Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data

doi:10.3390/ijgi9020102

	Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data
	Fan, Nai-Qing 1,2; Zhu, A-Xing 1,2,3,4,5; Qin, Cheng-Zhi 1,2,3; Liang, Peng 1,2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2020-02-01
卷号	9 期号:2 页码:17
关键词	digital soil mapping invalid data environmental covariate SoLIM uncertainty large areas China
DOI	10.3390/ijgi9020102
通讯作者	Qin, Cheng-Zhi(qincz@lreis.ac.cn)
英文摘要	Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil-environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.
资助项目	National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41871300] ; Chinese Academy of Sciences[XDA23100503]
WOS关键词	UNCERTAINTY ; KNOWLEDGE ; FOREST
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000522449700042
资助机构	National Natural Science Foundation of China ; Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133351]
专题	中国科学院地理科学与资源研究所
通讯作者	Qin, Cheng-Zhi
作者单位	1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 4.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 5.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Fan, Nai-Qing,Zhu, A-Xing,Qin, Cheng-Zhi,et al. Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2020,9(2):17.
APA	Fan, Nai-Qing,Zhu, A-Xing,Qin, Cheng-Zhi,&Liang, Peng.(2020).Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,9(2),17.
MLA	Fan, Nai-Qing,et al."Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 9.2(2020):17.