Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin
Mirzakhayot Ibrakhimova*, Usman Khalid Awanb, Murodjon Sultanova, Akmal Akramkhanovb, Kakhramon Djumaboevc, Christopher Conradd,e, John Lamersf
a Khorezm Rural Advisory Support Service (KRASS), Urgench, Khorezm, Uzbekistan
b International Center for Agricultural Research in Dry Areas (ICARDA), Tashkent, Uzbekistan
c International Water Management Institute (IWMI), Central Asia Regional Office, Tashkent, Uzbekistan
d University of Würzburg Institute of Geography and Geology, Department of Remote Sensing, Würzburg, Germany
e University of Halle, Institute of Geosciences and Geography, Halle, Germany
f Center for Development Research (ZEF), Department of Ecology and Resource Management, University of Bonn. Bonn, Germany
* Corresponding author: firstname.lastname@example.org://doi.org/10.29258/CAJWR/2019-R1.v5-2/64-81eng
Accurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable prediction with weak (R2=0.15–0.29) relationship of the salinity maps produced with RS and interpolation of electromagnetic EM38 during growth periods and more reliable (R2=0.35–0.56) beyond irrigation periods. Modeling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths showed that irrigation forces salts to move to deeper layers: salts reappear in the upper profile during dry periods. Beyond irrigation events, salts gradually accumulated in the upper soil layers without fluctuations. Coupling RS techniques with numerical modeling provided better insight into salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers since the combination of methods will allow for better planning and management.Download the article
How to cite: Ibrakhimov, M., Awanb, U. K., Sultanov, M., Akramkhanov, A., Djumaboev, K., Conrad, C., & Lamers, J. (2020). Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin. Central Asian Journal of Water Research, 5(2), 64–81. https://doi.org/10.29258/cajwr/2019-r1.v5-2/64-81eng
Abbas, A., Khan, S., Akbar, S., Hanjra, M.A. and Hussain, N., 2013. Characterizing Soil Salinity in Irrigated Agriculture using a Remote Sensing Approach. Physics and Chemistry of the Earth, Vol. 55–57, pp. 43-52. Available at: https://doi.org/10.1016/j.pce.2010.12.004.
Akramkhanov, A., Martius, C., Park, S. J., and Hendrickx, J. M. H., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, Vol. 163, No 1–2, pp. 55–62.
Akramkhanov, A., Kuziev, R., Sommer, R, Martius, C., Forkutsa, O. and Massucati, L., 2012. Soils and Soil Ecology in Khorezm.
Allen, R.G., Pereira, L.C., Raes, D. and Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements, pp. 1-300 FAO Irrigation and Drainage paper 56. FAO, Rome.
Awan, U. K., Tischbein, B., Conrad, C., Martius, C. and Hafeez, M., 2011. Remote Sensing and Hydrological Measurements for Irrigation Performance Assessments in a Water User Association in the Lower Amu Darya River Basin. Water Resources Management, Vol. 25, No 10, pp. 2467–2485.
Bannari, A., Guedon, A. M., El-Harti, A., Cherkaoui, F. Z. and El-Ghmari, A., 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, Vol. 39, No 19, pp. 2795–2811. Available at: https://doi.org/10.1080/00103620802432717.
Bastiaanssen, W.G.M., Allen, R.G., Droogers, P., D’Urso, G., Steduto, P., 2007. Review: twenty-five years modeling irrigated and drained soils: state of the art. Agricultural Water Management, Vol. 92, pp. 111–125. Available at: https://doi.org/10.1016/j.agwat.
Bucknall, J., Klytchnikova, I., Lampietti, J., Lundell, M., Scatasta, M. and Thurman, M., 2003. Irrigation in Central Asia: social, economic and environmental considerations. World Bank, Washington, DC, 52p.
Conrad, C., Schorcht, G., Tischbein, B., Davletov, S., Sultonov, M. and Lamers, J. P. A., 2012. Agro-Meteorological Trends of Recent Climate Development in Khorezm and Implications for Crop Production.
Dwivedi, R.S., and Rao, B.R.M., 1992. The selection of the best possible Landsat TM band combination for delineating salt-affected soils. International Journal of Remote Sensing, Vol. 13, pp. 2051–2058.
Farifteh, J., Farshad, A. and George, R. J., 2005. Assessing Salt-Affected Soils Using Remote Sensing, Solute Modelling, and Geophysics. Geoderma, Vol. 130, No 3-4, pp. 191-206. Available at: http://dx.doi.org/10.1016/j.geoderma. 2005.02.003.
FAO, 2000. FAO Global information and early warning system on food and agriculture. Special report: FAO/WFP crop and food supply assessment mission to the Karakalpakstan and Khorezm regions of Uzbekistan. Rome: FAO, 2000.
FAO,, 2003. Agriculture, Food and Water. ISBN 92-5-104943-2
Forkutsa, I., Sommer, R., Shirokova, Y., Lamers, J. P. A., Kienzler, K., Tischbein, B., Martius, C. and Vlek, P. L. G., 2009. Modeling irrigated cotton with shallow groundwater in the Aral Sea Basin of Uzbekistan: II. Soil salinity dynamics, Irrigation Science, Vol. 27, pp. 319–330.
Furby, S., Caccetta, P. and Wallace, J., 2010. Salinity monitoring in Western Australia using remotely sensed and other spatial data. Journal of Environmental Quality, Vol. 39, pp. 16–25.
Golovina, N. N., Minskiy, D., Pankova, Y. and Solovyev, D. A., 1992. Automated air photo interpretation in the mapping of soil salinization in cotton-growing zones. Mapping Sciences and Remote Sensing, Vol. 29, pp. 262– 268.
Hunt, G., Salisbury, J. and Lenhoff, C., 1972. Visible and near infrared spectra of minerals and rocks: V. Halides, phosphates, arsenates, venadates and borates. Modern Geology, Vol. 3, pp. 121– 132.
Ibrakhimov, M., Khamzina, A., Forkutsa, I., Paluasheva, G., Lamers, J. P. A., Tischbein, B., Vlek, P. L. G. and Martius, C., 2007. Groundwater table and salinity: Spatial and temporal distribution and influence on soil salinization in Khorezm region (Uzbekistan, Aral Sea Basin). Irrigation and Drainage Systems, Vol. 21, pp. 219–236. Available at: https://doi.org/10.1007/s10795-007-9033-3.
Lhissou, R., El Harti, A. and Chokmani, K., 2014. Mapping soil salinity in irrigated land using optical remote sensing data. Eurasian Journal of Soil Science, Vol. 3, No 2, pp. 82–88.
Marquardt, D. W., 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics., Vol. 11, pp. 431-441.
Metternicht, G.I. and Zinck, J. A., 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, Vol. 85, pp. 1–20.
Metternicht, G. and Zinck, A., 2008. Remote Sensing of Soil Salinity: Impact on Land Management. CRC Press. Available at: http://dx.doi.org/10.1201/9781420065039.
Metternicht, G., 1998. Analysing the relationship between ground based reflectance and environmental indicators of salinity processes in the Cochabamba Valleys (Bolivia). International Journal of Ecology and Environmental Sciences 24, 359– 370.
Mulder, V. L, de Bruin, S., Schaepman, M. E. and Mayr, T. R., 2011 The use of remote sensing in soil and terrain mapping—a review. Geoderma, Vol. 162, No 1–2, pp. 1–19.
Oster, J. D. and Rhoades, J. D., 1990. Steady state root zone salt balance. In: Tanji, K. K. (ed.), Agricultural salinity assessment and management manual. ASCE, New York, pp. 469–481.
Rao, B., Sankar, T., Dwivedi, R., Thammappa, S., Venkataratnam, L., Sharma, R. and Das, S., 1995. Spectral behaviour of salt-affected soils. International Journal of Remote Sensing, Vol. 16, pp. 2125– 2136.
Singh, R., van Dam, J. C. and Jhorar, R.K., 2003. Water and salt balances at farmer fields. In van Dam, J. C. and Malik, R. S. (eds.), Water Productivity of irrigated crops in Sirsa district, India, integration of remote sensing, crop and soil models and geographical information systems. Wageningen University, Department of Water Resources, The Netherlands, p. 41-58.
Vogel, T. and Cislerova, M., 1988. On the reliability of unsaturated hydraulic conductivity calculated from the moisture retention curve. Transport in porous media, Vol. 3, pp. 1-15.
Šimunek, J., Šejna, M., Saito, H., Sakai, M. and van Genuchten, M.Th., 2008. The HYDRUS-1D Software Package for Simulating the Movement of Water, Heat, and Multiple Solutes in Variably Saturated Media, Version 4.0, HYDRUS Software Series 3. Department of Environmental Sciences, University of California Riverside, Riverside, CA, USA, pp. 315.