Aerosol optical depth trends and variability over the Middle East from MODIS, MISR, OMI, and AERONET observations

This study compares the performance of the aerosol optical depth (AOD) retrievals from four satellite sensors including MODIS-Terra, MODIS-Aqua, MISR, and OMI by evaluating them against the AERONET ground-based observations across the nine Middle Eastern sites from 2007 to 2021. The analysis reveals that while all the satellite products show reasonable correlation with the AERONET data, their accuracy varies depending on the sensor, location, and environmental conditions. The MODIS-Aqua product showed the highest correlation with AERONET data, with R value of approximately 0.8, while OMI showed the lowest correlation. High AOD values are observed over industrial zones, densely populated urban areas and dust-prone regions. The MODIS sensors, utilizing the combined Deep Blue and Dark Target algorithms, provide broad spatial coverage with consistent performance but tend to underestimate AOD under high aerosol loading. MISR, with its multi-angle viewing capability, demonstrates strong agreement with AERONET, particularly over bright surfaces. Seasonal patterns were evident, with all sensors detecting peak AOD values during summer months of about 0.55, driven by increased dust activity. Retrieval errors increased with AOD magnitude, i.e., RMSE rose by approximately 40% when AOD exceeded 0.5. The study reveals that higher aerosol concentrations are associated with increased retrieval errors, underscoring the need for region-specific calibration and algorithm refinement. These findings highlight the importance of the integrating multi-sensor data and updated retrieval algorithms to improve AOD estimation accuracy in arid and semi-arid regions.

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Publication Details

Journal
Scientific Reports
Published
2025-11-25
DOI
https://doi.org/10.1038/s41598-025-25895-4
Citations
1
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
1.11
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article

Aerosol optical depth trends and variability over the Middle East from MODIS, MISR, OMI, and AERONET observations

Farhang Ahmadi‐Givi, Samaneh Sabetghadam, Dimitris G. Kaskaoutis, Negin Rezaei-Nokandeh
1 citations
Scientific Reports
Atmospheric aerosols and clouds
1.11
article

Aerosol optical depth trends and variability over the Middle East from MODIS, MISR, OMI, and AERONET observations

Farhang Ahmadi‐Givi, Samaneh Sabetghadam, Dimitris G. Kaskaoutis, Negin Rezaei-Nokandeh
article en
1 citations

Abstract

This study compares the performance of the aerosol optical depth (AOD) retrievals from four satellite sensors including MODIS-Terra, MODIS-Aqua, MISR, and OMI by evaluating them against the AERONET ground-based observations across the nine Middle Eastern sites from 2007 to 2021. The analysis reveals that while all the satellite products show reasonable correlation with the AERONET data, their accuracy varies depending on the sensor, location, and environmental conditions. The MODIS-Aqua product showed the highest correlation with AERONET data, with R value of approximately 0.8, while OMI showed the lowest correlation. High AOD values are observed over industrial zones, densely populated urban areas and dust-prone regions. The MODIS sensors, utilizing the combined Deep Blue and Dark Target algorithms, provide broad spatial coverage with consistent performance but tend to underestimate AOD under high aerosol loading. MISR, with its multi-angle viewing capability, demonstrates strong agreement with AERONET, particularly over bright surfaces. Seasonal patterns were evident, with all sensors detecting peak AOD values during summer months of about 0.55, driven by increased dust activity. Retrieval errors increased with AOD magnitude, i.e., RMSE rose by approximately 40% when AOD exceeded 0.5. The study reveals that higher aerosol concentrations are associated with increased retrieval errors, underscoring the need for region-specific calibration and algorithm refinement. These findings highlight the importance of the integrating multi-sensor data and updated retrieval algorithms to improve AOD estimation accuracy in arid and semi-arid regions.

Scientific ReportsVol. 15(1)
Cyprus Institute (CY), University of Tehran (IR), National Observatory of Athens (GR), McGill University (CA), University of Western Macedonia (GR)
Openalex Percentile: Top 17%
Atmospheric aerosols and clouds
1.11
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