干旱区生态环境知识资源中心(Arid): 基于光谱指数与机器学习算法的土壤电导率估算研究

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	基于光谱指数与机器学习算法的土壤电导率估算研究
其他题名	Estimation of Soil Electrical Conductivity Based on Spectral Index and Machine Learning Algorithm
	曹肖奕; 丁建丽; 葛翔宇; 王敬哲
来源期刊	土壤学报
ISSN	0564-3929
出版年	2020
卷号	57 期号:4 页码:867-877
中文摘要	土壤盐分是干旱区土壤盐渍化评价的重要指标。以新疆维吾尔自治区渭干河-库车河三角洲绿洲为例,基于土壤电导率(Electrical conductivity,EC)及可见光-近红外(Visible and near infrared,VIS-NIR)光谱数据,通过蒙特卡洛交叉验证(Monte Carlo cross validation,MCCV)确定364个有效样本。采用原始光谱(Raw reflectance,R)及其经过微分、吸光度(Absorbance,Abs)、连续统去除(Continuum removal,CR)等6种预处理后的数据构建光谱指数。基于遴选出的21个最优指数,采用BP神经网络(Back propagation neural network,BPNN)、支持向量机(Support vector machine,SVM)、极限学习机(Extreme learning machine,ELM)三种算法对EC进行估算,并引入偏最小二乘回归(Partial least squares regression,PLSR)进行比较。结果表明:在基于R与6种光谱预处理数据构建的21个最优光谱指数之中,R_FD_RSI(R_(1913),R_(2142))表现最佳(r = 0.649);与PLSR相比,机器学习算法能够显著提高模型的估算精度,R~2提高了34.55%。三种机器学习算法模型中,ELM表现最优(R~2 = 0.884,RMSE = 3.071 mS·cm~(-1),RPIQ = 2.535)。本研究中所构建的光谱指数在兼顾遥感机理的同时能深度挖掘更多的隐含信息,并且基于机器学习算法的土壤EC估算模型精度显著提高,为干旱区土壤盐分定量估算提供了科学参考。
英文摘要	【Objective】Soil salinity is an important indicator for evaluation of soil salinization in arid regions.It is of great practical significance to grasp real-time information about salinization to disaster prevention, ecology stabilization and harmonization of human-land relationship in this area.【Method】A total of 400 soil samples were collected from the Weigan River-Kucha River Delta Oasis in the Xinjiang Uygur Autonomous Region of China in October 2017, and prepared, separately,with distlled water into suspensions, 5︰1 in ratio, from which soil extracts were obtained for analysis of electrical conductivity(EC)and VIS-NIR(visible-near infrared)spectral reflectances in the laboratory.Based on the obtained data and the Monte Carlo cross validation(MCCV), 364 samples were determined to be valid.After the raw spectrum reflectances(R)were pre-processed with differential, absorbance(Abs), continuum removal(CR)and three others, 21 spectal indices were selected and established.Then based on the 21 optimal spectral indices, EC was assessed using the back propagation neural network(BPNN), support vector machine(SVM), and extreme learning machine(ELM), compared to the partial least squares regression(PLSR), and validated with its root mean square error(RMSE), determination coefficient(R~2)and ratio of performance to interquartile range(RPIQ).【Result】Results show: the 21 optimal spectral indices based on R and its pretreated data are R_NDSI(R_(2006), R_(2257)),R_DSI(R_(1882), R_(2010)), R_RSI(R_(2006), R_(2257)), R_FD_NDSI(R_(1376), R_(2142)), R_FD_DSI(R_(1376), R_(2124)), R_FD_RSI(R_(1913), R_(2142)),R_SD_NDSI(R416, R_(2470)), R_SD_DSI(R894, R1373), R_SD_RSI(R689, R_(2355)), ABS_NDSI(R_(2005), R_(2168)), ABS_DSI(R_(2006), R_(2257)),ABS_RSI(R_(2006), R_(2168)), ABS_FD_NDSI(R876, R_(2490)), ABS_FD_DSI(R_(1376), R_(2123)), ABS_FD_RSI(R_(1913), R_(2142)), ABS_SD_ NDSI(R1081, R1725), ABS_SD_DSI(R858, R1374), ABS_SD_RSI(R709, R_(2355)), CR_NDSI(R_(2119), R_(2261)), CR_DSI(R_(2119), R_(2261)), and CR_RSI(R_(2119), R_(2261)), among which R_FD-RSI(R_(1913), R_(2142))is the optimal(r = 0.649)one.Compared with the PLSR, the machine learning algorithm(MLA)could significantly improve accuracy of the model, with the R~2 increased by 34.55%.Among the three models using the machine learning algorithm, ELM was the best(R~2 = 0.884, RMSE = 3.071 mS·cm~(-1), RPIQ = 2.535).【Conclusion】In this study, different spectral pretreatment methods were used to obtain 21 optimal spectral indices.In constructing the spectral indices in this study, besides considering the remote sensing mechanism, it is advisable to explore in depth more implicit information.Compared with the traditional linear model, the MLA-based soil EC estimation model is obviously higher in accuracy.All the findings in this study may serve as a scientific reference for quantitative estimation of soil salinity in arid regions.
中文关键词	光谱 ; 土壤电导率 ; 光谱预处理 ; 光谱指数 ; 机器学习
英文关键词	Spectral Soil electrical conductivity Spectral pretreatment Spectral index Machine learning
类型	Article
语种	中文
收录类别	CSCD
WOS研究方向	Optics ; Physics
CSCD记录号	CSCD:6770502
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/2XILL650/336721
作者单位	曹肖奕, 新疆大学资源与环境科学学院;;新疆大学;;新疆大学, ;;绿洲生态教育部重点实验室;;智慧城市与环境建模新疆维吾尔自治区普通高校重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, ;;;; 830046;;830046;;830046.; 丁建丽, 新疆大学资源与环境科学学院;;新疆大学;;新疆大学, ;;绿洲生态教育部重点实验室;;智慧城市与环境建模新疆维吾尔自治区普通高校重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, ;;;; 830046;;830046;;830046.; 葛翔宇, 新疆大学资源与环境科学学院;;新疆大学;;新疆大学, ;;绿洲生态教育部重点实验室;;智慧城市与环境建模新疆维吾尔自治区普通高校重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, ;;;; 830046;;830046;;830046.; 王敬哲, 新疆大学资源与环境科学学院;;新疆大学;;新疆大学, ;;绿洲生态教育部重点实验室;;智慧城市与环境建模新疆维吾尔自治区普通高校重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, ;;;; 830046;;830046;;830046.
推荐引用方式 GB/T 7714	曹肖奕,丁建丽,葛翔宇,等. 基于光谱指数与机器学习算法的土壤电导率估算研究[J]. 新疆大学,2020,57(4):867-877.
APA	曹肖奕,丁建丽,葛翔宇,&王敬哲.(2020).基于光谱指数与机器学习算法的土壤电导率估算研究.土壤学报,57(4),867-877.
MLA	曹肖奕,et al."基于光谱指数与机器学习算法的土壤电导率估算研究".土壤学报 57.4(2020):867-877.