HR: 14:55h
AN: B43E-06    [Abstracts]
TI: VIIRS Proxy Data Using MODIS-to-VIIRS Spectral Transformations
AU: Vogel, R
EM: ron.vogel@gsfc.nasa.gov
AF: SAIC, Code 614.5 NASA Goddard Space Flight Center, Greenbelt, MD 20771 United States
AU: * Privette, J L
EM: jeff.privette@nasa.gov
AF: NASA, Code 614.4 NASA Goddard Space Flight Center, Greenbelt, MD 20771 United States
AU: Yu, Y
EM: yyu@pop900.gsfc.nasa.gov
AF: George Mason University, Code 614.4 NASA Goddard Space Flight Center, Greenbelt, MD 20771 United States
AU: Qu, J
EM: jqu@scs.gmu.edu
AF: George Mason University, Earth System and Geoinformation Sciences, GMU 4400 University Dr, Fairfax, VA 22030 United States
AU: Pinheiro, A C
EM: ana@hsb.gsfc.nasa.gov
AF: National Research Council Postdoctoral Associate, Code 614.3 NASA Goddard Space Flight Center, Greenbelt, MD 20771 United States
AU: Hauss, B
EM: bruce.hauss@ngc.com
AF: Northrop Grumman Space Technology, One Space Park, Redondo Beach, CA 90278 United States
AB: As demonstrated in past studies, assessing retrieval algorithms using synthetic or proxy data has its limitations. We wish to supplement modeled synthetic data in assessing Visible-Infrared Imager-Radiometer Suite (VIIRS) algorithms by creating proxy data based on the heritage sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS). The use of both types of data, synthetic and proxy, can reduce risk in the development and verification of the new VIIRS algorithms. Development of spectral transformations for the MODIS data involves a two-step approach: (1) generating the `best fit' functional form of the transformation equation using the MODTRAN radiative transfer model, and (2) derivation of the resulting equation's coefficients using MODIS and VIIRS brightness temperatures simulated from EOS AIRS data. We determined `best fit' transformation equations using multiple linear regression of various MODIS bands and satellite/sensor geometries. Single-band simple linear regression and `nearest spectral band-for-band matching' were also considered. We apply this approach for the five VIIRS moderate-resolution middle- and thermal infrared bands over each of 17 IGBP surface types. Results suggest that, in most cases, the standard error of the regressions with AIRS data were near or at the VIIRS band noise equivalent delta-T (NEDT). The approach was validated using AVHRR data and a coincident MODIS scene from which proxy AVHRR data were generated. Since AVHRR and VIIRS have similar thermal infrared band passes, the good agreement suggests that proxy VIIRS estimates will also be reasonable. VIIRS proxy data based on our transformation equations, in conjunction with synthetic data, are applicable to pre-launch testing of the NPOESS data systems and evaluation of the Environmental Data Record (EDR) algorithms.
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: Fall Meeting 2005