Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps
文献类型：期刊论文
| 作者 | Ma, Tianwu1,2,3; Brus, Dick J.1,2,7; Zhu, A-Xing1,2,3,4,5,6 ; Zhang, Lei1,2,3; Scholten, Thomas8,9
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| 刊名 | GEODERMA
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| 出版日期 | 2020-07-01 |
| 卷号 | 370页码:11 |
| 关键词 | Soil sampling Random forest Similarity-based predictive soil mapping K-means Simulated annealing Calibration sampling |
| ISSN号 | 0016-7061 |
| DOI | 10.1016/j.geoderma.2020.114366 |
| 通讯作者 | Brus, Dick J.(dick.brus@wur.nl) |
| 英文摘要 | This study investigates sampling design for mapping soil classes based on multiple environmental features associated with the soil classes. Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. The sample sizes used are 20, 30, 40, 50, 75, and 100 points, and at each sample size 100 sample sets were drawn using each of the three types of design. Each of these sample sets was then used to calibrate three prediction models: random forest (RF), individual predictive soil mapping (iPSM), and multinomial logistic regression (MLR). These sampling designs were compared based on the overall accuracy of predicted soil class maps obtained by these three prediction methods. The comparison was conducted in two study areas: Ammertal (Germany) and Raffelson (USA). For each of these two areas a detailed legacy soil class map is available. These soil class maps were used as references in a simulation study for the comparison. Results of both study areas show that on average FSCS outperforms CLHS and SRS for all three prediction methods. The difference in estimated medians of overall accuracy with CLHS and SRS was marginal. Moreover, the variation in overall accuracy among sample sets of the same size was considerably smaller for FSCS than that for CLHS. These results in the two study areas suggest that FSCS is a more effective sampling design. |
| WOS关键词 | LAW |
| 资助项目 | National Natural Science Foundation of China[41871300] ; National Natural Science Foundation of China[41431177] ; National Basic Research Program of China[2015CB954102] ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award from the University of Wisconsin-Madison ; Hammel Faculty Fellow Award from the University of Wisconsin-Madison ; Manasse Chair Professorship from the University of Wisconsin-Madison ; German Research Foundation (DFG) through the DFG Cluster of Excellence Machine Learning - New Perspectives for Science[EXC 2064/1] ; German Research Foundation (DFG) through the DFG Cluster of Excellence Machine Learning - New Perspectives for Science[390727645] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000528270900001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; National Basic Research Program of China ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award from the University of Wisconsin-Madison ; Hammel Faculty Fellow Award from the University of Wisconsin-Madison ; Manasse Chair Professorship from the University of Wisconsin-Madison ; German Research Foundation (DFG) through the DFG Cluster of Excellence Machine Learning - New Perspectives for Science |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/159834]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Brus, Dick J. |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 2.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China 3.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 6.Southern Univ Sci & Technol, Ctr Social Sci, Shenzhen 518055, Peoples R China 7.Wageningen Univ & Res, Biometris, POB 16, NL-6700 AA Wageningen, Netherlands 8.Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Rumelinstr 19-23, Tubingen, Germany 9.Univ Tubingen, DFG Cluster Excellence Machine Learning, AI Res Bldg,Maria Von Linden Str 6, D-72076 Tubingen, Germany |
| 推荐引用方式 GB/T 7714 | Ma, Tianwu,Brus, Dick J.,Zhu, A-Xing,et al. Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps[J]. GEODERMA,2020,370:11. |
| APA | Ma, Tianwu,Brus, Dick J.,Zhu, A-Xing,Zhang, Lei,&Scholten, Thomas.(2020).Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps.GEODERMA,370,11. |
| MLA | Ma, Tianwu,et al."Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps".GEODERMA 370(2020):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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