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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Yajie Huang, Zhen Li, Huichun Ye, Shiwen Zhang, Zhiqing Zhuo, An Xing, Yuanfang Huang

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. More... »

PAGES

270-282

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11769-019-1027-1

    DOI

    http://dx.doi.org/10.1007/s11769-019-1027-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112504888


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