Spatially distributed modeling of soil organic carbon across China with improved accuracy
文献类型：期刊论文
| 作者 | Li, Qi-quan1; Zhang, Hao1; Jiang, Xin-ye1; Luo, Youlin1; Wang, Chang-quan1; Yue, Tian-xiang2 ; Li, Bing1; Gao, Xue-song1
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| 刊名 | JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
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| 出版日期 | 2017-06-01 |
| 卷号 | 9期号:2页码:1167-1185 |
| ISSN号 | 1942-2466 |
| DOI | 10.1002/2016MS000827 |
| 通讯作者 | Wang, Chang-quan(wchangquan@126.com) |
| 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. |
| WOS关键词 | AUXILIARY INFORMATION ; HILLY AREA ; NITROGEN ; PREDICTION ; SYSTEM ; MATTER ; VARIABILITY ; PATTERNS ; TERRAIN ; STORAGE |
| 资助项目 | National Natural Science Foundation of China[41201214] ; Fundamental Research Funds for Education Department of Sichuan Province of China[16ZB0048] |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000406239300023 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for Education Department of Sichuan Province of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/62721]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Chang-quan |
| 作者单位 | 1.Sichuan Agr Univ, Coll Resources, Chengdu, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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