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Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau
文献类型：期刊论文
| 作者 | Dai, Fuqiang1,2,3; Zhou, Qigang1; Lv, Zhiqiang1; Wang, Xuemei2,3; Liu, Gangcai2,3
|
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2014-10-01 |
| 卷号 | 45页码:184-194 |
| 关键词 | Soil organic matter Digital soil mapping Artificial neural network Ordinary kriging Accuracy improvement |
| ISSN号 | 1470-160X |
| 通讯作者 | Liu, Gangcai |
| 英文摘要 | Soil organic matter (SOM) content is considered as an important indicator of soil quality. An accurate spatial prediction of SOM content is so important for estimating soil organic carbon pool and monitoring change in it over time at a regional scale. Due to the unfavourable natural conditions in Tibetan Plateau, soil sampling with high density is time consuming and expensive. As a result, little research has focused on the spatial prediction of SOM content in Tibet because of shortage of data. We used a two-stage process that integrated an artificial neural network (ANN) and the estimation of its residuals by ordinary kriging to produce accurate SOM content maps based on sparsely distributed observations and available auxiliary information. SOM content data were obtained from a soil survey in Tibet and were used to train and validate the ANN-kriging methodology. Available environmental information including elevation, temperature, precipitation, and normalized difference vegetation index were used as auxiliary variables in the ANN training. The prediction accuracy of SOM content was compared with those of ANN, universal kriging, and inverse distance weighting (IDW). A more accurate prediction of SOM content was obtained by ANN-kriging, with lower global prediction errors (root mean square error = 6.02 g kg(-1)) and higher Lin's concordance correlation coefficient (0.75) for validation sampling sites compared with other methods. Relative improvements of 26.94-37.10% over other methods were observed in the prediction of SUM content. In conclusion, the proposed ANN-kriging methodology is particularly capable of improving the accuracy of SOM content mapping at large scale. (C) 2014 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Biodiversity Conservation ; Environmental Sciences |
| 研究领域[WOS] | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 关键词[WOS] | AUXILIARY INFORMATION ; TERRAIN ATTRIBUTES ; REGIONAL-SCALE ; CARBON STORAGE ; LIMITED DATA ; VARIABILITY ; REGRESSION ; STOCKS ; SPECTROSCOPY ; DEGRADATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000340312100021 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/9951]  |
| 专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
| 作者单位 | 1.Chongqing Technol & Business Univ, Coll Tourism & Land Resources, Chongqing 400067, Peoples R China 2.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China 3.Minist Water Resources, Chengdu 610041, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dai, Fuqiang,Zhou, Qigang,Lv, Zhiqiang,et al. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau[J]. ECOLOGICAL INDICATORS,2014,45:184-194. |
| APA | Dai, Fuqiang,Zhou, Qigang,Lv, Zhiqiang,Wang, Xuemei,&Liu, Gangcai.(2014).Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau.ECOLOGICAL INDICATORS,45,184-194. |
| MLA | Dai, Fuqiang,et al."Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau".ECOLOGICAL INDICATORS 45(2014):184-194. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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