Chinese Geographical Science

, Volume 29, Issue 5, pp 784–797 | Cite as

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

  • Zhen Li
  • Yong Li
  • An Xing
  • Zhiqing Zhuo
  • Shiwen Zhang
  • Yuanpei Zhang
  • Yuanfang HuangEmail author


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.


soil salinity environmental variable random forest regression geographic weighted regression Yinchuan Plain irrigation oasis 


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  1. Abbas A, Khan S, 2007. Using remote sensing techniques for appraisal of irrigated soil salinity. MODSIM 2007: International Congress on Modelling and simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability.Google Scholar
  2. Abou Samra Rasha M, Ali R R, 2018. The development of an overlay model to predict soil salinity risks by using remote sensing and GIS techniques: a case study in soils around Idku Lake, Egypt. Environmental Monitoring and Assessment, 190(12): 706–722. doi: 10.1007/s10661-018-7079-3Google Scholar
  3. Aldabaa A A A, Weindorf D C, Chakraborty S et al., 2015. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma, 239: 34–46. doi: 10.1016/j.geoderma.2014.09.011CrossRefGoogle Scholar
  4. Allbed A, Kumar L, 2013. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in Remote Sensing, 2: 373–385. doi: 10.4236/ars.2013.24040CrossRefGoogle Scholar
  5. Allbed A, Kumar L, Sinha P, 2014. Mapping and modelling spatial variation in soil salinity in the Al Hassa oasis based on remote sensing indicators and regression techniques. Remote Sensing, 6: 1137–1157. doi: 10.3390/rs6021137CrossRefGoogle Scholar
  6. Bannari A, Guedon A M, El-Harti A et al., 2008. Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor. Communications in Soil Science and Plant Analysis, 39: 2795–2811. doi: 10.1080/0010362080243271CrossRefGoogle Scholar
  7. Bao Shidan, 2000. Soil and Agricultural Chemistry Analysis. Beijing: Chinese Agricultural press. (in Chinese)Google Scholar
  8. Bouaziz M, Matschullat J, Gloaguen R, 2011. Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil. Comptes Rendus Geoscience, 343: 795–803. doi: 10.1016/j.crte.2011.09.003CrossRefGoogle Scholar
  9. Breiman L, 2001. Classification and regression by randomForest. Machine Learning, 45(1): 5–32. doi: 10.1023/a:1010933404324CrossRefGoogle Scholar
  10. Cai S M, Zhang R Q, Liu L M et al., 2010. A method of salt-affected soil information extraction based on a support vector machine with texture features. Mathematical and Computer Modelling, 51: 1319–1325. doi: 10.1016/j.mcm.2009.10.037CrossRefGoogle Scholar
  11. Conrad O, Bechtel B, Bock M et al., 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development Discussions. 8(2): 2271–2312. doi: 10.5194/gmdd-8-2271-2015CrossRefGoogle Scholar
  12. Dou C Y, Kang Y H, Wan S Q et al., 2011. Soil salinity changes under cropping with lycium barbarum l. and irrigation with saline-sodic water. Pedosphere, 21: 539–548. doi: 10.1016/S1002-0160(11)60156-2CrossRefGoogle Scholar
  13. Douaoui A E K, Nicolas H, Walter C, 2006. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134: 217–230. doi: 10.1016/j.geod erma.2005.10.009CrossRefGoogle Scholar
  14. El Harti A, Lhissou R, Chokmani K et al., 2016. Spatiotemporal monitoring of soil salinization in irrigated Tadla plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation, 50: 64–73. doi: 10.1016/j.jag.2016.03.008CrossRefGoogle Scholar
  15. Elnaggar Abdelhamid A, Noller Jay S, 2010. Application of remote-sensing data and decision tree analysis to mapping salt-affected soils over large areas. Remote Sensing, 2: 151–165. doi: 10.3390/rs2010151CrossRefGoogle Scholar
  16. Fan X W, Liu Y B, Tao J M et al., 2015. Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression. Remote Sensing, 7: 488–511. doi: 10.3390/rs70100488CrossRefGoogle Scholar
  17. Farifteh J, Van der Meer F, Atzberger C et al., 2007. Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sensing of Environment, 110: 59–78. doi: 10.1016/j.rse.2007.02.005CrossRefGoogle Scholar
  18. Gill Bruce C, Terry Alister D, 2016. Keeping salt on the farm-Evaluation of an on-farm salinity management system in the Shepparton irrigation region of South-East Australia. Agricultural Water Management, 164: 291–303. doi: 10.1016/j.agwat.2015.10.014CrossRefGoogle Scholar
  19. Huang Yajie, Li Zhen, Ye Huichun et al., 2019. Mapping soil electrical conductivity using Ordinary Kriging combined with Back-propagation network. Chinese Geographical Science, 29(2): 270–282. doi: 10.1007/s11769-019-1027-1CrossRefGoogle Scholar
  20. Immitzer M, Atzberger C, Koukal T, 2012. Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data. Remote Sensing, 4: 2661–2693. doi: 10.3390/rs4092661CrossRefGoogle Scholar
  21. Jiang H, Rusuli Y, Amuti T, et al., 2019. Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network. International Journal of Remote Sensing, 40(1): 284–306, doi: 10.1080/01431161.2018.1513180CrossRefGoogle Scholar
  22. Khan N M, Rastoskuev V V, Sato Y et al., 2005. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77: 96–109. doi: 10.1016/j.agwat.2004.09.038CrossRefGoogle Scholar
  23. Konukcu F, Gowing J W, Rose D A, 2006. Dry drainage: A sustainable solution to waterlogging and salinity problems in irrigation areas? Agricultural Water Management, 83: 1–12. doi: 10.1016/j.agwat.2005.09.003CrossRefGoogle Scholar
  24. Koohafkan P, Stewart B A, 2008. Water and Cereals in Drylands. The Food and Agriculture Organization of the United Nations and Earth scan.Google Scholar
  25. Wang K, Zhang C R, Li W D, 2012. Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter. GIScience and Remote Sensing, 49: 915–932. doi: 10.2747/1548-1603.49.6.915CrossRefGoogle Scholar
  26. Li Zhen, Zhang Shiwen, Cao Meng et al., 2018. Spatial interpolation of soil mechanical composition based on the spherical coordinate transform method. Transactions of the Chinese society for Agricultural Machinery, 49(03): 295–302. (in Chinese)Google Scholar
  27. Liu M L, Liu X N, Jiang J L et al., 2013. Artificial Neural Network and Random Forest Approaches for Modeling of Sea Surface Salinity. International Journal of Remote Sensing Applications, 3(4): 229–235. doi: 10.14355/ijrsa.2013.0304.08CrossRefGoogle Scholar
  28. Lu D S, Li G Y, Moran E et al., 2014. The roles of textural images in improving land-cover classification in the Brazilian Amazon. International Journal of Remote Sensing, 35: 8188–8207. doi: 10.1080/01431161.2014.980920CrossRefGoogle Scholar
  29. Lu W, Lu D S, Wang G X G et al., 2018. Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data. Catena, 165: 576–589. doi: 10.1016/j.catena.2018.03.007CrossRefGoogle Scholar
  30. Ma L G, Yang S T, Simayi Z et al., 2018. Modeling variations in soil salinity in the oasis of Junggar Basin, China. Land Degradation and Development, 29: 551–562. doi: 10.1002/ldr.2890CrossRefGoogle Scholar
  31. Nanni M R, Demattê J A M, 2006. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal, 70: 393–407. doi: 10.2136/sssaj2003.0285CrossRefGoogle Scholar
  32. Peng J, Biswas A, Jiang Q S et al., 2019. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337: 1309–1349. doi: 10.1016/j.geoderma.2018.08.006CrossRefGoogle Scholar
  33. Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M et al., 2015. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71: 804–818. doi: 10.1016/j.oregeorev.2015.01.001CrossRefGoogle Scholar
  34. Shrestha R P, 2006. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation and Development, 17: 677–689. doi: 10.1002/ldr.752CrossRefGoogle Scholar
  35. Sidike A, Zhao S H, Wen Y M, 2014. Estimating soil salinity in Pingluo county of China using QuickBird data and soil reflectance spectra. International Journal of Applied Earth Observation and Geoinformation, 26: 156–175. doi: 10.1016/j.jag.2013.06.002CrossRefGoogle Scholar
  36. Taghizadeh-Mehrjardi R, Ayoubi S, Namazi Z et al., 2016. Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming. Arid Land Research and Management, 30(1): 49–64. doi: 10.1080/15324982.2015.1046092CrossRefGoogle Scholar
  37. Vermeeulen D, Van Niekert A, 2017. Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates. Geoderma, 299: 1–12. doi: 10.1016/j.geoderma.2017.03.013CrossRefGoogle Scholar
  38. Wang B, Waters C, Orgill S et al., 2018a. Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological Indicators, 88: 425–438. doi: 10.1016/j.ecolind.2018.01.049CrossRefGoogle Scholar
  39. Wang B, Waters C, Orgill S et al., 2018b. High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Science of Total Environment, 630: 367–378. doi: 10.1016/j.scitotenv.2018.02.204CrossRefGoogle Scholar
  40. Whitney K, Scudiero E, El-Askary H M et al., 2018. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecological Indicators, 93: 889–898. doi: 10.1016/j.ecolind.2018.05.069CrossRefGoogle Scholar
  41. Wu C S, Liu G H, Huang C, 2016. Prediction of soil salinity in the Yellow River Delta using geographically weighted regression. Archives of Agronomy and Soil Science, 63: 928–941. doi: 10.1080/03650340.2016.1249475CrossRefGoogle Scholar
  42. Yu R H, Liu T X, Xu Y P et al., 2010. Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China. Agriculture Water Management. 97: 1952–1960. Doi: 10.1016/j.agwat.2010.03.009CrossRefGoogle Scholar
  43. Zhang T T, Qi J G, Gao Y et al., 2015. Detecting soil salinity with MODIS time series VI data. Ecological Indicators, 52: 480–489. doi: 10.1016/j.ecolind.2015.01.004CrossRefGoogle Scholar
  44. Zhang Y P, Hu K L, Li B G et al., 2009. Spatial distribution pattern of soil salinity and saline soil in Yinchuan plain of China. Transactions of the CSAE, 25(7): 19–24. (in Chinese)Google Scholar
  45. Zhou D, Lin Z L, Liu L M, 2012. Regional land salinization assessment and simulation through cellular Automaton-Markov modeling and spatial pattern analysis. Science of Total Environment, 439: 260–274. doi: 10.1016/j.scitotenv.2012.09.013CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhen Li
    • 1
  • Yong Li
    • 1
  • An Xing
    • 1
  • Zhiqing Zhuo
    • 1
  • Shiwen Zhang
    • 2
  • Yuanpei Zhang
    • 3
  • Yuanfang Huang
    • 1
    Email author
  1. 1.College of Resources and Environment SciencesChina Agricultural UniversityBeijingChina
  2. 2.School of Earth and EnvironmentAnhui University of Science and TechnologyHuainanChina
  3. 3.Institute of Crop SciencesNingxia Academy of Agricultural and Forestry SciencesYinchuanChina

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