Phytoplankton cell size is a useful ecological indicator for evaluating the response of phytoplankton community structure to environmental changes. Ocean‐color remote observations and algorithms have allowed us to estimate phytoplankton size classes (PSCs) at decadal scale, helping us to understand their trends under ocean warming. Here a large data set of pigments, derived through high performance liquid chromatography, was collected in the Bohai Sea (BS) and Yellow Sea (YS) between 2014 and 2016. The data set was used to reparametrize the sea surface temperature (SST)‐dependent three‐component model of Brewin et al. (2017) to the region. The model was validated using independent in situ data set and subsequently applied to satellite chlorophyll‐a data from Ocean Colour Climate Change Initiative, spanning from 1997 to 2016, to derive percentages of three PSCs to total chlorophyll‐a. Monthly‐averaged PSCs exhibited spatial‐temporal variations in the study area, linked to topography, temperature, solar radiation, currents, and monsoonal winds. In the surface central south Yellow Sea (SYS), influenced by bottom Yellow Sea Cold Water Mass, tight relationships between PSCs and environmental factors were observed, where high SST, high sea level anomaly, low mixed‐layer depth, and low wind speed resulted in higher proportions of nanoplankton and picoplankton from June to October. Significant interannual anomlies in PSCs were found associated with El Niño events in the central SYS, related to anomalies in SST. The refined model characterized 20‐year variations in chlorophyll‐a concentration and PSCs in complicated optical, hydrodynamic, and biogeochemical environments in the BS and YS.

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.

Marine phytoplankton are essential to ocean biogeochemical cycles. However, our understanding of changes in phytoplankton rely largely on satellite data, which can only assess changes in surface phytoplankton. How climate variability is impacting their vertical structure remains unclear. Here we use 33 years’ worth of data from the Sargasso Sea to show distinct seasonal and long-term phytoplankton climate responses in the surface mixed layer compared with the subsurface. Seasonally, the surface community alters their carbon-to-chlorophyll ratio without changing their carbon biomass, whereas the chlorophyll a and carbon of the subsurface community covaries with no change in their carbon-to-chlorophyll ratio. Over the last decade, the subsurface phytoplankton biomass has increased in response to warming, whereas the surface phytoplankton have altered their carbon-to-chlorophyll ratio with minimal change in their carbon biomass. Given that satellites can only view the surface ocean, sustained subsurface monitoring is required to provide a full understanding of how phytoplankton are responding to climate change.