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We describe an approach to partition a vertical profile of chlorophyll‐a concentration into contributions from two communities of phytoplankton: one (community 1) that resides principally in the turbulent mixed‐layer of the upper ocean and is observable through satellite visible radiometry; the other (community 2) residing below the mixed‐layer, in a stably stratified environment, hidden from the eyes of the satellite. The approach is tuned to a time‐series of profiles from a Biogeochemical‐Argo float in the northern Red Sea, selected as its location transitions from a deep mixed layer in winter (characteristic of vertically well‐mixed systems) to a shallow mixed layer in the summer with a deep chlorophyll‐a maximum (characteristic of vertically stratified systems). The approach is extended to reproduce profiles of particle backscattering, by deriving the chlorophyll‐specific backscattering coefficients of the two communities and a background coefficient assumed to be dominated by non‐algal particles in the region. Analysis of the float data reveals contrasting phenology of the two communities, with community 1 blooming in winter and 2 in summer, community 1 negatively correlated with epipelagic stratification, and 2 positively correlated. We observe a dynamic chlorophyll‐specific backscattering coefficient for community 1 (stable for community 2), positively correlated with light in the mixed‐layer, suggesting seasonal changes in photoacclimation and/or taxonomic composition within community 1. The approach has the potential for monitoring vertical changes in epipelagic biogeography and for combining satellite and ocean robotic data to yield a three‐dimensional view of phytoplankton distribution.

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Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (a g (λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose a g from a dg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m −1 , and a g (λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m −1 . These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea.