Abstract
Abstract. Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.
Highlights
During the last ten years, improvements in sensors and computation capabilities of remote sensing technologies have improved exponentially
Query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine
This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter
Summary
During the last ten years, improvements in sensors and computation capabilities of remote sensing technologies have improved exponentially. The derived products exhibit improved characteristics with respect to previous systems, in terms of spatial, spectral, radiometric and temporal resolution, Signal-to-Noise Ratio (SNR), field of view and reliability of the acquired data. Such development that hyperspectral technology has undergone in recent years has caused the exponential growth of applied research and resources, especially in relation to environmental applications. There are some tools that facilitate the search for hyperspectral indices based on traditional search criteria These systems lack the ability to perform the search based on wavelength ranges.
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