Estimating variability in the quantum yield of Sun-induced chlorophyll fluorescence: A global analysis of oceanic waters

While empirical and semi-analytical algorithms that retrieve phytoplankton biomass from satellite ocean color have matured over the last few decades, the use of Sun-induced chlorophyll fluorescence data measured by spacebome sensors remains in its infancy. Sun-induced fluorescence has the potential to provide a synoptic global view of aspects of phytoplankton biology that go beyond biomass by observing the quantum yield of fluorescence. While several algorithms have been developed to retrieve the quantum yield, they are prone to biases from different sources. In this study, we assessed the accuracy of several ocean color algorithms to estimate phytoplankton chlorophyll or absorption when they are used together with Sun-induced fluorescence algorithms. Our analysis led us to develop a new type of algorithm for retrieving variability in the quantum yield of fluorescence. Based on a three dimensional lookup table, this algorithm avoided many of the biases present in older algorithms and provided distributions of the quantum yield that showed significant differences compared to previous methods. This algorithmic approach also has the advantage of being robust with respect to sensor characteristics and to the set of underlying proxies that are used, namely phytoplankton biomass, the absorption by chromophoric dissolved organic matter, and the incident irradiance. As such, it would be suitable for merging data from multiple satellite ocean color sensors. (C) 2013 Elsevier Inc. All rights reserved.