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GIS-BASED ELABORATE SPATIAL PREDICTION OF SOILNUTRIENT ELEMENTS USING ANCILLARY TERRAIN DATAISN CHONGQING TOBACCO PLANTING REGION, CHINA

  • Xuan Wang
  • Jiake Lv
  • Chaofu Wei
  • Deti Xie
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 293)

Abstract

The precision agriculture hopes to manage the variation in soil nutrient status continuously, which requires reliable predictions at places between sampling sites. For the long time, ordinary kriging has been used as one prediction method when the data are spatially dependent and a suitable variogram model exists. However, even if data are spatially correlated, there are often few soil sampling sites in relation to the area to be managed. Recently, Digital elevation models(DEMs) and remotely sensed data are becoming more readily available, these data are usually far more intensive than those from soil surveys. If these ancillary data are coregionalized with the sparse soil data, they might be used to increase the accuracy of predictions of the soil properties.

Under ArcGIS platform, this paper employed spatial predictions of the soil total N, P, K in Chongqing tobacco planting region, China, with cokriging and regression kriging respectively. For the both, intensive terrain data including elevation, slope and aspect were used with the soil data. Traditional ordinary kriging(OK) was investigated as comparison basis to determine which approach is appropriate for different soils properties mapping. And the results suggest that the use of intensive ancillary data can increase the accuracy of predictions of soil properties in arable fields provided that the variables are related spatially.

Keywords

Mean Square Error Ordinary Kriging Digital Elevation Model Spatial Prediction Ancillary Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Auemhammer, H. (2001). “Precision farming-the environment challenge.” Computers and Electronics in Agriculture (30): 31–43.CrossRefGoogle Scholar
  2. Cambardella, C. A. and T. B. Moorman, et al. (1994). “Field-scale variability of soil properties in central Iowa soils.” Soil science society of American Journal (58): 1501–1511.CrossRefGoogle Scholar
  3. Chaplot, V. and C. Walter, et al. (2004). “Using the topography of the saprolite upper boundary to improve the spatial prediction of the soil hydromorphic index.” Geoderma (123): 343–354.CrossRefGoogle Scholar
  4. Goovaerts, P. (1997). Geostatistics for natural resources evaluation, Oxford university press.Google Scholar
  5. Kay, S. and D. Rainer (2008). “Prediction of soil property distribution in paddy soil landscape using terrain data and satellite information as indicators.” Ecological indicators (8): 485–501.CrossRefGoogle Scholar
  6. Lopez-Granados, F. and M. Jurado-Exposito, et al. (2005). “Using geostatistical and remote sensing approaches for mapping soil properties.” Eur. J. Agron (23): 279–289.CrossRefGoogle Scholar
  7. Lu, M. and Y. Yang (1993). “Study on the combined digestion of total N, P and K in soil.” Acta Pedologica Sinica (3): 334–340.Google Scholar
  8. McBratney, A. B. and I. O. A. Odeh, et al. (2000). “An overview of pedometric techniques for use in soil survey.” Geoderma (97): 293–327.CrossRefGoogle Scholar
  9. Odeh, I. O. A. and A. B. McBratney, et al. (1995). “Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression kriging.” Geoderma (67): 215–226.CrossRefGoogle Scholar
  10. Sylvester-Bradley and L. R,. (1999). “An analysis of the potential of precision farming in northern Europe.” Soil use and management (15): 1–8.CrossRefGoogle Scholar
  11. Webster, R. (1985). “Quantitative spatial analysis of soil in the field.” Advance in soil science (3): 2–16.Google Scholar
  12. Wu, W. and Y. Fan, et al. (2008). “Assessing effects of digital elevation model resolutions on soil-landscape correlations in a hilly area.” Agriculture, Ecosystems and Environment (126): 209–216.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Xuan Wang
    • 1
  • Jiake Lv
    • 2
  • Chaofu Wei
    • 3
  • Deti Xie
    • 3
  1. 1.Chongqing key laboratory of digital agricultureChongqingP. R.China
  2. 2.College of computer and information scienceSouthwest UniversityChongqingP. R.China
  3. 3.College of resources and environmentSouthwest UniversityChongqingP. R.China

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