The East/Japan Sea (hereafter, the East Sea) is highly dynamic in its physical phenomena and biological characteristics, but it has changed substantially over the last several decades. In this study, a recent decadal trend of primary productivity in the East Sea was analyzed based on Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived monthly values to detect any long-term change. The daily primary productivities averaged using monthly values from 2003 to 2012 were 719.7 mg·C·m⁻²·d⁻¹ (S.D. ± 197.5 mg·C·m⁻²·d⁻¹, n = 120) and 632.3 mg·C·m⁻²·d⁻¹ (S.D. ± 235.1 mg·C·m⁻²·d⁻¹, n = 120) for the southern and northern regions of the East Sea, respectively. Based on the daily productivities, the average annual primary production in the East Sea was 246.8 g·C·m⁻²·y⁻¹, which was substantially higher than that previously reported in deep oceans. However, a decreasing trend (13% per 10 years) in the annual primary production was observed in the East Sea within the study period from 2003 to 2012. The shallower mixed layers caused by increased temperature could be a potential cause for the decline in annual production. However, this decline could also be part of an oscillation pattern that is strongly governed by the Pacific Decadal Oscillation (PDO). A better understanding of primary productivity patterns and their subsequent effects on the marine ecosystem is required for further interdisciplinary studies in the East Sea.

The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift in the fishing grounds of several fish species. The habitat suitability index (HSI), a reliable indicator of the capacity of a habitant to support selected species, has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the chub mackerel and satellite-derived environmental factors were used to calculate the HSI for the chub mackerel in the South Sea, South Korea. More than 80% of the total catch was found in areas with an SST of 14.72–25.72 °C, chlorophyll-a of 0.30–0.92 mg m⁻³, and primary production of 523.7–806.46 mg C m⁻² d⁻¹. Based on these results, the estimated climatological monthly HSI from 2002 to 2016 clearly showed that the wintering ground of the chub mackerel generally formed in the South Sea of South Korea, coinciding with the catch distribution during the same period. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for the chub mackerel in the East/Japan Sea and South Sea of South Korea.

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor, there have been no algorithms that were validated with in-situ quantitative measurements for the red tide occurring in the Korean seas. Furthermore, since the geostationary ocean color imager (GOCI) became available in June 2010, an algorithm that exploits its unprecedented observation frequency (every hour during the daytime) has been highly demanded to better track the changes in spatial distribution of red tide. This study developed a novel red tide quantification algorithm for GOCI that can estimate hourly chlorophyll-a (Chl a) concentration of Cochlodinium (Margalefidinium) polykrikoides, one of the major red tide species around Korean seas. The developed algorithm has been validated using in-situ Chl a measurements collected from a cruise campaign conducted in August 2013, when a massive C. polykrikoides bloom devastated Korean coasts. The proposed algorithm produced a high correlation (R2=0.92) with in-situ Chl a measurements with robust performance also for high Chl a concentration (300mg/m3) in East Sea areas that typically have a relatively low total suspended particle concentration (<0.5mg/m3).

Physical and biogeochemical changes induced by the Hurricane Fabian in the Northwest Atlantic in early September 2003 were observed using composite satellite images. After the passage of the hurricane, the mean sea surface temperature (SST) along the track decreased on average by about 1.3°C with maximum decrease of 10°C. At the same time, the mean Chl a concentration increased by about 42%. Entrainment of cold, nutrient-rich waters by vertical mixing induced by the hurricane seems to have enhanced the phytoplankton production. Asymmetric distribution of changes in SST and Chl a (strong intensity on the right side of the storm track) was observed from the satellite data. The storm-induced nitrate increase estimated from the satellite SST, using a local relationship between nitrate and temperature measurements was about 40% on average along the track of the storm. A numerical model study and climatological nutrient profile showed an increase in mixed-layer depth of 26 m and nitrate increase of about 0.2 μmol L⁻¹ after the storm passage. In addition to altering the physicochemical conditions of the water column, physical forcing by the hurricane also changed the taxonomic composition of phytoplankton. It is inferred that the dominance of diatoms after the storm is a result of the increase in nutrient concentration within the mixed layer due to the wind forcing of the storm.

Satellite ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) since 2012 and in situ water turbidity measurements from the U.S. Environmental Protection Agency’s Great Lakes Environmental Database System are used to develop a water turbidity algorithm for satellite ocean color applications in the Great Lakes for water quality monitoring and assessments. Results show that the proposed regional algorithm can provide reasonably accurate estimations of water turbidity from satellite observations in the Great Lakes. Therefore, VIIRS-derived water turbidity data are used to investigate spatial and temporal variations in water turbidity for the entirety of the Great Lakes. Water turbidity values are overall the highest in Lake Erie, moderate in Lake Ontario, and relatively low in lakes Superior, Michigan, and Huron. Significantly high values in water turbidity appear in the nearshore regions, particularly in Thunder Bay (Lake Superior), Green Bay (Lake Michigan), and Saginaw Bay (Lake Huron). Seasonal patterns of water turbidity are generally similar in lakes Superior, Michigan, Huron, and Ontario, showing relatively high values in the spring and autumn months and lows in the winter season, while the seasonal pattern in Lake Erie is apparently different from the other lakes, with the highest value in the winter season and the lowest in the summer season. A strong interannual variability in water turbidity is shown in the time series of the VIIRS-derived water turbidity data for most of the lakes.

Refined empirical algorithms for chlorophyll-a (Chl-a) concentration, using the maximum ratio of normalized water-leaving radiance nL_w(λ) at the blue and green bands, and Secchi depth (SD) from nL_w(λ) at 551 nm, nL_w(551), are proposed for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in the Great Lakes. We demonstrated that water quality properties and phytoplankton production can be successfully monitored and assessed using the new regional Chl-a and SD algorithms, with reasonably accurate estimates of Chl-a and SD from the VIIRS-SNPP ocean color data in the Great Lakes. VIIRS-derived Chl-a and SD products using the proposed algorithms provide the temporal and spatial variabilities in the Great Lakes. Overall, Chl-a concentrations are generally low in lakes Michigan and Huron, while Chl-a data are highest in Lake Erie. The seasonal pattern shows that overall low Chl-a concentrations appear in winter and high values in June to September in the lakes. The distribution of SD in the Great Lakes is spatially and temporally different from that of Chl-a. The SD data are generally lower in summer and higher in winter in most of the Great Lakes. However, the highest SD in Lake Erie appears in summer, and lower values in winter. Significantly high values in Chl-a, and lower values in SD, in the nearshore regions, such as Thunder Bay, Saginaw Bay, and Whitefish Bay, can be related to the very shallow bathymetry and freshwater inputs from the land. The time series of VIIRS-derived Chl-a and SD data provide strong interannual variability in most of the Great Lakes.