Influence of legacy soil map accuracy on soil map updating with data mining methods
文献类型：期刊论文
| 作者 | Liu, Xueqi7,8; Zhu, A-Xing3,4,5,6,8; Yang, Lin2; Pei, Tao4; Qi, Feng1; Liu, Junzhi5,6,8; Wang, Desheng8; Zeng, Canying9; Ma, Tianwu5,6,8 |
| 刊名 | GEODERMA
 |
| 出版日期 | 2022-06-15 |
| 卷号 | 416页码:12 |
| 关键词 | Digital soil mapping Machine learning Map accuracy Updating conventional soil map SoLIM Soil-landscape model |
| ISSN号 | 0016-7061 |
| DOI | 10.1016/j.geoderma.2022.115802 |
| 通讯作者 | Zhu, A-Xing(azhu@wisc.edu) |
| 英文摘要 | Over the past decades, conventional soil maps of various scales have been produced and become available in digital form. Efforts have been made to update these maps through various data mining methods to provide more detailed and precise information on soil spatial patterns. Key questions that remain unclear are: (1) How does the accuracy of legacy soil maps impact the update results; (2) Is the accuracy of inferred soil maps always improved regardless of the accuracy of the legacy maps. The current study aims to investigate these questions. Two noise production simulation methods were developed to simulate errors caused by inclusion and boundary displacement in the conventional maps, to generate a series of source maps with different accuracies and spatial patterns. Moreover, the impacts of two training sample selection methods and three data mining models on the accuracies and spatial patterns of the inferred soil maps were also evaluated. A case study was conducted in a small region, Raffelson study area, a typical ridge and valley terrain in La Crosse County, Wisconsin, USA. Results indicated that if the accuracies of the source soil maps ranged from 35% to 75%, the inferred soil map accuracies would be improved. These findings have important implications for updating conventional soil maps through data mining methods and understanding the situation in which the method is effective. |
| WOS关键词 | SPATIAL DISAGGREGATION ; REGIONAL-SCALE ; RANDOM FORESTS ; KNOWLEDGE ; UNITS ; PREDICTION ; POLARIS ; SERIES ; MODEL |
| 资助项目 | Na-tional Natural Science Foundation of China[41871300] ; Na-tional Natural Science Foundation of China[41971054] ; Na-tional Natural Science Foundation of China[41901062] ; 111 Program of China[D19002] ; PAPD[LY22D010009] ; Natural Science Foundation of Zhejiang Province ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; Na-tional Natural Science Foundation of China[41871300] ; Na-tional Natural Science Foundation of China[41971054] ; Na-tional Natural Science Foundation of China[41901062] ; 111 Program of China[D19002] ; PAPD ; Natural Science Foundation of Zhejiang Province[LY22D010009] ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000795905200005 |
| 出版者 | ELSEVIER |
| 资助机构 | Na-tional Natural Science Foundation of China ; 111 Program of China ; PAPD ; Natural Science Foundation of Zhejiang Province ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; Na-tional Natural Science Foundation of China ; 111 Program of China ; PAPD ; Natural Science Foundation of Zhejiang Province ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178231]  |
| 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.Kean Univ, Sch Environm & Sustainabil Sci, Union, NJ 07083 USA 2.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China 3.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China 6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 7.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 8.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China 9.Zhejiang Univ Finance & Econ, Inst Land & Urban Rural Dev, Hangzhou 310018, Zhejiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xueqi,Zhu, A-Xing,Yang, Lin,et al. Influence of legacy soil map accuracy on soil map updating with data mining methods[J]. GEODERMA,2022,416:12. |
| APA | Liu, Xueqi.,Zhu, A-Xing.,Yang, Lin.,Pei, Tao.,Qi, Feng.,...&Ma, Tianwu.(2022).Influence of legacy soil map accuracy on soil map updating with data mining methods.GEODERMA,416,12. |
| MLA | Liu, Xueqi,et al."Influence of legacy soil map accuracy on soil map updating with data mining methods".GEODERMA 416(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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