The spring phytoplankton bloom is a critical event in temperate oceans typically associated with the highest productivity levels throughout the year. To investigate the bloom process in the Yellow Sea, daily data on physical, chemical, and phytoplankton taxonomic group biomass, calculated via the chemotaxonomic approach, were collected from late March or early April to late May between 2018 and 2020 at the Socheongcho Ocean Research Station. During early spring (late March to mid-April), phytoplankton biomass increased, accompanied by a decrease in nutrient levels, with Bacillariophyceae and Cryptophyceae being the dominant groups. As water temperature increased, a pycnocline began to develop in late April, leading to a peak of the phytoplankton bloom dominated by chlorophytes and Cryptophyceae. Network analysis suggested that this phytoplankton bloom was caused by the onset of vertical stratification induced by increased sea surface temperature. The chlorophyte peak induced phosphate limitation above the pycnocline, resulting in succession to Prymnesiophyceae and Dinophyceae. Following pycnocline formation, phytoplankton biomass below the pycnocline was dominated by Bacillariophyceae and Cryptophyceae, with decreasing or fluctuating trends depending on phosphate concentration. Apart from these general patterns, 2019 and 2020 both had distinctive traits. The 2019 data revealed lower phosphate concentrations than the other 2 years, leading to a smaller chlorophyte peak at the surface compared to 2018 and extreme phosphate limitation above the pycnocline. This limitation resulted in decreased biomass of late successional groups, including Prymnesiophyceae and Dinophyceae. Pycnocline formation was delayed in year 2020, and stratification was significantly weaker compared to the previous 2 years. Due to the pycnocline delay, the surface chlorophyte peak did not develop and no succession to late successional groups was observed. Instead, high levels of Bacillariophyceae and Cryptophyceae biomass were observed throughout the water column with no surface bloom. Thus, among various environmental factors, increasing surface water temperature and phosphate concentrations play pivotal roles in shaping phytoplankton bloom dynamics. Distinct yearly variation points to the broader impacts of climate shifts, emphasizing the need for continued marine monitoring.

The CHEMTAX program has been widely used to estimate community composition based on major pigment concentrations in seawater. However, because CHEMTAX is an underdetermined optimization algorithm, underdetermined bias has remained an unsolved problem since its development in 1996. The risk of producing biased results increases when analyzing the picophytoplankton community; therefore, this study tested a new method for avoiding biased CHEMTAX results using the picophytoplankton community around the East Sea (Japan Sea). This method involves building a linear model between pigment concentration data and community composition data based on DNA sequencing to predict the pigment range for each operational taxonomic unit, based on the 95% prediction interval. Finally, the range data are transformed into an initial ratio and ratio limits for CHEMTAX analysis. Three combinations of initial ratios and ratio limits were tested to determine whether the modeled initial ratio and ratio limit could prevent underdetermined bias in the CHEMTAX estimates; these combinations were the modeled initial ratio and ratio limit, the modeled initial ratio with a default ratio limit of 500 s, and an initial ratio from previous research with the default ratio limit. The final ratio and composition data for each combination were compared with Bayesian compositional estimator-based final ratio and composition data, which are robust against underdetermined bias. Only CHEMTAX analysis using the modeled initial ratio and ratio limit was unbiased; all other combinations showed significant signs of bias. Therefore, the findings in this study indicate that ratio limits and the initial ratio are equally important in the CHEMTAX analysis of biased datasets. Moreover, we obtained statistically supported initial ratios and ratio limits through linear modeling of pigment concentrations and 16s rDNA composition data.

Microphytobenthos (MPB) are key benthic microalgae that support intertidal ecosystem functioning by driving primary production, stabilizing surface sediments, and contributing to carbon storage. Because these ecological roles are closely tied to the biomass and distribution of MPB, understanding their spatiotemporal patterns is essential for evaluating the functioning and resilience of tidal-flat ecosystems. Satellite-based approaches have demonstrated their value for characterizing seasonal variability and elevation-dependent differences in MPB biomass across a range of estuarine and coastal environments worldwide. Korean tidal flats, shaped by large tidal ranges and spatially variable Holocene sediments, present steep elevation–exposure gradients that are expected to influence MPB distribution. Yet how these gradients translate into seasonal and interannual biomass patterns has not been examined using multi-year satellite observations. We address this gap by proposing and implementing a satellite-based monitoring framework that couples a site-specific chlorophyll-a concentration (Chl-a) retrieval algorithm with Harmonized Landsat and Sentinel-2 (HLS) imagery and a waterline-derived elevation model. The algorithm developed for the Geunso Bay tidal flat achieved an R² of 0.70 and an RMSE of 18.33 mg/m² in estimating Chl-a of MPB. The framework produced multi-year seasonal maps and resolved elevation-dependent variability, exhibiting summer maxima in the upper flat, winter maxima in the middle flat, and mixed responses in the lower flat. The study delivers the first multi-year satellite record of MPB in a Korean tidal flat and demonstrates a transferable, cost-effective approach for benthic monitoring.