Marine Science Faculty Publications

Sensitivity of Satellite Ocean Color Data to System Vicarious Calibration of the Long Near Infrared Band

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Oceans, Static VAr compensators, Calibration, Image color analysis, Satellites, Sea measurements, Atmospheric measurements

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Satellite ocean color missions require accurate system vicarious calibrations (SVC) to retrieve the relatively small remote-sensing reflectance (R rs , sr -1 ) from the at-sensor radiance. However, the current atmospheric correction and SVC procedures do not include calibration of the “long” near infrared band (NIRL-869 nm for MODIS), partially because earlier studies, based primarily on simulations, indicate that accuracy in the retrieved R rs is insensitive to moderate changes in the NIRL vicarious gain (g). However, the sensitivity of ocean color data products to g(NIRL) has not been thoroughly examined. Here, we first derive 10 SVC “gain configurations” (vicarious gains for all visible and NIR bands) for MODIS/Aqua using current operational NASA protocols, each time assuming a different g(869). From these, we derive a suite of ~1.4E6 unique gain configurations with g(869) ranging from 0.85 to 1.2. All MODIS/A data for 25 locations within each of five ocean gyres were then processed using each of these gain configurations. Resultant time series show substantial variability in dominant R rs (547) patterns in response to changes in g(869) (and associated gain configurations). Overall, mean R rs(547) values generally decrease with increasing g(869), while the standard deviations around those means show gyre-specific minima for 0.97 <; g(869) <; 1.02. Following these sensitivity analyses, we assess the potential to resolve g(869) using such time series, finding g(869) = 1.025 most closely comports with expectations. This approach is broadly applicable to other ocean color sensors, and highlights the importance of rigorous cross-sensor calibration of the NIRL bands, with implications on consistency of merged-sensor data sets.

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EEE Transactions on Geoscience and Remote Sensing, v. 59, issue 3, p. 2562-2578