The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Satellite-derived remote-sensing reflectance (Rrs) just above the sea surface can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scales for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper, we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Data set (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, are ranked for spectrally resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking is tested using a Monto-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflect either their immunity to scale errors or instrument noise in Rrs data, or simply that data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggest the performance of some semi-analytical algorithms at retrieving chlorophyll were comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also performed with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.JRC.H.1-Water Resource