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17 December 1999 High-order derivative spectroscopy for selecting spectral regions and channels for remote sensing algorithm development
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A remote sensing reflectance model, which describes the transfer of irradiant light within a plant canopy or water column has previously been used to simulate the nadir viewing reflectance of vegetation canopies and leaves under solar induced or an artificial light source and the water surface reflectance. Wavelength dependent features such as canopy reflectance leaf absorption and canopy bottom reflectance as well as water absorption and water bottom reflectance have been used to simulate or generate synthetic canopy and water surface reflectance signatures. This paper describes how derivative spectroscopy can be utilized to invert the synthetic or modeled as well as measured reflectance signatures with the goal of selecting the optimal spectral channels or regions of these environmental media. Specifically, in this paper synthetic and measured reflectance signatures are used for selecting vegetative dysfunction variables for different plant species. The measured reflectance signatures as well as model derived or synthetic signatures are processed using extremely fast higher order derivative processing techniques which filter the synthetic/modeled or measured spectra and automatically selects the optimal channels for automatic and direct algorithm application. The higher order derivative filtering technique makes use of a translating and dilating, derivative spectroscopy signal processing (TDDS-SPR) approach based upon remote sensing science and radiative transfer theory. Thus the technique described, unlike other signal processing techniques being developed for hyperspectral signatures and associated imagery, is based upon radiative transfer theory instead of statistical or purely mathematical operational techniques such as wavelets.
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Charles R. Bostater Jr. "High-order derivative spectroscopy for selecting spectral regions and channels for remote sensing algorithm development", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999);

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