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Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors

Abstract : Chlorophyll fluorometers provide the largest in situ global data set for estimating phytoplankton biomass because of their ease of use, size, power consumption, and relatively low price. While in situ chlorophyll a (Chl) fluorescence is proxy for Chl a concentration, and hence phytoplankton biomass, there exist large natural variations in the relationship between in situ fluorescence and extracted Chl a concentration. Despite this large natural variability, we present here a global validation data set for the WET Labs Environmental Characterization Optics (ECO) series chlorophyll fluorometers that suggests a factor of 2 overestimation in the factory calibrated Chl a estimates for this specific manufacturer and series of sensors. We base these results on paired High Pressure Liquid Chromatography (HPLC) and in situ fluorescence match ups for which non-photochemically quenched fluorescence observations were removed. Additionally, we examined match-ups between the factory-calibrated in situ fluorescence and estimates of chlorophyll concentration determined from in situ radiometry, absorption line height, NASA's standard ocean color algorithm as well as laboratory calibrations with phytoplankton monocultures spanning diverse species that support the factor of 2 bias. We therefore recommend the factor of 2 global bias correction be applied for the WET Labs ECO sensors , at the user level, to improve the global accuracy of chlorophyll concentration estimates and products derived from them. We recommend that other fluorometer makes and models should likewise undergo global analyses to identify potential bias in factory calibration.
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Collin Roesler, Julia Uitz, Hervé Claustre, Emmanuel Boss, Xiaogang Xing, et al.. Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors. Limnology and Oceanography: Methods, Association for the Sciences of Limnology and Oceanography, 2017, ⟨10.1002/lom3.10185⟩. ⟨hal-01520511⟩

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