Abstract
Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved surface reflectance spectra. In the present spectral smoothing technique, model-derived reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived reflectance spectra, is then derived. This gain curve is finally applied to all of the reflectance spectra in a scene to obtain the spectrally smoothed surface reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented.
Highlights
Since the mid-1980s, hyperspectral imaging data have been collected with different types of imaging spectrometers from aircraft and satellite platforms
The surface reflectance spectra retrieved with radiative transfer models often contain residual atmospheric absorption and scattering effects
It is sometimes difficult to make reasonable matches over the entire spectral range. We describe another technique, which fits spectra “locally” in the spectral domain using moving filters derived with a cubic spline smoothing algorithm, for quick post processing of ATREM-derived reflectance spectra from imaging spectrometer data
Summary
Since the mid-1980s, hyperspectral imaging data have been collected with different types of imaging spectrometers from aircraft and satellite platforms. Because solar radiation along the sun-surface-sensor path in the 0.4−2.5 μm visible and near-IR spectral regions is subject to absorption and scattering by atmospheric gases and aerosols, hyperspectral imaging data contain atmospheric effects. In order to use hyperspectral imaging data for quantitative remote sensing of land surfaces and ocean color, the atmospheric effects must be removed. There are a number of model-based atmospheric correction algorithms for retrieving surface reflectances from hyperspectral imaging data. These include, but are not limited to, the ATmosphere REMoval algorithm (ATREM) [1,2], the. The surface reflectance spectra retrieved with radiative transfer models often contain residual atmospheric absorption and scattering effects.
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