A case-based method of selecting covariates for digital soil mapping

doi:10.1016/S2095-3119(19)62857-1

	A case-based method of selecting covariates for digital soil mapping
	Peng, Liang 2,3; Cheng-zhi, Qin 2,3,4; A-xing, Zhu 1,2,3,4,5; Zhi-wei, Hou 2,3; Nai-qing, Fan 2,3; Yi-jie, Wang 2,3
刊名	JOURNAL OF INTEGRATIVE AGRICULTURE
	2020-08-01
卷号	19 期号:8 页码:2127-2136
关键词	digital soil mapping covariates case-based reasoning Random Forest
ISSN号	2095-3119
DOI	10.1016/S2095-3119(19)62857-1
通讯作者	Cheng-zhi, Qin(qincz@lreis.ac.cn)
英文摘要	Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping (DSM). The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited samples. To solve the problem, this paper proposed a case -based method which could formalize the covariate selection knowledge contained in practical DSM applications. The proposed method trained Random Forest (RF) classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM application. In this study, we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer -reviewed journal articles to evaluate the performance of the proposed case -based method by Leave -One -Out cross validation. Compared with a novices? commonly -used way of selecting DSM covariates, the proposed case -based method improved more than 30% accuracy according to three quantitative evaluation indices (i.e., recall , precision , and F1 -score ). The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains, such as landslide susceptibility mapping, and species distribution modeling.
资助项目	National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41871300] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China ; Innovation Project of State Key Laboratory of Resources and Environmental Information System (LREIS), China[O88RA20CYA] ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison
WOS关键词	SPATIAL PREDICTION ; EXPERT KNOWLEDGE ; ORGANIC-MATTER ; STOCKS ; SCALE ; AREA
WOS研究方向	Agriculture
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000544587300009
资助机构	National Natural Science Foundation of China ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China ; Innovation Project of State Key Laboratory of Resources and Environmental Information System (LREIS), China ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162436]
专题	中国科学院地理科学与资源研究所
通讯作者	Cheng-zhi, Qin
作者单位	1.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Sch Geog, Nanjing 210097, Peoples R China 5.Univ Wisconsin Madison, Dept Geog, Madison, WI 53706 USA
推荐引用方式 GB/T 7714	Peng, Liang,Cheng-zhi, Qin,A-xing, Zhu,et al. A case-based method of selecting covariates for digital soil mapping[J]. JOURNAL OF INTEGRATIVE AGRICULTURE,2020,19(8):2127-2136.
APA	Peng, Liang,Cheng-zhi, Qin,A-xing, Zhu,Zhi-wei, Hou,Nai-qing, Fan,&Yi-jie, Wang.(2020).A case-based method of selecting covariates for digital soil mapping.JOURNAL OF INTEGRATIVE AGRICULTURE,19(8),2127-2136.
MLA	Peng, Liang,et al."A case-based method of selecting covariates for digital soil mapping".JOURNAL OF INTEGRATIVE AGRICULTURE 19.8(2020):2127-2136.