Prediction for spatial distribution of soil organic matter based on random forest model in cultivated area

doi:10.3969/j.issn.1004-1524.2018.07.15

›› 2018, Vol. 30 ›› Issue (7): 1211-1217.DOI: 10.3969/j.issn.1004-1524.2018.07.15

• Environmental Science • Previous Articles Next Articles

Prediction for spatial distribution of soil organic matter based on random forest model in cultivated area

YANG Yucen^{1, 2}, YANG Lian'an^{1, 2, *}, REN Li^{1, 2}, LI Congli³, ZHU Qun'e³, WANG Tiantai³, LI Xinyao^{1, 2}

1. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China;
2. College of Urban and Environmental Science, Northwest University, Xi'an 710127, China;
3. Soil and Fertilization Station of Zhouzhi County, Xi'an 710400, China

Received:2017-09-29 Online:2018-07-20 Published:2018-08-02

Abstract

Abstract: In the present study, the cultivated area of Zhouzhi County in Shaanxi Province was selected as study area with 192 soil samples collected, and the soil organic matter (SOM) content and distribution were predicted based on random forest (RF) model. The prediction accuracy was verified by 29 (15%) independent verification points, and the results were compared with ordinary kriging (OK) and cokriging (COK). It was shown that SOM contents in the training set and verification set were all moderately variable, and were classified into the medium low level. The SOM content was relatively high at east coast of Heihe River in middle and southern area, whereas was relatively low at Weihe River coast in the north-eastern area. By ranking the importance of variables, it was revealed that the main factors affecting the soil organic matter in the study area were elevation and rainfall. Compared with OK and COK, the correlation coefficient of prediction value and actual value of RF (0.782) was higher, yet the mean absolute error (0.618 g·kg^-1) and root mean squre error (2.062 g·kg^-1) were lower, which suggested that RF model yielded a more realistic spatial distribution of SOM.

Key words: spatial prediction, random forest, soil organic matter

CLC Number:

S153.6

YANG Yucen, YANG Lian'an, REN Li, LI Congli, ZHU Qun'e, WANG Tiantai, LI Xinyao. Prediction for spatial distribution of soil organic matter based on random forest model in cultivated area[J]. , 2018, 30(7): 1211-1217.

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Prediction for spatial distribution of soil organic matter based on random forest model in cultivated area

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