Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data
Introduction: A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Measurements made in the short-wavelength segment, such a...
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Shahid Beheshti University
2019-09-01
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| author | Azar Faryabi Hamid Reza Matinfar Seyyed Kazem Alavi Panah Ali Akbar Norouzi |
| author_facet | Azar Faryabi Hamid Reza Matinfar Seyyed Kazem Alavi Panah Ali Akbar Norouzi |
| author_sort | Azar Faryabi |
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| description | Introduction: A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Measurements made in the short-wavelength segment, such as the deep blue or ultraviolet section, are well-detectable in the desert area. Using short-range waves, the visual retention of fine-grained mass data, especially in desert areas, was carefully monitored. The western and southwestern Iran are always exposed to dusty systems due to its vicinity to the deserts of neighboring countries. With regard to the fact that most of the spectral indices proposed for the identification of dust have been tested and implemented based on satellite indicators for desert areas, these indicators and their related thresholds for complex topography areas need more accurate analyses. Therefore, in the western and southwestern Iran, which are mountainous with a diverse vegetation, it is necessary to test and evaluate dust detection methods. Material and methods: The study area included Khuzestan, Ilam and Kermanshah provinces, which is about 107307 square kilometers. In this study, MODIS L1B data from the Aqua satellite was used for dusty days on May 18 and June 25, 2013 and 2015. Before performing spectral calculations on various products, the data of this sensor was preprocessed, which included geometric correction of images, mask cloud and water masks with ENVI and the conversion tool module. After preprocessing (georges, separating the study area, and water mask, and cloud cover) the satellite data, the retrieved spatial radiance of TOA was normalized using satellite data considering the sun's conditions for each wavelength. Results and discussion: In general, it was found that all AOD maps generated from the direct method showed a very good spatial distribution of the local aerosol pattern compared to other methods. As expected, the retrieved AOD map from the L1B spectrum showed that the spatial distribution of the local AOD was very clear. The DAI index algorithm simulates the high-spectral dependence of the atmosphere in the blue wavelength for different surface and atmosphere conditions with a fully tested copy of the radiation-transfer code of -6 S, which is a trusted tool for measuring particle pumping over the oceans, different surfaces of the earth, and clouds. Conclusion: Unlike some of the dust detection algorithms that are carried out using measurements in the infrared thermal band, the advantage of this algorithm to detect dust is the use of spectral scattering, reflection of the surface, and absorption of dust in the air. The advantage of using measurments in the blue wavelength (410 to 490 nm) is to recover the optical properties of the aerosol. |
| format | Article |
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| spelling | doaj-art-5e10bbf229534689befbf7f0d5c156002025-08-20T02:19:31ZfasShahid Beheshti Universityعلوم محیطی1735-13242588-61772019-09-0117315116210.29252/envs.17.3.15198076Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral dataAzar Faryabi0Hamid Reza Matinfar1Seyyed Kazem Alavi Panah2Ali Akbar Norouzi3Soil Science Department, Faculty of Agricultural Science, University of Lorestan, Lorestan, IranSoil Science Department, Faculty of Agricultural Science, University of Lorestan, Lorestan, IranDepartment of Remote Sensing, University of Tehran, Tehran, IranFaculty of Soil Conservation and Watershed, Management Institute, Tehran, IranIntroduction: A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Measurements made in the short-wavelength segment, such as the deep blue or ultraviolet section, are well-detectable in the desert area. Using short-range waves, the visual retention of fine-grained mass data, especially in desert areas, was carefully monitored. The western and southwestern Iran are always exposed to dusty systems due to its vicinity to the deserts of neighboring countries. With regard to the fact that most of the spectral indices proposed for the identification of dust have been tested and implemented based on satellite indicators for desert areas, these indicators and their related thresholds for complex topography areas need more accurate analyses. Therefore, in the western and southwestern Iran, which are mountainous with a diverse vegetation, it is necessary to test and evaluate dust detection methods. Material and methods: The study area included Khuzestan, Ilam and Kermanshah provinces, which is about 107307 square kilometers. In this study, MODIS L1B data from the Aqua satellite was used for dusty days on May 18 and June 25, 2013 and 2015. Before performing spectral calculations on various products, the data of this sensor was preprocessed, which included geometric correction of images, mask cloud and water masks with ENVI and the conversion tool module. After preprocessing (georges, separating the study area, and water mask, and cloud cover) the satellite data, the retrieved spatial radiance of TOA was normalized using satellite data considering the sun's conditions for each wavelength. Results and discussion: In general, it was found that all AOD maps generated from the direct method showed a very good spatial distribution of the local aerosol pattern compared to other methods. As expected, the retrieved AOD map from the L1B spectrum showed that the spatial distribution of the local AOD was very clear. The DAI index algorithm simulates the high-spectral dependence of the atmosphere in the blue wavelength for different surface and atmosphere conditions with a fully tested copy of the radiation-transfer code of -6 S, which is a trusted tool for measuring particle pumping over the oceans, different surfaces of the earth, and clouds. Conclusion: Unlike some of the dust detection algorithms that are carried out using measurements in the infrared thermal band, the advantage of this algorithm to detect dust is the use of spectral scattering, reflection of the surface, and absorption of dust in the air. The advantage of using measurments in the blue wavelength (410 to 490 nm) is to recover the optical properties of the aerosol.https://envs.sbu.ac.ir/article_98076_fe9bd09d3b2a73f930f42d2b8e8c1be1.pdfdai algorithmmodis satellite imagesdust |
| spellingShingle | Azar Faryabi Hamid Reza Matinfar Seyyed Kazem Alavi Panah Ali Akbar Norouzi Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data علوم محیطی dai algorithm modis satellite images dust |
| title | Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data |
| title_full | Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data |
| title_fullStr | Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data |
| title_full_unstemmed | Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data |
| title_short | Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data |
| title_sort | dust detection in western and southwestern iran based on dai index algorithm and modis spectral data |
| topic | dai algorithm modis satellite images dust |
| url | https://envs.sbu.ac.ir/article_98076_fe9bd09d3b2a73f930f42d2b8e8c1be1.pdf |
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