干旱区生态环境知识资源中心(Arid): Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods

Arid

	Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods
	Gorji, Taha; Yildirim, Aylin; Hamzehpour, Nikou; Sertel, Elif; Tanik, Aysegul
通讯作者	Gorji, T (corresponding author), Istanbul Tech Univ ITU, Geog Informat Technol Program, Informat Inst, TR-34469 Maslak, Turkey.
会议名称	8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
会议日期	JUL 16-19, 2019
会议地点	Istanbul, TURKEY
英文摘要	Land degradation by salinity is one of the main environmental hazards threatening soil sustainability especially in arid and semi-arid regions of the world characterized by low precipitation and high evaporation. Geo-statistical approaches and remote sensing (RS) techniques have provided fast, accurate and economic prediction and mapping of soil salinity within the last two decades. Obtaining multi-temporal data via satellite images in different spatial domains with various scales is one of the key developments of monitoring spatial variability of soil salinity. In addition, geo-statistical methods have the capability of producing prediction surfaces from limited sample data. This study, aims to map spatial distribution of soil salinity in the selected pilot area which is located in the western part of Urmia Lake Basin, Iran, by applying geo-statistical methods. A kriging based map and three different co-kriging based maps were produced using electrical conductivity (EC) measurements as primary variable and three different soil salinity index values as secondary variable. Three soil salinity indices were created by using Sentinel-2A image that were acquired in the same date of field measurements to generate 3 various soil salinity prediction maps. Salinity maps obtained from geo-statistical methods were compared and validated to understand the performance of these approaches for soil salinity prediction. The results of this study demonstrated that co-kriging can provide promising estimation of spatial variability of soil salinity especially when there is relevant and abundant set of secondary data derived from satellite images.
英文关键词	Soil Salinity Co-kriging Remote Sensing Salinity Indices Sentinel 2-A Urmia Lake Basin
来源出版物	2019 8TH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS)
ISSN	2334-3168
出版年	2019
ISBN	978-1-7281-2116-1
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000562356600022
WOS关键词	SALINIZATION ; LAND ; AREA
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/370010
作者单位	[Gorji, Taha] Istanbul Tech Univ ITU, Geog Informat Technol Program, Informat Inst, TR-34469 Maslak, Turkey; [Yildirim, Aylin] Istanbul Tech Univ ITU, Satellite Commun & Remote Sensing Program, Informat Inst, TR-34469 Maslak, Turkey; [Hamzehpour, Nikou] Univ Maragheh, Fac Agr, Soil Sci Dept, Maragheh, Iran; [Sertel, Elif] Istanbul Tech Univ ITU, Dept Geomat Engn, Fac Civil Engn, TR-34469 Maslak, Turkey; [Tanik, Aysegul] Istanbul Tech Univ ITU, Dept Environm Engn, Fac Civil Engn, TR-34469 Maslak, Turkey
推荐引用方式 GB/T 7714	Gorji, Taha,Yildirim, Aylin,Hamzehpour, Nikou,et al. Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods[C]:IEEE,2019.