Dust detection in western and southwestern Iran based on DAI index algorithm and Modis spectral data

Document Type : Original Article


1 Soil Science Department, Faculty of Agricultural Science, University of Lorestan, Lorestan, Iran

2 Department of Remote Sensing, University of Tehran, Tehran, Iran

3 Faculty of Soil Conservation and Watershed, Management Institute, Tehran, Iran


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.
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.


  1. Ataee, Sh. 2014. Dust Detection from MODIS Images Using TIIDI Methods, Decision Tree and Neural Network, MS.c. Thesis. Khajehnesire Al-Din Tusi University of Technology.
  2. Bertina, H, Sayyad, G.A., Matin Far H.R. and Hojati, S., 2006. Detection of the dust of the Middle East on the basis of spectral data MODIS sensor, Physical Geography Research (Geographical Research): 45, 84-73.
  3. Bucholtz, A .1995. Rayleigh-scattering calculations for the terrestrial atmosphere Applied Optics 34 15 pp 2765-2773
  4. Bucholtz, A. 1995. Rayleigh-scattering calculations for the terrestrial atmosphere Applied Optics 34 15 pp 2765-2773)
  5. Chandrasekhar, S 1960 Radiative Transfer Dover NewYork Chap 1 p 49
  6. Chandrasekhar, S. 1960. Radiative Transfer Dover NewYork Chap 1 p 49
  7. Ciren P, Kondragunta, S. 2014. Dust aerosol index (DAI) algorithm for MODIS. Journal of Geophysical Research: Atmospheres.119 (8):4770-92
  8. Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., & Hoff, R. M. (2004). Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment, 38(16), 2495-2509.
  9. He, J., Zha, Y., Zhang, J., & Gao, J. (2014). Aerosol indices derived from MODIS data for indicating aerosol-induced air pollution. Remote Sensing, 6(2), 1587-1604.‏
  10. Hoyningen-Huene, W., Kokhanovsky, A. A., Wuttke, M. W., Buchwitz, M., Noël, S., Gerilowski, K., Burrows, J. P., Latter, B., Siddans, R. and Kerridge, B. J. 2007. Validation of SCIAMACHY top-of-atmosphere reflectance for aerosol remote sensing using MERIS L1 data Atmos. Chem. Phys 7 1 pp 96-106
  11. Hsu, N. C., Robinson, W. D., Bailey, S. W. and Werdell, P. J. 2000. the description of the SeaWiFS absorbing aerosol index SeaWiFS, NASA Tech. Memorandum, 2000-206892(10), 3–5.
  12. Hsu, N. C., Tsay, S.-C., King, M. D. and Herman J. R. 2006. Deep blue retrievals of Asian aerosol properties during ACE-Asia, IEEE Trans. Geosci. Remote Sens., 44(11), 3180–3195, doi:10.1109/TGRS.2006.879540
  13. Hsu, N. C., Tsay, S.C., King, M. D. and Herman, J. R. 2004. Aerosol properties over bright reflecting source regions, IEEE Trans. Geosci. Remote Sens., 42(3), 557–569, doi:10.1109/TGRS.2004.824067.
  14. Jayakumar, P. 1987. Modeling and identification in structural dynamics, Ph.D. dissertation, California Institute of Technology, Pasadena, California. (For thesis)
  15. Kaufman, Y.J., Tanre, D., Remer, L.A., Vermote, E.F., Chu, A., Holben, B.N. 1997. Operational Remote Sensing of Tropospheric Aerosol over Land from EOS Moderate Resolution Imaging Spectroradiometer, Journal of Geophysical Research: Atmospheres, Vol. 102, No. D14, PP. 17051-1706
  16. Kotchenova, S. Y., Vermote, E. F., Matarrese, R. and Klemm Jr. F. J. 2006. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data, Part I. Path Radiance, Appl. Opt., 45(26), 6726–6774.
  17. Li, L. J., Ying, W. A. N. G., Zhang, Q., Tong, Y. U., Yue, Z. H. A. O., & Jun, J. I. N. (2007). Spatial distribution of aerosol pollution based on MODIS data over Beijing, China. Journal of Environmental Sciences, 19(8), 955-960
  18. Mei, D., Xiushan, L., Lin, S. and Ping, W., 2008. "A Dust-Storm Process Dynamic Monitoring With Multi-Temporal MODIS Data", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B7, pp 965-970
  19. Savtchenko, A., Ouzounov, D., Ahmad, S., Acker, J., Leptoukh, G., Koziana, J., & Nickless, D. (2004). Terra and Aqua MODIS products available from NASA GES DAAC. Advances in Space Research, 34(4), 710-714
  20. Wahab, A. M. and Sarker, M. L. R. 2014. Aerosol retrieval algorithm for the characterization of local aerosol using MODIS L1B data. In IOP Conference Series: Earth and Environmental Science (Vol. 18, No. 1, p. 012098). IOP Publishing
  21. Wang, Z., Chen, L., Tao, J., Zhang, Y. and Su, L., 2009, Satellite-Based Estimation of Regional Particulate Matter (PM) in Beijing Using Vertical-and-RH Correcting Method, Remote Sensing of Environment, Vol. 114, No. 1, 50-63.