1 / 2018-05-10 15:24:48

Soil Property High Accuracy Surface Modeling Based on Ensemble Learning

Ensemble learning, Spatial interpolation, Geographical environment variable, Soil property

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). Regression Kriging (RK), Bayesian Kriging (BK), Ordinary Kriging, Inverse Distance Weighting (IDW), and HASMSP-EL were used to interpolate the soil potassium content in the complex geomorphological regions of Qinghai Lake, respectively. 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 RK, BK, IDW and 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.