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

HUANG Yajie,  LI Zhen,  YE Huichun,  ZHANG Shiwen,  ZHUO Zhiqing,  XING An,  HUANG Yuanfang

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文章导航 > Chinese Geographical Science  > 2019 >  20(2): 270-282
HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. 中国地理科学, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
引用本文: HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. 中国地理科学, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
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HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. Chinese Geographical Science, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
Citation: HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. Chinese Geographical Science, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
shu

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

doi: 10.1007/s11769-019-1027-1
  • 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

基金项目: Under the auspices of the National Natural Science Foundation of China (No. 41571217), the National Key Research and Development Program of China (No. 2016YFD0300801)
详细信息
    通讯作者:

    HUANG Yuanfang.E-mail:yfhuang@cau.edu.cn

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

  • 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

Funds: Under the auspices of the National Natural Science Foundation of China (No. 41571217), the National Key Research and Development Program of China (No. 2016YFD0300801)
More Information
    Corresponding author: HUANG Yuanfang

  • 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.
    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.
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出版历程
  • 收稿日期:  2017-11-29
  • 刊出日期:  2019-04-01

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

doi: 10.1007/s11769-019-1027-1
  • 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
    • 基金项目:  Under the auspices of the National Natural Science Foundation of China (No. 41571217), the National Key Research and Development Program of China (No. 2016YFD0300801)
    通讯作者: HUANG Yuanfang.E-mail:yfhuang@cau.edu.cn
  • 收稿日期: 2017-11-29
  • 刊出日期: 2019-04-01

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.

English Abstract

HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. 中国地理科学, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
引用本文: HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. 中国地理科学, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
shu
HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. Chinese Geographical Science, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
Citation: HUANG Yajie, LI Zhen, YE Huichun, ZHANG Shiwen, ZHUO Zhiqing, XING An, HUANG Yuanfang. Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network[J]. Chinese Geographical Science, 2019, 20(2): 270-282. doi: 10.1007/s11769-019-1027-1
shu

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