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Chinese Geographical Science

, Volume 29, Issue 5, pp 784–797 | Cite as

Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison

  • Zhen Li
  • Yong Li
  • An Xing
  • Zhiqing Zhuo
  • Shiwen Zhang
  • Yuanpei Zhang
  • Yuanfang HuangEmail author
Article
  • 1 Downloads

Abstract

Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity (EC) estimation systematically. Additionally, the performance of Multiple Linear Regression (MLR), Geographically Weighted Regression (GWR), and Random Forest regression (RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.

Keywords

soil salinity environmental variable random forest regression geographic weighted regression Yinchuan Plain irrigation oasis 

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Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhen Li
    • 1
  • Yong Li
    • 1
  • An Xing
    • 1
  • Zhiqing Zhuo
    • 1
  • Shiwen Zhang
    • 2
  • Yuanpei Zhang
    • 3
  • Yuanfang Huang
    • 1
    Email author
  1. 1.College of Resources and Environment SciencesChina Agricultural UniversityBeijingChina
  2. 2.School of Earth and EnvironmentAnhui University of Science and TechnologyHuainanChina
  3. 3.Institute of Crop SciencesNingxia Academy of Agricultural and Forestry SciencesYinchuanChina

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