干旱区生态环境知识资源中心(Arid): Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping

Arid

DOI	10.1007/s10661-019-7844-y
	Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping
	Sangani, Mahmood Fazeli 1; Khojasteh, Davood Namdar 2; Owens, Gary 3
通讯作者	Sangani, Mahmood Fazeli
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2019
卷号	191 期号:11
英文摘要	This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.
英文关键词	Deterministic method Geostatistics Interpolation Northern plains of Varamin city
类型	Article
语种	英语
国家	Iran ; Australia
收录类别	SCI-E
WOS记录号	WOS:000499199400003
WOS关键词	OASIS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
EI主题词	2019-11-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310553
作者单位	1.Univ Guilan, Fac Agr Sci, Dept Soil Sci, Rasht, Iran; 2.Shahed Univ, Fac Agr Sci, Dept Soil Sci, POB 18151-159, Tehran, Iran; 3.Univ South Australia, Future Ind Inst, Mawson Lakes, SA 5095, Australia
推荐引用方式 GB/T 7714	Sangani, Mahmood Fazeli,Khojasteh, Davood Namdar,Owens, Gary. Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping[J],2019,191(11).
APA	Sangani, Mahmood Fazeli,Khojasteh, Davood Namdar,&Owens, Gary.(2019).Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping.ENVIRONMENTAL MONITORING AND ASSESSMENT,191(11).
MLA	Sangani, Mahmood Fazeli,et al."Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping".ENVIRONMENTAL MONITORING AND ASSESSMENT 191.11(2019).