Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部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

	Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau
	Dai, Fuqiang 1,2,3; Zhou, Qigang 1; Lv, Zhiqiang 1; Wang, Xuemei 2,3; Liu, Gangcai2,3
刊名	ECOLOGICAL INDICATORS
	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
内容类型	期刊论文
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.