Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea

Abstract Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages’ dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE , are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately