Chinese Geographical Science

, Volume 25, Issue 3, pp 283–294 | Cite as

Mapping soil salinity using a similarity-based prediction approach: A case study in Huanghe River Delta, China

  • Lin Yang
  • Chong Huang
  • Gaohuan Liu
  • Jing Liu
  • A-Xing Zhu


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abbas A, Khan S, Hussain N et al., 2013. Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Physics and Chemistry of the Earth, 55–57: 43–52. doi: 10.1016/j.pce.2010.12.004 CrossRefGoogle Scholar
  2. Akramkhanov A, Martius C, Park S J et al., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163(1–2): 55–62. doi: 10.1016/j.geoderma.2011.04.001 CrossRefGoogle Scholar
  3. Cui B S, Tang N, Zhao X S et al., 2009. A management-oriented valuation method to determine ecological water requirement for wetlands in the Yellow River Delta of China. Journal for Nature Conservation, 17(3): 129–141. doi: 10.1016/j.jnc.2009.01.003 CrossRefGoogle Scholar
  4. Fan X, Pedroli B, Liu G H et al., 2012. Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Degradation and Development, 23(2): 175–189. doi: 10.1002/ldr.1071 CrossRefGoogle Scholar
  5. Fang H L, Liu G H, Kearney M, 2005. Georelational analysis of soil type, soil salt content, landform, and land use in the Yellow River Delta, China. Environment Management, 35(1): 72–83. doi: 10.1007/s00267-004-3066-2 CrossRefGoogle Scholar
  6. Farifteh J, Farshad A, George R J, 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3–4): 191–206. doi: 10.1016/j.geoderma.2005.02.003 CrossRefGoogle Scholar
  7. Ghassemi F, Jakerman A J, Nix H A, 1995. Salinization of Land Water Resources. Wallingford: CAB International.Google Scholar
  8. Gower J C, 1971. A general coefficient of similarity and some of its properties. Biometrics, 27(4): 857–871. doi: 10.2307/2528823 CrossRefGoogle Scholar
  9. Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regression- kriging: from equations to case studies. Computers & Geosciences, 33(10): 1301–1315. doi: 10.1016/j.cageo.2007.05.001 CrossRefGoogle Scholar
  10. Jafari A, Finke P A, de Wauw J V et al., 2012. Spatial prediction of USDA-great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. European Journal of Soil Science, 63(2): 284–298. doi: 10.1111/j.1365-2389.2012.01425.x CrossRefGoogle Scholar
  11. Li S N, Wang G X, Deng W et al., 2009. Influence of hydrology process on wetland landscape pattern: a case study in the Yellow River Delta. Ecological Engineering, 35(12): 1719–1726. doi: 10.1016/j.ecoleng.2009.07.009 CrossRefGoogle Scholar
  12. Liu Jing, 2010. Mapping Soil Properties Using Individual Representativeness of Samples over Large Area. Beijing, Beijing Normal University. (in Chinese)Google Scholar
  13. Ma Yulei, Wang De, Liu Junmin et al., 2013. Relationships between typical vegetations, soil salinity, and groundwater depth in the Yellow River Delta of China. Chinese Journal of Applied Ecology, 24(9): 2423–2430. (in Chinese)Google Scholar
  14. 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 CrossRefGoogle Scholar
  15. Minasny B, McBratney A B, Mendonca-Santos M L et al., 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal of Soil Research, 44(3): 233–244. doi: 10.1071/SR05136 CrossRefGoogle Scholar
  16. Mougenot B, Pouget M, Epema G, 1993. Remote sensing of salt-affected soils. Remote Sensing Reviews, 7(3–4): 241–259. doi: 10.1080/02757259309532180 CrossRefGoogle Scholar
  17. Qin C Z, 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: 10.1080/13658810601073240 CrossRefGoogle Scholar
  18. Qin C Z, Lu Y J, Li B L et al., 2009. Simple digital terrain analysis software (SimDTA 1.0) and its application in fuzzy classification of slope positions. Journal of Geo-Information Science, 11(6): 737–743. (in Chinese)CrossRefGoogle Scholar
  19. Quinn P, Beven K J, Lamb R, 1995. The ln(a/tanb) index: how to calculate it and how to use it within the TOPMODEL framework. Hydrological Processes, 9(2): 161–182. doi: 10.1002/hyp.3360090204 CrossRefGoogle Scholar
  20. Rodgers J L, Nicewander W A, 1988. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1): 59–66. doi: 10.1080/00031305.1988.10475524 CrossRefGoogle Scholar
  21. Scull P, Franklin J, Chadwick O A, 2005. The application of classification of tree analysis to soil type prediction in a desert landscape. Ecological Modelling, 181(1): 1–15. doi: 10.1016/j.ecolmodel.2004.06.036 CrossRefGoogle Scholar
  22. Sheng J D, Ma L C, 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: 1944–1951. doi: 10.1016/j.agwat.2009.04.011 CrossRefGoogle Scholar
  23. Shi X, Zhu A X, Burt J E et al., 2004. A case-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal, 68(3): 885–894. doi: 10.2136/sssaj2004.8850 CrossRefGoogle Scholar
  24. Song Chuangye, Huang Chong, Liu Huimin, 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 CrossRefGoogle Scholar
  25. Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F et al., 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213: 15–28. doi: 10.1016/j.geoderma.2013.07.020 CrossRefGoogle Scholar
  26. Triantafilis J, Odeh I O A, Mcbratney A B, 2001. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of America Journal, 65(3): 869–878. doi: 10.2136/sssaj2001.653869x CrossRefGoogle Scholar
  27. Wang X G, Lian Y, Huang C et al., 2011. Environmental flows and its evaluation of restoration effect based on LEDESS model in Yellow River Delta wetlands. Mitigation and Adaptation Strategies for Global Change, 17(4): 357–367. doi: 10.1007/s11027-011-9330-x CrossRefGoogle Scholar
  28. Webster R, Oliver M A, 2001. Geostatistics for Environmental Science. Toronto, Canada, John Wiley and Sons, LTD.Google Scholar
  29. Xie T, Liu X H, Sun T, 2011. The effects of groundwater table and flood irrigation strategies on soil water and salt dynamics and reed water use in the Yellow River Delta, China. Ecological Modelling, 222(2): 241–252. doi: 10.1016/j.ecolmodel.2010.01.012 CrossRefGoogle Scholar
  30. Xu Xuegong, 1997. An analysis on the land structure in the Yellow River Delta. Acta Geographical Sinica, 64(1): 18–26. (in Chinese)Google Scholar
  31. Yang L, Zhu A X, Qi F et al., 2013. An integrative hierarchical stepwise sampling strategy and its application in digital soil mapping. International Journal of Geographical Information Science, 27(1): 1–23. doi: 10.1080/13658816.2012.658053 CrossRefGoogle Scholar
  32. Yao R J, Yang J S, 2010. Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method. Agricultural Water Management, 97(12): 1961–1970. doi: 10.1016/j.agwat.2010.02.001 CrossRefGoogle Scholar
  33. Ye Q H, Liu G H, Tian G L, 2004. Geospatial-temporal analysis of land-use changes in the Yellow River Delta in the last 40 years. Science in China Series D Earth Sciences, 47(11): 1008–1024. doi: 10.1360/03yd0151 CrossRefGoogle Scholar
  34. Zhang T T, Zeng S L, Gao Y et al., 2011. Assessing impact of land uses on land salinization in the Yellow River Delta, China using an integrated and spatial statistical model. Land Use Policy, 28(4): 857–866. doi: 10.1016/j.landusepol.2011.03.002 CrossRefGoogle Scholar
  35. Zhao X S, Cui B S, Sun T et al., 2010. The relationship between the spatial distribution of vegetation and soil environmental factors in the tidal creek areas of the Yellow River Delta. Ecology and Environmental Sciences, 19(8): 1855–1861. (in Chinese)Google Scholar
  36. Zhou W Z, Tian Y Z, Zhu L F, 2007. Land use/land cover change in Yellow River Delta China during fast development period. Conference on Remote Sensing and Modelling of Ecosystems for Sustainability IV, San Antonio, CA. doi: 10.1117/12.734015 Google Scholar
  37. Zhu A X, 1997. A similarity model for representing soil spatial information. Geoderma, 77: 217–242. doi: 10.1016/S0016-7061(97)00023-2 CrossRefGoogle Scholar
  38. Zhu A X, Band L E, 1994. A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing, 20: 408–418. doi: 10.1080/07038992.1994.10874583 CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Lin Yang
    • 1
  • Chong Huang
    • 1
  • Gaohuan Liu
    • 1
  • Jing Liu
    • 2
  • A-Xing Zhu
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.Department of GeographyUniversity of Wisconsin-MadisonMadisonUSA
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of GeographyNanjing Normal UniversityNanjingChina

Personalised recommendations