Vicarious Calibration of the Long Near Infrared Band: Cross-Sensor Differences in Sensitivity

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 $R_{\mathrm {RS}}$ .