Bryan A. Franz, Sean W. Bailey, P. Jeremy Werdell, and Charles R. McClain, "Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry," Appl. Opt. 46, 5068-5082 (2007)
The retrieval of ocean color radiometry from space-based sensors requires
on-orbit vicarious calibration to achieve the level of accuracy desired for quantitative oceanographic applications. The approach developed by the NASA Ocean Biology Processing Group (OBPG) adjusts the integrated instrument and atmospheric correction system to retrieve normalized water-leaving radiances that are in agreement with ground truth measurements.
The method is independent of the satellite sensor or the source of the ground truth data, but it is specific to the atmospheric correction algorithm.
The OBPG vicarious calibration approach is described in detail, and results are presented for the operational calibration of SeaWiFS using data from the Marine Optical Buoy (MOBY) and observations of clear-water sites in the South Pacific and southern Indian Ocean. It is shown that the vicarious calibration allows SeaWiFS to reproduce the MOBY radiances and achieve good agreement with radiometric and chlorophyll a measurements from independent in situ sources. We also find that the derived vicarious gains show no significant temporal or geometric dependencies, and that the mission-average calibration reaches stability after
high-quality calibration samples. Finally, we demonstrate that the performance of the vicariously calibrated retrieval system is relatively insensitive to the assumptions inherent in our approach.
Robert E. Eplee, Wayne D. Robinson, Sean W. Bailey, Dennis K. Clark, P. Jeremy Werdell, Menghua Wang, Robert A. Barnes, and Charles R. McClain Appl. Opt. 40(36) 6701-6718 (2001)
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Number of gain samples, gi, used to compute the mean gain, , for .
Standard deviation of the distribution of gi about .
Standard error on the mean, , computed as σ∕sqrt(N).
Median ratio of satellite to in situ Lwn.
Median percent difference.
Linear regression slope of satellite versus in situ Lwn.
Mean bias [=Σ(satellite − in situ)∕N].
Table 4
Validation of Vicarious Calibration Against Deep-Water In Situ Measurements
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 7
Sensitivity of Deep-Water Validation to Calibration Assumptions +4% 865-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 8
Sensitivity of Deep-Water Validation to −4% 865-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 9
Sensitivity of Deep-Water Validation to M50 765-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 10
Sensitivity of Deep-Water Validation to O99 765-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Tables (10)
Table 1
Glossary of Symbols
Symbol
Description
References
λ
Sensor wavelength
[1,2]
Lt, Ltt
TOA radiance, observed or (t) predicted
[1,2]
Lr
Radiance due to Rayleigh scattering from air molecules
[12,13]
La
Radiance due to scattering by aerosols, including Rayleigh–aerosol interactions
[5]
Lf
Radiance associated with whitecaps (foam) on the sea surface
[14–16]
Lw, Lwt
Water-leaving radiance, retrieved or (t) targeted
[21]
Lwn, Lwnt
Normalized water-leaving radiance, retrieved or (t) targeted
[21,23–25]
, ,
Transmittance due to gaseous absorption (e.g., ozone) for solar path (s) and sensor view path (v)
[5]
, ,
Rayleigh-aerosol diffuse transmittance for solar path (s) and sensor view path (v)
[5,17]
fp
Polarization correction factor
18,19]
fs, fst
Earth–Sun distance correction
[22]
fb, fbt
Bidirectional reflectance correction
[23–25]
fλ, fλt
Band-pass adjustment to Lwn or Lwnt
[10,26]
θs, θv
Zenith angles for solar path (s) and sensor view path (v)
Number of gain samples, gi, used to compute the mean gain, , for .
Standard deviation of the distribution of gi about .
Standard error on the mean, , computed as σ∕sqrt(N).
Median ratio of satellite to in situ Lwn.
Median percent difference.
Linear regression slope of satellite versus in situ Lwn.
Mean bias [=Σ(satellite − in situ)∕N].
Table 4
Validation of Vicarious Calibration Against Deep-Water In Situ Measurements
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 7
Sensitivity of Deep-Water Validation to Calibration Assumptions +4% 865-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 8
Sensitivity of Deep-Water Validation to −4% 865-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 9
Sensitivity of Deep-Water Validation to M50 765-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).
Table 10
Sensitivity of Deep-Water Validation to O99 765-nm Calibration;
As defined in Table 3.
Number of satellite-to-in situ match-up cases (a common set was used for Tables 6–10 and for the analysis presented in Table 4).