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

	Spatial Prediction of Soil Salinity in a Semiarid Oasis:Environmental Sensitive Variable Selection and Model Comparison
	Li Zhen; Li Yong; Xing An; Zhuo Zhiqing; Zhang Shiwen; Zhang Yuanpei; Huang Yuanfang
来源期刊	Chinese Geographical Science
ISSN	1002-0063
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
开放获取类型	Bronze
收录类别	CSCD
WOS研究方向	Science & Technology - Other Topics
CSCD记录号	CSCD:6567964
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/336168
作者单位	Li Zhen, College of Resources and Environment Sciences,China Agricultural University, Beijing 100193, China.; Li Yong, College of Resources and Environment Sciences,China Agricultural University, Beijing 100193, China.; Xing An, College of Resources and Environment Sciences,China Agricultural University, Beijing 100193, China.; Zhuo Zhiqing, College of Resources and Environment Sciences,China Agricultural University, Beijing 100193, China.; Huang Yuanfang, College of Resources and Environment Sciences,China Agricultural University, Beijing 100193, China.; Zhang Shiwen, School of Earth and Environment,Anhui University of Science and Technology, Huainan, Anhui 232001, China.; Zhang Yuanpei, Institute of Crop Sciences,Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan, Ningxia 750002, 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],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.