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Soil Property Surface Modeling Based on Ensemble Learning for Complex Landforms

  • Wei LiuEmail author
  • Yongkun Liu
  • Mengyuan Yang
  • Meng Xie
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

It is difficult to simulate soil property with a single global interpolation model. For the characteristics of spatial discontinuity, limited precision of global interpolation model and poor adaptability, a high accuracy surface modeling for soil property based on ensemble learning and fusion geographical environment variables was proposed (HASMSP-EL). The simulation accuracy of different interpolation methods was evaluated by using Mean Error (ME), Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Accuracy (AC). The results showed that: (1) In the interpolation method of fusion geographical environment variables, the estimation deviation of HASMSP-EL was lower. Compared with other interpolation methods, ME, MRE, RMSE and AC of HASMSP-EL were better. HASMSP-EL had more advantages in describing spatial variation and local detail information of soil potassium content, and its accuracy was 6.42%, 7.28%, 11.56% and 9.38% higher than that of Regression Kriging (RK), Bayesian Kriging (BK), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), respectively. (2) The HASMSP-EL can provide more details in the geographical boundary, which made the simulation results consistent with the real auxiliary variables. HASMSP-EL not only considered the nonlinear relationship between geographical environmental variables and soil property, but also combined the adaptive advantages of multiple models. It is a new method to simulate soil property in complex geomorphological regions with higher precision.

Keywords

Ensemble learning Spatial interpolation Geographical environment variable Soil property 

Notes

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant No. 41601405). We are grateful to the Qinghai Environmental Monitoring Center for providing topsoil sampling approval. Thanks to the China Soil Investigation Office and the Bureau of Geological Exploration & Development of Qinghai Province for providing secondary datasets.

References

  1. 1.
    Li, J., Heap, A.D., Potter, A., Daniell, J.J.: Application of machine learning methods to spatial interpolation of environmental variables. Environ. Model. Softw. 26, 1647–1659 (2011)CrossRefGoogle Scholar
  2. 2.
    Zhao, Q.G.: Strategic thinking of soil science in China. Soils 41, 681–688 (2009)Google Scholar
  3. 3.
    Yi, X.S., Li, G.S., Yin, Y.Y., Peng, J.T.: Comparison on soil depth prediction among different spatial interpolation methods: a case study in the three-river headwaters region of Qinghai Province. Geogr. Res. 31, 1793–1805 (2012)Google Scholar
  4. 4.
    Wang, J.F., Ge, Y., Li, L.F., Meng, B., Wu, J.L., Bai, Y.C.: Spatiotemporal data analysis in geography. Acta Geogr. Sin. 69, 1326–1345 (2014)Google Scholar
  5. 5.
    Yue, T.X., Wang, S.H.: Adjustment computation of HASM: a high-accuracy and high-speed method. Int. J. Geogr. Inf. Sci. 24, 1725–1743 (2010)CrossRefGoogle Scholar
  6. 6.
    Shi, W., Liu, J., Du, Z., Yue, T.: Development of a surface modeling method for mapping soil properties. J. Geogr. Sci. 22, 752–760 (2012)CrossRefGoogle Scholar
  7. 7.
    Wu, C., Wu, J., Luo, Y., Zhang, L., DeGloria, S.D.: Spatial estimation of soil total nitrogen using cokriging with predicted soil organic matter content. Soil Sci. Soc. Am. J. 73, 1676–1681 (2009)CrossRefGoogle Scholar
  8. 8.
    Bashir, B., Fouli, H.: Studying the spatial distribution of maximum monthly rainfall in selected regions of Saudi Arabia using geographic information systems. Arab. J. Geosci. 8, 1–15 (2015)CrossRefGoogle Scholar
  9. 9.
    Kravchenko, A.: Influence of spatial structure on accuracy of interpolation methods. Soil Sci. Soc. Am. J. 67, 1564–1571 (2003)CrossRefGoogle Scholar
  10. 10.
    Li, Q., Dehler, S.A.: Inverse spatial principal component analysis for geophysical survey data interpolation. J. Appl. Geophys. 115, 79–91 (2015)CrossRefGoogle Scholar
  11. 11.
    Panagopoulos, T., Jesus, J., Antunes, M., Beltrao, J.: Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. Eur. J. Agron. 24, 1–10 (2016)CrossRefGoogle Scholar
  12. 12.
    Gotway, C.A., Ferguson, R.B., Hergert, G.W., Peterson, T.A.: Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci. Soc. Am. J. 60, 1237–1247 (1996)CrossRefGoogle Scholar
  13. 13.
    Montealegre, A., Lamelas, M., Riva, J.: Interpolation routines assessment in ALS-derived digital elevation models for forestry applications. Remote Sens. 7, 8631–8654 (2015)CrossRefGoogle Scholar
  14. 14.
    Xie, Y.F., Chen, T.B., Lei, M., Zheng, G.D., Song, B., Li, X.Y.: Impact of spatial interpolation methods on the estimation of regional soil cd. Acta Sci. Circum. 30, 847–854 (2010)Google Scholar
  15. 15.
    Triantafilis, J., Odeh, I., McBratney, A.: Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Sci. Soc. Am. J. 65, 869–878 (2001)CrossRefGoogle Scholar
  16. 16.
    Liu, W., Zhang, H.R., Yan, D.P., Wang, S.L.: Adaptive surface modeling of soil properties in complex landforms. ISPRS Int. J. Geo Inf. 6, 178 (2017)CrossRefGoogle Scholar
  17. 17.
    Zhang, H., Lu, L., Liu, Y., Liu, W.: Spatial sampling strategies for the effect of interpolation accuracy. ISPRS Int. J. Geo Inf. 4, 2742–2768 (2015)CrossRefGoogle Scholar
  18. 18.
    Liu, W., Du, P.J., Wang, D.C.: Ensemble learning for spatial interpolation of soil potassium content based on environmental information. Plos One 10, e0124383 (2015)CrossRefGoogle Scholar
  19. 19.
    Shi, W.J., Liu, J.Y., Du, Z.P., Yue, T.X.: High accuracy surface modeling of soil properties based on geographic information. Acta Geogr. Sin. 66, 1574–1581 (2011)Google Scholar
  20. 20.
    Collins, F.C., Bolstad, P.V.: A comparison of spatial interpolation techniques in temperature estimation (1996)Google Scholar
  21. 21.
    Asli, M., Marcotte, D.: Comparison of approaches to spatial estimation in a bivariate context. Math. Geol. 27, 641–658 (1995)CrossRefGoogle Scholar
  22. 22.
    Odeh, I.O., McBratney, A., Chittleborough, D.: Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67, 215–226 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Geography, Geomatics and PlanningJiangsu Normal UniversityXuzhouChina

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