Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000–2012) satellite-derived products from MODIS and SeaWiFS observations. Based on linear regression analysis, the results showed that the entire Bohai Sea experienced an increase in Chl-a on a long-term scale, with the largest increase in the central Bohai Sea and the smallest increase in the Bohai strait. Distinct seasonal patterns of Chl-a existed in different sub-regions of the Bohai Sea. A long-lasting Chl-a peak was observed from May to September in coastal waters (Liaodong bay, Qinhuangdao coast, and Bohai bay) and the central Bohai Sea, whereas Laizhou bay had relatively low Chl-a in early summer. In the Bohai strait, two pronounced Chl-a peaks occurred in March and September, but the lowest Chl-a was in summer. This pattern was quite different from those in other regions of the Bohai Sea. The water column condition (stratified or mixed) was likely an important physical factor that affects the seasonal pattern of Chl-a in the Bohai Sea. Meanwhile, increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in both coastal waters and the central Bohai Sea, especially in summer. The increasing trend of Chl-a in the Bohai Sea might be attributed to the increase in nutrient contents from riverine inputs. The Chl-a dynamics documented in this study provide basic knowledge for the future exploration of marine biogeochemical processes and ecosystem evolution in the Bohai Sea.

Several algorithms have been proposed to detect floating macroalgae blooms in the global ocean. However, some of them are difficult or even impossible to routinely apply by non-experts because of performing a sophisticated atmospheric correction scheme or due to the mismatch in spectral bands from one sensor to another. Here, a generic, simple and effective method, referred to as the Floating Green Tide Index (FGTI), was proposed to detect floating green macroalgae blooms (GMB). The FGTI was defined as the difference between greenness and wetness features extracted from digital number (DN) observation through Tasseled Cap Transformation analysis, providing the advantage of bypassing the atmospheric correction procedure. Through cross-index and cross-sensor comparisons, the FGTI showed similar performance to the existing VB-FAH (Virtual-Baseline Floating macroAlgae Height) and FAI (Floating Algae Index) algorithms but proved more robust than the traditional NDVI (Normalized Difference Vegetation Index) in terms of response to perturbations by environmental conditions, viewing geometry, sun glint, and thin cloud contamination. Given the requirement for spectral bands in the current and planned satellite sensors, the FGTI design can easily be extended to any satellite sensor, and therefore provide an excellent data resource for studying GMB in any part of the global ocean.

Knowledge on the phenology and distribution of phytoplankton taxonomic groups (PTGs) represent valuable information when studying marine ecosystem, especially in the Arctic Ocean where rapid warming has drastic effects on sea-ice dynamics, which affect the marine food web. Taxonomic groups of phytoplankton can be discriminated based on their pigment signatures, which, in turn, impact their absorption spectra, given that different pigments have different absorption windows in the visible. Using concurrent measurements of phytoplankton diagnostic pigments and absorption spectra (a_ph) collected in the Bering and Chukchi Seas, a novel and direct approach was designed for simultaneously estimating the biomass concentrations of several PTGs (C_i) as well as their specific absorption coefficient. The chemotaxonomic tool CHEMTAX was applied to twelve diagnostic pigments measured by high-performance liquid chromatography (HPLC). Their results revealed that the phytoplankton community composition was made of nine groups, from which six dominant were identified: diatoms, dinoflagellates, c3-flagellate, haptophytes type 7, two types of prasinophytes. Out of 117 samples, twenty pairs of C_i derived by CHEMTAX and measured a_ph were randomly selected and used in a linear unmixing model to extract the specific absorption spectral of each group. This step was repeated 1000 times to provide the mean specific absorption of a given phytoplankton group. These specific absorption spectra were used to reconstruct total a_ph, which was consistent with the measured a_ph (R² from 0.8 to 0.95) at all visible wavelengths (400-700 nm). The derived specific absorption spectra were further used with the measured a_ph(λ) at ten Moderate Resolution Imaging Spectroradiometer (MODIS) wavebands in a linear unmixing model to test the ability to retrieve the concentrations of PTGs from satellite remote sensing. A comparison between estimated and measured C_i showed that the approach used in this study performed best when retrieving five groups (i.e., dinoflagellates, c3-flagellate, haptophytes, two types of prasinophytes) from the nine initially identified using CHEMTAX with a mean absolute percentage error (MAPE) <35%, except for diatoms with a MAPE value of about 45%. Our approach provides a practical basis for estimation of PTGs using a_ph(λ) derived from satellite observations and field measurements.