DOI: 10.1002/2016MS000827
Scopus记录号: 2-s2.0-85019882555
论文题名: Spatially distributed modeling of soil organic carbon across China with improved accuracy
作者: Li Q ; -Q ; , Zhang H ; , Jiang X ; -Y ; , Luo Y ; , Wang C ; -Q ; , Yue T ; -X ; , Li B ; , Gao X ; -S
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 2 起始页码: 1167
结束页码: 1185
语种: 英语
英文关键词: Forecasting
; Interpolation
; Land use
; Neural networks
; Organic carbon
; Regression analysis
; Soils
; Spatial distribution
; China
; Environmental factors
; Soil organic carbon
; Spatially distributed modeling
; Surface modeling
; Principal component analysis
; abiotic factor
; accuracy assessment
; artificial neural network
; conceptual framework
; environmental factor
; error analysis
; integrated approach
; kriging
; land use
; organic carbon
; performance assessment
; prediction
; principal component analysis
; regression analysis
; soil organic matter
; soil type
; China
英文摘要: There 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. © 2017. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75776
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: College of Resources, Sichuan Agricultural University, Chengdu, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Recommended Citation:
Li Q,-Q,, Zhang H,et al. Spatially distributed modeling of soil organic carbon across China with improved accuracy[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(2)