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 NIRL 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 NIRL errors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases in $R_{\mathrm {RS}}$. [ABSTRACT FROM AUTHOR]