Evaluation of the Origin and Synoptic Analysis of Dust Storm Phenomena Using Satellite Image Processing in Western Iran

Abstract

This study investigates the identification and analysis of dust sources using satellite imagery and synoptic meteorological data, focusing on a significant dust event originating from Syria on September 1, 2015. Visual interpretation of satellite images, complemented by the Brightness Temperature Difference (BTD) index, confirmed the accuracy of dust source identification. The analysis revealed that an active low-pressure system in the eastern Mediterranean facilitated dust formation due to low humidity conditions. Dust movement was predominantly directed from northwest to southeast, impacting regions in southwestern Iran, including Kermanshah, Ilam, and Khuzestan. Additionally, the study examined wind patterns, demonstrating how zonal and meridional winds contributed to dust transport and dissipation. A comparative analysis of vegetation cover over a decade indicated a significant decline at the dust formation site, suggesting a correlation between reduced vegetation and increased dust emissions. This research underscores the complex interplay between atmospheric dynamics and regional environmental changes, highlighting the need for further investigation into the long-term impacts of vegetation loss on dust storm frequency and intensity. The findings aim to enhance our understanding of dust storm mechanisms and inform strategies for mitigating their adverse effects on human health and the environme

Keywords

• Dust storms
• MODIS sensors
• Terra and Aqua
• Monitoring and synoptic analysis

References

1. SSivakumar, M. V. K. (2005). Impacts of sand storms and dust storms on agriculture. In M. V. K. Sivakumar, R. P. Motha, & H. P. Das (Eds.), Natural Disasters and Extreme Events in Agriculture (pp. 159-177). Springer. https://doi.org/10.1007/3-540-28307-2
2. Abbasi, H., Rafiei Emam, A., & Rouhi Pour, H. (1999). Analysis of the origin of dust in Bushehr and Khuzestan using satellite images. Forest and Rangeland Quarterly, 78, 48-51.
3. Zolfaghari, H., & Abedzadeh, H. (2005). Synoptic analysis of dust storms in western Iran. Journal of Geography and Development, 6, 27.
4. Raeispour, K. (2008). Statistical and synoptic analysis of dust phenomenon in Khuzestan (Master's thesis, Climatology in Environmental Planning, University of Sistan and Baluchestan), p. 157.
5. Maghrabi, A., Alharbi, B., & Tapper, N. (2011). Impact of the March 2009 dust event in Saudi Arabia on aerosol optical properties, meteorological parameters, sky temperature and emissivity. Atmospheric Environment, 45(12), 2164-2173. https://doi.org/10.1016/j.atmosenv.2011.01.018
6. Mie, D., Xiushan, L., Lin, S., & Ping, W. (2008). A dust-storm process dynamic monitoring with multi-temporal MODIS data. In the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XXXVII, Part B7, pp. 965–969). https://doi.org/10.5194/isprsarchives-XXXVII-B7-965-2008
7. Tsolmon, R., Ochirkhuyag, L., & Sternberg, T. (2008). Monitoring the source of transnational dust storms in North East Asia. International Journal of Digital Earth, 1, 119-129. https://doi.org/10.1080/17538940701782593
8. Solomos, S., Ansmann, A., Mamouri, R.-E., Binietoglou, I., Patlakas, P., Marinou, E., & Amiridis, V. (2017). Remote sensing and modelling analysis of the extreme dust storm hitting the Middle East and eastern Mediterranean in September 2015. Atmospheric Chemistry and Physics, 17, 4063–4079. https://doi.org/10.5194/acp-17-4063-2017

9. Darvishi, B. A., Kazemi, Y., Sadeghi, A., Nadizadeh, S. S., & Argany, M. (2020). Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin. Environmental Pollution, 263, 114404. https://doi.org/10.1016/j.envpol.2020.114404
10. Darvishi Boloorani, A., Papi, R., Soleimani, M., Al-Hemoud, A., Amiri, F., Karami, L., Neysani, S. N., Bakhtiari, M., & Mirzaei, S. (2023). Visual interpretation of satellite imagery for hotspot dust sources identification. Computers in Earth Sciences, 235, 1963. https://doi.org/10.1016/j.cgs.2022.1963
11. Yakar, M. (2009). Digital elevation model generation by robotic total station instrument. Experimental Techniques, 33(2), 52-59.
12. Çelik, M. Ö., Kuşak, L., & Yakar, M. (2024). Assessment of groundwater potential zones utilizing geographic information system-based analytical hierarchy process, Vlse Kriterijumska Optimizacija Kompromisno Resenje, and technique for order preference by similarity to ideal solution methods: a case study in Mersin, Türkiye. Sustainability, 16(5), 2202.
13. Valizadeh Kamran, K. and Khorrami, B.: Change Detection and Prediction of Urmia Lake and its Surrounding Environment During the Past 60 Years Applying Geobased Remote Sensing Analysis, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 519–525, https://doi.org/10.5194/isprs-archives-XLII-3-W4-519-2018, 2018.
14. Nasir, S. M., Kamran, K. V., Blaschke, T., & Karimzadeh, S. (2022). Change of land use/land cover in kurdistan region of Iraq: A semi-automated object-based approach. Remote Sensing Applications: Society and Environment, 26, 100713. https://doi.org/10.1016/j.rsase.2022.100713
15. Rezaeimoghadam, M., Valizadeh Kamran, K., Rostamzadeh, H., & Rezaee, A. (2013). Evaluating the Adequacy of MODIS in the Assessment of Drought (Case Study: Urmia Lake Basin). Geography and Environmental Sustainability, 2(4), 37-52.
16. Yilmaz, H. M., Yakar, M., Mutluoglu, O., Kavurmaci, M. M., & Yurt, K. (2012). Monitoring of soil erosion in Cappadocia region (Selime-Aksaray-Turkey). Environmental Earth Sciences, 66(1), 75-81.
17. Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69.
19. Yakar, M., & Dogan, Y. (2018). 3D Reconstruction of residential areas with SfM photogrammetry. In Conference of the Arabian Journal of Geosciences (pp. 73-75). Cham: Springer International Publishing.
20. Zeynalov, I., Akhmedova , R. ., Akhmedova , A. ., & Rustamova , A. . (2024). Analysis of the sea surface temperature (SST) of the Caspian Sea from NOAA satellites. Advanced Remote Sensing, 4(1), 1–10. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/1164
21. Tahsin, A. ., Abdullahi, J. ., Karabulut, A. İzzeddin, & Yesilnacar, M. I. . (2023). Spatiotemporal prediction of reference evapotranspiration in Araban Region, Türkiye: A machine learning based approach. Advanced Remote Sensing, 3(1), 27–37. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/833
22. Boutallaka, M., Miloud, T., El Mazi, M., Hmamouchi, M., et al. (2025). Assessment of current and future land sensitivity to degradation under climate change in the upstream Ouergha watershed (Morocco) using GIS and AHP method. International Journal of Engineering and Geosciences, 10(1), 46-58. https://doi.org/10.26833/ijeg.1521350
23. Yılmaz, V. (2025). Climate patterns in Europe: A focus on ten countries through remote sensing. International Journal of Engineering and Geosciences, 10(3), 398-418. https://doi.org/10.26833/ijeg.1583206
24. Yaman, Ş., & Yaman, M. (2024). Creation of Turkiye risk map for Cydalima perspectalis (box tree moth) by weighted overlay analysis. International Journal of Engineering and Geosciences, 9(3), 345-355. https://doi.org/10.26833/ijeg.1434437