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Article Navigation >  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]. 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

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
  • Received Date: 2013-12-16
  • Rev Recd Date: 2014-04-14
  • Publish Date: 2015-03-27
  • 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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    [59] Hu S, Zhao R, Tian C et al., 2009. Empirical models of calculating phreatic evaporation from bare soil in Tarim river basin, Xinjiang. Environmental Earth Sciences, 59(3): 663-668. doi:  10.1007/s12665-009-0063-z
    [60] Karl J W, Maurer B A, 2010. Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information. Ecological Informatics, 5(3): 194-202. doi: 10.1016/j.ecoinf. 2010.02.004
    [61] Li Q, Chen Y, Shen Y et al., 2011. Spatial and temporal trends of climate change in Xinjiang, China. Journal of Geographical Sciences, 21(6): 1007-1018. doi:  10.1007/s11442-011-0896-8
    [62] Liu D M, Abuduwaili J, Lei J Q et al., 2011. Deposition rate and chemical composition of the aeolian dust from a bare saline playa, Ebinur Lake, Xinjiang, China. Water Air and Soil Pollution, 218(1-4): 175-184. doi:  10.1007/s11270-010-0633-4
    [63] Liu F, Geng X, Zhu A X et al., 2012. Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS. Geoderma, 171-172: 44-52. doi:  10.1016/j.geoderma.2011.05.007
    [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
    [65] MacMillan R A, Moon D E, Coupé R A et al., 2010. Predictive ecosystem mapping (PEM) for 8.2 million ha of forestland, British Columbia, Canada. In: Boettinger J L (eds). Digital Soil Mapping: Bridging Research, Environmenatal Application, and Oper ation. Netherlands: Springer, 337-356.
    [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
    [67] McBratney A B, Mendonca S M L, Minasny B, 2003. On digital soil mapping. Geoderma, 117(1-2): 3-52. doi: 10.1016/ S0016-7061(03)00223-4
    [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
    [69] Mulder V L, de Bruin S, Schaepman M E et al., 2011. The use of remote sensing in soil and terrain mapping—A review. Geoderma, 162(1-2): 1-19. doi: 10.1016/j.geoderma.2010.12. 018
    [70] Nachtergaele F, Velthuizen H Y, Verelst L et al., 2008. Harmonized World Soil Database (version 1.1). Food and Agriculture Organization of the United Nations, Rome, Italy.
    [71] Ohmaan J L, Gregory M J, 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastral Oregon, USA. Canadian Journal of Forest Research, 32(4): 725-741. doi:  10.1139/x02-011
    [72] Qadir M, Oster J D, 2004. Crop and irrigation management strategies for saline-sodic soils and waters aimed at environ­mentally sustainable agriculture. Science of the Total Envi­ronment, 323(1-3): 1-19. doi: 10.1016/j.scitotenv.2003. 10.012
    [73] Qadir M, Tubeileh A, Akhtar J et al., 2008. Productivity enhancement of salt-affected environments through crop diversification. Land Degradation & Development, 19(4): 429-453. doi:  10.1002/ldr.853
    [74] Qiao Mu, Zhou Shengbin, Lu Lei et al., 2011. Temporal and spatial changes of soil salinization and improved countermeasures of Tarim Basin Irrigation District in recent 25a. Arid Land Geography, 34(4): 604-613. (in Chinese)
    [75] Qiao Mu, Zhou Shengbin, Lu Lei et al., 2012. Causes and spatial-temporal changes of soil salinization in Weigan River basin, Xinjiang. Progress in Geography, 31(7): 904-910. (in Chinese)
    [76] Qin C, Zhu A X, Pei T et al., 2007. An adaptive approach to selecting a flow-partition exponent for a multiple-flow- direction algorithm. International Journal of Geographical Information Science, 21(4): 443-458. doi: 10.1080/136588106 01073240
    [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)
    [81] Snyder W C, Wan Z, Zhang Y et al., 1998. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote sensing, 19(14): 2753-2774. doi:  10.1080/014311698214497
    [82] Soil Survey Staff of Xinjiang, 1996. Soil of Xinjiang Province. Beijing: Science Press, 52-53. (in Chinese)
    [83] Song Chuangye, Huang Chong, Liu Huiming, 2013. Predictive vegetation mapping approach based on spectral data, DEM and Generalized Additive Models. Chinese Geographical Science, 23(3): 331-343. doi:  10.1007/s11769-013-0590-0
    [84] Szabolcs I, 1992. Salinization of soil and water and its relation to desertification. Desertification Control Bulletin, 21: 32-37.
    [85] Wang F, Chen X, Luo G P et al., 2013. Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery. Journal of Arid Land, 5(3): 340-353. doi:  10.1007/s40333-013-0183-x
    [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)
    [87] Wang K, Wang P, Sparrow M et al., 2005. Estimation of surface long wave radition and broadband emissivity using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature/emissivity products. Journal of Gephysical Research, 110: D11109. doi:  10.1029/2004JD005566
    [88] Wang Q, Li P H, Chen X, 2012. Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma, 170: 103-111. doi:  10.1016/j.geoderma.2011.10.015
    [89] Wang Y, Li Y, 2013. Land exploitation resulting in soil salinization in a desert-oasis ecotone. Catena, 100: 50-56. doi:  10.1016/j.catena.2012.08.005
    [90] Wei S, Dai Y, Liu B et al., 2012. A soil particle-size distribution dataset for regional land and climate modelling in China. Geoderma, 171-172: 85-91. doi: 10.1016/j.geoderma. 2011.01. 013
    [91] Yang L, Zhu A X, Qi F et al., 2012. An integrative hierarchical stepwise sampling strategy for spatial sampling and its application in digital soil mapping. International Journal of Geographical Information Science, 27(1): 1-23. doi:  10.1080/13658816.2012.658053
    [92] Zhang F, Tiyip T, Ding J et al., 2012. Spectral reflectance properties of major objects in desert oasis: A case study of the Weigan-Kuqa river delta oasis in Xinjiang, China. Environ­mental Monitoring and Assessment, 184(8): 5105-5119. doi:  10.1007/s10661-011-2326-x
    [93] Zhang H , Wu J W, Zheng Q H et al., 2003. A preliminary study of oasis evolution in the Tarim Basin, Xinjiang, China. Journal of Arid Environments, 55(3): 545-553. doi: 10.1016/ S0140-1963(02)00283-5
    [94] Zhu A X, Band L E, Dutton B et al., 1996. Automated soil inference under fuzzy logic. Ecological Modelling, 90(2): 123-145. doi:  10.1016/0304-3800(95)00161-1
    [95] Zhu A X, Band L, Vertessy R et al., 1997. Derivation of soil properties using a Soil Land Inference Model (SoLIM). Soil Science Society of America Journal, 61(2): 523-533. doi:  10.2136/sssaj1997.03615995006100020022x
    [96] Zhu A X, Hudson B, Burt J et al., 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal, 65(5): 1463-1472. doi: 10.2136/sssaj2001. 6551463x
    [97] Zhu A X, Qi F, Moore A et al., 2010b. Prediction of soil properties using fuzzy membership values. Geoderma, 158(3-4): 199-206. doi:  10.1016/j.geoderma.2010.05.001
    [98] Zhu A X, Yang L, Li B et al., 2008. Purposive sampling for digital soil mapping for areas with limited data. In: Hartemink A E (eds). Digital Soil Mapping with Limited Data. Netherlands: Springer, 233-245.
    [99] Zhu A X, Yang L, Li B et al., 2010a. Construction of membership functions for predictive soil mapping under fuzzy logic. Geoderma, 155(3-4): 164-174. doi: 10.1016/j.geoderma.2009. 05.024
    [100] Zhu B Q, Yang X P, Rioual P et al., 2011. Hydrogeochemistry of three watersheds (the Erlqis, Zhungarer and Yili) in northern Xinjiang, NW China. Applied Geochemistry, 26(8): 1535-1548. doi:  10.1016/j.apgeochem.2011.06.018
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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
Funds:  Under the auspices of Key Program of National Science Foundation of China (No. 41361140361)
    Corresponding author: CHEN Xi. E-mail:Chenxi@ms.xjb.ac.cn
  • Received Date: 2013-12-16
  • Rev Recd Date: 2014-04-14
  • Publish Date: 2015-03-27

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.

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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