中国地理科学 ›› 2015, Vol. 25 ›› Issue (3): 283-294.doi: 10.1007/s11769-015-0740-7

• 论文 • 上一篇    下一篇

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

YANG Lin1, HUANG Chong1, LIU Gaohuan1, LIU Jing2, ZHU A-Xing1,2,3   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA;
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210023, China
  • 收稿日期:2014-06-03 修回日期:2014-08-29 出版日期:2015-03-27 发布日期:2015-05-07
  • 通讯作者: HUANG Chong. E-mail:huangch@lreis.ac.cn E-mail:huangch@lreis.ac.cn
  • 基金资助:

    Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research (No. 201105020), National Natural Science Foundation of China (No. 41471178, 41023010, 41431177), National Key Technology Innovation Project for Water Pollution Control and Remediation (No. 2013ZX07103006)

Mapping Soil Salinity Using a Similarity-based Prediction Approach:A Case Study in Huanghe River Delta, China

YANG Lin1, HUANG Chong1, LIU Gaohuan1, LIU Jing2, ZHU A-Xing1,2,3   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA;
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210023, China
  • Received:2014-06-03 Revised:2014-08-29 Online:2015-03-27 Published:2015-05-07
  • Contact: HUANG Chong. E-mail:huangch@lreis.ac.cn E-mail:huangch@lreis.ac.cn
  • Supported by:

    Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research (No. 201105020), National Natural Science Foundation of China (No. 41471178, 41023010, 41431177), National Key Technology Innovation Project for Water Pollution Control and Remediation (No. 2013ZX07103006)

摘要:

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

关键词: soil salinization, similarity-based prediction approach, digital soil mapping, Huanghe (Yellow) River Delta, environmental factor

Abstract:

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Key words: soil salinization, similarity-based prediction approach, digital soil mapping, Huanghe (Yellow) River Delta, environmental factor