Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network

作     者：HUANG Yajie LI Zhen YE Huichun ZHANG Shiwen ZHUO Zhiqing XING An HUANG Yuanfang HUANG Yajie;LI Zhen;YE Huichun;ZHANG Shiwen;ZHUO Zhiqing;XING An;HUANG Yuanfang

作者机构：Key Laboratory of Arable Land Conservation (North China) Ministry of Agriculture/Key Laboratory of Agricultural Land Quality Monitoring Ministry of Land and Resources College of Resources and Environmental Sciences China Agricultural University Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences School of Earth and Environment Anhui University of Science and Technology 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2019年第29卷第2期

页      面：270-282页

核心收录：

学科分类：0303[法学-社会学] 09[农学] 0708[理学-地球物理学] 0903[农学-农业资源与环境] 0705[理学-地理学] 0813[工学-建筑学] 0704[理学-天文学] 0833[工学-城乡规划学] 

基　　金：Under the auspices of the National Natural Science Foundation of China(No.41571217) the National Key Research and Development Program of China(No.2016YFD0300801) 

主　　题：ordinary kriging neural network soil electrical conductivity variability mapping Ningxia,China 

摘      要：Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.