IGSNRR OpenIR
Identification of representative samples from existing samples for digital soil mapping
An Yiming1,2; Yang Lin1,3; Zhu A-Xing1,4,5,6; Qin Chengzhi1; Shi JingJing1,2
2018-02-01
Source PublicationGEODERMA
ISSN0016-7061
Volume311Pages:109-119
Corresponding AuthorYang Lin(yanglin@lreis.ac.cn)
AbstractExisting sample data are important for digital soil mapping. Different sample points possess different representativeness. The representativeness of samples influences the soil mapping result greatly. However, few study focus on assessing the representativeness of single sample. In this paper, we proposed a method to identify representative samples from existing samples collected from multiple resources. The basic idea of the method was to use clusters of environmental covariates to approximate types of soil variations, and check the occupancy of the existing samples in centroids of environmental clusters. Those samples locating in typical locations or centroids of environmental clusters were considered as representative samples. In this paper, the proposed method was used to discern representative samples in 282 soil samples in Anhui Province, China. SOM content was mapped using a similarity based mapping method. Two cases with different training samples (representative samples, non -representative samples, and training samples including representative and non-representative samples) and validation samples were set to compare the mapping results and accuracies. The results showed that the SOM content maps predicted using representative training samples had generally higher accuracy than the results produced using non -representative samples, and comparative accuracies with the results produced using full training samples. To discern representative samples is helpful for understanding the soil-landscape relationships in an area and the proposed method can be used to design supplementary samples for a better soil mapping result. Mapping results and accuracies showed that different training and validation sample sets impacted the mapping results and accuracies greatly, which indicates that researchers should be cautious when using randomization to obtain training and validation subsets for soil mapping. (C) 2017 Elsevier B.V. All rights reserved.
KeywordDigital soil mapping Similarity based method under soil-landscape inference model (SoLIM) Representative samples
DOI10.1016/j.geoderma.2017.03.014
WOS KeywordALGORITHM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41471178] ; National Natural Science Foundation of China[41530749] ; National Natural Science Foundation of China[41431177] ; National Key Technology Innovation Project for Water Pollution Control and Remediation[2013ZX07103006] ; Featured Institute Construction Services Program[TSYJS03] ; National Basic Research Program of China[2015CB954102] ; Natural Science Research Program of Jiangsu[14KJA170001] ; University of Wisconsin-Madison ; One-Thousand Talents Program of China
Funding OrganizationNational Natural Science Foundation of China ; National Key Technology Innovation Project for Water Pollution Control and Remediation ; Featured Institute Construction Services Program ; National Basic Research Program of China ; Natural Science Research Program of Jiangsu ; University of Wisconsin-Madison ; One-Thousand Talents Program of China
WOS Research AreaAgriculture
WOS SubjectSoil Science
WOS IDWOS:000415771300012
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56743
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang Lin
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing, Jiangsu, Peoples R China
4.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China
5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
6.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
An Yiming,Yang Lin,Zhu A-Xing,et al. Identification of representative samples from existing samples for digital soil mapping[J]. GEODERMA,2018,311:109-119.
APA An Yiming,Yang Lin,Zhu A-Xing,Qin Chengzhi,&Shi JingJing.(2018).Identification of representative samples from existing samples for digital soil mapping.GEODERMA,311,109-119.
MLA An Yiming,et al."Identification of representative samples from existing samples for digital soil mapping".GEODERMA 311(2018):109-119.
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