The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data

Yerong Wu, Martin de Graaf, Massimo Menenti

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
27 Downloads (Pure)


This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading τ≤0.5 . With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of >3% when τ> 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors.
Original languageEnglish
Article number765
Pages (from-to)1-16
Number of pages16
JournalRemote Sensing
Issue number9
Publication statusPublished - 2016


  • Aerosol Optical Depth (AOD)
  • satellite data
  • simulation
  • retrieval
  • aerosol type
  • aerosol vertical distribution
  • OA-Fund TU Delft


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