Aerosol uncertainty assessment: an integrated approach of remote AQUA MODIS and AERONET data

  • Moncef BouazizEmail author
  • Henda Guermazi
  • Khawla Khcharem
  • Sascha Meszner
  • Mohamed Moncef Sarbeji
Original Paper


The moderate resolution imaging spectroradiometer (MODIS) is one of the widely used sensors to address environmental and climate change subjects with a daily global coverage. MODIS Collection 6 aerosol products at 10-km resolution are used in this study to monitor aerosol variability and assess its uncertainty using ground-based measurements. The aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. Using data collected from Sidi Salem Aerosol Robotic Network (AERONET) station, we computed the accuracy for aerosol optical depth (AOD) retrieved from MODIS aboard the AQUA satellite using two validation methods. The results show a good agreement between MODIS and AERONET data for the study period using both the algorithms. We obtained high values of the correlation coefficient. These findings indicate that MODIS data perform well over Ben Salem AERONET station and are recommended for air quality monitoring over Tunisia. The conducted validation throughout the AERONET leads to a degree of confidence that allows a deep investigation of the AOD spatial variability over Tunisia. Then, MODIS data shows high performance with good certainty to identify the principal dust sources and typical transport paths occurring on the study region.


Remote sensing MODIS Aerosol AQUA AOD AERONET 



We would like to express our special appreciation and thanks to the Deutsche Akademische Austausch dienst (DAAD) for their support. Many thanks are expressed to NASA Goddard Space Flight Center (GSFC) and Atmosphere Archive and Distribution System (LAADS) ( for making available the L2 MODIS AQUA C6 aerosol data. The authors are grateful to the AERONET scientific team for making data level 2 available.


  1. Al-Dousari AM, Al-Awadhi J, Ahmed M (2013) Dust fallout characteristics within global dust storms major trajectories. Arab J Geosci 6(10):3877–3884. CrossRefGoogle Scholar
  2. Chu DA, Kaufman YJ, Ichoku C, Remer LA, Tanr´e D, Holben BN (2002) Validation of MODIS aerosol optical depth retrieval overland. Geophys Res Lett 29.
  3. Evan AT, Fiedler S, Zhao C, Menut L, Schepanski K, Flamant C, Doherty O (2015) Derivation of an observation-based map of North African dust emission. Aeolian Res 16:153–162CrossRefGoogle Scholar
  4. Fiedler S, Schepanski K, Knippertz P, Heinold B, Tegen I (2014) How important are atmospheric depressions and mobile cyclones for emitting mineral dust aerosol in North Africa. Atmos Chem Phys 14:8983–9000CrossRefGoogle Scholar
  5. Fraser RS (1976) Satellite measurement of mass of Sahara dust in the atmosphere. Appl Opt 15:2471–2479CrossRefGoogle Scholar
  6. Griggs M (1975) Measurements of atmospheric aerosol optical thickness over water using ERTS-1 data. J Air Pollut Control Assoc 25:622–626CrossRefGoogle Scholar
  7. Ichoku C, Allen CD, Mattoo S, Kaufman YJ, Remer LA, Tanré D, Slutsker I, Holben BN, (2002) A spatio temporal approach for global validation and analysis of MODIS aerosol products, Geophys Res Lett 29:1616Google Scholar
  8. Kaufman YJ, Tanr’e D, Remer LA, Vermote EF, Chu A, Holben BN (1997) Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J Geophys Res 102(D14):17051–17067CrossRefGoogle Scholar
  9. Kinne S (2003) Monthly averages of aerosol properties: a global comparison among models, satellite data, and AERONET ground data. J Geophys Res 108(4634, D20).
  10. Levy RC, Remer LA, Mattoo S, Vermote EF, Kaufman YJ (2007a) A second-generation algorithm for retrieving aerosol properties over land from MODIS spectral reflectance. J Geophys Res 112(D13211)Google Scholar
  11. Levy RC, Remer LA, Dubovik O (2007b) Global aerosol optical properties and application to moderate resolution imaging spectroradiometer aerosol retrieval over land. J Geophys Res 112(D13210).
  12. Levy RC, Leptoukh GG, Kahn R, Zubko V, Gopalan A, Remer LA (2009) A critical look at deriving monthly aerosol optical depth from satellite data. IEEE Trans Geosci Remote Sens 47:2942–2956CrossRefGoogle Scholar
  13. Li Z, Niu F, Lee KH, Xin J, Hao WM, Nordgren B, Wang Y, Wang P (2007) Validation and understanding of MODIS aerosol products using ground-based measurements from the handheld sunphotometer network in China. J Geophy Res 112(D22S07).
  14. Mekler Y, Quenzel H, Ohring G, Marcus I (1977) Relative atmospheric aerosol content from ERTS observations. J Geophys Res 82:967–972CrossRefGoogle Scholar
  15. Mi W, Li Z, Xia X, Holben B, Levy R, Zhao F, Chen H, and Cribb M (2007) Evaluation of the moderate resolution imaging spectroradiometer aerosol products at two Aerosol Robotic Network stations in China. J Geophys Res 112(D22S08), doi:
  16. Pantillon F, Knippertz P, Marsham JH, Panitz HJ, Bischoff-Gauss I (2016) Modeling haboob dust storms in large-scale weather and climate models. J Geophys Res Atmos 121:2090–2109CrossRefGoogle Scholar
  17. Remer LA, Kaufman YJ, Tanr’e D, Mattoo S, Chu DA, Martins JV, Li RR, Ichoku C, Levy RC, Kleidman RG, Eck TF, Vermote E, Holben BN (2005) The MODIS aerosol algorithm, products and validation. J Atmos Sci 62:947–973CrossRefGoogle Scholar
  18. Remer LA, Tanre D, Kaufman YJ, Levy R, Mattoo S (2006) Algorithm for remote sensing of Tropospheric aerosol from MODIS: Collection 005, Product ID MOD04/MYD04 Ref. No. ATBD-MOD-96. Available at
  19. Salomonsen V, Barnes W, Maymon P, Montgomery H, Ostrow H (1989) MODIS – advanced facility instrument for studies of the earth as a system. IEEE T Geosci Remote 27:145–153, 1989CrossRefGoogle Scholar
  20. Schepanski K, Tegen I, Macke A (2012) Comparison of satellite based observations of Saharan dust source areas. Remote Sens Environ 123:90–97CrossRefGoogle Scholar
  21. Shepherd G, Terradellas E, Baklanov A, Kang U, Sprigg WA, Nickovic S, Boloorani AD, Al-Dousari AM, Basart S, Benedetti A, Sealy A, Tong D, Zhang X, Sh J. Guillemot, Kebin Z, Knippertz P, Mohammed AAA ,Al-Dabbas M, Cheng L, Otani Sh, Wang F, Zhang Ch, Ryoo SB, Cha J. Global assessment of sand and dust storms. (2016). United Nations Environment Programme UNEP, Nairobi.Google Scholar
  22. Smirnov A, Holben BN, Eck TF, Dubovik O, Slutsker I (2000) Cloud-screening andquality control algorithms for the AERONET database. Remote Sens Environ 73:337_349–337_349. CrossRefGoogle Scholar
  23. Tanr’e D, Kaufman YJ, Herman M, Mattoo S (1997) Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J Geophys Res 102(D14):16971–16988CrossRefGoogle Scholar
  24. Tanr’e D, Remer LA, Kaufman YJ, Mattoo S, Hobbs PV, Livingston JM, Russell PB, Smirnov A (1999) Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS airborne simulator during TARFOX. J Geophys Res 104(D2):2261–2278CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Moncef Bouaziz
    • 1
    • 2
    Email author
  • Henda Guermazi
    • 2
  • Khawla Khcharem
    • 3
  • Sascha Meszner
    • 1
  • Mohamed Moncef Sarbeji
    • 2
  1. 1.Faculty of Environmental Sciences, Institute of Geography, TU-DresdenDresdenGermany
  2. 2.National School of Engineers of Sfax, Water, Energy and Environment Laboratory L3EUniversity of SfaxSfaxTunisia
  3. 3.Faculty of Sciences, Department of Earth SciencesUniversity of SfaxSfaxTunisia

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