Mapping the Spatial Variability of Soil Properties: A Comparative Study of Spatial Interpolation Methods in Northeast China

Abstract:

We analyzed the variance characteristics of soil organic matter (SOM), total nitrogen (TN), extractable phosphorus (EP), and extractable potassium (EK), in Jiutai County, Northeast China, and compared different prediction methods for mapping of these four soil variables. The prediction methods used were geostatistical interpolation (ordinary kriging), inverse distance weight method, and the hybrid techniques (regression-kriging). A modified jackknifing method involving 40% partitions was used to examine the stability of validate the indices. Root mean square error (RMSE) was used as validation index, and mean RMSE was used to judge the prediction quality. The results showed that the hybrid interpolation regression-kriging cant be used in the region influenced by frequent and high-intensity human activity when the relationship between soil properties and environment factors were not obvious. The ordinary kriging was found to be the best method to fit the experimental semivariogram of SOM and EK. The inverse distance weight method fit well to predict the distribution of TN and EP. For SOM and EK, results showed that data values in the western part were higher than those in the eastern part. However, for TN and EP, there is no clear trend. Water and tillage erosion caused by human activity has weakened the structural influence and elevation and slope played key roles in the distribution of soil variables in the local area.

• Periodical: Applied Mechanics and Materials (Volume 535)
• Pages: 483-488
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.535.483
• Online since: February 2014
• Authors: Xiao Yan Li, Hong Li, Hui Jia Liu
• Keywords: Geostatistics, Inverse Distance Weight Method, Jiutai County, Regression, Soil Prediction

References

[1] T.F.A. Bishop, A.B. McBratney, A comparison of prediction method for the creation of field-extent soil property maps, Geoderma. 103 (2001) 149–160.

DOI: 10.1016/s0016-7061(01)00074-x

[2] Z.M. Wang, B. Zhang, K. Sh. Song, D.W. Liu, Soil organic carbon under different landscape attributes in croplands of Northeast China, Plant, Soil and Environment. 54(2008) 420-427.

DOI: 10.17221/402-pse

[3] D. W Liu, Z.B. Wang, B. Zhang, K. SH. Song, Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China, Agriculture, Ecosystems and Environment. 113(2006)73-81.

DOI: 10.1016/j.agee.2005.09.006

[4] Z.M. Wang, B. Zhang, K. Sh. Song, Spatial variability of soil organic carbon under maize monoculture in the Song-nen Plain, Northeast China, Pedosphere. 20(2010) 80-89.

DOI: 10.1016/s1002-0160(09)60285-x

[5] N. Pettorelli, J.O. Vik, A. Mysterud, J.M. Gaillard, C.J. Tucker, N.C. Stenseth, Using the satellite-derived NDVI to assess ecological responses to environmental chang, Trends in Ecology & Evolution. 20(2005) 503–510.

DOI: 10.1016/j.tree.2005.05.011

[6] G. Rondeaux, M. Steven, F Baret, Optimization of soil adjusted vegetation indices, Remote sensing of Environment. 55(1996). 95–107.

DOI: 10.1016/0034-4257(95)00186-7

[7] I.D. Moore, P.E. Gessler, G.A. Nielsen, G.A. Peterson, Soil attribute prediction using terrain analysis, Soil Science Society of America Journal. 9(1993)443–452.

DOI: 10.2136/sssaj1993.03615995005700020026x

[8] S.M. Lesch, D. L. Corwin, Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests, Geoderma. 14(200) 130–140.

DOI: 10.1016/j.geoderma.2008.09.014

[9] A.B. McBratney, I.O.A. Odeh, T.F.A. Bishop, M.S. Dunbar, M. Shatar, An overview of pedometric techniques for use in soil survey, Geoderma. 97(2000) 293–32.

DOI: 10.1016/s0016-7061(00)00043-4

[10] X.Y. Li, S.W. Zhang, Z. M. Wang, Spatial variability and pattern analysis of soil properties in Dehui City, Jilin Province, Journal of Geographical Sciences. 14(2004) 503-511.

DOI: 10.1007/bf02837495

[11] B. Sun, S. L. Zhou, Q. G. Zhao, Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of sub-tropical, China, Geoderma. 115(2003)5-99.

DOI: 10.1016/s0016-7061(03)00078-8

[12] Z. Q. Wang, Geostatistics and its application in ecology. Beijing: Science Press, (1999).