The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method
文献类型：期刊论文
| 作者 | Zeng, Canying2,3; Zhu, A-Xing1,2,3,5; Liu, Feng4; Yang, Lin5; Rossiter, David G.2,6; Liu, Junzhi2,3; Wang, Desheng2,3 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2017 |
| 卷号 | 72页码:297-309 |
| 关键词 | Land surface dynamic feedbacks Individual predictive soil mapping Rainfall magnitude Soil texture |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2016.08.023 |
| 通讯作者 | Zhu, A-Xing(azhu@wisc.edu) |
| 英文摘要 | Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low -relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0-40 mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude (Pearson's r between root mean squared error of prediction and rainfall magnitude = -0.943 for percentage of sand and -0.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 20 mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying. (C) 2016 Elsevier Ltd. All rights reserved. |
| WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; SOUTHERN GREAT-PLAINS ; ORGANIC-MATTER ; PATTERNS ; CHINA ; DIFFERENTIATION ; CLASSIFICATION ; INTERPOLATION ; INFORMATION ; TEMPERATURE |
| 资助项目 | National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41201207] ; National Natural Science Foundation of China[41571212] ; Natural Science Research Program of Jiangsu[14KJA170001] ; National Basic Research Program of China[2015CB954102] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; National Key Technology Innovation Project for Water Pollution Control and Remediation[2013ZX07103006] ; Project of One-Three-Five Strategic Planning & Frontier Sciences of the Institute of Soil Science, Chinese Academy of Sciences[ISSASIP1622] ; Xing Zhu through the Vilas Associate Award ; Hammel Faculty Fellow Award ; University of Wisconsin-Madison ; One-Thousand Talents Program of China ; D.G. Rossiter through the NJNU Visiting Prof. Programme |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000398426200030 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Research Program of Jiangsu ; National Basic Research Program of China ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; National Key Technology Innovation Project for Water Pollution Control and Remediation ; Project of One-Three-Five Strategic Planning & Frontier Sciences of the Institute of Soil Science, Chinese Academy of Sciences ; Xing Zhu through the Vilas Associate Award ; Hammel Faculty Fellow Award ; University of Wisconsin-Madison ; One-Thousand Talents Program of China ; D.G. Rossiter through the NJNU Visiting Prof. Programme |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/64711]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhu, A-Xing |
| 作者单位 | 1.Univ Wisconsin Madison, Dept Geog, Madison, WI USA 2.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, 1 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, 1 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China 4.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Jiangsu, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Cornell Univ, Sch Integrat Plant Sci, Sect Soil & Crop Sci, Ithaca, NY 14853 USA |
| 推荐引用方式 GB/T 7714 | Zeng, Canying,Zhu, A-Xing,Liu, Feng,et al. The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method[J]. ECOLOGICAL INDICATORS,2017,72:297-309. |
| APA | Zeng, Canying.,Zhu, A-Xing.,Liu, Feng.,Yang, Lin.,Rossiter, David G..,...&Wang, Desheng.(2017).The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method.ECOLOGICAL INDICATORS,72,297-309. |
| MLA | Zeng, Canying,et al."The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method".ECOLOGICAL INDICATORS 72(2017):297-309. |
入库方式： OAI收割
来源：地理科学与资源研究所
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