Remote‐Sensing Estimation of Phytoplankton Size Classes FromGOCISatellite Measurements in Bohai Sea and Yellow Sea

Abstract

Phytoplankton size class (PSC), a measure of different phytoplankton functional and structural groups, is a key parameter to the understanding of many marine ecological and biogeochemical processes. In turbid waters where optical properties may be influenced by terrigenous discharge and nonphytoplankton water constituents, remote estimation of PSC is still a challenging task. Here based on measurements of phytoplankton diagnostic pigments, total chlorophyll a, and spectral reflectance in turbid waters of Bohai Sea and Yellow Sea during summer 2015, a customized model is developed and validated to estimate PSC in the two semienclosed seas. Five diagnostic pigments determined through high‐performance liquid chromatography (HPLC) measurements are first used to produce weighting factors to model phytoplankton biomass (using total chlorophyll a as a surrogate) with relatively high accuracies. Then, a common method used to calculate contributions of microphytoplankton, nanophytoplankton, and picophytoplankton to the phytoplankton assemblage (i.e., Fm, Fn, and Fp) is customized using local HPLC and other data. Exponential functions are tuned to model the size‐specific chlorophyll a concentrations (Cm, Cn, and Cp for microphytoplankton, nanophytoplankton, and picophytoplankton, respectively) with remote‐sensing reflectance (Rrs) and total chlorophyll a as the model inputs. Such a PSC model shows two improvements over previous models: (1) a practical strategy (i.e., model Cp and Cn first, and then derive Cm as C‐Cp‐Cn) with an optimized spectral band (680 nm) for Rrs as the model input; (2) local parameterization, including a local chlorophyll a algorithm. The performance of the PSC model is validated using in situ data that were not used in the model development. Application of the PSC model to GOCI (Geostationary Ocean Color Imager) data leads to spatial and temporal distribution patterns of phytoplankton size classes (PSCs) that are consistent with results reported from field measurements by other researchers. While the applicability of the PSC model together with its parameterization to other optically complex regions and to other seasons is unknown, the findings of this study suggest that the approach to develop such a model may be extendable to other cases as long as local data are used to select the optimal band and to determine the model coefficients.

Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

“…The spatial and temporal distribution of satellite-derived chlorophyll-a concentration and PSCs in this study are in agreement with previous in situ observations and remote sensing estimations (Fu et al, 2010(Fu et al, , 2009Huang et al, 2010;Sun et al, 2017Sun et al, , 2019Sun et al, , 2002Sun et al, , 2012Sun et al, , 2018. High chlorophyll-a concentration and large cell phytoplankton biomass were observed in entire BS and nearshore YS (Figures 6 and 7).…”

“…Each approach has its own advantages and limitations (Mouw et al, ). Many of these approaches have been designed for global ocean applications (Brewin et al, ; Bricaud et al, ), and some regional studies have also been conducted (Brotas et al, ; Lamont et al, ; Sun et al, , ).…”

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

“…GOCI-derived PSCs' distribution showed that microplankton and nanoplankton were generally the major contributors to coastal and transitional regions, and picoplankton was found to be abundant in the oligotrophic regions ( Figure 5), which was consistent with in situ investigations in our study (Figure 2), previous studies in the BS, the YS, and ECS (e.g., Fu et al, 2009;Huang et al, 2006;Sun et al, 2002Sun et al, , 2012, and from other study areas (e.g., Arin et al, 2002;Chisholm et al, 1988;Madariaga & Orive, 1989;Marañ on et al, 2001). Similar distribution of GOCI-derived PSCs was observed from our study ( Figure 5) and the study of Sun et al (2017). However, slight differences existed in the nearshore BS and SYS where microplankton instead of nano and picoplankton was dominant in our study, which might result from regional data sets for modeling and time scales utilized in the two studies.…”

Monitoring Water Transparency in Shallow and Eutrophic Lake Waters Based on GOCI Observations

“…The geostationary ocean color imager (GOCI), the world's first geostationary ocean color satellite sensor, observes the Northeast Asian region eight times within a day from 08:15 am to 3:15 p.m. local time (temporal resolution of 1 h). In the visible region, GOCI contains six visible wavebands with central wavelengths of 412, 443, 490, 555, 660, and 680 nm and two NIR wavebands with central wavelengths of 745 and 865 nm, and has a spatial resolution of 500 m [18,36]. The GOCI satellite observations with fine temporal and spatial resolutions facilitate more detailed analysis (e.g., diurnal dynamics) for monitoring Z sd in our study area.…”

“…Compared to polar-orbiting satellite sensor, the geostationary satellite sensors can provide observations at a short revisit period to monitor the daily dynamics of water clarity. As the world's first ocean color geostationary satellite sensor, the geostationary ocean color imager (GOCI) provides near real-time observations over northeast Asia (including Lake Taihu as our study region), with hourly intervals up to eight times a day and the spatial resolution of 500 m [18]. Thus, the GOCI satellite data are very useful to monitor the water transparency in submesoscale regions such as the estuaries and large individual lakes.…”