干旱区生态环境知识资源中心(Arid): Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison

Arid

DOI	10.1007/s11769-019-1071-x
	Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison
	Li, Zhen 1; Li, Yong 1; Xing, An 1; Zhuo, Zhiqing 1; Zhang, Shiwen 2; Zhang, Yuanpei 3; Huang, Yuanfang 1
通讯作者	Huang, Yuanfang
来源期刊	CHINESE GEOGRAPHICAL SCIENCE
ISSN	1002-0063
EISSN	1993-064X
出版年	2019
卷号	29 期号:5 页码:784-797
英文摘要	Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity (EC) estimation systematically. Additionally, the performance of Multiple Linear Regression (MLR), Geographically Weighted Regression (GWR), and Random Forest regression (RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.
英文关键词	soil salinity environmental variable random forest regression geographic weighted regression Yinchuan Plain irrigation oasis
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000494481000005
WOS关键词	SUPPORT VECTOR MACHINE ; REMOTE-SENSING DATA ; MODIS TIME-SERIES ; ELECTRICAL-CONDUCTIVITY ; RANDOM FOREST ; IRRIGATION ; SALT ; SALINIZATION ; REGRESSION ; CLASSIFICATION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
EI主题词	2019-10-01
来源机构	中国农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310099
作者单位	1.China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China; 2.Anhui Univ Sci & Technol, Sch Earth & Environm, Huainan 232001, Peoples R China; 3.Ningxia Acad Agr & Forestry Sci, Inst Crop Sci, Yinchuan 750002, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhen,Li, Yong,Xing, An,et al. Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison[J]. 中国农业大学,2019,29(5):784-797.
APA	Li, Zhen.,Li, Yong.,Xing, An.,Zhuo, Zhiqing.,Zhang, Shiwen.,...&Huang, Yuanfang.(2019).Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison.CHINESE GEOGRAPHICAL SCIENCE,29(5),784-797.
MLA	Li, Zhen,et al."Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison".CHINESE GEOGRAPHICAL SCIENCE 29.5(2019):784-797.