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Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas

文献类型：期刊论文

作者Fan, Nai-Qing1,2; Zhao, Fang-He1,2; Zhu, Liang-Jun1,2; Qin, Cheng-Zhi1,2,6; Zhu, A-Xing1,2,3,4,5,6
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2022-09-01
卷号113页码:12
ISSN号1569-8432
关键词Digital soil mapping Soil-environment relationship Large area Individual predictive soil mapping (iPSM) Covariates applicability Uncertainty Digital soil mapping Soil-environment relationship Large area Individual predictive soil mapping (iPSM) Covariates applicability Uncertainty
DOI10.1016/j.jag.2022.102986
英文摘要The effective use of environmental covariates in characterizing soil-environment relationships is key to successful digital soil mapping. The typical way to use environmental covariates in digital soil mapping is by selecting diverse environmental covariates considering the overall geographical characteristics of the study area and considering these covariates to have consistent applicability across the whole area. However, this practice ignores the fact that the applicability of each environmental covariate in characterizing soil-environment relationships varies over complex environmental conditions, especially in large areas. This study proposed a method to adaptively consider covariate applicability in large-area digital soil mapping using soil-environment relationships. The applicability of each covariate at each location was quantified from the terrain conditions using the newly designed fuzzy functions in the study. Then the covariate applicability was regarded as the importance weight and integrated into an existing representative method, iPSM (individual predictive soil mapping). The integration was separately performed at the similarity calculation and soil estimation stages of iPSM to generate two new methods: iPSM weighting on the applicability of all covariates (iPSM_WCovar_all), and iPSM weighting on the applicability of the limiting covariate (i.e., the covariate with the minimum similarity between two locations that constrains the overall similarity) (iPSM_WCovar_limit). Experiments were carried in Anhui Province, China. The two new methods were used to predict the soil organic matter content of topsoil and outperformed the original iPSM and random forest kriging methods. The root mean square error of the iPSM_WCovar_all, iPSM_WCovar_limit, iPSM and random forest kriging methods were 8.14, 8.00, 8.88 and 9.65 g/kg, respectively, while the mean absolute error of those methods were 6.48, 6.31, 6.61 and 6.82 g/kg. Both proposed methods outperformed the iPSM method and the other commonly used method, i.e., random forest kriging. Moreover, the performance was stable under different parameter settings. Experimental results indicate that the idea of adaptively considering covariate applicability in digital soil mapping is feasible and effective.
WOS关键词SPATIAL PREDICTION ; DIFFERENTIATION ; PATTERNS ; IMAGERY ; REGION ; CARBON ; LAW
资助项目National Natural Science Foundation of China[41871362] ; National Natural Science Foundation of China[41871300] ; Chinese Academy of Sciences[XDA23100503] ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin -Madison
WOS研究方向Remote Sensing
语种英语
出版者ELSEVIER
WOS记录号WOS:000857298300001
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin -Madison
源URL[http://ir.igsnrr.ac.cn/handle/311030/184782]  
专题中国科学院地理科学与资源研究所
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, 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.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Jiangsu, Peoples R China
4.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Jiangsu, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI USA
6.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Nai-Qing,Zhao, Fang-He,Zhu, Liang-Jun,et al. Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,113:12.
APA Fan, Nai-Qing,Zhao, Fang-He,Zhu, Liang-Jun,Qin, Cheng-Zhi,&Zhu, A-Xing.(2022).Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,113,12.
MLA Fan, Nai-Qing,et al."Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 113(2022):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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