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.
Highlights
Hyperspectral imaging sensors record the at-sensor radiance reflected from a surface, for hundreds of narrow contiguous spectral bands
From the experiments with the noise levels and following the signal-to-noise ratios (SNR) values given in the literature, we found that sensor noise with SNR = 60 dB: SRE (dB) was a realistic choice for Hyperspectral Mapper (HyMap) sensor
From our experience with real images processed through the Central Data Processing Center (CDPC) we learned that a reflectance estimate is not sensitive to visibility values that are less than 5 km
Summary
Hyperspectral imaging sensors record the at-sensor radiance reflected from a surface, for hundreds of narrow contiguous spectral bands. In the absence of the Earth’s atmosphere, a reflectance obtained from the recorded radiation is the spectral signature that characterises the underlying surface within the Instantaneous Field of View of the sensor. In the presence of the Earth’s atmosphere, the apparent reflectance differs from the target reflectance This is primarily because of the complex interaction of the surface reflected radiation with the atmospheric constituents while propagating along the path from the target surface to the sensor. On the path of beam to the sensor two major scattering components distort the at sensor radiance: reflection by the surrounding area of the target pixel and the radiance backscattered by the atmosphere
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