中国地理科学 ›› 2019, Vol. 20 ›› Issue (5): 784-797.doi: 10.1007/s11769-019-1071-x

• 论文 • 上一篇    下一篇

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

LI Zhen1, LI Yong1, XING An1, ZHUO Zhiqing1, ZHANG Shiwen2, ZHANG Yuanpei3, HUANG Yuanfang1   

  1. 1. College of Resources and Environment Sciences, China Agricultural University, Beijing 100193, China;
    2. School of Earth and En-vironment, Anhui University of Science and Technology, Huainan 232001, China;
    3. Institute of Crop Sciences, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China
  • 收稿日期:2018-12-20 修回日期:2019-04-10 出版日期:2019-10-27 发布日期:2019-09-26
  • 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 National Natural Science Foundation of China (No. 41571217), National Program on Key Basic Research Project (No. 2016YFD0300801)

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

LI Zhen1, LI Yong1, XING An1, ZHUO Zhiqing1, ZHANG Shiwen2, ZHANG Yuanpei3, HUANG Yuanfang1   

  1. 1. College of Resources and Environment Sciences, China Agricultural University, Beijing 100193, China;
    2. School of Earth and En-vironment, Anhui University of Science and Technology, Huainan 232001, China;
    3. Institute of Crop Sciences, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China
  • Received:2018-12-20 Revised:2019-04-10 Online:2019-10-27 Published:2019-09-26
  • Contact: HUANG Yuanfang.E-mail:yfhuang@cau.edu.cn E-mail:yfhuang@cau.edu.cn
  • Supported by:

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

摘要:

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.

关键词: soil salinity, environmental variable, random forest regression, geographic weighted regression, Yinchuan Plain irrigation oasis

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.

Key words: soil salinity, environmental variable, random forest regression, geographic weighted regression, Yinchuan Plain irrigation oasis