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Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)
Qin, Chengzhi1,2,3,4; An, Yiming1,2; Liang, Peng1,2; Zhu, Axing1,3,4,5,6; Yang, Lin7
2021-08-01
Source PublicationPEDOSPHERE
ISSN1002-0160
Volume31Issue:4Pages:638-644
Corresponding AuthorZhu, Axing(azhu@wisc.edu)
AbstractThe Soil Land Inference Model (SoLIM) was primarily proposed by Zhu et al. (Zhu A X, Band L, Vertessy R, Dutton B. 1997. Derivation of soil properties using a soil land inference model (SoLIM). Soil Sci Soc Am J. 61: 523-533.) and was based on the Third Law of Geography. Based on the assumption that the soil property value at a location of interest will be more similar to that of a given soil sample when the environmental condition at the location of interest is more similar to that at the location from which the sample was taken, SoLIM estimates the soil property value of the location of interest using the soil property values of known samples weighted by the similarity between those samples and the location of interest in terms of an attribute domain of environmental conditions. However, the current SoLIM method ignores information about the spatial distances between the location of interest and those of the sample. In this study, we proposed a new method of soil property mapping, SoLIM-IDW, which incorporates spatial distance information into the SoLIM method by means of inverse distance weighting (IDW). The proposed method is based on the assumption that the soil property value at a location of interest will be more similar to that of a known sample both when the environmental conditions are more similar and when the distance between the location of interest and the sample location is shorter. Our evaluation experiments on A-horizon soil organic matter mapping in two study areas with independent evaluation samples showed that the proposed SoLIM-IDW method can obtain lower prediction errors than the original SoLIM method, multiple linear regression, geographically weighted regression, and regression-kriging with the same modeling points. Future work mainly includes the determination of optimal power parameter values and the appropriate setting of the parameter under different application contexts.
Keyworddigital soil mapping location of soil sample inverse distance weighting soil organic matter Third Law of Geography
DOI10.1016/S1002-0160(20)60016-9
WOS KeywordGEOGRAPHICALLY WEIGHTED REGRESSION ; ORGANIC-MATTER ; PREDICTION ; UNCERTAINTY ; DESIGN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41871300] ; National Natural Science Foundation of China[41422109] ; National Natural Science Foundation of China[41431177] ; National Basic Research Program of China[2015CB954102] ; Priority Academic Program Development of Jiangsu Higher Education Institutions, China[164320H116] ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China ; Innovation Project of State Key Laboratory of Resources and Environmental Information System of China[O88RA20CYA] ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison, USA
Funding OrganizationNational Natural Science Foundation of China ; National Basic Research Program of China ; Priority Academic Program Development of Jiangsu Higher Education Institutions, China ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China ; Innovation Project of State Key Laboratory of Resources and Environmental Information System of China ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison, USA
WOS Research AreaAgriculture
WOS SubjectSoil Science
WOS IDWOS:000631178900013
PublisherSCIENCE PRESS
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/161901
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhu, Axing
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, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210097, Peoples R China
4.Nanjing Normal Univ, Sch Geog, Nanjing 210097, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
6.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China
7.Nanjing Univ, Sch Geog & Oceanog Sci, Nanjing 210093, Peoples R China
Recommended Citation
GB/T 7714
Qin, Chengzhi,An, Yiming,Liang, Peng,et al. Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)[J]. PEDOSPHERE,2021,31(4):638-644.
APA Qin, Chengzhi,An, Yiming,Liang, Peng,Zhu, Axing,&Yang, Lin.(2021).Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM).PEDOSPHERE,31(4),638-644.
MLA Qin, Chengzhi,et al."Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)".PEDOSPHERE 31.4(2021):638-644.
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