中国地理科学 ›› 2019, Vol. 20 ›› Issue (2): 270-282.doi: 10.1007/s11769-019-1027-1

• 论文 • 上一篇    下一篇

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

HUANG Yajie1, LI Zhen1, YE Huichun2, ZHANG Shiwen1,3, ZHUO Zhiqing1, XING An1, HUANG Yuanfang1   

  1. 1. 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, Beijing 100193, China;
    2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
    3. School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
  • 收稿日期:2017-11-29 出版日期:2019-04-27 发布日期:2019-02-23
  • 通讯作者: HUANG Yuanfang.E-mail:yfhuang@cau.edu.cn E-mail:yfhuang@cau.edu.cn
  • 基金资助:

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

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

HUANG Yajie1, LI Zhen1, YE Huichun2, ZHANG Shiwen1,3, ZHUO Zhiqing1, XING An1, HUANG Yuanfang1   

  1. 1. 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, Beijing 100193, China;
    2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
    3. School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
  • Received:2017-11-29 Online:2019-04-27 Published:2019-02-23
  • Contact: HUANG Yuanfang E-mail:yfhuang@cau.edu.cn
  • Supported by:

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

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.

关键词: ordinary kriging, neural network, soil electrical conductivity, variability, mapping, Ningxia, China

Abstract:

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.

Key words: ordinary kriging, neural network, soil electrical conductivity, variability, mapping, Ningxia, China