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Spatially distributed modeling of soil organic carbon across China with improved accuracy
Li, Qi-quan1; Zhang, Hao1; Jiang, Xin-ye1; Luo, Youlin1; Wang, Chang-quan1; Yue, Tian-xiang2; Li, Bing1; Gao, Xue-song1
2017-06-01
Source PublicationJOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
ISSN1942-2466
Volume9Issue:2Pages:1167-1185
Corresponding AuthorWang, Chang-quan(wchangquan@126.com)
AbstractThere is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_ EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_ EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_ LS and HASM_ LS), and regression kriging combined with land uses and soil types (RK_ LS). Results showed that HASM_ EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_ EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_ EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.
DOI10.1002/2016MS000827
WOS KeywordAUXILIARY INFORMATION ; HILLY AREA ; NITROGEN ; PREDICTION ; SYSTEM ; MATTER ; VARIABILITY ; PATTERNS ; TERRAIN ; STORAGE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41201214] ; Fundamental Research Funds for Education Department of Sichuan Province of China[16ZB0048]
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for Education Department of Sichuan Province of China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000406239300023
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62722
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Chang-quan
Affiliation1.Sichuan Agr Univ, Coll Resources, Chengdu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Li, Qi-quan,Zhang, Hao,Jiang, Xin-ye,et al. Spatially distributed modeling of soil organic carbon across China with improved accuracy[J]. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,2017,9(2):1167-1185.
APA Li, Qi-quan.,Zhang, Hao.,Jiang, Xin-ye.,Luo, Youlin.,Wang, Chang-quan.,...&Gao, Xue-song.(2017).Spatially distributed modeling of soil organic carbon across China with improved accuracy.JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,9(2),1167-1185.
MLA Li, Qi-quan,et al."Spatially distributed modeling of soil organic carbon across China with improved accuracy".JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 9.2(2017):1167-1185.
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