Management of Natural Ecosystems

Management of Natural Ecosystems

Monitoring the spatial-temporal distribution of soil salinity in the Eshtehard Plain of Karaj using remote sensing

Document Type : Original Article

Authors
Leila Esmaeeelnejad 1
Saeed Saadat 2
Fatemeh Siavashi 3
1 Assistant professor, Monitoring and improvement of soil and water research department, Soil and water research institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
2 Associated professor, Monitoring and improvement of soil and water research department, Soil and water research institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
3 Monitoring and improvement of soil and water research department, Soil and water research institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
10.22034/emj.2026.2088012.1166
Abstract
Salinization and sodification of soils, by degrading soil structure, causing a loose surface structure, and salt accumulation at the soil surface, increase thethe sensitivity of lands to various types of erosion, especially wind erosion. Eshtehard County is one of the critical dust hotspots in Alborz Province, where approximately 24,500 hectares of county’s agricultural and natural resources are among these dust sources. Therefore, it is essential to predict and monitor soil salinity trends in order to implement protective measures against further land degradation. Recent advancements in remote sensing technology have enabled accurate detection and effective monitoringof soil salinity. Consequently, this study examined the levels of surface soil salinity and the likelihood of saltstorm occurrences in 7,118 hectares of agricultural land in the arid region of Eshtehard, Karaj, over the period from 2011 to 2021 using machine learning algorithms in the Google Earth Engine (GEE). The results indicated that the level of salinity in agricultural soils increased by approximately 18%, and the salinity class shifted from non-saline soils to low to moderate salinity. This could be the result of a combination of climatic factors, including reduced rainfall and increased evaporation from the soil surface, as well as human factors, including the continued use of low-quality groundwater, the lack of effective drainage systems, and improper irrigation management.Additionally, the class of very low probability for saltstorm occurrences increased by 160%, while the classes for low and moderate probability increased by 47% and approximately 500%, respectively, from 2011 to 2021. Increased salinity reduces vegetation growth and destroys soil structure, resulting in a decrease in the resistance of the soil surface to wind erosion and an increase in salt storms.
Keywords
Random forest
salinity index
Google Earth Engine
Landsat
machine learning
Subjects
سعادت، س.، رضایی، ح.، اسماعیل نژاد، ل.، میرخانی، ر.، و باقری، ی. ر. (1402) نقشه شوری خاک‌های کشاورزی ایران. نشریه فنی شماره 30 موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی.
Abbas, A., and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. In International Congress on Modelling and Simulation (MODSIM) (pp. 2632-2638). Modelling and Simulation Society of Australia and New Zealand. 2632–2638.
Arabameri, A., Roy, J., Saha, S., Blaschke, T., Ghorbanzadeh, O., and Tien Bui, D. (2019). Application of probabilistic and machine learning models for groundwater potentiality mapping in Damghan sedimentary plain, Iran. Remote Sensing, 11(24), 3015.
Bai, S.B., Wang, J., Lu, G.N., Kanevski, M., and Pozdnoukhov, A. (2008). GIS-based landslide susceptibility mapping with comparisons of results from machine learning methods process versus logistic regression in Bailongjiang river basin, China. Geophysical Research Abstracts. 10, 1-2.
Bittencourt, H.R., and Clarke, R.T. (2003). Use of classification and regression trees (CART) to classify remotely-sensed digital images. IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477) (Vol. 6, pp. 3751-3753). IEEE.
Chen, H., Yunus, A. P., Nukapothula, S., and Avtar, R. (2022). Modelling Arctic coastal plain lake depths using machine learning and Google Earth Engine. Physics and Chemistry of the Earth, Parts A/B/C, 126(3), 103138.
Douaoui A., Nicolas, Hervé N., Christian, W. (2006). Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma. 134(1). 217-230.
FAO. 2018. Handbook for saline soil management. Editors: R. Vargas, E.I. Pankova, S.A. Balyuk, P.V. Krasilnikov and G.M. Khasankhanova.Published by the Food and Agriculture Organization of the United Nations and Lomonosov Moscow State University.
Feizizadeh, B., Garajeh, M.K., Lakes, T., and Blaschke, T. (2021). A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran. Catena, 207(1), 105585.
El Imanni, H.S., El Harti, A., Bachaoui, E.M., Mouncif, H., Eddassouqui, F., Hasnai, M.A., and Zinelabidine, M.I. (2023). Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco. Remote Sensing Applications: Society and Environment, 30, 100941.
Jafari, A., Khademi, H., Finke, P. A., Van de Wauw, J., and Ayoubi, S. (2014). Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma, 232(1), 148-163.
Kazemi Garajeh, M., Li, Z., Hasanlu, S., Zare Naghadehi, S., and Hossein Haghi, V. (2022). Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping. Scientific Reports, 12(1), 21396.
Khan, N.M., Rastoskuev, V.V., Sato, Y., and 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.
Mahdianpari, M., Brisco, B., Salehi, B., Granger, J., Mohammadimanesh, F., Lang, M., and Toure, S. (2022). Toward a North American continental wetland map from space: wetland classification using satellite imagery and machine learning algorithms on Google Earth Engine. In Book: Radar Remote Sensing (pp. 357-373), Elsevier.
Meier, M., Souza, E.D., Francelino, M.R., Fernandes Filho, E.I., and Schaefer, C.E.G.R. (2018). Digital soil mapping using machine learning algorithms in a tropical mountainous area. Revista Brasileira de Ciência do Solo, 42, e0170421.
Rengasamy, P. (2016). Soil salinization. In Oxford Research Encyclopedia of Environmental Science.
Shahabi, M., Jafarzadeh, A.A., Neyshabouri, M.R., Ghorbani, M.A., and Valizadeh Kamran, K. (2017). Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods. Archives of Agronomy and Soil Science, 63(2), 151-160.
Singh, A. (2021). Soil salinization management for sustainable development: A review. Journal of Environmental Management, 277(3), 111383.
Stumpf, A., and Kerle, N. (2011). Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment, 115(10), 2564-2577.
Sulova, A., and Jokar Arsanjani, J. (2020). Exploratory analysis of driving force of wildfires in Australia: An application of machine learning within Google Earth engine. Remote Sensing, 13(1), 10.
Waleed, M., Sajjad, M., Shazil, M.S., Tariq, M., and Alam, M.T. (2023). Machine learning-based spatial-temporal assessment and change transition analysis of wetlands: An application of Google Earth Engine in Sylhet, Bangladesh (1985–2022). Ecological Informatics,75(2), 102075.
Yahiaoui, I., Douaoui, A., Zhang, Q., and Ziane, A. (2015). Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis. Journal of Arid Land, 7(6), 794-805.
You, N., and Dong, J. (2020). Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 109-123.
Zhang, F. (2023). Factors influencing the spatio- temporal variability of aerosol optical depth over the arid region of Northwest China. Atmosphere, 15(1), 54.
Zhao, F., Feng, S., Xie, F., Zhu, S., and Zhang, S. (2023). Extraction of long time series wetland information based on Google Earth Engine and random forest algorithm for a plateau lake basin- A case study of Dianchi Lake, Yunnan Province, China. Ecological Indicators, 146(02), 109813.
Zurqani, H.A., Post, C.J., Mikhailova, E.A., Schlautman, M.A., and Sharp, J.L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69(5), 175-185.

  • Receive Date 09 May 2026
  • Revise Date 20 May 2026
  • Accept Date 30 May 2026