Utilizing NASA Giovanni and Google Earth Engine for Dust Analysis in Northeast and East Iran: A 40-Year Study Using MODIS, MERRA-2, and Sentinel-5 Data to Assess Relationships with Wind and Vegetation
Subject Areas : EnvironmentSajad Jalil 1 , Alireza Nouri 2 , Ali Akbar Jamali 3
1 - Department of Geography and Urban Planning, Yazd Branch, Islamic Azad University, Yazd, Iran
2 - Department of Geography and Urban Planning, Yazd Branch, Islamic Azad University, Yazd, Iran
3 - Associate Professor, Department of GIS-RS and Watershed Management, Maybod Branch, Islamic Azad University, Maybod, Iran
Keywords: Dust, EVI, Google Earth Engine, Remote Sensing, Wind speed,
Abstract :
Background and objective: Dust storms have been a persistent environmental challenge in eastern and northeastern Iran, exacerbating air pollution and affecting human health, agriculture, and infrastructure. This study aims to analyze the spatial and temporal variations of dust levels over the last 40 years, with a focus on assessing the Enhanced Vegetation Index (EVI) and wind speed on dust intensity and distribution. The research uses satellite data to explore the relationship between environmental factors and the occurrence of dust storms, providing insights into the role of drought-induced vegetation loss in dust formation. Materials and methods: This study utilized data from MODIS, MERRA-2, and Sentinel-5, processed through the NASA Giovanni and Google Earth Engine (GEE) platforms. Dust concentration levels were analyzed using satellite imagery and meteorological data. The relationship between wind speed, EVI, and dust levels was assessed by extracting time-series data for the study region. Temporal and spatial variations were analyzed to identify trends, with a particular focus on the influence of environmental factors such as, wind speed, and vegetation decline. Results and conclusion: The results indicate a strong inverse correlation between vegetation cover and dust intensity, with declining EVI values corresponding to increased dust levels. Wind speed also significantly influenced dust distribution patterns, with higher speeds contributing to more severe dust storms. Over the 40-year period, a marked increase in dust levels was observed, particularly in areas where vegetation cover had diminished due to recurrent droughts. The findings highlight the critical role of wind and vegetation in controlling dust storms and suggest that improving vegetation cover could mitigate dust storm severity in the region.
Al Ameri, I. D., Briant, R., & Engels, S. (2019). Drought severity and increased dust storm frequency in the Middle East: A case study from the Tigris-Euphrates alluvial plain, central Iraq. Weather, 74(12), 416-426. https://doi.org/10.1002/wea.3445
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., ... & Brisco, B. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350. doi: 10.1109/JSTARS.2020.3021052
Azizi, Q., Miri, M., & Nabavi, S. O. (2012). Dust detection in the western half of Iran. Geographical Studies of Arid Areas, 2(7), 63-81.
Bakker, N. L., Drake, N. A., & Bristow, C. S. (2019). Evaluating the relative importance of northern African mineral dust sources using remote sensing. Atmospheric Chemistry and Physics, 19(16), 10525-1053 https://doi.org/10.5194/acp-19-10525-2019
Berrick, S. W., Leptoukh, G., Farley, J. D., & Rui, H. (2008). Giovanni: a web service workflow-based data visualization and analysis system. IEEE Transactions on Geoscience and Remote Sensing, 47(1), 106-113 doi: 10.1109/TGRS.2008.2003183
Breckle, S. W. (2002). Salt deserts in Iran and Afghanistan. Sabkha ecosystems, 1, 109-122.
Duniway, M. C., Pfennigwerth, A. A., Fick, S. E., Nauman, T. W., Belnap, J., & Barger, N. N. (2019). Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere, 10(3), e02650. https://doi.org/10.1002/ecs2.2650
Ebrahimi-Khusfi, Z., Mirakbari, M., Ebrahimi-Khusfi, M., & Taghizadeh-Mehrjardi, R. (2020). Impacts of vegetation anomalies and agricultural drought on wind erosion over Iran from 2000 to 2018. Applied Geography, 125, 102330. https://doi.org/10.1016/j.apgeog.2020.102330
Feuerstein, S., & Schepanski, K. (2018). Identification of dust sources in a Saharan dust hot-spot and their implementation in a dust-emission model. Remote Sensing, 11(1), 4. https://doi.org/10.3390/rs11010004
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., ... & Zhao, B. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of climate, 30(14), 5419-5454. https://doi.org/10.1175/JCLI-D-16-0758.1
Khusfi, Z. E., Khosroshahi, M., Roustaei, F., & Mirakbari, M. (2020). Spatial and seasonal variations of sand-dust events and their relation to atmospheric conditions and vegetation cover in semi-arid regions of central Iran. Geoderma, 365, 114225. https://doi.org/10.1016/j.geoderma.2020.114225
Kok, J. F., Adebiyi, A. A., Albani, S., Balkanski, Y., Checa-Garcia, R., Chin, M., ... & Wan, J. S. (2021). Contribution of the world's main dust source regions to the global cycle of desert dust. Atmospheric Chemistry and Physics, 21(10), 8169-8193. https://doi.org/10.5194/acp-21-8169-2021
Liu, L., Wang, Z., Che, H., Wang, D., Gui, K., Liu, B., ... & Zhang, X. (2024). Climate factors influencing springtime dust activities over Northern East Asia in 2021 and 2023. Atmospheric Research, 303, 107342. https://doi.org/10.1016/j.atmosres.2024.107342
Li, J., Garshick, E., Al-Hemoud, A., Huang, S., & Koutrakis, P. (2020). Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries. Science of the total environment, 712, 136597. https://doi.org/10.1016/j.scitotenv.2020.136597
Namdari, S., Zghair Alnasrawi, A. I., Ghorbanzadeh, O., Sorooshian, A., Kamran, K. V., & Ghamisi, P. (2022). Time series of remote sensing data for interaction analysis of the vegetation coverage and dust activity in the middle east. Remote Sensing, 14(13), 2963. https://doi.org/10.3390/rs14132963
Papi, R., Kakroodi, A. A., Soleimani, M., Karami, L., Amiri, F., & Alavipanah, S. K. (2022). Identifying sand and dust storm sources using spatial-temporal analysis of remote sensing data in Central Iran. Ecological Informatics, 70, 101724. https://doi.org/10.1016/j.ecoinf.2022.101724
Priya, M. V., Kalpana, R., Pazhanivelan, S., Kumaraperumal, R., Ragunath, K. P., Vanitha, G., ... & Vasumathi, V. (2023). Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu. Journal of Applied and Natural Science, 15(3), 1170-1177. https://doi.org/10.31018/jans.v15i3.4803
Rahmdel, M., Javanshiri, Z., & Sanaei Nejad, S. H. (2022). Exploratory Analysis and Detection of In-homogeneities in Temperature and Precipitation Series of Meteorological Stations in Iran (Period 1959-2018). Iranian Journal of Geophysics, 16(1), 213-233. https://doi.org/10.30499/ijg.2022.308829.1367
Shi, L., Zhang, J., Yao, F., Zhang, D., & Guo, H. (2021). Drivers to dust emissions over dust belt from 1980 to 2018 and their variation in two global warming phases. Science of The Total Environment, 767, 144860. https://doi.org/10.1016/j.scitotenv.2020.144860
Song, H., Min, R., Song, G., Zhai, S., Wang, D., Wang, Y., & Bai, T. (2024). Impacts of land cover changes on dust emissions in northern China (2000–2020). Land Degradation & Development, 35(8), 2800-2812. https://doi.org/10.1002/ldr.5094
Xiong, C., Ma, H., Liang, S., He, T., Zhang, Y., Zhang, G., & Xu, J. (2023). Improved global 250 m 8-day NDVI and EVI products from 2000–2021 using the LSTM model. Scientific Data, 10(1), 800. https://doi.org/10.1038/s41597-023-02695-x
Xuan, J., Sokolik, I. N., Hao, J., Guo, F., Mao, H., & Yang, G. (2004). Identification and characterization of sources of atmospheric mineral dust in East Asia. Atmospheric Environment, 38(36), 6239-6252. https://doi.org/10.1016/j.atmosenv.2004.06.042
Yao, W., Che, H., Gui, K., Wang, Y., & Zhang, X. (2020). Can MERRA-2 reanalysis data reproduce the three-dimensional evolution characteristics of a typical dust process in East Asia? A case study of the dust event in May 2017. Remote Sensing, 12(6), 902. https://doi.org/10.3390/rs12060902
Zhang, M., Chen, S., Zhang, X., Guo, S., Wang, Y., Zhao, F., ... & Bilal, M. (2023). Characters of particulate matter and their relationship with meteorological factors during winter Nanyang 2021–2022. Atmosphere, 14(1), 137. https://doi.org/10.3390/atmos14010137