AVIRIS SURFACE REFLECTANCE PROCESSING AND PRODUCTS
Last Modified 10 August 2014 by D. R. Thompson

Contacts 
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Bo Cai Gao, Naval Research Laboratory
Robert O. Green, Jet Propulsion Laboratory
Sarah R. Lundeen, Sarah.R.Lundeen@jpl.nasa.gov
David R. Thompson, David.R.Thompson@jpl.nasa.gov 

Summary
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The HyspIRI Preparatory Campaign’s Level 2 processing pipeline transforms calibrated AVIRIS radiance data into surface reflectance.  This document describes the algorithm, its theoretical assumptions, and products.   We use reflectance in a generic sense, but acknowledge that this measurement has more specific formulations [Schaepman-Strub 2006]. 

The atmospheric correction is based on the Atmosphere Removal Algorithm (ATREM) program developed by Gao et al. [1993].  It retrieves scaled surface reflectance by modeling the absorption due to water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, and oxygen.  These absorption models are combined with observation geometry to estimate atmospheric transmittance from 400-2500 nanometers.  Scattering effects are modeled with the 6S code [Vermote, 1997].  The algorithm retrieves the integrated water vapor amount on a per-pixel basis using 940 and 1140nm water absorption features, and then estimates the Lambertian surface reflectance.   

The HyspIRI preparatory campaign adds several additional features: absorption cross sections from the HITRAN 2012 line list [Rothman 2013], and a three-phase H2O retrieval that simultaneously estimates absorption by liquid, ice, and vapor.  Pressure altitude is now retrieved using the depth of the oxygen A band, which improves performance for images with large elevation changes and variable optical path.  

Finally,  an empirical correction is used to correct the resulting surface reflectance using invariant targets measured in situ.  We fit one multiplicative coefficient per band, using the value which best aligns the remote spectrum to the in situ measurement.  The correction factors are supplied as an additional file with the .smth prefix, and an investigator that wants to use the original data can simply divide by these channel-wise gains to recover the original *_refl_img file.  

References
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Dudhia, A. Reference Forward Model. visited 22 August 2013. 
    http://www.atm.ox.ac.uk/RFM/
Gao, B.C., K. H. Heidebrecht, and A. F. H. Goetz, Derivation of scaled surface 
    reflectances from AVIRIS data, Remote Sens. Env., 44, 165-178, 1993
Mlawer E. J., et al.. Development and recent evaluation of the MT_CKD model of 
    continuum absorption. Phil. Trans. Royal Soc. A, 370, 2012.
Rothman, L. S. et al., The HITRAN 2012 molecular spectroscopic database. Journ.
    Quant. Spectroscopy and Radiative Transfer,  2013 (in press).
Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S. and  
    Martonchik, J. V.  Reflectance quantities in optical remote sensing — 
    definitions and case studies. Remote Sensing of Environment, 103, 27−42, 
    2006.
Vermote, Eric F., et al. Second simulation of the satellite signal in the solar 
    spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens. 35.3. 675-686,
    1997.

Acknowledgements
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This research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration (NASA).  Copyright 2013 California Institute of Technology.  All Rights Reserved.  U.S. Government Support Acknowledged.