Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)

doi:10.1016/S1002-0160(20)60016-9

	Soil property mapping by combining spatial distance information into the Soil Land Inference Model (SoLIM)
	Qin, Chengzhi 1,2,3,4; An, Yiming 1,2; Liang, Peng 1,2; Zhu, Axing 1,3,4,5,6; Yang, Lin 7
刊名	PEDOSPHERE
	2021-08-01
卷号	31 期号:4 页码:638-644
关键词	digital soil mapping location of soil sample inverse distance weighting soil organic matter Third Law of Geography
ISSN号	1002-0160
DOI	10.1016/S1002-0160(20)60016-9
通讯作者	Zhu, Axing(azhu@wisc.edu)
英文摘要	The 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.
资助项目	National 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
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; ORGANIC-MATTER ; PREDICTION ; UNCERTAINTY ; DESIGN
WOS研究方向	Agriculture
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000631178900013
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/161900]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhu, Axing
作者单位	1.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
推荐引用方式 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.