17 Some Reflections on Thirty-Five Years of Ocean Color Remote Sensing
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facilitated the vicarious calibration of these sensors over time. SeaWiFS, which is
in its 11th year of operation, has been an enormous success.
From the end of CZCS to the approval of SeaWiFS, most of my effort was
devoted to enhancements to the atmospheric correction algorithm, with some work
on a semi-analytic model of water-leaving radiance (Gordon et al., 1988). We
replaced the single scattering computation of L r (λ i ) with a full multiple scattering
computation, including polarization (Gordon et al., 1988), and tried to understand
the influence of sea surface roughness on atmospheric correction (Gordon and
Wang, 1992a, b). We also examined calibration requirements and enhancements
and signal-to-noise considerations for future sensors (Gordon, 1987, 1990). During
this time André Morel and coworkers examined the influence of the variation of
Ozone concentration and the variation of atmospheric pressure on atmospheric correction (André and Morel, 1989) and developed the first atmospheric correction
algorithm that truly coupled a model of ocean color to first-order radiative transfer (Equations (17.1), (17.2), (17.3), (17.4), (17.5) and (17.6)) (Bricaud and Morel,
1987). However, even with all of the algorithm enhancements, it became clear
that significant improvement in CZCS processing was unlikely simply because of
instrument limitations.
With the improved radiometric sensitivity of SeaWiFS (and later MODIS) over
that of CZCS, the atmospheric correction was still not up to the task because of the
partial neglect of multiple scattering effects, particularly the interaction between
aerosol and Rayleigh scattering. Menghua Wang and I set out to try to modify the correction algorithm to include multiple scattering effects. Our idea was
(1) to use the basic structure of the algorithm, but to rewrite Equation (17.1) to
explicitly include the Rayleigh-aerosol interaction (L ra (λ i )), i.e., L t (λ i ) = L r (λ i ) +
L ra (λ i ) + L a (λ i ) + t(λ i )L w (λ i ), (2) compute L t (λ i ) for various aerosol models and
concentrations with L w (λ i ) = 0 including all orders of multiple scattering, (3)
compute L r (λ i ) as before using a full multiple scattering code, and (4) compute
L t (λ i ) − L r (λ i ) = L ra (λ i ) + L a (λ i ) as a function of the aerosol optical thickness
and model and store the computations for later use in look-up-tables (LUTs). We
tested such a scheme using (as before) an aerosol model for which the scattering
phase function was independent of wavelength, and then re-evaluated it using more
realistic aerosol models. Our final algorithm was still being used in SeaWiFS and
MODIS processing into 2009 (Gordon and Wang, 1994).
CZCS, SeaWiFS, and MODIS are the only sensor programs with which I have
had direct involvement. Although I have no first-hand knowledge of them, for completeness I mention the other sensors that were flown in the mid to late 1990s. These
include OCTS (JAXA) and POLDER (CNES) on ADEOS (JAXA) and MOS (DLR)
on IRS-P3 (ISRO). 1
1 For information on these sensors, see http://www.ioccg.org/sensors_ioccg.html
303
facilitated the vicarious calibration of these sensors over time. SeaWiFS, which is
in its 11th year of operation, has been an enormous success.
From the end of CZCS to the approval of SeaWiFS, most of my effort was
devoted to enhancements to the atmospheric correction algorithm, with some work
on a semi-analytic model of water-leaving radiance (Gordon et al., 1988). We
replaced the single scattering computation of L r (λ i ) with a full multiple scattering
computation, including polarization (Gordon et al., 1988), and tried to understand
the influence of sea surface roughness on atmospheric correction (Gordon and
Wang, 1992a, b). We also examined calibration requirements and enhancements
and signal-to-noise considerations for future sensors (Gordon, 1987, 1990). During
this time André Morel and coworkers examined the influence of the variation of
Ozone concentration and the variation of atmospheric pressure on atmospheric correction (André and Morel, 1989) and developed the first atmospheric correction
algorithm that truly coupled a model of ocean color to first-order radiative transfer (Equations (17.1), (17.2), (17.3), (17.4), (17.5) and (17.6)) (Bricaud and Morel,
1987). However, even with all of the algorithm enhancements, it became clear
that significant improvement in CZCS processing was unlikely simply because of
instrument limitations.
With the improved radiometric sensitivity of SeaWiFS (and later MODIS) over
that of CZCS, the atmospheric correction was still not up to the task because of the
partial neglect of multiple scattering effects, particularly the interaction between
aerosol and Rayleigh scattering. Menghua Wang and I set out to try to modify the correction algorithm to include multiple scattering effects. Our idea was
(1) to use the basic structure of the algorithm, but to rewrite Equation (17.1) to
explicitly include the Rayleigh-aerosol interaction (L ra (λ i )), i.e., L t (λ i ) = L r (λ i ) +
L ra (λ i ) + L a (λ i ) + t(λ i )L w (λ i ), (2) compute L t (λ i ) for various aerosol models and
concentrations with L w (λ i ) = 0 including all orders of multiple scattering, (3)
compute L r (λ i ) as before using a full multiple scattering code, and (4) compute
L t (λ i ) − L r (λ i ) = L ra (λ i ) + L a (λ i ) as a function of the aerosol optical thickness
and model and store the computations for later use in look-up-tables (LUTs). We
tested such a scheme using (as before) an aerosol model for which the scattering
phase function was independent of wavelength, and then re-evaluated it using more
realistic aerosol models. Our final algorithm was still being used in SeaWiFS and
MODIS processing into 2009 (Gordon and Wang, 1994).
CZCS, SeaWiFS, and MODIS are the only sensor programs with which I have
had direct involvement. Although I have no first-hand knowledge of them, for completeness I mention the other sensors that were flown in the mid to late 1990s. These
include OCTS (JAXA) and POLDER (CNES) on ADEOS (JAXA) and MOS (DLR)
on IRS-P3 (ISRO). 1
1 For information on these sensors, see http://www.ioccg.org/sensors_ioccg.html