| Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable | |
| Yao, R. J.1,2; Yang, J. S.1,2; Shao, H. B.3,4,5 | |
| 发表期刊 | ENVIRONMENTAL MONITORING AND ASSESSMENT
 |
| ISSN | 0167-6369 |
| 2013-06-01 | |
| 卷号 | 185期号:6页码:5151-5164 |
| 关键词 | Uncertainty Assessment Geostatistical Simulation Soil Salinity Coastal Farmland Apparent Electrical Conductivity |
| 产权排序 | [Yao, R. J.; Yang, J. S.] Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Jiangsu, Peoples R China; [Yao, R. J.; Yang, J. S.] Chinese Acad Sci, Dongtai Inst Tidal Flat Res, Nanjing Branch, Dongtai 224200, Peoples R China; [Shao, H. B.] Chinese Acad Sci, Key Lab Coastal Environm Proc, Yantai Inst Coastal Zone Res YIC, Yantai 264003, Peoples R China; [Shao, H. B.] YICCAS, Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai 264003, Peoples R China; [Shao, H. B.] Qingdao Univ Sci & Technol, Inst Life Sci, Qingdao 266042, Peoples R China |
| 通讯作者 | Yang, JS (reprint author), Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Jiangsu, Peoples R China. jsyang@issas.ac.cn ; shaohongbochu@126.com |
| 作者部门 | 海岸带生物学与生物资源利用所重点实验室 |
| 英文摘要 | Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (ECe 4-8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.; Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (ECe 4-8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable. |
| 文章类型 | Article |
| 资助机构 | National Natural Science Foundation of China [41101199]; Special Fund for Public Welfare Industrial (Agriculture) Research of China [200903001]; Natural Science Foundation of Jiangsu Province [BK2011423]; Key Technology R&D Program of Jiangsu Province [BE2010313]; Prospective Project of production education research cooperation of Jiangsu Province [BY2011195]; Fund Project for Transformation of Scientific and Technological Achievements of Jiangsu Province [BA2010116] |
| 收录类别 | SCI |
| 语种 | 英语 |
| 关键词[WOS] | ELECTRICAL-CONDUCTIVITY ; ELECTROMAGNETIC INDUCTION ; STOCHASTIC SIMULATION ; SPATIAL VARIABILITY ; CO-SIMULATION ; WATER CONTENT ; AGRICULTURE ; PRECISION ; PATTERNS |
| 研究领域[WOS] | Environmental Sciences & Ecology |
| WOS记录号 | WOS:000318503100050 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.yic.ac.cn/handle/133337/6408 |
| 专题 | 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保护实验室 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Soil & Sustainable Agr, Inst Soil Sci, Nanjing 210008, Jiangsu, Peoples R China 2.Chinese Acad Sci, Dongtai Inst Tidal Flat Res, Nanjing Branch, Dongtai 224200, Peoples R China 3.Chinese Acad Sci, Key Lab Coastal Environm Proc, Yantai Inst Coastal Zone Res YIC, Yantai 264003, Peoples R China 4.YICCAS, Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai 264003, Peoples R China 5.Qingdao Univ Sci & Technol, Inst Life Sci, Qingdao 266042, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, R. J.,Yang, J. S.,Shao, H. B.. Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable[J]. ENVIRONMENTAL MONITORING AND ASSESSMENT,2013,185(6):5151-5164. |
| APA | Yao, R. J.,Yang, J. S.,&Shao, H. B..(2013).Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable.ENVIRONMENTAL MONITORING AND ASSESSMENT,185(6),5151-5164. |
| MLA | Yao, R. J.,et al."Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable".ENVIRONMENTAL MONITORING AND ASSESSMENT 185.6(2013):5151-5164. |
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