Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity
2022
Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (
$R_{\mathrm {RS}}$
) for ocean color applications. Among these is the vicarious calibration coefficient (
$g$
) of the “long” near infrared band (NIR
$_{\mathrm {L}}$
) used for atmospheric correction. For this band, the prelaunch calibration has always been deemed sufficient [thus
$g$
(NIR
$_{\mathrm {L}}) =1.00$
] as long as other bands are vicariously calibrated. Recent research, however, suggests that Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua
$R_{\mathrm {RS}}$
time series is quite sensitive to
$g$
(NIR
$_{\mathrm {L}}$
) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP)
$R_{\mathrm {RS}}$
to NIR
L
calibration and compared our results to previous MODIS/Aqua and Sea-viewing Wide Field-of-View Sensor onboard OrbView2 (SeaWiFS)/OrbView2 analysis. In doing so, we note that
$g$
(NIR
$_{\mathrm {L}}$
) sensitivities of mission-averaged
$R_{\mathrm {RS}}$
time series are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies (MCs), however, all sensors show prominent
$g$
(NIR
$_{\mathrm {L}}$
) sensitivity with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts on
$g$
(NIR
$_{\mathrm {L}}$
) sensitivity. As such,
$g$
(NIR
$_{\mathrm {L}}$
) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within
$g$
(NIR
$_{\mathrm {L}}$
) sensitivity, such studies should be coupled with cross-sensor intercalibrations [e.g., using simultaneous same view (SSV) measurements] toward minimizing NIR
L
errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in
$R_{\mathrm {RS}}$
.
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