›› 2018, Vol. 30 ›› Issue (4): 640-648.DOI: 10.3969/j.issn.1004-1524.2018.04.16

• Environmental Science • Previous Articles     Next Articles

Prediction of soil organic matter distribution based on auxiliary variables and regression-radial basis function neural network (R-RBFNN) model

JIANG Yefeng, GUO Xi*   

  1. Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agricultural University, Nanchang 330045, China
  • Received:2017-05-02 Online:2018-04-20 Published:2018-04-19

Abstract: Accurate spatial distribution information about soil organic matter (SOM) is critical for farmland use and soil environmental protection. In order to find the best interpolation method of SOM in Qibu Town in Wannian County, Jiangxi Province, a regression-radial basis function neural network (R-RBFNN) model was proposed based on environmental factors and neighbor information, regression Kriging (RK), based on environmental factors and neighbor information, and ordinary Kriging (OK) were also dopted to predict SOM distribution. Environmental factors were extracted from digital terrain and remote sensing image, and the four-direction search method was applied to get the neighbor information. To establish and validate this method, 78 soil samples were collected and randomly divided into two groups, as modeling points (62) and validation points (16). Results showed that, SOM content ranged from 17.30 to 53.58 g·kg-1, with an average of 35.03 g·kg-1, indicating a moderate variability. The nugget/sill ratio was 0.59, indicating a moderate spatial dependence for SOM. The prediction map obtained by RK and R-RBFNN was similar and more consistent with the true geographical information than OK. Moreover, compared to OK model with the validation points, RK model and R-RBFNN reduced the prediction errors, as the root mean square errors (RMSE), the mean absolute errors (MAE) and the mean relative errors (MRE) of RK were all reduced to those of OK, and the relative improvement was 66.67% and 71.79%, respectively. Therefore, both RK and R-RBFNN significantly improved the interpolation accuracy of SOM distribution due to the consideration of the environmental factors and neighbor information. In addition, R-RBFNN did not require calculation of semi-variogram, and thus exhibited better application potential.

Key words: soil organic matter, ordinary Kriging, regression Kriging, radial basis function neural network, prediction

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