ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 169-179

Regional-scale assessment of multi-year soil salinity using MODIS in the Syr Darya River valley, Kazakhstan

A.G. Terekhov 1 , G.N. Sagatdinova 1 , B.A. Murzabaev 2 
1 Institute of Information and Computational Technologies, Almaty, Kazakhstan
2 M. Auezov South Kazakhstan State University, Shymkent, Kazakhstan
Accepted: 27.04.2022
DOI: 10.21046/2070-7401-2022-19-2-169-179
In the Syr Darya River valley there is a large irrigated region — the Hungry Steppe with a total area of about 10 thousand km2. The lower part of this region, which is about 140 thousand hectares of irrigated arable land, belongs to the territory of Kazakhstan. After the collapse of the USSR, hydroelectric power plants in the upper reaches of the river basin changed their operating modes from irrigation to energy. In addition, the regional drainage system has partially lost its functionality. All this contributed to the activation of the secondary soil salinity processes. In this study, two satellite indices, normalized differential vegetation and salinity indexes (NDVI and NDSI) based on the MODIS MCD43A4 product of the period April – July 2001–2021, were tested as a basis for regional mapping of many-year soil salinity in the Kazakhstan’s Syr Darya River valley. Official state information on the soil salinity of rural districts (district — an administrative unit with an area of irrigated arable land of 4–11 thousand hectares) of the Maktaaral and Zhetysai Regions of the Turkestan Oblast of Kazakhstan in the period 2007–2021 was used to calibrate satellite data. It was found that the best correlation with the long-term salinity of irrigated arable land has the average long-term maximum NDVI of the period June 20 – July 5 (Pearson correlation coefficient R2 = 0.88), as well as the average long-term maximum NDSI of the periods April 10–25 (R2 = 0.85) and June 26 – July 10 (R2 = 0.87). Also, a close correlation between the considered indices with a Pearson correlation coefficient up to 0.97 is recorded. The physical basis linking satellite data with secondary soil salinization, apparently, is the procedure of winter-spring irrigation, which, in the process of removing salts, leads to spring soil waterlogging and delays in the early summer development of agricultural vegetation. Thus, the average long-term values of the NDVI and NDSI satellite indexes built on the MODIS MCD43A4, on certain calendar dates, can serve as the basis for regional mapping of the average long-term soil salinity in the test site region. One of the important purposes of the many years soil salinity mapping may be to identify areas that are systematically subjected to secondary soil salinization.
Keywords: remote sensing, monitoring of irrigated arable land, secondary soil salinity, winter irrigation, soil salinity mapping
Full text


  1. Mamedov E. A., The study of saline soil and salt marshes using space techniques, Issledovanie Zemli iz kosmosa, 1985, No. 1, pp. 60–61 (in Russian).
  2. Pankova E. I., Mazikov V. M., Isaev V. A., Yamnova I. A., Experience in the use of aerial photographs for the characteristics of soil salinity rainfed areas serozem area, Pochvovedenie, 1978, No. 3, pp. 82–85 (in Russian).
  3. Terekhov A. G., Satellite estimation of agriculture water availability using the 2002–2019 irrigation cooling effect over Xinjiang, Northwest China, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 7, pp. 131–141 (in Russian), DOI: 10.21046/2070-7401-2020-17-7-131-141.
  4. Terekhov A. G., Abayev N. N., Maglinets Yu. A., Satellite monitoring of River Amu Darya oases during 2003–2020 based on irrigation cooling effect, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 5, pp. 123–132 (in Russian), DOI: 10.21046/2070-7401-2021-18-5-123-132.
  5. Abbas A., Khan S., Hussain N., Hanjra M., Akbar S., Characterizing soil salinity in irrigated agriculture using a remote sensing approach, Physics Chemistry of the Earth, 2013, Vol. 55–57, pp. 43–52, DOI: 10.1016/j.pce.2010.12.004.
  6. Adejumobi M. A., Alonge T. A., Ojo O. I., A review of the techniques for monitoring soil salinity in irrigated fields, Advanced Multidisciplinary Research J., 2016, Vol. 2, No. 3, pp. 167–170, DOI: 10.1080/01431168908903849.
  7. Alexakis D. D., Daliakopoulos I. N., Panagea I. S., Tsanis I. K., Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece, Geocarto Intern., 2018, Vol. 33, No. 4, pp. 321–338, DOI: 10.1080/10106049.2016.1250826.
  8. Al-Khaier F., Soil salinity detection using satellite Remote Sensing: Master thesis, Intern. Inst. Geo-information Science and Earth Observation, Enschede, Netherlands, 2003, 61 p.
  9. Allbed A., Kumar L., Aldakheel Y., Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: applications in a date palm dominated region, Geoderma, 2014, Vol. 230–231, pp. 1–8, DOI: 10.1016/j.geoderma.2014.03.025.
  10. Bannari A., Guédon A. M., Communications in soil science and plant analysis mapping slight and moderate saline soils in irrigated agricultural land using advanced land imager sensor (EO-1) data and semi-empirical models, Communications in Soil Science and Plant Analysis, 2016, Vol. 47, pp. 1883–1906, DOI: 10.1080/00103624.2016.1206919.
  11. Fan X., Weng Y., Tao J., Towards decadal soil salinity mapping using Landsat time series data, Intern. J. Applied Earth Observation and Geoinformation, 2016, Vol. 52, pp. 32–41, DOI: 10.1016/j.jag.2016.05.009.
  12. Laiskhanov S. U., Otarov A., Savin I. Y., Tanirbergenov S. I., Mamutov Z. U., Duisekov S. N., Zhogolev A., Dynamics of soil salinity in irrigation areas in South Kazakhstan, Polish J. Environmental Studies, 2016, Vol. 25, pp. 2469–2475, DOI: 10.15244/pjoes/61629.
  13. Li L., Liu H., He X., Lin E., Yang G., Winter Irrigation Effects on Soil Moisture, Temperature and Salinity, and on Cotton Growth in Salinized Fields in Northern Xinjiang, China, Sustainability, 2020, Vol. 12, No. 18, Art. No.7573, DOI: 10.3390/su12187573.
  14. Li Y., Research Progress of Remote Sensing Monitoring of Soil Salinization, IOP Conf. Ser.: Earth and Environmental Science, 2021, Vol. 692, Art. No. 042007, DOI: 10.1088/1755-1315/692/4/042007.
  15. Lobell D. B., Lesch S. M., Corwin D. L., Ulmer M. G., Anderson K. A., Potts D. J., Doolittle J. A., Matos M. R., Baltes M. J., Regional-scale Assessment of Soil Salinity in the Red River Valley Using Multi-year MODIS EVI and NDVI, J. Environmental Quality, 2010, Vol. 39, Issue 1, pp. 35–41, DOI: 10.2134/jeq2009.0140.
  16. Metternicht G. I., Zinck J. A., Remote sensing of soil salinity: potentials and constraints, Remote Sensing of Environment, 2003, Vol. 85, Issue 1, pp. 1–20, DOI: 10.1016/S0034-4257(02)00188-8.
  17. Rahmati M., Hamzehpour N., Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data, Intern. J. Remote Sensing, 2017, Vol. 38, pp. 123–140, DOI: 10.1080/01431161.2016.1259681.
  18. Ramos T. B., Castanheira N., Oliveira A. R., Paz A. M., Darouich H., Simionesei L., Farzamian M., Gonçalves M. C., Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal, Agricultural Water Management, 2020, Vol. 241, Art. No. 106387, DOI: 10.1016/j.agwat.2020.106387.
  19. Rukhovich D. I., Pankova E. I., Chernousenko G. I., Koroleva P. V., Long-term salinization dynamics in irrigated soils of the Golodnaya Steppe and methods of their assessment on the basis of remote sensing data, Eurasian Soil Science, 2010, Vol. 43, pp. 682–692, DOI: 10.1134/S1064229310060098.
  20. Scudiero E., Corwin D. L., Anderson R. G., Yemoto K., Clary W., Luke Z., Todd W., Remote sensing is a viable tool for mapping soil salinity in agricultural lands, California Agriculture, 2017, Vol. 71, No. 4, pp. 231–238, DOI: 10.3733/ca.2017a0009.
  21. Singh A. N., Dwivedi R. S., Delineation of salt-affected soils through digital analysis of Landsat MSS data, Intern. J. Remote Sensing, 1989, Vol. 10, No. 1, pp. 83–92, DOI: 10.1080/01431168908903849.
  22. Terekhov A. G., Abayev N. N., Irrigation cooling effect: opportunities in task of estimation of international irrigation water usage in transboundary River Syrdarya basin, Central Asia, E3S Web Conf., 2020, Vol. 223, Art. No. 02009, DOI: 10.1051/e3sconf/202022302009.
  23. Whitney K., Scudiero E., El-Askary H. M., Skaggs T. H., Allali M., Corwin D. L., Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA, Ecological Indicators, 2018, Vol. 93, pp. 889–898, DOI: 10.1016/j.ecolind.2018.05.069.
  24. Zare S., Shamsi S. Fallah R., Abtahi S. A., Weakly-coupled geo-statistical mapping of soil salinity to Stepwise Multiple Linear Regression of MODIS spectral image products, J. African Earth Sciences, 2019, Vol. 152, pp. 101–114, DOI: 10.1016/j.jafrearsci.2019.01.008.
  25. Zhang T., Qi J., Gao Yu, Ouyang Z., Zeng S., Zhao B., Detecting soil salinity with MODIS time series VI data, Ecological Indicators, 2015, Vol. 52, pp. 480–489, DOI: 10.1016/j.ecolind.2015.01.004.