A graded proportion method of training sample selection for updating conventional soil maps

doi:10.1016/j.geoderma.2019.113939

	A graded proportion method of training sample selection for updating conventional soil maps
	Liu, Xueqi 4,6; Zhu, A-Xing 2,3,5,6; Yang, Lin 1,2; Pei, Tao 2; Liu, Junzhi 6; Zeng, Canying 6; Wang, Desheng 6
刊名	GEODERMA
	2020
卷号	357 页码:9
关键词	Training sample selection method Data mining model Update conventional soil map Soil-environmental relationships
ISSN号	0016-7061
DOI	10.1016/j.geoderma.2019.113939
通讯作者	Yang, Lin(yanglin@nju.edu.cn)
英文摘要	Selection of training samples is a vital step in updating conventional soil maps when utilizing data mining models. Quality of training samples significantly affects the mapping results and accuracies of the updated soil maps. The area-weighted proportion method was a common method for generating training samples. However, this method usually assigns too small weight to those soil types of small areas and large weight to those of large areas in sample size allocation, which causes the unreasonable proportions of sample numbers for soil types and thereby biases the representation of soil-environmental relationships for those soil types. Meanwhile, random selection of training samples from a soil type may generate some 'noise' samples located in the transition areas between soil types. These two aspects in training sample selection could probably reduce the accuracy of the updated soil maps. In this study, a new method was developed to select training samples based on soil type grading according to their area coverages. The method consists of two steps. The first step is to determine the numbers of training samples for each soil type based on soil type grading so as to maintain the reasonable proportion in sample numbers among soil types with different area coverages. The second step is to select typical (representative) samples for each soil type from conventional soil map, to avoid generation of 'noise samples'. To evaluate the proposed method, the method was compared with three other training sample selection methods with four training sample sizes. Each method was ran for 100 times to generate training sample datasets with each sample size to evaluate their effectiveness and stability. Random forest was employed to generate updated soil maps in a small watershed in Raffelson, Wisconsin (USA). The validation results showed that the graded proportion method effectively solved the imbalanced issue of training samples among soil types with area coverages in big differences caused by the area-weighted proportion strategy. Thus training samples generated using the proposed method usually obtained more accurate and reasonable mapping results than those using the area-weighted proportion strategy. Furthermore, the performance of the proposed method was more stable than that of the area-weighted proportion strategy with the training sample size increasing. It is concluded that the proposed method is an effective training sample selection method for data mining model to update conventional soil maps.
资助项目	National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41971054 41871300] ; National Basic Research Program of China[2015CB954102] ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; Vilas Associate Award ; Hammel Faculty Fellow Award ; University of Wisconsin-Madison
WOS关键词	RANDOM FORESTS ; KNOWLEDGE ; UNITS ; TREE
WOS研究方向	Agriculture
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000496837300026
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 ; Hammel Faculty Fellow Award ; University of Wisconsin-Madison
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132010]
专题	中国科学院地理科学与资源研究所
通讯作者	Yang, Lin
作者单位	1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, 1 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China 4.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 6.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, 1 Wenyuan Rd, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xueqi,Zhu, A-Xing,Yang, Lin,et al. A graded proportion method of training sample selection for updating conventional soil maps[J]. GEODERMA,2020,357:9.
APA	Liu, Xueqi.,Zhu, A-Xing.,Yang, Lin.,Pei, Tao.,Liu, Junzhi.,...&Wang, Desheng.(2020).A graded proportion method of training sample selection for updating conventional soil maps.GEODERMA,357,9.
MLA	Liu, Xueqi,et al."A graded proportion method of training sample selection for updating conventional soil maps".GEODERMA 357(2020):9.