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Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management

WANG Fei,  CHEN Xi,  LUO Geping,  HAN Qifei

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文章导航 > Chinese Geographical Science  > 2015 >  25(3): 321-336
WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. 中国地理科学, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
引用本文: WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. 中国地理科学, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
shu
WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. Chinese Geographical Science, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
Citation: WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. Chinese Geographical Science, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
shu

Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management

doi: 10.1007/s11769-014-0718-x
  • 1.

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China;

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

基金项目: Under the auspices of Key Program of National Science Foundation of China (No. 41361140361)
详细信息
    通讯作者:

    CHEN Xi. E-mail:Chenxi@ms.xjb.ac.cn

Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management

  • 1.

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China;

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds: Under the auspices of Key Program of National Science Foundation of China (No. 41361140361)
More Information
    Corresponding author: CHEN Xi. E-mail:Chenxi@ms.xjb.ac.cn

  • 摘要: Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R2=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R2=0.75, RPD=1.66, RMSE=10.92), that in Fubei Oasis (R2=0.77, RPD=2.01, RMSE=6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R2=0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R2=0.60, RPD=1.47, RMSE=14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.
    Abstract: Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R2=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R2=0.75, RPD=1.66, RMSE=10.92), that in Fubei Oasis (R2=0.77, RPD=2.01, RMSE=6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R2=0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R2=0.60, RPD=1.47, RMSE=14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.
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    [64] Lobell D B, Lesch S M, Corwin D L et al., 2010. Regional-scale assessment of soil salinity in the red river valley using multi-year MODIS EVI and NDVI. Journal of Environmental Quality, 39(1): 35-41. doi:  10.2134/jeq2009.0140
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    [66] Masoud A A, Koike K, 2006. Arid land salinization detected by remotely-sensed landcover changes: A case study in the Siwa region, NW Egypt. Journal of Arid Environments, 66(1): 151-167. doi:  10.1016/j.jaridenv.2005.10.011
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    [68] Metternicht G I, Zinck J A, 2003. Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85(1): 1-20. doi:  10.1016/S0034-4257(02)00188-8
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    [77] Rodríguez-Pérez J, Plant R, Lambert J J et al., 2011. Using apparent soil electrical conductivity (ECa) to characterize vineyard soils of high clay content. Precision Agriculture, 12(6): 775-794. doi:  10.1007/s11119-011-9220-y
    [78] Sheng J, Ma L, Jiang P A et al., 2010. Digital soil mapping to enable classification of the salt-affected soils in desert agro-ecological zones. Agricultural Water Management, 97(12): 1944-1951. doi:  10.1016/j.agwat.2009.04.011
    [79] Shi X, Franklin J, Chadwick O A et al., 2009. Integrating different types of knowledge for digital soil mapping. Soil Science Society of America, 73: 1682-1692. doi: 10.2136/ sssaj2007.0158
    [80] Shi Xingmin, Li Youlin, Yang Jingchun, 2008. Climatic and tectonic analysis of Manas Lake changes. Scientia Geogra­phica Sinica, 28(2): 266-271. (in Chinese)
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    [86] Wang Fangfang, Wu Shixin, Qiao Mu et al., 2009. Investigation and analysis on the salinization degree of cultivated land in Xinjiang based on 3S technology. Arid Zone Research, 26(3): 366-371. (in Chinese)
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出版历程
  • 收稿日期:  2013-12-16
  • 修回日期:  2014-04-14
  • 刊出日期:  2015-03-27

Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management

doi: 10.1007/s11769-014-0718-x
  • 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China;
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
    • 基金项目:  Under the auspices of Key Program of National Science Foundation of China (No. 41361140361)
    通讯作者: CHEN Xi. E-mail:Chenxi@ms.xjb.ac.cn
  • 收稿日期: 2013-12-16
  • 修回日期: 2014-04-14
  • 刊出日期: 2015-03-27

摘要: Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R2=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R2=0.75, RPD=1.66, RMSE=10.92), that in Fubei Oasis (R2=0.77, RPD=2.01, RMSE=6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R2=0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R2=0.60, RPD=1.47, RMSE=14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.

English Abstract

WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. 中国地理科学, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
引用本文: WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. 中国地理科学, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
shu
WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. Chinese Geographical Science, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
Citation: WANG Fei, CHEN Xi, LUO Geping, HAN Qifei. Mapping of Regional Soil Salinities in Xinjiang and Strategies for Ame-lioration and Management[J]. Chinese Geographical Science, 2015, 25(3): 321-336. doi: 10.1007/s11769-014-0718-x
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