Characterization of fine resolution field spectrometers using solar Fraunhofer lines and atmospheric absorption features.

The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval.

[1]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[2]  B. Edĺen The Refractive Index of Air , 1966 .

[3]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[4]  C J Sansonetti,et al.  Irradiances of spectral lines in mercury pencil lamps. , 1996, Applied optics.

[5]  R. Green,et al.  Spectral calibration requirement for Earth-looking imaging spectrometers in the solar-reflected spectrum. , 1998, Applied optics.

[6]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[7]  M E Schaepman,et al.  Solid laboratory calibration of a nonimaging spectroradiometer. , 2000, Applied optics.

[8]  Robert O. Green,et al.  On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina , 2003, IEEE Trans. Geosci. Remote. Sens..

[9]  E. V. Thomas,et al.  Non‐parametric statistical methods for multivariate calibration model selection and comparison , 2003 .

[10]  Marcos J. Montes,et al.  Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique , 2004 .

[11]  L. Guanter,et al.  Spectral calibration of hyperspectral imagery using atmospheric absorption features. , 2006, Applied optics.

[12]  M. Schildhauer,et al.  Spectral Network (SpecNet)—What is it and why do we need it? , 2006 .

[13]  Nigel P. Fox,et al.  Progress in Field Spectroscopy , 2006 .

[14]  Benoit Rivard,et al.  Comparison of spectral indices obtained using multiple spectroradiometers , 2006 .

[15]  N. Coops,et al.  Instrumentation and approach for unattended year round tower based measurements of spectral reflectance , 2007 .

[16]  L. Guanter,et al.  Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data , 2007 .

[17]  Robert A. Neville,et al.  Spectral calibration of imaging spectrometers by atmospheric absorption feature matching , 2008 .

[18]  Robert A. Neville,et al.  Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features , 2008 .

[19]  D. Baldocchi ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[20]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[21]  Luis Alonso,et al.  Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers. , 2009, Optics express.

[22]  Shunlin Liang,et al.  Earth system science related imaging spectroscopy — an assessment , 2009 .

[23]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[24]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[25]  Luis Alonso,et al.  Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications , 2009 .