Soil Property Surface Modeling Based on Ensemble Learning for Complex Landforms
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.
KeywordsEnsemble learning Spatial interpolation Geographical environment variable Soil property
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.
- 2.Zhao, Q.G.: Strategic thinking of soil science in China. Soils 41, 681–688 (2009)Google Scholar
- 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.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
- 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
- 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.Collins, F.C., Bolstad, P.V.: A comparison of spatial interpolation techniques in temperature estimation (1996)Google Scholar