Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 海岸带生物学与生物资源利用重点实验室 > 海岸带生物学与生物资源利用所重点实验室

	Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland
	Yao, R. J.1,2; Yang, J. S.1,2; Gao, P.3; Shao, H. B.4; Liu, G. M.1; Yu, S. P.1,2
刊名	ENVIRONMENTAL EARTH SCIENCES
	2014
卷号	71 期号:1 页码:233-243
关键词	Soil salinity Electrical conductivity Spatial prediction Kriging Yellow River Delta
ISSN号	1866-6280
通讯作者	Yang, JS (reprint author), Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Peoples R China. jsyang@issas.ac.cn
产权排序	[Yao, R. J.; Yang, J. S.; Liu, G. M.; Yu, S. P.] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Peoples R China; [Yao, R. J.; Yang, J. S.; Yu, S. P.] Chinese Acad Sci, Dongtai Inst Tidal Flat Res, Nanjing Branch, Dongtai 224200, Peoples R China; [Gao, P.] Univ S Carolina, Dept Geog, Columbia, SC 29208 USA; [Shao, H. B.] Chinese Acad Sci, Key Lab Coastal Biol & Bioresources Utilizat, Yantai Inst Coastal Zone Res YIC, Yantai 264003, Peoples R China
中文摘要	Soil salinity has been known to be problematic to land productivity and environment in the lower Yellow River Delta due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity has gained increasing importance for the demand of management and sustainable utilization of arable land in this area. Apparent electrical conductivity, as measured by electromagnetic induction instrument in a fairly quick manner, has succeeded in mapping soil salinity and many other soil physical and chemical properties from field to regional scales. This was done based on the correlation that existed between apparent electrical conductivity and many other soil properties. In this paper, four spatial prediction methods, i.e., local polynomial, inverse distance weighed, ordinary kriging and universal kriging, were employed to estimate field-scale apparent electrical conductivity with the aid of an electromagnetic induction instrument (type EM38). The spatial patterns estimated by the four methods using EM38 survey datasets of various sample sizes were compared with those generated by each method using the entire sample size. Spatial similarity was evaluated using difference index (DI) between the maps created using various sample sizes (i.e., target maps) and the maps generated with the entire sample size (i.e., the reference map). The results indicated that universal kriging had the best performance owing to the inclusion of residuals and spatial detrending in the kriging system. DI showed that spatial similarity between the target and reference maps of apparent electrical conductivity decreased with the reduction in sample size for each prediction method. Under the same reduction in sample size, the method retaining the most spatial similarity was universal kriging, followed by ordinary kriging, inverse distance weighed, and local polynomial. Approximately, 70 % of total survey data essentially met the need for retaining 90 % details of the reference map for universal kriging and ordinary kriging methods. This conclusion was that OK and UK were two most appropriate methods for spatial estimation of apparent electrical conductivity as they were robust with the reduction in sample size.
英文摘要	Soil salinity has been known to be problematic to land productivity and environment in the lower Yellow River Delta due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity has gained increasing importance for the demand of management and sustainable utilization of arable land in this area. Apparent electrical conductivity, as measured by electromagnetic induction instrument in a fairly quick manner, has succeeded in mapping soil salinity and many other soil physical and chemical properties from field to regional scales. This was done based on the correlation that existed between apparent electrical conductivity and many other soil properties. In this paper, four spatial prediction methods, i.e., local polynomial, inverse distance weighed, ordinary kriging and universal kriging, were employed to estimate field-scale apparent electrical conductivity with the aid of an electromagnetic induction instrument (type EM38). The spatial patterns estimated by the four methods using EM38 survey datasets of various sample sizes were compared with those generated by each method using the entire sample size. Spatial similarity was evaluated using difference index (DI) between the maps created using various sample sizes (i.e., target maps) and the maps generated with the entire sample size (i.e., the reference map). The results indicated that universal kriging had the best performance owing to the inclusion of residuals and spatial detrending in the kriging system. DI showed that spatial similarity between the target and reference maps of apparent electrical conductivity decreased with the reduction in sample size for each prediction method. Under the same reduction in sample size, the method retaining the most spatial similarity was universal kriging, followed by ordinary kriging, inverse distance weighed, and local polynomial. Approximately, 70 % of total survey data essentially met the need for retaining 90 % details of the reference map for universal kriging and ordinary kriging methods. This conclusion was that OK and UK were two most appropriate methods for spatial estimation of apparent electrical conductivity as they were robust with the reduction in sample size.
学科主题	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
研究领域[WOS]	Environmental Sciences & Ecology ; Geology ; Water Resources
关键词[WOS]	ELECTROMAGNETIC INDUCTION TECHNIQUES ; MAPPING CLAY CONTENT ; YELLOW-RIVER DELTA ; SOIL-SALINITY ; PRECISION AGRICULTURE ; IRRIGATED COTTON ; UNCERTAINTY ; MANAGEMENT ; LANDSCAPE ; SIMULATION
收录类别	SCI
资助信息	National Natural Science Foundation of China [41101199, 41171181]; Special Fund for Agro-scientific Research in the Public Interest of China [200903001]; Prospective Project of Production Education Research Cooperation of Jiangsu Province [BY2011195]; Natural Science Foundation of Jiangsu Province [BK2011423]; Key Technology R&D Program of Jiangsu Province [BE2010313]; Fund Project for Transformation of Scientific and Technological Achievements of Jiangsu Province [BA2010116]
原文出处	http://dx.doi.org/10.1007/s12665-013-2427-7
语种	英语
WOS记录号	WOS:000329828900022
公开日期	2014-07-08
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/7025]
专题	烟台海岸带研究所_海岸带生物学与生物资源利用所重点实验室
作者单位	1.Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Peoples R China 2.Chinese Acad Sci, Dongtai Inst Tidal Flat Res, Nanjing Branch, Dongtai 224200, Peoples R China 3.Univ S Carolina, Dept Geog, Columbia, SC 29208 USA 4.Chinese Acad Sci, Key Lab Coastal Biol & Bioresources Utilizat, Yantai Inst Coastal Zone Res YIC, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Yao, R. J.,Yang, J. S.,Gao, P.,et al. Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland[J]. ENVIRONMENTAL EARTH SCIENCES,2014,71(1):233-243.
APA	Yao, R. J.,Yang, J. S.,Gao, P.,Shao, H. B.,Liu, G. M.,&Yu, S. P..(2014).Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland.ENVIRONMENTAL EARTH SCIENCES,71(1),233-243.
MLA	Yao, R. J.,et al."Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland".ENVIRONMENTAL EARTH SCIENCES 71.1(2014):233-243.