Soil Column Sample Height Influences Soil Spectral Reflectance in Laboratory Experiment

  • Lu XuEmail author
  • Zhi-chun Wang
  • John Maina Nyongesah
Research Article


Soil salt inversion models are basically calibrated in laboratory experiments and validated in the field. Unified standard about settings of soil column sample height (SCSH) is lacking. The main objective of this study was to identify the optimal SCSH for soil salinity estimation. Three types of soil particle size simulating soil roughness and ten different heights of soil column samples from 1 to 10 cm were designed, according to the previous studies, and soil spectral reflectance was measured before and after irrigation with deionized water. The spectrum, soil water content and soil salt amount (SA) on the surface of various soil column SHs in complex soil water system and soil roughness conditions were compared. Two-way ANOVA was applied to assess the effect of soil particle and salt crystallization on the spectral reflectance. Results indicated that SCSH caused distinct difference in salt accumulation at the surface (critical height is 6 cm) and in soil spectral reflectance (critical height is 2 cm) at the final state. Soil roughness influenced spectra significantly between the low-height samples (1–5 cm) and was covered up with SA at the surface between the high samples (6–10 cm). Considering the actual condition in the field, greater than 6 cm was deemed as an exact selection for saline soil in the region (Na2SO4). These results were critical to model soil salinity in laboratory experiment based on hyperspectral reflectance.


Salt-affected soil Soil column sample height Hyperspectral reflectance Laboratory experiment 



This study was supported by the Science Foundation of Jiangsu Normal University (16XLR036), the National Key Research and Development Program of China (2016YFC0501200), and the National Natural Science Foundation of China (41571210, 41807001, 41671395). We thank the staff at Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences and Wulanwusu Agricultural Meteorological Station for their facilities and support.


  1. Bauer, P., Thabeng, G., Stauffer, F., & Kinzelbach, W. (2004). Estimation of the evapotranspiration rate from diurnal groundwater level fluctuations in the Okavango Delta, Botswana. Journal of Hydrology, 288(3–4), 344–355. Scholar
  2. Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., et al. (2006). Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1), 54–67. Scholar
  3. Ben-Dor, E. (2002). Quantitative remote sensing of soil properties. Advances in Agronomy, 75(75), 173–243. Scholar
  4. Bittelli, M., Ventura, F., Campbell, G. S., Snyder, R. L., Gallegati, F., & Pisa, P. R. (2008). Coupling of heat, water vapor, and liquid water fluxes to compute evaporation in bare soils. Journal of Hydrology, 362(3–4), 191–205. Scholar
  5. Boufadel, M. C., Suidan, M. T., & Venosa, A. D. (1999). Numerical modeling of water flow below dry salt lakes: Effect of capillarity and viscosity. Journal of Hydrology, 221(1–2), 55–74. Scholar
  6. Cetin, M., & Kirda, C. (2003). Spatial and temporal changes of soil salinity in a cotton field irrigated with low-quality water. Journal of Hydrology, 272(1–4), 238–249. Scholar
  7. Chanzy, A., & Bruckler, L. (1993). Significance of soil surface moisture with respect to daily bare soil evaporation. Water Resources Research, 29(4), 1113–1125. Scholar
  8. Chen, J. G., Chen, J., Wang, Q. J., Zhang, Y., Ding, H. F., & Huang, Z. (2016). Retrieval of soil dispersion using hyperspectral remote sensing. Journal of the Indian Society of Remote Sensing, 44(4), 563–572. Scholar
  9. Corwin, D. L., & Lesch, S. M. (2005). Characterizing soil spatial variability with apparent soil electrical conductivity. Computers and Electronics in Agriculture, 46(1–3), 135–152. Scholar
  10. Dehaan, R. L., & Taylor, G. R. (2002). Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sensing of Environment, 80(3), 406–417. Scholar
  11. Dehni, A., & Lounis, M. (2012). Remote sensing techniques for salt affected soil mapping: Application to the Oran region of Algeria. Procedia Engineering, 33, 188–198. Scholar
  12. Farifteh, J. (2011). Interference of salt and moisture on soil reflectance spectra. International Journal of Remote Sensing, 32(23), 8711–8724. Scholar
  13. Farifteh, J., Tolpekin, V., Van Der Meer, F., & Sukchan, S. (2010). Salinity modelling by inverted Gaussian parameters of soil reflectance spectra. International Journal of Remote Sensing, 31(12), 3195–3210. Scholar
  14. Fauck, R. (1977). Influences of agricultural practices on soil degradation. In FAO Soil Bulletin No. 34: Assessing soil degradation.Google Scholar
  15. Gao, J. F., Li, Y., Chen, S. P., & Ma, X. Y. (2010). Effects of soil column heights on movement of soil water and soil salt during evaporation under perforated plastic mulch. Transactions of the Chinese Society of Agricultural Machinery, 41(9), 50–55.Google Scholar
  16. Gholizadeh, A., Amin, M. S. M., Boruvka, L., & Saberioon, M. M. (2014). Models for estimating the physical properties of paddy soil using visible and near infrared reflectance spectroscopy. Journal of Applied Spectroscopy, 81(3), 534–540. Scholar
  17. Leone, A. P., & Sommer, S. (2000). Multivariate analysis of laboratory spectra for the assessment of soil development and soil degradation in the southern Apennines (Italy). Remote Sensing of Environment, 72(3), 346–359. Scholar
  18. Li, W. H., Weeks, R., & Gillespie, A. R. (1998). Multiple scattering in the remote sensing of natural surfaces. International Journal of Remote Sensing, 19(9), 1725–1740. Scholar
  19. Liang, S. L. (2004). Quantitative remote sensing of land surfaces. Hoboken: Wiley.Google Scholar
  20. Lobell, D. B., & Asner, G. P. (2002). Moisture effects on soil reflectance. Soil Science Society of America Journal, 66(2), 722–727.CrossRefGoogle Scholar
  21. Luo, G. P., Chen, X., Zhou, K. F., & Ye, M. Q. (2003). Temporal and spatial variation and stability of the oasis in the Sangong River watershed, Xinjiang, China. Science in China Series D-Earth Sciences, 46(1), 62–73. Scholar
  22. Mahfouf, J. F., & Noilhan, J. (1991). Comparative-study of various formulations of evaporation from bare soil using insitu data. Journal of Applied Meteorology, 30(9), 1354–1365.;2.CrossRefGoogle Scholar
  23. Mashimbye, Z. E., Cho, M. A., Nell, J. P., De Clercq, W. P., Van Niekerk, A., & Turner, D. P. (2012). Model-based integrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data: A case study of selected South African soils. Pedosphere, 22(5), 640–649. Scholar
  24. Melendez-Pastor, I., Navarro-Pedreno, J., Koch, M., & Gomez, I. (2010). Applying imaging spectroscopy techniques to map saline soils with ASTER images. Geoderma, 158(1–2), 55–65. Scholar
  25. Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85(1), 1–20. Scholar
  26. Mitran, T., Ravisankar, T., Fyzee, M. A., Suresh, J. R., Sujatha, G., & Sreenivas, K. (2014). Retrieval of soil physicochemical properties towards assessing salt-affected soils using hyperspectral data. Geocarto International. Scholar
  27. Parlange, M. B., Cahill, A. T., Nielsen, D. R., Hopmans, J. W., & Wendroth, O. (1998). Review of heat and water movement in field soils. Soil & Tillage Research, 47(1–2), 5–10. Scholar
  28. Qadir, M., Schubert, S., Ghafoor, A., & Murtaza, G. (2001). Amelioration strategies for sodic soils: A review. Land Degradation and Development, 12(4), 357–386. Scholar
  29. Shamsi, S. R. F., Zare, S., & Abtahi, S. A. (2013). Soil salinity characteristics using moderate resolution imaging spectroradiometer (MODIS) images and statistical analysis. Archives of Agronomy and Soil Science, 59(4), 471–489. Scholar
  30. Srivastava, R., Sarkar, D., Mukhopadhayay, S. S., Sood, A., Singh, M., Nasre, R. A., et al. (2015). Development of hyperspectral model for rapid monitoring of soil organic carbon under precision farming in the Indo-Gangetic Plains of Punjab, India. Journal of the Indian Society of Remote Sensing, 43(4), 751–759. Scholar
  31. Srivastava, R., Sethi, M., Yadav, R. K., Bundela, D. S., Singh, M., Chattaraj, S., et al. (2017). Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the Indo-Gangetic plains of Haryana, India. Journal of the Indian Society of Remote Sensing, 45(2), 307–315. Scholar
  32. Toya, T., & Yasuda, N. (1988). Parameterization of evaporation from a non-saturated bare surface for application in numerical prediction models. Journal of the Meteorological Society of Japan, 66(5), 729–739.CrossRefGoogle Scholar
  33. Wang, Q., Li, P., & Chen, X. (2012). Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma, 170, 103–111. Scholar
  34. Wang, Q., Li, P., Maina, J. N., & Chen, X. (2013). Study of how salt types greatly shape soil reflectance spectra versus salt concentrations. Communications in Soil Science and Plant Analysis, 44(9), 1503–1510. Scholar
  35. Wang, Q., Li, P., Pu, Z., & Chen, X. (2011). Calibration and validation of salt-resistant hyperspectral indices for estimating soil moisture in arid land. Journal of Hydrology, 408(3–4), 276–285. Scholar
  36. Wang, Y. G., Xiao, D. N., Li, Y., & Li, X. Y. (2008). Soil salinity evolution and its relationship with dynamics of groundwater in the oasis of inland river basins: Case study from the Fubei region of Xinjiang Province, China. Environmental Monitoring and Assessment, 140(1–3), 291–302. Scholar
  37. Yanful, E. K., & Mousavi, S. M. (2003). Estimating falling rate evaporation from finite soil columns. Science of the Total Environment, 313(1–3), 141–152. Scholar
  38. Zribi, M., Baghdadi, N., Holah, N., & Fafin, O. (2005). New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sensing of Environment, 96(3–4), 485–496. Scholar

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© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.School of Geography, Geomatics and PlanningJiangsu Normal UniversityXuzhouChina
  2. 2.Xinjiang Institute of Ecology and Geography, CASUrumqiChina
  3. 3.Northeast Institute of Geography and Agroecology, CASChangchunChina
  4. 4.Jaramogi Oginga, Odinga University of Science and TechnologyBondoKenya

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