Document Type : Original Article


1 Department of Science Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

2 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran


Introduction: One of the most important factors that play a major role in reducing soil fertility and agricultural land degradation is soil salinization. Soil salinity problem is more severe in agricultural lands of arid and semi-arid regions. In many cases, human activities and irrigation of agricultural lands with saline water are the cause of salinization. This is a serious problem in different regions of Iran, especially in Khuzestan Province. Therefore, the present study was conducted with the aim of monitoring and evaluating the effect of Gotvand Dam on the salinization of the downstream area and changing its plant ecosystem before and after water intake using remote sensing imagery.
Material and methods: The time series of two ETM+ and OLI sensors from 2019-1999 were collected using plant indices (NDVI, SAVI), biophysical index of leaf cover (LAI), and salinity indices. The soil was classified by salient decision-making method of changes in halophyte and non-halophyte plants according to the threshold obtained from the indicators used in each year. Then, the final results were evaluated according to the trend of changes obtained from the used indicators and their correlation with changes in the plant ecosystem of the region.
Results and discussion: The rate of vegetation changes in the four years of 2018, 2013, 2002, and 1999 was more than other years, which was prepared by the method of supervised classification of the area under normal vegetation and saline plants. According to the results obtained from 1999, the total vegetation area of the groves was about 1117 hectares, of which about 134 hectares were related to halophyte vegetation. However in 2018, these values were estimated at 921 hectares, with areas covered by halophyte changing to 445 hectares and halophyte to 476 hectares.
Conclusion: The results of the study indicate the onset of the highest stresses in the plant ecosystem of the region and the simultaneous decline in leaf cover and NDVI with the water intake of Gotvand Dam since 2011. This coincidence, which is due to the salinity of the water of Gotvand Dam Lake and consequently Karun River, has a significant effect on increasing salinity and changes in soil quality of the region and thus increasing halophyte plants as well as high vegetation degradation in the region. These conditions can create more serious challenges for the ecosystem of this area and in the long period change the ecosystem and vegetation cover of this region to halophyte plants.


Abbas, A., Khan, S., Hussain, N., Hanjra, M.A., and Akbar, S., 2013. Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Physics and Chemistry of the Earth. 55–57, 43–52.
Allbed, A. and Kumar, L., 2013. Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology : A Review. (January).
Allbed, A., Kumar, L. and 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(2), 1137–1157.
Belward, A.S. and Skøien, J.O., 2015. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing. 103, 115–128.
Benedek, C., and Sziranyi, T., 2009. Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model. IEEE Transactions on Geoscience and Remote Sensing, 47-10-, 3416–3430.
Chang, X., Gao, Z., Wang, S., & Chen, H., 2019. Modelling long-term soil salinity dynamics using SaltMod in Hetao Irrigation District, China. Computers and Electronics in Agriculture, 156, 447–458.
Chen, J., Zhu, X., Vogelmann, J. E., Gao, F., & Jin, S., 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 115(4), 1053–1064.
Chi, Y., Shi, H., Zheng, W., & Sun, J., 2018. Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing. Science of The Total Environment, 628–629, 384–399.
Chi, Y., Sun, J., Liu, W., Wang, J., & Zhao, M., 2019. Mapping coastal wetland soil salinity in di ff erent seasons using an improved comprehensive land surface factor system. Ecological Indicators, 107(391), 105517.
Corwin, D. L., 2021. Climate change impacts on soil salinity in agricultural areas. European Journal of Soil Science, 72(2), 842–862.
Dai, X., Huo, Z., & Wang, H., 2011. Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crops Research, 121(3), 441–449.
Dewan, M. L., & Famuri, J., 1964. The Soils of Iran, FAO. Rome. Italy.
Ding, J., & Yu, D., 2014. Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan–Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments. Geoderma, 235–236, 316–322.
Elagib, N. A., & Basheer, M., 2021. Would Africa’s largest hydropower dam have profound environmental impacts? Environmental Science and Pollution Research, 28(7), 8936–8944.
Fan, X., Liu, Y., Tao, J., & Weng, Y., 2015. Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression. Remote Sensing, 7(1), 488–511.
Fan, X., Weng, Y., & Tao, J., 2016. Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32–41.
Gorji, T., Sertel, E., & Tanik, A., 2017. Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecological Indicators, 74, 384–391.
Guo, B., Han, B., Yang, F., Fan, Y., Jiang, L., Chen, S., … Liang, T., 2019. Salinization information extraction model based on VI – SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image feature space combinations in the Yellow River Delta. Geomatics, Natural Hazards and Risk, 10(1), 1863–1878.
Hamzeh, S., Naseri, A. A., Alavi Panah, S. K., Mojaradi, B., Bartholomeus, H. M., & Herold, M., 2012, October 19. Mapping salinity stress in sugarcane fields with hyperspecteral satellite imagery (C. M. U. Neale & A. Maltese, eds.).
Hamzeh, S., Naseri, A. A., AlaviPanah, S. K., Mojaradi, B., Bartholomeus, H. M., Clevers, J. G. P. W., & Behzad, M., 2013. Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices. International Journal of Applied Earth Observation and Geoinformation, 21(1), 282–290.
Hamzeh, Saeid, Naseri, A. A., AlaviPanah, S. K., Bartholomeus, H., & Herold, M., 2016. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields. International Journal of Applied Earth Observation and Geoinformation, 52, 412–421.
Han, L., Liu, D., Cheng, G., Zhang, G., & Wang, L., 2019. Spatial distribution and genesis of salt on the saline playa at Qehan Lake, Inner Mongolia, China. CATENA, 177, 22–30.
Hassani, A., Azapagic, A., & Shokri, N., 2020. Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences, 117(52), 33017–33027.
Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., … Shi, Z., 2019. Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images. Remote Sensing, 11(7), 736.
Huete, A., Didan, K., Miura, T., Rodriguez, E. ., Gao, X., & Ferreira, L. ., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213.
Huete, A., Justice, C., & Liu, H., 1994. Develpmeclassification and soil indices for MODIS-EOS. Remote Sensing of Environment, 49, 224–234.
Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., & de Sousa, L., 2019. Global mapping of soil salinity change. Remote Sensing of Environment, 231(March), 111260.
Kasim, N., Tiyip, T., Abliz, A., Nurmemet, I., Sawut, R., & Maihemuti, B., 2018. Mapping and Modeling of Soil Salinity Using WorldView-2 Data and EM38-KM2 in an Arid Region of the Keriya River, China. Photogrammetric Engineering & Remote Sensing, 84(1), 43–52.
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. Agricultural Water Management, 77(1–3), 96–109.
Knyazikhin, Y., Schull, M. A., Stenberg, P., Mottus, M., Rautiainen, M., Yang, Y., … Myneni, R. B., 2013. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences, 110(3), E185–E192.
Li, S., Shi, Z., Chen, S., Ji, W., Zhou, L., Yu, W., & Webster, R., 2015. In Situ Measurements of Organic Carbon in Soil Profiles Using vis-NIR Spectroscopy on the Qinghai–Tibet Plateau. Environmental Science & Technology, 49(8), 4980–4987.
Masoud, A. A., 2014. Predicting salt abundance in slightly saline soils from Landsat ETM+ imagery using Spectral Mixture Analysis and soil spectrometry. Geoderma, 217–218, 45–56.
nasiri, mina, hamidi, mehdi, & Kardan moghaddam, H., 2020. Evaluating the effect of supplying drinking water and agriculture water of Sari-Neka aquifer on the salinity movement with the utilization of Gelvard dam. Journal of Soil and Water Resources Conservation, 9(2), 71–88.
Neuenschwander, A. L., Crawford, M. M., & Ringrose, S., 2005. Results from the EO‐1 experiment—A comparative study of Earth Observing‐1 Advanced Land Imager (ALI) and Landsat ETM+ data for land cover mapping in the Okavango Delta, Botswana. International Journal of Remote Sensing, 26(19), 4321–4337.
Nguyen, K.-A., Liou, Y.-A., Tran, H.-P., Hoang, P.-P., & Nguyen, T.-H., 2020. Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7(1), 1.
Rekha, S., Jenita, R., Mrunalini, B., Kannan, V., & E, N. M. V., 2011. Development And Demonstration Of Satellite Image Salinity Analyzer - A Tool development and demonstration of satellite image salinity analyzer-a tool for salinity mapping. International Journal on Applied Bio-Engineering, 5(1), 25–29.
Ren, D., Wei, B., Xu, X., Engel, B., Li, G., Huang, Q., Huang, G., 2019. Analyzing spatiotemporal characteristics of soil salinity in arid irrigated agro-ecosystems using integrated approaches. Geoderma, 356, 113935.
Rome, F. A. O., 2015. Status of the World’s Soil Resources (SWSR)—Main Report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils. Rome, FAO, 650 p.
Russ, J. D., Zaveri, E. D., Damania, R., Desbureaux, S. G., Escurra, J. J., & Rodella, A.-S., 2020. Salt of the Earth: Quantifying the Impact of Water Salinity on Global Agricultural Productivity. World Bank Policy Research Working Paper, (9144).
Sakizadeh, M., & Chua, L. H. C., 2020. Environmental impact of Karkheh Dam in the southern part of Iran on groundwater quality by intervention and trend analysis. Environmental Monitoring and Assessment, 192(11), 683.
Sayyari, M. H., & Mahmoodi, S., 2002. Investigation on reason of soil salinity and alkalinity on some part of Khorasan province (Dizbad-e-Pain Region). World Congress of Soil Science,, Bangkok (Thailand), 14-21 Aug 2002.
Scudiero, E., Skaggs, T. H., & Corwin, D. L., 2014. Regional scale soil salinity evaluation using Landsat 7, Western San Joaquin Valley, California, USA. Geoderma Regional, 2–3, 82–90.
Scudiero, E., Skaggs, T. H., & Corwin, D. L., 2015. Regional-scale soil salinity assessment using Landsat ETM + canopy reflectance. Remote Sensing of Environment, 169, 335–343.
Skaggs, T. H., Anderson, R. G., Corwin, D. L., & Suarez, D. L., 2014. Analytical steady-state solutions for water-limited cropping systems using saline irrigation water. Water Resources Research, 50(12), 9656–9674.
Slonecker, E. T., 2018. Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation (2nd Editio; P. S. Thenkabail, J. G. Lyon, & A. Huete, Eds.).
Squires, V. R., & Glenn, E. P., 2011. Salination, desertification and soil erosion. The Role of Food, Agriculture, Forestry and Fisheries in Human Nutrition, VR Squires, Ed.(EOLSS Publications, 2011), 3, 102–123.
Taghadosi, M. M., Hasanlou, M., & Eftekhari, K., 2019. Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. International Journal of Remote Sensing, 40(1), 237–252.
Tran, T. V., Tran, D. X., Myint, S. W., Huang, C., Pham, H. V., Luu, T. H., & Vo, T. M. T., 2019. Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach. Science of The Total Environment, 687, 1087–1097.
Wang, J., Ding, J., Yu, D., Ma, X., Zhang, Z., Ge, X., Guo, Y., 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(June), 172–187.
Wang, J., Ding, J., Yu, D., Teng, D., He, B., Chen, X., … Su, F., 2020. Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Science of The Total Environment, 707, 136092.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., … Zhu, Z., 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147.
Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P., 2019. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma, 338, 445–452.
Zhang, T.-T., Zeng, S.-L., Gao, Y., Ouyang, Z.-T., Li, B., Fang, C.-M., & Zhao, B., 2011. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecological Indicators, 11(6), 1552–1562.