Estimation of AOD under uncertainty: An approach for Hyperspectral Airborne data

Nitin Bhatia* (Corresponding Author), V.A. Tolpekin, A. Stein, Ils Reusen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
101 Downloads (Pure)


A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (Lrs,t(λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference.We propose a method that determines pre-estimates of surface reflectance (ρt,pre) where effects associated with Lrs,t(λ) are less influential. The method identifies pixels comprising pure materials from ρt,pre. AOD values at the pure pixels are iteratively estimated using l2-norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

Original languageEnglish
Article number947
Pages (from-to)1-30
Number of pages30
JournalRemote sensing
Issue number6
Publication statusPublished - 1 Jun 2018


  • Adjacency range
  • Aerosol optical depth
  • Atmospheric correction
  • Hyperspectral unmixing
  • Sensitivity
  • Uncertainty


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