Water and Irrigation Management

Water and Irrigation Management

Evaluation and Preparation of Soil Salinity Map Using Vegetation Indicators and Sentinel-2 and Landsat-8 Multispectral Images in Salt Marsh Qazvin Plain

Document Type : Research Paper

Authors
Mohadese Sadat Fakhar 1
Bijan Nazari 2
1 Department of Water Science and Engineering Faculty of Agriculture and Natural Resources Imam Khomeini International University, Qazvin, Iran.
2 Department Faculty of Agriculture and Natural Resources Imam Khomeini International University, Qazvin, Iran.
10.22059/jwim.2023.357320.1065
Abstract
In this research, 23 soil samples with specific geographical characteristics were collected to investigate and monitor salinity changes in the region. Using the Sentinel-2 and Landsat-8 sensors, seven vegetation cover indices and five salinity indices were examined and evaluated in the GEE environment, resulting in a total of 240 outputs from the two sensors. To assess the modeled values, several statistical indices including root mean square error (RMSE), coefficient of determination (R2), normalized root mean square error (NRMSE), and percent bias (PBIAS) were utilized. The results indicated that the SI-2 index exhibited the highest correlation with the measured salinity values in the region, with an R2 value of 0.91, demonstrating its accuracy in estimating salinity levels. In the next step, a multiple regression model was employed to investigate the mean values of measured ECe (electrical conductivity of the saturation extract) and the vegetation indices GDVI (Green Difference Vegetation Index) and CRSI (Crop Salt Stress Index) obtained from the Sentinel-2 sensor, which showed the highest correlation with the salinity data. The results demonstrated that the two-variable regression model achieved a satisfactory accuracy with an R2 value of 0.84 and a PBIAS value of 0.01 in producing a salinity map of the area. Therefore, this model can be utilized as a cost-effective approach for salinity mapping in the region with minimal ground-based data. Furthermore, the investigation of the impact of constructing a barrier drain in the area revealed that the construction of a barrier drain within a distance of 250 meters had a significant effect of approximately 40 percent in controlling salinity. It was able to prevent a substantial increase in salinity levels in the region. Therefore, if a barrier drain is not constructed in the area, salinity progression in the upstream agricultural lands could significantly escalate.
Keywords
Qazvin plain salt marsh
remote sensing
soil salinity
GDVI
Subjects
  1. Akram, M., & Sotoodehnia, A. )2011(. Monitoring plan of interceptor drain in Qazvin. Co. Reports, Kamab Pars Saman Abran. (In Persian).
  2. Aldakheel, Y.Y., Elprince, A.M., & Al-Hosaini, A.I. )2005(. Mapping of salt-affected soils of irrigated lands in arid regions using remote sensing and GIS, in: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005. IEEE, pp. 467-472.
  3. Allbed, A., & Kumar, L. (2013(. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Remote Sens (2), 373-385.
  4. Allbed, A., Kumar, L., & Sinha, P. )2018(. Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia. Geocarto Int., (33), 830-846.
  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 Sens (6), 1137-1157.
  6. Asfaw, E., Suryabhagavan, K. V., & Argaw, M. )2018(. Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Saudi Soc. Agric. Sci., (17), 250-258.
  7. Corwin, D.L. )2021(. Climate change impacts on soil salinity in agricultural areas. Eur. J. Soil Sci. (72), 842-862.
  8. Corwin, D.L., & Scudiero, E. )2019(. Advances in Agronomy.
  9. El Harti, A., Lhissou, R., Chokmani, K., Ouzemou, J., Hassouna, M., Bachaoui, E.M., & El Ghmari, A. )2016(. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. J. Appl. Earth Obs. Geoinf., (50), 64-73.
  10. Elnaggar, A.A., & Noller, J.S. )2009(. Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sens, (2), 151-165.
  11. )2015(. Status of the world’s soil resources. Main Report. Food and Agriculture
    Organization of the United Nations and Intergovernmental Technical Panel on Soils.
    Rome, Italy.
  12. Fakhar, M.S., Kaviani, A., )2022(. Evaluation of FAO WaPOR product and PYSEBAL algorithm in estimating The amount of water consumed. Iran. Soil Water Res. ISNN 2423, 7833.
  13. Ghavam, M. )2018(. Effect of silver nanoparticles on seed germination and seedling growth in Thymus vulgaris and Thymus daenensis Celak under salinity stress. J. Rangel. Sci., 8, 93-100.
  14. Gorji, T., Sertel, E., & Tanik, A. )2017(. Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Indic., 74, 384-391.
  15. Huete, A.K., Didan, T.M., EP, R., Gao, n.d. X., & Ferreira, L. G. )2002(. Overv. Radiom. Biophys. Perform. MODIS Veg. I Remote Sens. Environ., 83, 195-213.
  16. Katawatin, R., & Kotrapat, W. )2005(. Use of LANDSAT-7 ETM+ with ancillary data for soil salinity mapping in Northeast Thailand, in: Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics. SPIE, pp. 708-716.
  17. Khan, N.M., Rastoskuev, V. V, Sato, Y., & Shiozawa, S. )2005(. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Water Manag, 77, 96-109.
  18. Khan, S., & Abbas, A. )2007(. Using remote sensing techniques for appraisal of irrigated soil salinity. Int. Congr. Model. Simul.(MODSIM), Model. Simul. Soc. Aust. New Zealand, Bright, 2632-2638.
  19. Koohafkan, P., & STEWART, B.A. )2008(. Water and cereals in drylands, 113pp. Food Agric. Organ. United Nations Earthscan, London-Sterling, VA, UK.
  20. 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. )2010(. Regional‐scale assessment of soil salinity in the Red River Valley using multi‐year MODIS EVI and NDVI. Environ. Qual., (39), 35-41.
  21. Maas, E.V., & Hoffman, G.J. )1977(. Crop salt tolerance-current assessment. Irrig. Drain. Div., 103, 115-134.
  22. Marco, E.S., Herrmann, D., Schwab, K., Schweitzer, K., Almengor, R., Berndt, F., Sommer, C., & Probeck, M. )2019(. Improvement of existing and development of future copernicus land monitoring products–the ecolass project. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., (42), 201-208.
  23. Minhas, P.S., Ramos, T.B., Ben-Gal, A., & Pereira, L.S. )2020(. Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues. Agric. Water Manag, 227, 105832.
  24. Morshed, M., Islam, M., & Jamil, R. )2016(. Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data. Monit. Assess., 188, 1-10.
  25. Mohammadi, M., Mohammadi Ghaleney, M., & Ebrahimi, K. (2011). Spatial and Temporal
    Variations of Groundwater Quality of Qazvin plain, Water Research Iran, 5(8), 41-51. (In
    Persian)
  26. 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.
  27. Nohegar, A., & Zare, G. )2012(. Extraction of soil salinity zone in arid and semi arid regional using of remote sensing data (Case study: Darab Township). Geogr. Environ. Hazards, 1, 49-64.
  28. Pereira, L.S., Cordery, I., & Iacovides, I. )2009(. Coping with water scarcity: Addressing the challenges. Springer Science & Business Media.
  29. Rouse Jr, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. )1973(. Paper a 20, in: Third Earth Resources Technology Satellite-1 Symposium: The Proceedings of a Symposium Held by Goddard Space Flight Center at Washington, DC On. p. 309.
  30. Scudiero, E., Skaggs, T.H., & Corwin, D.L. )2014( Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Reg., 2, 82-90.
  31. Scudiero, E., Teatini, P., Corwin, D.L., Ferro, N.D., Simonetti, G., & Morari, F. )2014b). Spatiotemporal response of maize yield to edaphic and meteorological conditions in a saline farmland. J., 106, 2163-2174.
  32. Sotoodehnia, A., Jafari, M., & Daneshkar Arasteh, P. )2014(. The Role of Qazvin Central Marsh Interceptor Drain in Controlling Shallow Groundwater Salinity. J. Soil Water Res., 45, 447-452.
  33. Taghadosi, M.M., Hasanlou, M., & Eftekhari, K. )2019(. Retrieval of soil salinity from Sentinel-2 multispectral imagery. J. Remote Sens., 52, 138-154.
  34. Vickers, N.J. (2017). Animal communication: when i’m calling you, will you answer too? Biol., 27, R713-R715.
  35. Wang, J., Ding, J., Yu, D., Ma, X., Zhang, Z., Ge, X., Teng, D., Li, X., Liang, J., & Lizaga, I. )2019(. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma, 353, 172-187.
  36. Whitney, K., Scudiero, E., El-Askary, H.M., Skaggs, T.H., Allali, M., & Corwin, D.L. )2018(. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Indic., 93, 889-898.
  37. Wu, W., Al-Shafie, W.M., Mhaimeed, A.S., Ziadat, F., Nangia, V., & Payne, W.B. )2014(. Soil salinity mapping by multiscale remote sensing in Mesopotamia, Iraq. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4442-4452.
  38. Yang, Z., Gao, J., Zhou, C., Shi, P., Zhao, L., Shen, W., & Ouyang, H. )2011(. Spatio-temporal changes of NDVI and its relation with climatic variables in the source regions of the Yangtze and Yellow rivers. Geogr. Sci., 21, 979-993.
  39. Yu, H., Liu, M., Du, B., Wang, Z., Hu, L., & Zhang, B. )2018(. Mapping soil salinity/sodicity by using Landsat OLI imagery and PLSR algorithm over semiarid West Jilin Province, China. Sensors, 18, 1048.
  40. Zhang, T.-T., Qi, J.-G., Gao, Y., Ouyang, Z.-T., Zeng, S.-L., & Zhao, B. )2015(. Detecting soil salinity with MODIS time series VI data. Indic., 52, 480-489.

 

Water and Irrigation Management
Volume 13, Issue 3
Autumn 2023
Pages 783-799