Resources Science ›› 2016, Vol. 38 ›› Issue (4): 704-713.doi: 10.18402/resci.2016.04.12

• Orginal Article • Previous Articles     Next Articles

Spatial prediction of soil salinity in the Yellow River Delta based on geographically weighted regression

WU Chunsheng1,2(), HUANG Chong1,3(), LIU Gaohuan1, LIU Qingsheng1   

  1. 1. State Key Lab. of Resources and Environment Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
    2. University of Chinese Academy of Sciences,Beijing 100049,China
    3. Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • Received:2015-10-19 Revised:2016-01-08 Online:2016-04-25 Published:2016-04-25

Abstract:

The content and spatial distribution of soil salinity is closely related to agriculture development and land productivity at a regional scale. It is essential to determine the content and spatial distribution of soil salinity in a timely manner as soil salinization could cause land degradation and influence human lives. Geographically weighted regression (GWR)is a local regression interpolation method that can achieve spatial extension of the dependent variable based on the relationships between the dependent variable and environmental variables and the spatial distances between sample points and predicted locations. GWR has been successfully applied to studies on some soil properties,such as soil organic matter. This study aimed to explore the feasibility of GWR in predicting soil salinity through comparisons with multiple linear regression (MLR)and Cokriging. Environmental factors,including the normalized difference vegetation index (NDVI),elevation and the distances of sample points from the rivers,were selected as auxiliary variables for GWR. The result generated by GWR showed a strong regularity in the spatial distribution of soil salinity,which has an increasing trend from coastal to inland areas,and the values of soil salinity near rivers were smaller than other regions. When compared to the map produced by Cokriging,GWR weakened the smoothing effect and many details became apparent. These findings indicate that GWR is applicable to predicting soil salinity. The prediction accuracy was higher than those of MLR and Cokriging. The RMSE,correlation coefficient,regression coefficient and adjust coefficient were 0.305,0.649,0.572 and 0.421,respectively. In addition,the prediction map generated by GWR reduced the smoothing effect compared to that of Cokriging and showed more spatial details than that of MLR.

Key words: soil salinity, geographically weighted regression, environmental variables, Yellow River Delta