干旱区生态环境知识资源中心(Arid): Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network

Arid

DOI	10.1080/01431161.2018.1513180
	Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network
	Jiang, Hong 1,2,3; Rusuli, Yusufujiang 1,2; Amuti, Tureniguli 2; He, Qing 3
通讯作者	Rusuli, Yusufujiang
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2019
卷号	40 期号:1 页码:284-306
英文摘要	Salinization of soil is one of the most important environmental issues in arid and semi-arid areas. Accordingly, agricultural production and ecological development have been profoundly influenced in these regions. Therefore, it is becoming increasingly important to assess soil salinization and its driving factors. However, soil salinity is difficult to accurately characterize by using single-factor and linear models. Thus, it is necessary to develop a robust modeling technique by integrating multiple biophysical indicators to quantitatively monitor soil salinity. In this paper, the Support Vector Machine (SVM) regression algorithm and Artificial Neural Network (ANN) algorithm were employed to better estimate the soil salinity in the Yanqi Basin, Xinjiang, China. The soil backscattering coefficient (), Groundwater Depth (GD), Salinity Index (SI) and Surface Evapotranspiration (SET) were used as model parameters. was obtained from Sentinel-1A Synthetic Aperture Radar (SAR) data; GD and SI were calculated from Landsat-8 imagery; and SET was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration product (MOD16). The performances of SVM and ANN in evaluating the nonlinear relationship between , GD, SI, SET, and soil Electrical Conductivity (EC) were compared. The results showed that, the SVM regression algorithm performs better than ANN algorithm in monitoring soil salinity. The Root Mean Square Error (RMSE) and the coefficient of determination (R-2) in the estimation obtained using the SVM regression algorithm were about 2.01 and 0.82, respectively, versus the training data set; the RMSE and R-2 were 1.36 and 0.88, respectively, versus the testing data set. The accuracy was significantly higher than that using the ANN algorithm, which obtained an RMSE of 2.20 and R-2 of 0.79 versus the training data set, and 2.25 and 0.68 versus the testing data set. The results of this study indicated that about 56.82% of the soil in the study area was affected by different degrees of salinity. It is obvious that SVM regression algorithm has great potential for estimating soil salinity using multi-source remote sensing data.
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000457037300017
WOS关键词	DIFFERENCE WATER INDEX ; DIELECTRIC-PROPERTIES ; LAND DEGRADATION ; VEGETATION ; SALT ; MODEL ; SALINIZATION ; MOISTURE ; BACKSCATTERING ; EXTRACTION
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216492
作者单位	1.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing, Peoples R China; 2.Xinjiang Normal Univ, Inst Geog Sci & Tourism, Lab Informat Integrat & Ecosecur, Urumqi, Peoples R China; 3.Meteorol Bur Xinjiang Uygur Autonomous Reg, Xinjiang Meteorol Observ, Urumqi, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Hong,Rusuli, Yusufujiang,Amuti, Tureniguli,et al. Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network[J],2019,40(1):284-306.
APA	Jiang, Hong,Rusuli, Yusufujiang,Amuti, Tureniguli,&He, Qing.(2019).Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(1),284-306.
MLA	Jiang, Hong,et al."Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.1(2019):284-306.