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Remote Sens. 2018, 10(6), 947; https://doi.org/10.3390/rs10060947

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

1
Remote Sensing Unit, Flemish Institute for Technological Research, 2400 Mol, Belgium
2
Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 4 May 2018 / Revised: 9 May 2018 / Accepted: 12 May 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
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Abstract

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. View Full-Text
Keywords: aerosol optical depth; uncertainty; sensitivity; adjacency range; atmospheric correction; hyperspectral unmixing aerosol optical depth; uncertainty; sensitivity; adjacency range; atmospheric correction; hyperspectral unmixing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bhatia, N.; Tolpekin, V.A.; Stein, A.; Reusen, I. Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data. Remote Sens. 2018, 10, 947.

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