Application of GEE in Dust Actual Sources Detection using Sentinel- 5 and Modis

Document Type : Original Article

Authors

1 Assistant professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

2 PhD Student of Environmental Science, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

Abstract

Dust storms originate in many of the world’s drylands and may impact a wide range of negative effects on ecological, public health, and socio-economic issues. The phenomenon of dust is one of the most important environmental challenges nowadays. Therefore, Identifying the sources of dust storms is the first step to combating these devastating phenomena. Using satellite images is the most up-to-date method to identify dust sources. The present study aims to identify areas of dust generation potential in Hamadan province and the effective range. Modis and Sentinel-5 satellite imagery were used for the 2008-2019 and 2018-2020 study periods using GEE, respectively. Land-use maps, BSI, and MNDVI were considered useful indices to detect and monitor the dust generation centers. Classification of aerosol concentrations in three classes showed that the area of the first class (the highest concentration class) in Modis and Sentinel-5 images are 9875.1 and 7100.5 km2, respectively, which are Continuous polygons in Sentinel-5 and Scattered polygons in Modis images. By focusing on quantifying and overlapping land-use 2018 and actual dust centers, the results showed that most aerosols are concentrated in poor pastures and uncultivated lands in Sentinel-5 images and are concentrated in poor pastures and rainfed-agriculture in Madis images. The correlation coefficient between the two images is 81%.  Finally, Sentinel-5 satellite imagery can be used for dust detecting and monitoring. To manage these actual dust sources, reducing bare soils, increasing vegetation covers, improving water-use efficiency in agriculture, and reducing the use of groundwater in Qahvand plain are recommended.

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References

References (in Persian)
Arjmand, M., Rashki, A., Sargazi, H. (2018). Monitoring of spatial and temporal variability of desert dust over the Hamoun e Jazmurian, Southeast of Iran based on the Satellite Data. Scientific- Research Quarterly of Geographical Data (SEPEHR), 27(106), 153-168. DOI: 10.22131/sepehr.2018.32339. [In Persian].
Ahmadi, M., Dadashiroudbari, A. (2000). Spatio-Temporal Distribution of Particulate Matter (PM2.5) with an Environmental Approach in West and Southwest of Iran Based on SeaWiFS, MISR and MODIS Sensors. Journal of Environmental Studies, 45(3), 379-394. doi:10.22059/jes.2019.282101.1007867. [In Persian].
Analyzing dust crisis in the ISNA office. (16 Jun 2017). Iranian Student’s News Agency. Hamedan-49443. [In Persian].
Shahrisvand, M., Akhoondzadeh Hanzaei, M., & Souri A. (2015). Comparison of Support Vector Machine, Artificial Neural Network and Decision Tree Classifiers for Dust Detection in Modis Imagery. JGST, 4 (3), 131-144. URL: http://jgst.issge.ir/article-1-284-fa.html. [In Persian].
Shamshiri, S., Jafari, R., Soltani, S., & Ramezani, N. (2014). Dust Detection and Mapping in Kermanshah Province Using MODIS Satellite Imagery. ijae. 3 (8), 29-42. http://ijae.iut.ac.ir/article-1-516-fa.html. [In Persian].
Mirakbari, M., Ebrahimi Khusfi, Z. (2020). Investigation of spatial and temporal changes in atmospheric aerosol using aerosol optical depth in Southeastern Iran, Journal of Rs and Gis for Natural Resources, 11(3), 87-105. magiran.com/p2189684. [In Persian].
Mohammadi, F., Kamali, S., Eskandary, M. (2016). Tracing Dust Sources in Different Atmosphere Levels of Tehran Using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. Journal of Geography and Environmental Hazards, 4(4), 39-54. DOI: 10.22067/geo.v4i4.41109. [In Persian].
zazuli, M.F., Vafaeinezhad, A., Kheirkhah zarkesh, M.M., Ahmadi dehka, F. (2014). Source routing of dust haze phenomenon in the west and southwest of Iran and its synoptic analysis by using remote sensing and GIS. Journal of RS and GIS for Natural Resources, 5(4), 61-78. https://girs.bushehr.iau.ir/article_516681.html?lang=en. [In Persian]
 
References (in English)
Ahn, C., Torres, O., Loyola, D.G., Tiruchirapalli, R., & Jethva, H.T. (2018). Aerosol Index Products from Sentinel-5P/TROPOMI and Suomi-NPP/OMPS Measurements. AGUFM, 33-3292. 2018AGUFM.A33J3292A.
Al-Hurban, A.E., & Al-Ostad, A.N. (2010). Textural characteristics of dust fallout and potential effect on public health in Kuwait City and suburbs. Environ Earth Sci, 60, 169–181. 10.1007/s12665-009-0177-3
Baghbanan, P., Ghavidel, Y., & Farajzadeh, M. (2020). Temporal long-term variations in the occurrence of dust storm days in Iran. Meteorol Atmos Phys, 132, 885–898. 10.1007/s00703-020-00728-3
Cao, H., Liu, J., Wang, G., Yang, G., & Luo, L. (2015). Identification of sand and dust storm source areas in Iran. Journal of Arid Land, 7(5), 567-578. 10.1007/s40333-015-0127-8
Grousset F.E. & Biscaye, P.E. (2005). Tracing dust sources and transport patterns using Sr, Nd, and Pb isotopes. Chemical Geology, 222(3-4):149–67. 10.1016/j.chemgeo.2005.05.006.
Guo, J.P., Zhang, X.Y., Che, H.Z., Gong, S.L., An, X., Cao, C.X., et al. (2009). Correlation between PM concentrations and aerosol optical depth in eastern China. Atmospheric Environment, 43(37):5876-86. 10.1016/j.atmosenv.2009.08.026.
Ji, L., Zhang, L., Wylie, B. (2009). Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing, 75(11):1307–1317. 10.14358/PERS.75.11.1307.
Kasturi, D.K, Yaso, N. (2010). Preliminary analysis of the spatial and temporal patterns of aerosols and their impact on climate in Malaysia using MODIS satellite data. International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Science, Volume XXXVIII(8), Kyoto Japan.
Kaufman, Y.J., Koren, I., Remer, L., Tanré, D., Ginoux, P., & Fan, S. (2005). Dust transport and deposition observed from the Terra‐Moderate Resolution Imaging Spectroradiometer (MODIS) spacecraft over the Atlantic Ocean. Journal of Geophysical Research: Atmospheres; 110(D10). 10.1029/2003jd004436.
Klüser, L., Schepanski,  K. (2009), Remote sensing of mineral dust over land with MSG inferred channels: A new Bitemporal Mineral Dust Index, Remote Sens. Environ., 113, 9. doi.org/10.1016/j.rse.2009.04.012
Kumar, A. (2020). Spatio-temporal variations in satellite-based aerosol optical depths & aerosol index over Indian subcontinent: Impact of urbanization and climate change. Urban Climate, 32:100598. 10.1016/j.uclim.2020.100598.
Liu, J., Ding, J., Li, L., Li, X., Zhang, Z., Ran, S. et al. (2020). Characteristics of aerosol optical depth over land types in central Asia. Science of the Total Environment, 727, 138676. 10.1016/j.scitotenv.2020.138676.
Loi, D., Chou, T.Y., & Fang, Y.M. (2017). Integration of GIS and Remote Sensing for Evaluating Forest Canopy Density Index in Thai Nguyen Province, Vietnam. International Journal of Environmental Science and Development, 8:539-42. 10.18178/ijesd.2017.8.8.1012.
McFeeters, S.K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7):3544–61. doi.org/10.3390/rs5073544.
Munkhtsetseg, E., Shinoda, M., Gillies, J.A., Kimura, R., King, J., Nikolich, G. (2016). Relationships between soil moisture and dust emissions in a bare sandy soil of Mongolia. Particuology, 28, 131-137. 10.1016/j.partic.2016.03.001.
Ogren, J.A. (1995). A systematic approach to in situ observations of aerosol properties, in Aerosol Forcing of Climate, edited by: Charlson, R. J., and Heintzenberg, J., John Wiley & Sons, Ltd., 215–226.
Powell, J.T., Chatziefthimiou, A.D., Banack, S.A., Cox, P.A., Metcalf, J.S. (2015). Desert crust microorganisms, their environment, and human health. Journal of Arid Environments, 112:127-33. 10.1016/j.jaridenv.2013.11.004
Raygani, B., Barati, S., Goshtasb, H., Gachpaz, S., Ramezani, J. & Sarkheil, H. (2020). Sand and dust storm sources identification: A remote sensing approach. Ecological Indicators, 112:106099. 10.1016/j.ecolind.2020.106099.
Taghavi, F., Owlad, E. & Ackerman, S.A. (2017). Enhancement and identification of dust events in the southwest region of Iran using satellite observations. Journal of Earth System Science, 126(2), 28. 10.1007/s12040-017-0808-0
Wischmeier, W.H., Smith, D.D. (1978). Predicting rainfall erosion losses: a guide to conservation planning: Department of Agriculture, Science and Education Administration, (537). https://handle.nal.usda.gov/10113/CAT79706928.
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 27(14):3025–33. 10.1080/01431160600589179.
Zhang, Y., Liu, Y., Kucera, P.A., Alharbi, B.H., Pan, L., Ghulam, A. (2015). Dust modeling over Saudi Arabia using WRF-Chem: March 2009 severe dust case. Atmospheric Environment, 119,118–30. 10.1016/j.atmosenv.2015.08.032
Zhang, P., Lu, N.m., Hu, X.q. & Dong, C.h. (2009). Identification and physical retrieval of dust storms using three MODIS thermal IR channels. Global and Planetary Change, 52(1-4):197–206. 10.1016/j.gloplacha.2006.02.014.

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History

  • Receive Date: 01 June 2021
  • Revise Date: 02 August 2022
  • Accept Date: 24 August 2022

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