inversion relationship between soil spectrum and electrical conductivity (EC) by using multiple spectral pretreatment methods,and then the multiple linear regression (MLR) and the partial least squares regression (PLSR) modelling were applied to establish the salt content model based on the hyperspectral analysis technique. The effective and predictive capacities of different models were validated. This study took the typical arid area in South Xinjiang as the research object,obtained the hyperspectral data and EC by using Near-infrared spectrometer (Zolix Gaia Sorter) and conductivity meter (DDS -307), 142 soil samples at 0 ~20 cm depth were collected and these samples were highly representative for the EC. Seven pretreatment methods were used to pretreat the original spectral data,such as vector normalization (VN),multiplicative scatter correction (MSC),standard normal variate (SNV),moving-average (MA) smoothing,Savitzky-Golay (SG) smoothing,first derivative (1 - Der) and second derivative (2 - Der),then the characteristic wavelengths were extracted with stepwise multiple regression (SMR) and successive projection algorithm (SPA),which were used as input variables of MLR and PLSR modeling. The results showed that the optimum pretreatment methods were VN,MSC,SNV and 1 -Der. According to different pretreatments,in the stepwise multiple linear regression (SMLR) prediction model Rval2 was greater than 0.95,RPD was greater than 6.2,and RMSEP was less than 0.44. In the multiple linear regression model based on the successive projections algorithm (SPA-MLR) Rval2 was greater than 0.96, RPD was greater than 6.9,and RMSEP was less than 0.36,which were better than those in SMLR. In the partial least squares regression prediction model R_(val)~2 was greater than 0.88,RPD was greater than 4.4,RMSEP was less than 0.62 and in the partial least squares regression model based on the successive projections algorithm (SPA-PLSR) R_(val)~2 was greater than 0.59,RPD was greater than 2.4, and RMSEP was less than 1.1 which were less than those in SMLR and SPA-MLR. The best prediction of SMLR and SPA-MLR after vector normalization (VN) was as the follows:RMSEP = 0.287 6,R_(val)~2=0.979 2, RPD =9.907 8 and RMSEP =0.278 3,R_(val)~2 =0.980 5, RPD= 11.50 which had fewer characteristic wavelengths. So the VN is a best effective pretreatment method. It is more suitable to establish the prediction model of soil conductivity in jujube tree by MLR with PLSR. It could be an important part in future researches how to choose the right algorithm to analyze the soil salt content with the spectral data.
中文关键词	土壤电导率 ; 多元线性回归 ; 偏最小二乘回归方法 ; 高光谱
英文关键词	soil electrical conductivity multiple linear regression partial least squares regression hyperspectral
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6386369
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237621
作者单位	1.塔里木大学信息工程学院, 阿拉尔, 新疆 843300, 中国; 2.塔里木大学信息工程学院;;新疆南疆农业信息化研究中心, ;;, 阿拉尔;;阿拉尔, 新疆;;新疆 843300;;843300, 中国; 3.塔里木大学信息工程学院;;Gembloux Agro-Bio Tech,University of Liege, ;;, 阿拉尔;;Gembloux, 新疆;;Belgium 843300;;25030, 中国
推荐引用方式 GB/T 7714	王涛,喻彩丽,姚娜,等. MLR和PLSR的沙壤土盐分含量光谱检测对比研究[J],2018,41(6):1295-1302.
APA	王涛,喻彩丽,姚娜,张楠楠,&白铁成.(2018).MLR和PLSR的沙壤土盐分含量光谱检测对比研究.干旱区地理,41(6),1295-1302.
MLA	王涛,et al."MLR和PLSR的沙壤土盐分含量光谱检测对比研究".干旱区地理 41.6(2018):1295-1302.