Pedosphere 14(3): 289--296, 2004
ISSN 1002-0160/CN 32-1315/P
©2004 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Neural network ensemble residual kriging application for spatial variability of soil properties |
SHEN Zhang-Quan1,3 , SHI Jie-Bin2, WANG Ke1, KONG Fan-Sheng3 and J. S. BAILEY4 |
1 Institute of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029 (China). E-mail: zhqshen@xju.edu.cn;
2 Department of Agriculture and Rural Development for Northern Ireland;
2 Zhejiang University Library, Hangzhou 310029 (China);
3 College of Computer Science, Zhejiang University, Hangzhou 310027 (China);
4 Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science Division, Newforge Lane, Belfast BT9 5PX (UK) |
| ABSTRACT |
| High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S, was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble, the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area. |
| Key Words: kriging, neural networks ensemble, residual, soil properties, spatial variability |
| Citation: Shen, Z. Q., Shi, J. B., Wang, K., Kong, F. S. and Bailey, J. S. 2004. Neural network ensemble residual kriging application for spatial variability of soil properties. Pedosphere. 14(3): 289-296. |
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