A Flexible, Automated, Processing and Decision System to Support Ocean Color Satellite Vicarious Calibration Using Hyperspectral Radiometric Instrumentation on Autonomous Profiling Floats.

Ocean color satellites require routine in-orbit verification and system vicarious calibration (SVC) to maintain accuracy over the mission lifetime and between satellites. A spatially extensive network of vicarious calibration match-up data points would aid in reducing vicarious calibration uncertainty of ocean color satellites. To enable this network, we have developed a new approach to ocean color satellite vicarious calibration and validation that combines accurate, reliable and stable hyperspectral radiometric instruments with autonomous profiling float technologies to provide an unattended means for vicarious calibration over periods of years in the open ocean. Advances in robotic sampling platforms over the past 2 decades has brought about a definitive change in the way ocean data is collected and provide a new opportunity to combine sensing technologies with autonomous platforms. Autonomous floats are now ubiquitously used to monitor the worlds physical and biogeochemical conditions with high vertical resolution with the established Argo and BGC-Argo programs. In parallel with these technological advances have been advances in modelling efforts to track, predict and even optimize float deployment locations for improved research and event based monitoring. Our current efforts build off of these extensive works to revolutionize the concept and operation of SVC in situ programs while addressing quality data assurance. Our conceptual approach is to enable an automated control, processing and decision support system to acquire SVC data that is of the highest quality. Elements required for this system span float configuration and mission tools, to data processing systems, to predictive models. In combining these elements, we aim to establish a continuous in situ program that is optimized for producing the highest quality and quantity of in situ matchup data for SVC purposes while minimizing the cost of data acquisition. We present our conceptual approach and elucidate advanced processing techniques needed to enable such an approach.